Handbook of Smart Cities [1 ed.] 3030696979, 9783030696979

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Juan Carlos Augusto Editor

Handbook of Smart Cities

Handbook of Smart Cities

Juan Carlos Augusto Editor

Handbook of Smart Cities With 370 Figures and 91 Tables

Editor Juan Carlos Augusto Department of Computer Science Middlesex University London, UK

ISBN 978-3-030-69697-9 ISBN 978-3-030-69698-6 (eBook) ISBN 978-3-030-69699-3 (print and electronic bundle) https://doi.org/10.1007/978-3-030-69698-6 © Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

“To Celeste and Axel, with the hope they live in a better world” London, December 2020

Preface

Our planet is transitioning through an exciting new phase in its rich evolutionary process. We humans have inhabited this place in the universe for some time and are among the most sophisticated living entities in existence, at least for now. A combination of powerful inner forces, some innate positive motivations, combined with some practical needs are fostering this new quest from humanity to upgrade these urban spaces called cities. Cities come in different sizes and each one is significantly different to each other. All are big and complex conglomerates of humans. However, each one has a different history, inhabited by a different mix of people, with different needs, with a different mix of available resources, existing under different governments that shape in some way or another which aspirations and actions are fostered or discouraged. Throw to the picture described above an increasingly sophisticated mix of technological tools. Cities have obtained in the last half century unprecedented access to information, knowledge, and connectivity. Technological diversity and its potential impact on society have been growing steadily. It is true many technological products and current areas of exploration are prematurely presented as more sophisticated that what they really are. Still, technological progress is continuously moving forward in an undulating manner. From time to time some area of exploration comes to a momentary halt, the vast majority makes their way to market and evolution, like fridges, TV sets, phones, cars, and planes did in the past. Step by step, model by model, year by year, getting gradually and permanently accepted by society and incorporated in our lives. Cities create a supra entity out of the synergies of those humans so intimately sharing space, time, and resources. This Major References Works project on Smart Cities considers from various fundamental perspectives this growing phenomenon at this stage of our civilization where technology is consciously considered at such a level that can be used to bring benefits at a massive scale. Although technology is one of the main enablers, we should keep in mind it is after all only a collection of tools to support human existence. The five broad frameworks we selected to structure this publication are Humans and Institutions as main recipients, Technology as enabler, and Energy and Data as fundamental resources.

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The project provides a forum for leading experts in this area to discuss fundamental concepts and applications, how the infrastructure both enables and adds dependability, how current services can be improved and new ones conceived, what are the current affordances and what needs to be developed, what do we know well, and what else we need to investigate deeper if we want to make progress. Diversity is an important part of this project and as such this forum is open to all because we understand everyone is a stakeholder. We encourage citizens from all regions of the planet, from diverse professions, cultural backgrounds, ages, genders, and any other significant dimension of society, to provide their views, expectations, needs, and preferences as, after all, this technology will only be considered a progress if it helps us humans to better experience life. This is a complex enterprise, one for which is difficult, to pinpoint a beginning and an end; as with most things in this world, this concept also flows, will grow in waves, and morph with other aspects of life on this planet as it progressively embeds in our civilization. This intends to be one of the first major organized landmarks in this important theme and we hope it contributes to a mature reflection on the subject and into a healthy use of technology for all. London, UK December 2020

Juan Carlos Augusto

Contents

Volume 1 Part I

Basic Concepts and Frameworks . . . . . . . . . . . . . . . . . . . . . . .

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Smart Cities: Fundamental Concepts . . . . . . . . . . . . . . . . . . . . . . . Peggy James, Ross Astoria, Theresa Castor, Christopher Hudspeth, Denise Olstinske, and John Ward

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2

Smart Cities Can Be More Humane and Sustainable Too . . . . . . . Eduardo M. Costa

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Smart Energy Frameworks for Smart Cities: The Need for Polycentrism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Joseph Nyangon

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Urban Computing: The Technological Framework for Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mélanie Bouroche and Ivana Dusparic

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Smart Cities Data: Framework, Applications, and Challenges . . . Muhammad Bilal, Raja Sher Afgun Usmani, Muhammad Tayyab, Abdullahi Akibu Mahmoud, Reem Mohamed Abdalla, Mohsen Marjani, Thulasyammal Ramiah Pillai, and Ibrahim Abaker Targio Hashem

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Smart Institutions: Concept, Index, and Framework Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hans Wiesmeth, Dennis Häckl, and Christopher Schrey

Part II 7

Current Exemplary Smart Cities . . . . . . . . . . . . . . . . . . . . . .

Smart City Edmonton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Katie Hayes, Soumya Ghosh, Wendy Gnenz, Janice Annett, and Mary Beth Bryne

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177 179

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From Invention City to Innovation City: The Case of Racine Wisconsin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Peggy James and William Martin

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Urban Innovation Ecosystem and Humane and Sustainable Smart City: A Balanced Approach in Curitiba . . . . . . . . . . . . . . . Luiz Márcio Spinosa and Eduardo M. Costa

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Holistic, Multifaceted, and Citizen-Centric Smart Taipei Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chen-Yu Lee and Taipei Smart City Project Mangement Office (TPMO) Smart City Transformation for Mid-Sized Cities: Case of Canakkale, Turkey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Berrin Benli, Melih Gezer, and Ezgi Karakas

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Stockholm: Smart City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gustaf Landahl

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Smart City Wien: A Sustainable Future Starts Now . . . . . . . . . . . Thomas Madreiter, Angela Djuric, Nikolaus Summer, and Florian Woller

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NEOM Smart City: The City of Future (The Urban Oasis in Saudi Desert) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Somayya Madakam and Pragya Bhawsar

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Tehran in the Path of Transition to a Smart City: Initiatives, Implementation, and Governance . . . . . . . . . . . . . . . . . . . . . . . . . . Kiarash Fartash, Amirhadi Azizi, and Mohammadsadegh Khayatian Yazdi

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Rebranding Umhlanga as an Intelligent City . . . . . . . . . . . . . . . . . C. Erwee, L. Chipungu, and H. Magidimisha-Chipungu

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Bandung Smart City: The Digital Revolution for a Sustainable Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dody Arfiansyah and Hoon Han

Part III 18

Human Dimension

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Social Inclusion in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . Víctor Manuel Padrón Nápoles, Diego Gachet Páez, José Luis Esteban Penelas, Olalla García Pérez, Fernando Martín de Pablos, and Rafael Muñoz Gil

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Malaysia Smart City Framework: A Trusted Framework for Shaping Smart Malaysian Citizenship? . . . . . . . . . . . . . . . . . . . . . Seng Boon Lim, Jalaluddin Abdul Malek, Mohd Yusof Hussain, and Zurinah Tahir Making Smart Cities “Smarter” Through ICT-Enabled Citizen Coproduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Paula Rodriguez Müller

Part IV

Energy Dimension

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Smart Cities and the Challenge of Cities’ Energy Autonomy Vassiliki Meleti and Vasiliki Delitheou

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Energy Harvesting in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . Zheng Jun Chew, Yang Kuang, Tingwen Ruan, and Meiling Zhu

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Greenhouse Gas Mitigation in Smart Cities: Political Economy and Strategic Mitigation Alliances . . . . . . . . . . . . . . . . . . . . . . . . . Ross Astoria

Part V

Technology Dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Technology: Person Identification . . . . . . . . . . . . . . . . . . . . . . . . . . Igor Bezukladnikov, Anton Kamenskih, Aleksander Tur, Andrey Kokoulin, and Aleksander Yuzhakov

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User Interfaces in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . Torin Hopkins, S. Sandra Bae, Julia Uhr, Clement Zheng, Amy Banić, and Ellen Yi-Luen Do

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Vehicular Network Systems in Smart Cities . . . . . . . . . . . . . . . . . . Edna Iliana Tamariz-Flores and Richard Torrealba-Meléndez

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How Technology Makes a Difference: Digital, Agile, and Design Thinking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Muni Prabaharan

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Building Smart City Solutions with Focus on Health Care and GDPR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Emirhan Enler, Istvan Pentek, and Attila Adamko

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Smart Mobility Ontology: Current Trends and Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ali Yazdizadeh and Bilal Farooq

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Contents

Volume 2 Part VI 30

Data Dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Towards Autonomous Knowledge Creation from Big Data in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sławomir Nowaczyk, Thorsteinn Rögnvaldsson, Yuantao Fan, and Ece Calikus

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Interoperability Effect in Big Data . . . . . . . . . . . . . . . . . . . . . . . . . José Delgado

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Data Protection and Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . Goran Vojković and Tihomir Katulić

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Multitier Intelligent Computing and Storage for IoT Sensor Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Osamah Ibrahiem Abdullaziz, Mahmoud M. Abouzeid, and Mohamed Faizal Abdul Rahman

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Deep Learning for LiDAR-Based Autonomous Vehicles in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vinay Ponnaganti, Melody Moh, and Teng-Sheng Moh

Part VII 35

Institutions Dimension . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Corporate Social Responsibility (CSR): Governments, Institutions, Businesses, and the Public Within a Smart City Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andrew D. Roberts

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Social Emergence, Cornerstone of Smart City Governance as a Complex Citizen-Centric System . . . . . . . . . . . . . . . . . . . . . . . . . 1009 Claude Rochet and Amine Belemlih

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Exploiting Big Data for Smart Government: Facing the Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1035 Sunil Choenni, Niels Netten, Mortaza S. Bargh, and Susan van den Braak

Part VIII

Smart Cities Infrastructure Ecosystem . . . . . . . . . . . . . . .

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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1061

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Feeding a Smart City Jonathan Lodge

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IoT and Blockchain-Based Smart Agri-food Supply Chains . . . . . 1109 Lehan Hou, Ruizhi Liao, and Qiqi Luo

Contents

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A Primer on Smart Contracts and Blockchains for Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1131 Srini Bhagavan, Praveen Rao, and Laurent Njilla

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Technology-Led Disruptions and Innovations: The Trends Transforming Urban Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1163 Hussein Dia, Saeed Bagloee, and Hadi Ghaderi

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Advances on Urban Mobility Using Innovative Data-Driven Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1199 Marcelo O. Rosa, Keiko V. O. Fonseca, Nádia P. Kozievitch, Anderson A. De-Bona, Jeferson L. Curzel, Luciano U. Pando, Olga M. Prestes, and Ricardo Lüders

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Towards Interoperability of Data Platforms for Smart Cities . . . . 1237 Matthias Buchinger, Peter Kuhn, and Dian Balta

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Future Urban Smartness: Connectivity Zones with Disposable Identities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1259 Rob van Kranenburg, Loretta Anania, Gaëlle Le Gars, Marta Arniani, Delfina Fantini van Ditmar, Mantalena Kaili, and Petros Kavassalis

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Problem-Driven and Technology-Enabled Solutions for Safer Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1289 Johan Barthelemy, Mehrdad Amirghasemi, Bilal Arshad, Cormac Fay, Hugh Forehead, Nathanael Hutchison, Umair Iqbal, Yan Li, Yan Qian, and Pascal Perez

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Crowdsourcing for Smart Cities That Realizes the Situation of Cities and Information Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . 1317 Kenro Aihara and Hajime Imura

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Layer-Based Reference Model for Smart City Implementation . . . 1359 Patrick-Benjamin Bök and Ute Paukstadt

Part IX

Ethical Challenges

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“Eyes and Ears”: Surveillance in the Indian Smart City . . . . . . . . 1387 Uttara Purandare and Khaliq Parkar

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Reclaiming the Smart City: Toward a New Right to the City . . . . 1419 Maša Galič and Marc Schuilenburg

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Application of the General Data Protection Regulation for Social Robots in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1437 Gizem Gültekin-Várkonyi, Attila Kertész, and Szilvia Váradi

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Contents

Part X

Bottle Necks and Potential Enablers . . . . . . . . . . . . . . . . . .

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Optimization Problems Under Uncertainty in Smart Cities . . . . . . 1465 Edoardo Fadda, Lohic Fotio Tiotsop, Daniele Manerba, and Roberto Tadei

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Information Technology Macro Trends Impacts on Cities: Guidelines for Urban Planners . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1493 Keiko V. O. Fonseca, Nádia P. Kozievitch, Rita C. G. Berardi, and Oscar R. M. Schmeiske

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Advanced Visualization of Neighborhood Carbon Metrics Using Virtual Reality: Improving Stakeholder Engagement . . . . . 1517 A. Houlihan Wiberg, Sondre Løvhaug, Mikael Mathisen, Benedikt Tschoerner, Eirik Resch, Marius Erdt, and Ekaterina Prasolova-Førland

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Smart City Needs a Smart Urban-Rural Interface: An Overview on Romanian Urban Transformations . . . . . . . . . . . 1551 Ioan Ianoş, Andreea-Loreta Cercleux, Radu-Matei Cocheci, Cristian Tălângă, Florentina-Cristina Merciu, and Cosmina-Andreea Manea

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Journeys in the Age of Smart Cities: Some Fresh Perspectives . . . 1571 V. Callaghan, J. Chin, F. Doctor, T. Kymäläinen, A. Peña-Rios, C. Phengdy, A. Reyes-Munoz, A. Tisan, M. Wang, H. Y. Wu, V. Zamudio, S. Zhang, and P. Zheng

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Openness: A Key Factor for Smart Cities . . . . . . . . . . . . . . . . . . . 1611 Simge Özdal Oktay, Sergio Trilles Oliver, Albert Acedo, Fernando Benitez-Paez, Shivam Gupta, and Christian Kray

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The Importance of Creative Practices in Designing MoreThan-Human Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1643 Annika Wolff, Anne Pässilä, Antti Knutas, Teija Vainio, Joni Lautala, and Lasse Kantola

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Influence of Smart Cities Sustainability on Citizen’s Quality of Life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1665 Manuel Pedro Rodríguez Bolívar

Part XI 59

Closing Words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Smart Cities: State of the Art and Future Challenges . . . . . . . . . . 1693 Juan Carlos Augusto

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1703

About the Editor

Dr. Juan Carlos Augusto is professor of computer science at Middlesex University London, and head of the Research Group on Development of Intelligent Environments and of Smart Spaces Lab, which won the first prize at the 2019 edition of Machine Intelligence Competition that took place at the British Computer Society Headquarters. With a technical background in artificial intelligence, software engineering, and human–computer interfaces, his research interest lies in design and implementation of sensing systems that provide a practical benefit to humans. The application domain he most often explored has been ambient-assisted living, smart education, and smart cities. His interests intersect with several computer science areas, for example, ambient intelligence user-centered computing, context awareness, Internet of Things, and ubiquitous computing. Dr. Augusto has contributed to the research community with more than 260 publications, including several co-edited books on various types of smart systems. He has given more than a dozen invited talks and tutorials at international workshops and conferences and has also chaired numerous technical events. Dr. Augusto has been appointed co-editor-in-chief of the Journal on Ambient Intelligence and Smart Environments (IOS Press) and the Journal on Reliable Intelligent Environments (Springer), and he is the editorial board member of other international journals. He has led several UK-/EU-funded quadruple helix in style-innovation projects. He has advised several international funding bodies, including being external referee and monitoring expert for the European Commission.

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Contributors

Reem Mohamed Abdalla School of Hospitality and Tourism, Taylor’s University, Subang Jaya, Malaysia Jalaluddin Abdul Malek School of Social, Development and Environmental Studies, National University of Malaysia, Bangi, Selangor, Malaysia Mohamed Faizal Abdul Rahman International College of Semiconductor Technology, National Chiao Tung University, Taiwan, China Osamah Ibrahiem Abdullaziz Department of Electrical Engineering and Computer Science, National Chiao Tung University, Taiwan, China Mahmoud M. Abouzeid Department of Electrical Engineering and Computer Science, National Chiao Tung University, Taiwan, China Albert Acedo ITI/LARSyS, Instituto Superior Tcnico (IST), Universidade de Lisboa, Lisbon, Portugal Attila Adamko Department of Information Technology, University of Debrecen, Debrecen, Hungary Kenro Aihara Digital Content and Media Sciences Research Division, National Institute of Informatics, Tokyo, Japan Mehrdad Amirghasemi SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW, Australia Loretta Anania European Commission, Brussels, Belgium Janice Annett Open City and Technology, City of Edmonton, Edmonton, AB, Canada Dody Arfiansyah School of Built Environment, University of New South Wales, Sydney, NSW, Australia Marta Arniani Futuribile, Nice/Milan, Italy Bilal Arshad SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW, Australia xvii

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Contributors

Ross Astoria Political Science, University of Wisconsin-Parkside, Kenosha, WI, USA Juan Carlos Augusto Department of Computer Science, Middlesex University, London, UK Amirhadi Azizi Institute for Science and Technology Studies, Shahid Beheshti University, Tehran, Iran S. Sandra Bae University of Colorado, Boulder, CO, USA Saeed Bagloee Department of Civil and Construction Engineering, Swinburne University of Technology, Melbourne, VIC, Australia Dian Balta fortiss GmbH, Munich, Germany Amy Banić University of Wyoming, Laramie, WY, USA Mortaza S. Bargh Research and Documentation Centre, Ministry of Justice and Security, The Hague, The Netherlands Research Center Creating 010, Rotterdam University of Applied Sciences, Rotterdam, The Netherlands Johan Barthelemy SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW, Australia Amine Belemlih Paris Dauphine PSL University, Paris, France EM Lyon Casablanca Campus, Casablanca, Morocco Transilience Institute for Territory Resilience and Transformation, Casablanca, Morocco Fernando Benitez-Paez Institute of New Imaging Technologies (INIT), Universitat Jaume I, Castellón de la Plana, Spain Berrin Benli Novusens Smart City Institute, Kale Group, Turkish Informatics Foundation, Canakkale, Turkey Rita C. G. Berardi Department of Informatics, Federal University of Technology, Curitiba, PR, Brazil Igor Bezukladnikov Department of Automation and Remote Control, Perm National Research Polytechnic University, Perm, Russia Srini Bhagavan University of Missouri-Kansas City, Kansas City, MO, USA Pragya Bhawsar Strategic Management, Indian Institute of Management, Sirmaur, India Muhammad Bilal School of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia Patrick-Benjamin Bök HSPV NRW, Münster, Germany

Contributors

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Mélanie Bouroche School of Computer Science and Statistics, Trinity College, Dublin, Ireland Mary Beth Bryne Open City and Technology, City of Edmonton, Edmonton, AB, Canada Matthias Buchinger fortiss GmbH, Munich, Germany Ece Calikus Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden V. Callaghan School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK The Business School, Canterbury Christ Church University, Canterbury, UK Theresa Castor Communication, University of Wisconsin-Parkside, Kenosha, WI, USA Andreea-Loreta Cercleux Department of Human and Economic Geography, Faculty of Geography, University of Bucharest, Bucharest, Romania Zheng Jun Chew University of Exeter, Exeter, UK J. Chin School of Computing Sciences, University of East Anglia, Norwich, UK L. Chipungu University of KwaZulu-Natal, SOBEDS, Durban, South Africa Sunil Choenni Research and Documentation Centre, Ministry of Justice and Security, The Hague, The Netherlands Research Center Creating 010, Rotterdam University of Applied Sciences, Rotterdam, The Netherlands Radu-Matei Cocheci Department of Urban Planning and Territorial Development, “Ion Mincu” University of Architecture and Urban Planning, Bucharest, Romania Eduardo M. Costa LabCHIS – Humane Smart City Lab, Federal University of Santa Catarina (BR), Florianópolis, Brazil Knowledge Engineering and Management Dept., Federal University of Santa Catarina (BR), Florianópolis, Brazil Jeferson L. Curzel Instituto Federal de Santa Catarina (IFSC), Joinville, Brazil Anderson A. De-Bona Centro Universitário Dinâmica das Cataratas (UDC), Foz do Iguacu, Brazil José Delgado Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal Vasiliki Delitheou Department of Economics and Regional Development, Panteion University of Social and Political Sciences, Athens, Greece Hussein Dia Department of Civil and Construction Engineering, Swinburne University of Technology, Melbourne, VIC, Australia

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Contributors

Angela Djuric Smart City Agency, UIV Urban Innovation Vienna GmbH, Wien, Austria Ellen Yi-Luen Do University of Colorado, Boulder, CO, USA F. Doctor School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK Ivana Dusparic School of Computer Science and Statistics, Trinity College, Dublin, Ireland Emirhan Enler Department of Information Technology, University of Debrecen, Debrecen, Hungary Marius Erdt Fraunhofer Singapore, Nanyang Technological University, Singapore, Singapore C. Erwee University of KwaZulu-Natal, SOBEDS, Durban, South Africa José Luis Esteban Penelas Universidad Europea de Madrid (Diseño, Arquitectura y Construcciones Civiles), Madrid, Spain Edoardo Fadda Department of Control and Computer Engineering, Politecnico di Torino, Torino, Italy Yuantao Fan Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden Bilal Farooq Laboratory of Innovations in Transportation (LiTrans), Ryerson University, Toronto, ON, Canada Kiarash Fartash Institute for Science and Technology Studies, Shahid Beheshti University, Tehran, Iran Cormac Fay SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW, Australia Keiko V. O. Fonseca Department of Informatics, Federal University of Technology, Curitiba, PR, Brazil Hugh Forehead SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW, Australia Lohic Fotio Tiotsop Department of Control and Computer Engineering, Politecnico di Torino, Torino, Italy Diego Gachet Páez Universidad Europea de Madrid (Ciencias y Tecnología de la Información y las Comunicaciones), Madrid, Spain Maša Galič Department of Criminal Law and Criminology, VU University Amsterdam, Amsterdam, The Netherlands Olalla García Pérez Universidad Europea de Madrid (Ingeniería Industrial y Aeroespacial), Madrid, Spain

Contributors

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Gaëlle Le Gars Brussels, Belgium Melih Gezer Novusens Smart City Institute, Kale Group, Turkish Informatics Foundation, Canakkale, Turkey Hadi Ghaderi Department of Business Technology and Entrepreneurship, Swinburne University of Technology, Melbourne, VIC, Australia Soumya Ghosh Open City and Technology, City of Edmonton, Edmonton, AB, Canada Wendy Gnenz Open City and Technology, City of Edmonton, Edmonton, AB, Canada Gizem Gültekin-Várkonyi Faculty of Law and Political Sciences, University of Szeged, Szeged, Hungary Shivam Gupta Bonn Alliance for Sustainability Research/Innovation Campus Bonn (ICB), Bonn, Germany Dennis Häckl WIG2 GmbH, Wissenschaftliches Institut für Gesundheitsökonomie und Gesundheitssystemforschung, Leipzig, Germany Hoon Han School of Built Environment, University of New South Wales, Sydney, NSW, Australia Katie Hayes Open City and Technology, City of Edmonton, Edmonton, AB, Canada Torin Hopkins University of Colorado, Boulder, CO, USA Lehan Hou School of Data Science, The Chinese University of Hong Kong, Shenzhen, China A. Houlihan Wiberg The Research Centre for Zero Emission Neighbourhoods in Smart Cities (ZEN), Department of Architecture and Technology, Norwegian University of Science and Technology, Trondheim, Norway The Belfast School of Architecture and the Built Environment, Ulster University, Belfast, UK Christopher Hudspeth Philosophy, University of Wisconsin-Parkside, Kenosha, WI, USA Mohd Yusof Hussain School of Social, Development and Environmental Studies, Faculty of Social Sciences and Humanities, National University of Malaysia, Bangi, Selangor, Malaysia Nathanael Hutchison SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW, Australia Ioan Ianoş Interdisciplinary Centre for Advanced Research on Territorial Dynamics, University of Bucharest, Bucharest, Romania

xxii

Contributors

Hajime Imura Graduate School of Interdisciplinary Information Studies, The University of Tokyo, Tokyo, Japan Umair Iqbal SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW, Australia Peggy James Political Science, College of Social Sciences and Professional Studies, University of Wisconsin-Parkside, Kenosha, WI, USA Mantalena Kaili European Law Observatory on New Technologies-ELONTech, Athens, Greece Anton Kamenskih Department of Automation and Remote Control, Perm National Research Polytechnic University, Perm, Russia Lasse Kantola Theatrum Olga, Diakonia College of Finland, Lahti, Finland Ezgi Karakas Novusens Smart City Institute, Kale Group, Turkish Informatics Foundation, Canakkale, Turkey Tihomir Katulić Faculty of Law, University of Zagreb, Zagreb, Croatia Petros Kavassalis University of the Aegean, Chios, Greece Attila Kertész Faculty of Law and Political Sciences, University of Szeged, Szeged, Hungary Mohammadsadegh Khayatian Yazdi Institute for Science and Technology Studies, Shahid Beheshti University, Tehran, Iran Antti Knutas LUT University, Lappeenranta, Finland Andrey Kokoulin Department of Automation and Remote Control, Perm National Research Polytechnic University, Perm, Russia Nádia P. Kozievitch Department of Informatics, Federal University of Technology, Curitiba, PR, Brazil Rob van Kranenburg IoT Council, Resonance Design BV, Gent, Belgium Christian Kray Institute for Geoinformatics (ifgi), University of Münster, Münster, Germany Yang Kuang University of Exeter, Exeter, UK Peter Kuhn fortiss GmbH, Munich, Germany T. Kymäläinen VTT Technical Research Centre of Finland Ltd, Tampere, Finland Gustaf Landahl Environment and Health Administration, City of Stockholm, Stockholm, Sweden Joni Lautala Theatrum Olga, Diakonia College of Finland, Lahti, Finland Chen-Yu Lee Taipei, Taiwan

Contributors

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Yan Li SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW, Australia Ruizhi Liao School of Humanities and Social Science, The Chinese University of Hong Kong, Shenzhen, China Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology, Shenzhen, China Jonathan Lodge City Farm Systems Ltd, Slough, UK Sondre Løvhaug Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Trondheim, Norway Ricardo Lüders Universidade Tecnológica Federal do Paraná (UTFPR), Curitiba, Brazil Qiqi Luo School of Management and Economics, The Chinese University of Hong Kong, Shenzhen, China Somayya Madakam Information Technology, FORE School of Management, New Delhi, India Thomas Madreiter Executive Group for Construction and Technology, City of Vienna, Vienna, Austria H. Magidimisha-Chipungu University of KwaZulu-Natal, SOBEDS, Durban, South Africa Abdullahi Akibu Mahmoud School of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia Cosmina-Andreea Manea “Simion Mehedinti – Nature and Sustainable Development” Doctoral School, Faculty of Geography, University of Bucharest, Bucharest, Romania Daniele Manerba Department of Information Engineering, Università degli Studi di Brescia, Brescia, Italy Mohsen Marjani School of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia William Martin City of Racine, Racine, WI, USA Fernando Martín de Pablos Universidad Europea de Madrid (Ciencias y Tecnología de la Información y las Comunicaciones), Madrid, Spain Mikael Mathisen Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Trondheim, Norway

xxiv

Contributors

Vassiliki Meleti Department of Economics and Regional Development, Panteion University of Social and Political Sciences, Athens, Greece Florentina-Cristina Merciu Department of Human and Economic Geography, Faculty of Geography, University of Bucharest, Bucharest, Romania Melody Moh Department of Computer Science, San Jose State University, San Jose, CA, USA Teng-Sheng Moh San Jose State University, San Jose, CA, USA Rafael Muñoz Gil Universidad Europea de Madrid (Ciencias y Tecnología de la Información y las Comunicaciones), Madrid, Spain Niels Netten Research and Documentation Centre, Ministry of Justice and Security, The Hague, The Netherlands Research Center Creating 010, Rotterdam University of Applied Sciences, Rotterdam, The Netherlands Laurent Njilla Air Force Research Lab, Rome, NY, USA Sławomir Nowaczyk Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden Joseph Nyangon Center for Energy and Environmental Policy (CEEP), University of Delaware, Newark, DE, USA Sergio Trilles Oliver Institute of New Imaging Technologies (INIT), Universitat Jaume I, Castellón de la Plana, Spain Denise Olstinske Applied Professional Studies, University of Wisconsin-Parkside, Kenosha, WI, USA Simge Özdal Oktay Institute for Geoinformatics (ifgi), University of Münster, Münster, Germany Víctor Manuel Padrón Nápoles Universidad Europea de Madrid (Ingeniería Industrial y Aeroespacial), Madrid, Spain Luciano U. Pando Instituto Federal do Paraná (IFPR), Campo Largo, Brazil Khaliq Parkar CESSMA, University of Paris, Paris, France Anne Pässilä LUT University, Lappeenranta, Finland Ute Paukstadt HSPV NRW, Münster, Germany A. Peña-Rios BT Research Labs, Adastral Park, Ipswich, UK Istvan Pentek Department of Information Technology, University of Debrecen, Debrecen, Hungary Pascal Perez SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW, Australia

Contributors

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C. Phengdy Learning Design and Technology, San Diego State University, San Diego, CA, USA Thulasyammal Ramiah Pillai School of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia Vinay Ponnaganti San Jose State University, San Jose, CA, USA Muni Prabaharan Chennai, India Ekaterina Prasolova-Førland Department of Education and Lifelong Learning, Norwegian University of Science and Technology, Trondheim, Norway Olga M. Prestes Instituto de Pesquisa e Planejamento Urbano de Curitiba (IPPUC), Curitiba, Brazil Uttara Purandare IITB-Monash Research Academy, Mumbai, India Yan Qian SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW, Australia Praveen Rao University of Missouri-Columbia, Columbia, MO, USA Thorsteinn Rögnvaldsson Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden Eirik Resch The Research Centre for Zero Emission Neighbourhoods in Smart Cities (ZEN), Department of Architecture and Technology, Norwegian University of Science and Technology, Trondheim, Norway A. Reyes-Munoz Telecommunications and Aerospace Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain Andrew D. Roberts School of Business and Law, Central Queensland University, Melbourne, Australia Claude Rochet Paris Dauphine PSL University, Paris, France Fondation Robert de Sorbon, Institut Franco Allemand d’Etudes Européennes, Paris, France Manuel Pedro Rodríguez Bolívar Department of Accounting and Finance, University of Granada, Granada, Spain A. Paula Rodriguez Müller Public Governance Institute, KU Leuven, Leuven, Belgium Marcelo O. Rosa Universidade Tecnológica Federal do Paraná (UTFPR), Curitiba, Brazil Tingwen Ruan University of Exeter, Exeter, UK Oscar R. M. Schmeiske Department of Informatics, Federal University of Technology, Curitiba, PR, Brazil

xxvi

Contributors

Christopher Schrey WIG2 GmbH, Wissenschaftliches Institut für Gesundheitsökonomie und Gesundheitssystemforschung, Leipzig, Germany Marc Schuilenburg Department of Criminal Law and Criminology, VU University Amsterdam, Amsterdam, The Netherlands Seng Boon Lim School of Social, Development and Environmental Studies, Faculty of Social Sciences and Humanities, National University of Malaysia, Bangi, Selangor, Malaysia Luiz Márcio Spinosa LabCHIS / Federal University of Santa Catarina (BR), Triple Helix Association (IT), Curitiba, Brazil LabCHIS – Humane Smart City Lab, Federal University of Santa Catarina (BR), Florianópolis, Brazil Nikolaus Summer Smart City Agency, UIV Urban Innovation Vienna GmbH, Wien, Austria Roberto Tadei Department of Control and Computer Engineering, Politecnico di Torino, Torino, Italy Zurinah Tahir School of Social, Development and Environmental Studies, Faculty of Social Sciences and Humanities, National University of Malaysia, Bangi, Selangor, Malaysia Taipei Smart City Project Mangement Office (TPMO) Taipei, Taiwan Cristian Tălângă Interdisciplinary Centre for Advanced Research on Territorial Dynamics, University of Bucharest, Bucharest, Romania Edna Iliana Tamariz-Flores Faculty of Computer Sciences, Autonomous University of Puebla, Puebla, México Ibrahim Abaker Targio Hashem Future Technology Research Center, National Yunlin University of Science and Technology, Douliu, Taiwan Muhammad Tayyab School of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia A. Tisan Department of Electronic Engineering, Royal Holloway, University of London, Surrey, UK Richard Torrealba-Meléndez Faculty of Electronics Sciences, Autonomous University of Puebla, Puebla, México Benedikt Tschoerner Fraunhofer Singapore, Nanyang Technological University, Singapore, Singapore Aleksander Tur Department of Automation and Remote Control, Perm National Research Polytechnic University, Perm, Russia Julia Uhr University of Colorado, Boulder, CO, USA

Contributors

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Raja Sher Afgun Usmani School of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia Teija Vainio Aalto University, Espoo, Finland Susan van den Braak Research and Documentation Centre, Ministry of Justice and Security, The Hague, The Netherlands Delfina Fantini van Ditmar Royal College of Art, London, UK Szilvia Váradi Faculty of Law and Political Sciences, University of Szeged, Szeged, Hungary Goran Vojković Faculty of Transport and Traffic Sciences, University of Zagreb, Zagreb, Croatia M. Wang Learning Design and Technology, San Diego State University, San Diego, CA, USA John Ward Geography/GIS, University of Wisconsin-Parkside, Kenosha, WI, USA Hans Wiesmeth Graduate School of Economics and Management, Ural Federal University, Yekaterinburg, Russia Faculty of Business and Economics, TU Dresden, Dresden, Germany Annika Wolff LUT University, Lappeenranta, Finland Florian Woller Smart City Agency, UIV Urban Innovation Vienna GmbH, Wien, Austria H. Y. Wu National Taipei University of Technology, Taipei, Taiwan Ali Yazdizadeh Laboratory of Innovations in Transportation (LiTrans), Ryerson University, Toronto, ON, Canada Aleksander Yuzhakov Department of Automation and Remote Control, Perm National Research Polytechnic University, Perm, Russia V. Zamudio Division of Graduate Studies and Research, TecNM / Instituto Tecnológico de León, León, México S. Zhang Department of Computer Science, Shijiazhuang University, Shijiazhuang, PR China P. Zheng The Business School, Canterbury Christ Church University, Canterbury, UK Clement Zheng National University of Singapore, Singapore, Singapore Meiling Zhu University of Exeter, Exeter, UK

Part I Basic Concepts and Frameworks

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Smart Cities: Fundamental Concepts Peggy James, Ross Astoria, Theresa Castor, Christopher Hudspeth, Denise Olstinske, and John Ward

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Human Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fundamental Beginnings of the City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qualitative and Quantitative Changes in Human Interactions Within the City . . . . . . . . . . . . . . Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Information And Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Technology, Integrated Technology, and Responsive Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . Architecture of a Technology Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 7 7 8 10 10 13 13 14

P. James (*) Political Science, College of Social Sciences and Professional Studies, University of WisconsinParkside, Kenosha, WI, USA e-mail: [email protected] R. Astoria Political Science, University of Wisconsin-Parkside, Kenosha, WI, USA e-mail: [email protected] T. Castor Communication, University of Wisconsin-Parkside, Kenosha, WI, USA e-mail: [email protected] C. Hudspeth Philosophy, University of Wisconsin-Parkside, Kenosha, WI, USA e-mail: [email protected] D. Olstinske Applied Professional Studies, University of Wisconsin-Parkside, Kenosha, WI, USA e-mail: [email protected] J. Ward Geography/GIS, University of Wisconsin-Parkside, Kenosha, WI, USA e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_2

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Institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Triple Helix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Institutional Logics Connecting Actors, Activities, and Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Climate and Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction: The Green, Resilient Cosmo-Polity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The “Old” Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Smart Grid, Distributed Energy Resources, and the City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

This introductory chapter identifies key fundamental charactistics of a smart city. First and foremost, however, a smart city is based on humans and human interaction for a common purpose. These interactions are often successfully and synergistically organized through university-industry-government-citizens collaborations. A further distinguishing feature of smart cities is the unprecedented amount and types of data that can now be collected and produced through digital technologies. Technology also allows the city to prioritize interactions and establish dynamic relationships, allowing continuous identification and resolution of challenges. Regardlesss of the evolution of cities, they continue to be political and social entities; technology must be developed and implemented in line with the needs of citizens so that the end result is an increase in well-being and responsiveness. The top four research themes in smart city research are: (1) Technology (29%), (2) The nature of smart cities (17%), (3) Models and frameworks (13%), and (4) Policy and strategy ( 8%). Smart city innovators may want to start at the end and work backward.

Introduction Popular attention turned to the smart cities concept in 2010 when IBM initiated the first smart cities challenge, donating $50 million in technology and services to 100 cities. In 2014, Songdo, South Korea, was declared to be the first smart city. Songdo was built from the ground up with an intentional technological foundation supported through Cisco Systems; in 2019, the technology works but the city inhabitants number only one third of the projected population. Technology without community. While there are more “new” cities like Songdo (King Abdullah Economic City, Saudi Arabia, Treasure Island, San Francisco Bay Area, Masdar City, and Abu Dhabi), the great majority of development is in existing cities. All cities need to be smarter, as 68% of the population in 2050 is expected to be living in urban areas. Development plans will increasingly be focused on the modification and modernization of infrastructure, services, and economic systems; the demands on physical space will require the expansion of digital space utilization. Add concerns for equity, inclusivity, resilience, and responsiveness, and there is a concomitant need to

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integrate and connect across all city activities, including the public and private sector, and the most important component – the citizens. In 2017, the largest market for smart city products and development was in Europe; however, spending on smart cities development is projected to grow by 37% in Asia-Pacific, 27% in Latin America, 23% in the ME and Africa, and 14% in North America by 2020 (Smart city strategies: A global review 2017. Catapult Future Cities). Smart city development has proceeded in two phases. Initially, private companies promoting technology dominated the activity, resulting in the insertion of isolated technological products that were not strategically embedded in the dynamics of city life (Townsend 2013). While this was an important first move to introduce, and gain acceptance for, the innovative changes that technology might offer, it became apparent that the technology needed to be more thoughtfully embedded into the larger governance relationship with its citizens. With this move to the social and political consideration of citizen engagement and wellbeing, more consideration was given to equity, inclusivity, resilience, and responsiveness in the second phase. A result of this realization was that technology needed to be considered with a larger urban development framework, accessible to citizens, and integrated/connected within an analytic process. Smart city applications have not necessarily followed this linear description. Mosco (2019) identified three drivers that can catalyze the development of a smart city: state-driven; private or corporate driven; and, citizendriven. Depending on the primary driver, the operational logic of a smart city may have implications not only for its general functionality but for the impact that the smart city network will have on citizens and the impact citizens will have on the city. Many cities remain focused on technology; others rely on a governance model; others focus on citizen wellbeing and engagement; still others focus on sustainability. It is due to this diversity of definitions and applications that various indices will identify different cities as being smart, according to different criteria. Table 1 lists the three top ranking smart cities according to three separate indices with variable Table 1 Select indices ranking smart cities, with top three cities Source Smart City Strategy Index (SCSI) (Roland Berger 2019) IESE Cities in Motion Index (2018)

Statista (2019)

Dimensions Action Fields; Policy & Infrastructure; Strategic Planning

First Vienna, Austria

Second London, UK

Third St. Albert, Canada

Human Capital; Social Cohesion; Economy; Governance; Environment; Mobility & Transportation; Urban Planning; International Outreach; Technology transport & mobility; sustainability; governance; innovation economy; digitalization; living standard; expert perception

New York City, US

London, UK

Paris, France

Gotheberg, Sweden

Bergen, Norway

Stockholm, Sweden

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dimensions of “smartness.” The multiplicity of indicators demonstrates that smart cities are diverse in their planning, applications, and values. A broad definition of a smart city is an urban environment where technology allows for an efficient relationship between data and its applications in order to provide an environment that is responsive, resilient, and healthy. The functional aspects of this relationship are that a smart city is more immediately responsive, predictive, adaptive, and is capable of learning. The outcomes of a smart city include sustainable and healthy lifestyles, economic efficiency, political and social inclusivity through equitable engagement, and ability for all public and private residents to flourish. These ideal outcomes must be balanced with the increasing possibility of surveillance, lack of control and consent in regards to privacy in data collection, and the profiling of “normal” populations (Sadowski and Pasquale 2015). The smart city concept may appear to be revolutionary, but upon close inspection is more evolutionary. Five areas will be introduced in this chapter, to be elaborated by others in future chapters: (1) Human Interaction, (2) Institutions, (3) Data, (4) Technology, and (5) Climate and Energy. Human interaction focuses on issues of privacy and security, media richness, attitudinal adaptation, and human flourishing in a technological environment. Individual interactions are taken to an institutional level, where the emergence of the triple helix model offers the ability to frame technological development within and between a partnership of universities, governments, and private interests. Data driven decision-making for individuals and organizations is a logical progression from the ability to process big data and to provide linkages between edge computing and deeper learning. Information Technology focuses on the foundation of IoT platforms, wireless connectivity and access, and the use of blockchains to manage information, records, and ownerships in a secure cloud setting. Finally, Climate and Energy considers the efficient and sustainable use of energy, through monitoring and adaptation systems, as well as the distributed energy resources (DER) available within a smart city environment, and serves as an illustrative application of previously discussed concepts. Cities are large human settlements (Kuper and Kuper 1996). Although scholars debate when the earliest cities were formed and have attempted to define criteria for identifying ancient large settlements as cities (Childe 2008), it is generally agreed that sometime around 5000 years ago humans first began to engage in agriculture and to settle in large population densities. Whether agriculture led to the development of cities, or cities led to the development of agriculture is still a matter of debate (Bairoch 1988). Regardless, settling together people created access to benefits and shared resources such as ideas, transportation networks, natural resources, markets, and cultural amenities all of which continue to spur the growth of cities. Thus, cities are social and political entities; technology must be developed and implemented in line with the needs of citizens so that the end result is an increase in well-being and efficiency. The top four most common research themes is smart city research are: (1) technology (29%), (2) nature of smart cities (17%), (3) model and

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frameworks (13%), and (4) policy and strategy (8%). Smart city innovators may want to start at the end with the fourth category and move backwards.

Human Interaction Fundamental Beginnings of the City In Plato’s Republic, Socrates gives an account of the perfect city. He suggests that cities are “the product, apparently, of our needs” (Plato 2000, p. 51). He then begins with the necessities – food, shelter, and clothing – and proceeds to the things necessary for those necessities – ploughs, tools, hides, and cloth. But meeting bare necessities is not enough because, as Glaucon notes, “we are not a city of pigs” (Plato 2000, p. 55). So, residents turn their attention to luxuries necessary for a city – incense, perfumes, and cakes. Thus, the underlying question that drives Plato’s account is “what helps human flourishing?” Anything that harms the well-being of citizens is excluded from Socrates’ city and anything that enhances their well-being is included. Although the world has become more complicated as technology has advanced, this question always remains. Perhaps it is masked by other questions but it always remains fundamental to the design and creation of a city. Should the city have a public water supply? Should the city have a sewer system? Should it have walls? Should it have open spaces? Should it have schools, police departments, and fire stations? Should it have roads, sidewalks, paths? Should it have public Wi-Fi and Internet? No matter how technology changes, the question of human flourishing remains the guiding thread of a city’s development. Politicians and developers that ignore this question do so at their peril. This question is carried forward into the Modern Era. For example, Jane Jacobs claims that “We need [diversity] so city life can work decently and constructively, and so the people of cities can sustain (and further develop) their society and civilization” (Jacobs 1992, p. 241). Working decently and constructively is not an end in itself; those are only means towards the ends of people developing their society and civilization. Jeff Speck argues that walkability answers the question “how can these typical cities provide their citizens a quality of life that makes them want to stay?” (Speck 2013, p. 4). In his account, improving the walkability of a city improves the well-being of the people that live there. And Charles Montgomery argues that “the city should strive to maximize joy and minimize hardship” (Montgomery 2013, p. 43). It is easy to get caught up in the flash and sparkle of new technology, to get lost in the marvel of innovation, but one must never lose sight of that fundamental, underlying goal: to help humans to flourish. Focusing on that goal will, of course, lead to another question: what does it mean to flourish as a human being? Although it may seem that no answer is possible to this question because of the complexity of human experience, the problem is not, in the broad strokes, that difficult – Plato has already given us a road-map for that.

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Food, shelter, clothing, and some things that raise existence above mere subsistence are necessary not merely for survival but also for well-being. In addition, in order to flourish, humans need to interact with others – humans are social animals and, so, cities must not only take that aspect into consideration but they must create spaces to encourage and support social lives. Humans like various forms of entertainment, which means that cities need to provide venues for games, music, theater, and other forms of expression. Humans also need to be productive. This does not mean that humans need to be a cog in some industrial machine but rather that humans like accomplishing things. Some fulfill that need through their jobs while others do it through their hobbies. A successful city, then, must also provide for the opportunity to engage in pursuits that let people feel that sense of accomplishment. So, while smart city technology and governance can provide more efficiency and responsiveness, it must not do so at the expense of the individual’s need to be unique, independent, and free of what Foucault calls “governmentality,” a system of modern government that overly shapes and affects the conduct of the population as a whole. Humans also have a need for privacy and security. These two are extremely important when considering Smart Cities because of the way technology challenges the relationship between these two. The first thing to note is that privacy and security are not synonymous. Privacy is about limiting access often to the point of being alone. Security is about being protected from danger or harm. The Hope diamond is not private but is very secure while a walk in the woods is not secure but private. For centuries, one of the surest ways to keep something secure was to keep it private – a Polaroid can only be seen by those it is shown it to but a jpeg can be posted on the Internet for the world to see. The interconnectivity of the myriad of household devices, when combined with the power of artificial intelligence will make public much of what citizens have been accustomed to be private. Information that had previously only been available to close associates is now collected and stored on remote servers in the hands of corporations, governments, and data brokers. Are the changes being wrought to conceptions of privacy and security – of which things are private and which things are secure – helping human beings to flourish? Would losing this or that bit of privacy enhance well-being? How can one promote happiness in Smart Cities knowing that by their nature, they fundamentally change which things are private and degree to which those things are private?

Qualitative and Quantitative Changes in Human Interactions Within the City Advanced technologies and Smart Cities alter forms of human interaction by changing how human interaction is mediated and through the inclusion of nonhuman entities as interactors. Because of the emphasis of Smart Cities on technology, attention to communication in smart cities has tended to focus on the use of communication technologies, information dissemination, and the use of data. Media scholar, Marshall McLuhan in the mid-twentieth century wrote of technological determinism, as epitomized through his phrase “the medium is the message”

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(1967). Through this concept, McLuhan highlighted how the very form of a given media shapes interaction through the type of physical cues that are available. For example, a basic telephone allows one access to sound (aural information) and to speak to another. However, video-conferencing now allows one access to sound as well as sight (visual information). While various technologies enable communication, there are ways that they constrain communication because of the limitations in media richness or number of available cues afforded by those technologies. There are four dimensions to this concept: personal focus, immediacy of feedback, conveyance of multiple cues, and variety of languages that can be conveyed. In one sense, the Smart City is an enabler of human interactions through increased technology use. For example, in the South Korean smart city of Songdo, educational instruction can take place through video conferencing (Tanaka 2012), and neighbors can talk to neighbors through video (Poon 2018). However, this ubiquity of technology has also earned Songdo the reputation of being a “lonely” city as neighborly contact often takes the form of video calls rather than face-to-face chats (Poon). Also, a consequence of over-emphasizing mediated interaction is decreased attention to those who are physically present, digital addiction, and decreased “immediacy” and closeness. Interaction in the digitally mediated city includes information, but it also includes other functions of communication such as relationship formation. Castells (2011) writes of the “rise of the network society” where networks of technologically mediated human interactions characterize the new economy and will hail radical changes in organizational structures. Social media and the mass sharing of data also allow for increased human interaction in the sharing of individual information on a mass scale. As this data is collected, however, it can have unintended consequences. For example, Strava is a fitness app that tracks one’s walking and running information. This information in turn can be uploaded and shared via social media to share one’s fitness progress with others for support and to be part of a fitness community. However, Strava data also had unintentional consequences when in 2017, Department of Defense officials realized that military base locations and configurations could be determined by identifying Strava running route heat maps generated by military personnel overseas (Kronisch 2019; also see Cooren 2019). Another area of change in human interaction in smart cities is that of humancomputer and human-machine interaction. Increasingly, humans will be interacting with computers, robots, cobots, machines, and artificial intelligence rather than solely with other humans. Guzman (2018) elaborates on this distinction by explaining that “in human-machine communication, technology is conceptualized as more than a channel or medium: it enters into the role of a communicator” (emphasis in original, p. 3). The distinction is one where interaction is not just about information dissemination but one of “meaning making.” Computers, machines, and other advanced technologies are not just tools but are interactants that affect how people make sense of the world. For example, some of the collective research findings in Guzman (2018) include identifying how robots are socially constructed through various marketing materials, how knowledge that one’s communication will be evaluated by a robot will cause more anxiety as compared to human evaluation,

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and how social robot persuasion can be just as effective as human-generated persuasion. In 2015, Boston Dynamics created an Internet stir when they released a video of a man kicking a robot dog (Parke 2015). While this video was intended to be a demonstration of robot dog Spot’s agility and maneuverability, viewers of the video expressed concern about “robot abuse” and the ethics of how robots may be treated. Within smart cities, there may be several opportunities for human-computer or human-machine interactions in areas such as transportation (e.g., taxi robots), service, delivery drones, and more. In addition to service areas, humans may interact with robots in the workplace. Each of these areas has their own advantages as well as concerns. Attitudes towards such technologies may range from positive due to assistance afforded through these technologies to concern or feeling threatened. The advanced technologies of smart cities are not just tools but are now a part of the equation of human interaction.

Data Cities require data in order to operate. Cities provide systems for housing, transportation, sanitation, safety, utilities, land use, and communication that provide benefits to people. On the other hand, large settlements also involve costs such as higher crime and pollution. Cities grow because the benefits of living in large settlements outweigh the costs. Whether it is deciding how much food to grow, where to build housing, how much to tax people, or which areas are impacted the most by crime, data has played an essential role in the operation of a city. Smart technologies enable smart cities to gather and process a greater variety of data at a scale and speed that hitherto has not been seen in human society.

Big Data In the past, data existed on paper. Today and in the future, an increasing amount of data used to manage cities is collected, stored, and processed over the Internet, and thus exists in cyberspace. The term “cyberspace” first appeared when Danish artists Susanne Ussing and Carsten Hoff began creating works of art under the moniker Atelier Cyberspace (Lillemose and Kryger 2015). However, in its modern usage, the term can be credited to William Gibson who described “cyberspace” in his 1982 short story “Burning Chrome” as “A graphic representation of data abstracted from banks of every computer in the human system” (Gibson 1982). Others credit John Perry Barlow with using “cyberspace” in its current definition (Barlow 1990). The Pentagon has formalized the definition for their own purposes (Shachtman 2008). The term “big data” first appeared sometime in the 1990s (Lohr 2013). “Big data” is a term with no solid definition. Big data refers to the large and increasing volume of data available (Hashem et al. 2015). In general, it refers to having an amount of

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data that surpasses the technological ability to store, manage, and process (Manyika et al. 2011). Big data is often in a nonhuman readable format and also requires large computational power for processing and analysis (Hashem et al. 2015) in order to translate the data into valuable insights (Hashem et al. 2015). The most important thing about data is that it informs decision-making (and can be used by machines to make decisions using artificial intelligence) and shapes the decision framework. In the cities of the future, people will be increasingly connected to the internet and will be both creating data as well as serving as entities about which data is collected. In addition, an Internet of Things will exist whereby smart devices such as vehicles and buildings will be connected to the Internet via sensors, software cameras, microphones, radio frequency identification, and wireless sensors that allow these devices to collect, exchange, and utilize data. This will produce a tremendous amount of data. The amount of data is almost unimaginable and necessitates a new paradigm for dealing with it. The volume of data in the world is predicted to grow 40% per year and 90% of the data has been created in the last 2 years (Waal-Montgomery 2016). Today it is estimated that the world produces about 2.5 quintillion bytes of data per day. The United States alone produces 2,657,700 gigabytes of data on the Internet every minute (Hale 2017). This presents many challenges for utilizing big data in decisionmaking. Current technology is not capable of dealing with this amount of data in terms of data capture, storage, analysis, searching and querying, sharing and transfer, visualization, updating, privacy, and security. Big data is often described by characteristics called the “Vs” that provide a framework for understanding and utilizing it. The “Vs” are (1) volume, (2) variety, (3) velocity, and (4) veracity (Marr 2014; Zikopoulos et al. 2012; Manyika et al. 2011). Volume refers to the quantity of data being collected and stored using various devices. Volume plays an important role in the utilization of data. Big data offers greater insight and has the potential to reveal hidden patterns during analysis, but also requires new technologies to collect, store, and process. Variety refers to the type and nature of the data being collected and stored from various devices. Examples of data types include text, video, images, audio, and data logs. About 80% of data is considered “unstructured” and therefore cannot easily be put into the type of database tables currently in use (Marr 2014) Velocity refers to the speed of data generation and transfer. Big data is often collected in real time and can be continuously produced creating a very large volume of data. Velocity can also refer to the processing and turn-around time for data utilization. Veracity refers to the quality and value of the data. This is sometimes called the “messiness” or “trustworthiness” of big data sets. Quality and value can vary quite a bit and directly affects the usefulness of data in analysis and predictive modeling. In addition to the characteristics described by the “Vs”, big data can be classified into categories based on five additional aspects (Hashem et al. 2015). These aspects are (1) data sources, (2) content format, (3) data stores, (4) data staging, and (5) data processing (Harshem et al 2015).

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Data sources include social media (data generated by URL to share or exchange information and ideas in virtual communities and networks), machine-generated data (automatically generated by computers without human intervention), sensing (using devices to measure physical quantities), transactions (financial and other data that require a time dimension), and Internet of Things (smart phones, cameras, other devices connected through the Internet that serve economic, environmental, and health needs). Content format refers to whether data is structured (numbers, words, dates that are easily formatted using current database technology), semi-structured (data that do not follow conventional database system design), and unstructured (text messages, videos, social media data, etc. that do not follow a specified format). Data stores refer to the structure of data storage. This includes document-oriented (storing documents in a database based on rows or records), column-oriented (storing data in rows/records and columns), graph database (storage in a graph model utilizing nodes, edges, properties), and key-value (utilizing row keys to store large datasets in relational databases). Data staging refers to the processes of cleaning (identifying incomplete or unreasonable data), transforming (changing data into a usable format), and normalizing (structuring data to minimize redundancy) data. Data processing refers to ways in which data is collected and utilized. This includes batch processing of large subsets of data, real-time collection, performing analysis using cloud computing, performing operations using the distributed power of decentralized networks, etc. In addition, smart cities will likely rely on a combination of centralized computers as well as a decentralized network. Whether it is the location of buildings, people, or data collection devices, location plays a major role in the management of cities. Geographic Information Systems (GIS) can play a major role in smart planning, smart policy-making, sustainable practices, smart management, smart services, and smart end-to-end solutions (Deogawanka 2016). In a future of smart cities, big data on the Internet will play a primary role. Data will be collected everywhere on everything. As the twenty-first century begins, some of the major concerns surrounding big data involve privacy and security. Data will continue to be collected not only on “who” people are and “what” people are doing but also “where” they are doing it. Crampton (2010) discussed this in the context of what he calls the “biopolitics of fear.” This involves the utilization of data for nefarious purposes in order to wield power over others. The three stages discussed are: (1) divide the population into groups of “us” and “them,” (2) engage in geosurveillant technologies, and (3) creating a risk-based society. Smart cities must create ways to keep the data they collect both private and secure. Yet this also requires experts to analyze the actual level of privacy and security provided by technology. Claims about increased security of data lead to an increase in the quantity of confidential data being communicated, yet in the absence of end to end encryption, data is not truly private or secure despite corporate claims (Dellinger 2019). This same concern about the true level of privacy and security applies to

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cellular network security as well (The Threat Lab 2019), a technology that will be used widely for data collection by smart cities. In addition, the accuracy and utilization of things like facial recognition (Ravani 2019) and aggression detectors (Gillum and Kao 2019), and the rise of surveillance capitalism and data brokers (MacMillan 2019), present challenges for smart cities, as does the application of algorithms in types of data analysis and decision-making such as predictive policing (Weisburd et al. 2004). At the same time, big data provides an opportunity for subjugated voices to be heard. As more and more people create data and have data collected about them, opportunities will be created for everyone to get their voice out. Smart cities need data, lots of data. What is called “big data.” This data will be collected, stored, and processed in order to aid in decision-making. However, big data is so large in volume that current technologies are limited in terms of its use. As technologies are developed to work with big data, it is of the utmost importance that measures are taken to ensure data privacy and security. In addition, it has been argued that at best big data provide information on the past and present but not the future. In order to effectively plan for smart city, development predictive models must be developed to work with big data.

Information And Technology Technology, Integrated Technology, and Responsive Technology Advances in technology in the last three decades have made smart cities possible. Even while technology enhances the ability to collect data, it is the advances in connecting, analyzing, and relating data, both in real time and in predictive analyses, that provide the foundation for smart cities. The relationship between smart devices and their potential to connect to the real world was coined in 1999 as “The Internet of Things” Ashton (2009). Statista estimates that there will be 75 billion devices in 2025 (https://www.statista.com/statistics/471264/iot-number-of-connected-devicesworldwide/). These devices are not only talking to us; they are talking to each other, which provides the power of the network, which is the foundation of a smart city. Smart cities are built upon the creation of technology relationships able to dynamically contextualize lots of data. This allows for dynamic decision-making at the point of collection and the technology infrastructure to increase efficiency in government services and activities. It also allows for long-term adaptations to create resiliency in the system. Both long- and short-term activities result in a city platform that is continuously responsive to the needs of its citizens. This capability does not emerge all at once; technologically speaking, there is an evolutionary pattern towards the smart city that allows movement from technology to integrated technology to responsive technology. Five stages can be identified: (1) Measurement technology allows sensors to be implemented to collect data that can be used to monitor operational status, (2) Networked technology connects these

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sensors allowing the exchange of data, (3) Managed systems provide for real time analysis of the information collected from sensor arrays, (4) Integrated systems make the data and analyses available across intra and intercity systems, (5) Smart technology systems provide software as a service (SaaS) so that individuals, businesses, and community organizations can access services, manage participation, and directly integrate with the platform.

Architecture of a Technology Platform A full smart city platform can be conceptualized as having four “layers”: a sensor array to be the gateway to edge computing; the fog as a decentralized distributed network to connect devices to a remote server; a cloud which is more centralized and offers the capability for analysis, prediction, and adaptation; and finally a neural network core where independent learning occurs without human initiated or managed intervention. Arguably the neural network is not separate from the cloud; indeed, as a connected system, none of the four layers are separable from each other. As a heuristic image, however, this provides some capability to separate according to primary functions. Sensors represent that level of technology that is closest to real time data creation. Sensors, themselves, are not new; the first sensor was a thermostat invented by Warren S Johnson in 1883. The first motion detecting sensor was invented in the 1950’s. However, the exponential link in sensor technology occurred with low cost– low power sensors that, in addition to triggering a local actuator, could transmit data to the cloud to be used for long term analysis, interpretation, and system adaptation. The maturation of sensor technology, in step with the connective capacity of 5G, has resulted in speculations that trillions of sensors could be implemented in the coming years. These embedded systems will be in light poles, buildings, cars, and potentially in humans (Maenaka 2016). In the foggy cloud, the most promising development for political, economic, and legal institutions is block chain technology. This peer to peer decentralized recordkeeping and transaction enabling system was developed as a foundation for the bitcoin trade invented in 2009. Blockchain offers significant changes for banking, trade, and currency management by providing transparency and security in an unalterable ledger system between peers. But it can do the same for the judicial system in the provision, interpretation, and verification of evidence and testimony. It can be used in the management of a smart electrical grid. And, it can be used for a transparent and secure voting process. And it does this without the need of any external management entity. As a peer to peer transactional system, it allows the participants to set their transactional rules, and removes the need for the middleman. Traditional banking institutions, voting systems, and any recordkeeping system based on the legitimacy of secure sites for holding currency, securities, information, etc., will no longer be needed. And, the removal of these participants as mediators will speed up the transaction process, regardless of spatial distance. As of 2019, blockchain voting apps have been piloted in West

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Virginia midterm elections and Denver’s municipal elections. San Francisco and New York City are among the cities using the most block chain applications in 2019. At the core of smart city platforms is the deep learning in neural networks. In a neural network, the algorithmic system prioritizes problems and works to solve them on its own. Neural networks have the ability to identify submerged and complex patterns, learn from their interpretation, and make decisions that reflect the existing data. Yet, because they are connected to the edge computing sensor devices and the processes in the cloud, they are constantly open to modification by the addition of new data. The deep neural network processes data by sifting and winnowing raw data, identifying high level features at various levels of analysis; this learning process is then stored in memory and accessed as needed in analyzing similar problems in the future. The random access does not prioritize the immediate past, and it provides a sample of experiences that might be different each time, thus allowing a more complete learning process. Deep learning permits the smart city to reassess policies and services, not in an immediate frame, but rather in a process that allows for large amounts of dynamically accessed data to be used to make decisions on major modifications or adaptations in city activities. Use cases for deep learning include water consumption and conservation, energy management, public health, and economic development (Mohammadi and Al-Fuqaha 2018). As cities become smarter and IoT devices more integrated, the need for reliable and rapid connectivity will increase dramatically. This is especially important for transportation systems, so that information communication and feedback can be consistent at all times. This area has yet to be completely solved, even with 5G technology. 5G allows for high density usage but can be expensive to install and, even though it provides more bandwidth and speed than its predecessors, has more difficulty travelling at long distances and around infrastructure. For cities, this means that uneven coverage can be expected, and certain areas might experience degraded signals. Cities can explore combinations of other technologies with 5G such as a mesh network and dynamic network slicing to partition the 5G network to customize the network according to various use cases.

Institutions The Triple Helix As was suggested earlier, technology enables smart cities, but it does not define them. All cities are political social and economic systems; smart cities utilize technology to improve governance, but also to introduce new dynamics into governance through integration and systems learning. Although there are many actors that can shape the development and implementation of technology in a smart city, there is a growing acceptance that the triple helix model of university-industry-government relationships (Etzkowitz and Leydesdorff 1995) can provide the balanced

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configuration needed for innovation and knowledge development. (Etzkowitz and Leydesdorff 2000) At the points of intersection of the three institutions in the helix, innovation is most likely. The complex coexistence within the helix also suggests that the institutional interactions differ from “business as usual” in that they are collaborative rather than competitive, parallel rather than hierarchical, and connected rather than separate. Citizens in a smart city desire collaboration and participation in accord with the culture of their communities. For the sustainability of smart cities, citizens must contribute to the formulation of policies and urban development. This contribution is in the form of direct citizen participation in urban policy decisions, not just as beneficiaries. As active citizens, they express their wishes and seek solutions. Within the triple helix model, the University, as a key actor, supports the growth of “Smart people” through educational advancement opportunities, attracting engaged citizens, and assisting in identifying entrepreneurial opportunity for citizen participation Giffinger et al. (2007). The intersection of Industry and Citizen interests is found in the iterative decision-making between producers and consumers, where each actor can take on either role. The triple helix model combines the academic resources of universities, industries, and governments of civil society. As the smart city movement evolves across the world, the progressive leaders are focusing on a new quadruple helix model where citizens are embraced as partners and are becoming an integral fourth agent. With citizens as partners, the silos of universities, industry, and government begin to break down and all dimensions within the quadruple helix work collaboratively to drive modernization and transformation for the betterment of the city as a whole. A smart city designation is not simply a city that has integrated more technology; the goal of a smart city is to enhance the quality of life of its citizens through advanced technology that allows for a bottom-up policy approach rather than a top-down approach dictating passive citizen engagement. The citizens are the knowledge base when it comes to characteristics and problems within areas of the city and must not be excluded from the policy decision-making process. The quadruple helix provides an elevated and macro level perspective on the actors in a smart city; when unpacked it demonstrates a complexity and variability within cities depending upon the urban culture. Smart cities are difficult to define because of this characteristic. Technology is neutral; different cities manifest different combinations of actors, agendas, capacities, and values which will affect the way in which the technology is prioritized, utilized, and integrated across services and policies. Raven et al. (2017) take an institutional perspective on three cities in Japan, Germany, and the Netherlands and find that they present very differently depending upon the degree to which public private partnerships exist, the involvement of citizens in decision-making, and the influence of national governments, among others. The institutional logics of these cities also represent varying levels of actor involvement and intersection, and the nature of the core organizing structures. So as smart cities mature, it becomes apparent that they are not unique social or political forms; rather, they represent the next stage of urban development in a larger framework of governance and engagement.

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Institutional Logics Connecting Actors, Activities, and Roles As technology becomes more ubiquitous, attention is increasingly turning towards the institutional and organizational patterns that affect its implementation and impact. Still, research into the political and social influences on smart city development is relatively recent. Pierce et al. (2017) provide a useful framework for understanding the political processes that can affect the amount, type, and sustainability of smart city developments. The framework is composed of five dimensions: actors, city subsystems (for example, water), activity layers, actor roles, and institutional logic. The five dimensions can be conceptualized as follows: within a specific city subsystem, select actors will adopt roles that are located at one or more of the activity layers of the subsystem. The ways in which the actors interact, adopt their roles and implement their agendas will be defined by an institutional logic, which consists of rules, regulation, and behavioral expectations. Pierce et al. identify three activity layers within a subsystem: (1) Service, (2) Digital, and (3) Environmental and the Roles are categorized as (1) Idea generation and development, (2) Creation and maintenance, (3) Analysis, and (4) Governance. See Fig. 1. The immediate takeaway from the organizational framework is that it has the capacity to represent a large number of combinations, even for each actor. As more actors become involved, the character and culture of the development and implementation of smart city technology becomes increasingly complex and unique to the particular case. Pierce et al. identify eight distinct institutional logics that may be present to regulate actor behavior within these configurations: innovation logic, bureaucratic logic, equality logic, environmental logic, commons logic, co-creation logic, predatory logic, and classic market logic. Because the last two forms describe behaviors that are representative of competition, control, and exploitation, they may not be appropriate for a smart cities framework. However, the other six logics can regulate smart city relations. Even while citizen engagement has been touted as one of the benefits of smart cities, less work has been done on the means by which citizens can be engaged and in what capacity. The quadruple helix suggests centricity of citizen involvement in smart city culture, but little work has been done on how well citizens are engaged Fig. 1 A one actor, one city subsystem, illustration of the Pierce, et al. model

Actor

Activity Layer

Roles

Service

Idea generation & development

Digital

Creation & maintenance Analysis

Environmental

Governance

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into the smart city decision-making framework and how that process depends upon existing institutional logics and city culture. Castelnovo et al. (2016) provide a policy assessment framework on five dimensions intersecting to create four quadrants of agenda defining behavior for citizen engagement (Fig. 2). Using the logic structure of the proposed smart city’s governance holistic assessment framework for citizen engagement and governance, six of Pierce et al.’s logic models can be superimposed as to where they might be most descriptive as behavioral expectations and regulations for the inclusion of citizen engagement into the organizational fields of decision-making. Co-creation logic, representing data and knowledge sharing, collaboration, and participatory decision-making, is at the center of citizen participation, where citizens are both consumers and producers of products and services. Information can be dynamically and iteratively revised, and the leaderfollower roles are interchangeable. Depending upon the quadrants of policy-making defined by the dimensions above, one institutional logic can be more helpful than others.

Fig. 2 Super-imposition of Pierce et al.’s institutional logics on Castelnovo et al.’s model of decision-making content emerging from the intersection of policy dimensions. Responsibility for this combination is solely on the author

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The remaining institutional logics can all enable citizen engagement, albeit to different degrees and manners, reflecting again the complexity of smart city development as it reflects the city’s values and culture. Institutional logics need not be static; while they reflect a culture, they can also be used to change a culture towards a different direction and, as such, are instruments of change as well as descriptors of states. This approach was successfully accomplished in the Reno-Sparks region of Nevada. Reno-Sparks became a host for Tesla when the company opened production facilities in 2016. Reno-Sparks had little time to plan for the challenges posed by this opening in terms of transportation, housing, population density, and utilities. And, the region needed a cultural shift in its institutional logic. The actor that was most influential in shifting the logic was the Economic Development Authority of Western Nevada (EDAWN). EDAWN manifests qualities much like other economic development and workforce development associations throughout the United States, in that it has the capability of bridging private and public interests, and can initiate shifts in culture with less perceived bias than others more solidly located in the public and private sectors. Successful Public Private Partnerships (P3) in the United States have often benefited from the integrating work of a middleman organization to interpret, translate, and negotiate relations between traditionally isolated actors. In addition to the relationship characteristics of actors, exogenous factors such as economic capacity, experience with public private partnerships, and population characteristics are relevant factors. In the United States, emergent smart cities are large population centers with strong economies. In 2019, Racine, Wisconsin became the smallest city to receive a smart cities grant, signaling that smaller urban centers have the potential to use technology effectively. With the increasing interest in technologically enabled change, smart cities are being joined by smart regions, smart counties, and smart states. The diversity in the political units utilizing technology to become smarter suggests that the configuration of organizational fields and institutional logics require careful attention.

Climate and Energy Introduction: The Green, Resilient Cosmo-Polity Municipal governments are at the nexus of several ground-level climate governance challenges. Municipalities often manage the immediate harm of climate disasters and implement long-term clean-up and rebuilding efforts. They also have primary jurisdiction over various land-use policies which must anticipate the new climatic regime and buffer its citizens against its deleterious impacts. With respect to response, rebuilding, and anticipating, then, municipalities are responsible for incorporating biophysical resilience into their built environments. At the same time that global warming places physical structures under duress, humans are expected to continue their migration to urban environments, some because of climate-induced distress. Upper end estimates of climate-distressed dislocations approach 1 billion people by 2050, or about 1 of every 9 humans. The

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welcoming of this diversity of individuals into settled social patterns is expected to strain social cohesion, so much so that, some think the Westphalian nation-state shall not endure the new climate regime. In addition to biophysical resilience, global warming, then, calls upon municipalities to build social resilience into their political, economic, and social forms. According to the recent IPCC 1.5C report, humans must abate greenhouse gas emissions to zero by 2050 in order to avoid increasing global average temperature by more than 1.5C. Virtually all mitigation pathways which avoid 1.5C increase also require the extensive deployment of carbon dioxide removal (CDR) technologies and methods. Further, at the present rate of emissions, the carbon budget will be exhausted by 2030 (IPCC 1.5C). Globally, cities account for about 70% of all greenhouse gas emissions and municipalities have jurisdiction over zoning laws, building codes, and infrastructure decisions which “lock in” patterns of energy use and greenhouse can gas emissions for decades. Climate governance, then, also calls upon municipalities to configure the built and social urban environments so as to abate greenhouse gas emissions. Accelerated in reaction to the Trump Administration, municipalities around the world have pledged to meet international greenhouse gas mitigation obligations and are leading the development of climate policy and climate governance. Under the auspices of various compacts, such as the C40, these municipal pledges operate across national jurisdictions, imparting a trans-national, multilateral element to municipal management. An adequate response to global warming then requires of municipal governments that they respond to and anticipate climate-disasters, increase the total amount of energy resources available to their expanding constituents, sharply reduce both total and per capita greenhouse gas emissions, secure equitable access to the energy resources, and to do so in transnational dialogue and partnership. In the context of climate and energy, the emerging smart city ideal is the green, resilient cosmo-polity. Amongst other trends, individuals, utilities, and municipalities are imbricating new energy technologies into both the greater electrical grid and personal spaces. This smartening and greening of the use of energy technologies exemplifies in many ways the challenge cities confront when they attempt to become smart. New governance structures are required to support green energy technologies; those technologies generate big data which must be transitioned into management decisions, the privacy and security of the individual is endangered, and those technologies’ promise of inclusion could vanish into even more extreme social and economic divisions.

The “Old” Grid In the United States during the early twentieth century, two governance models competed for control of the urban electrical power industry. Under the now-prevailing model, investor owned utilities (IOUs) received a franchise monopoly from the state. The franchise monopoly preserves the utility’s return on investment while obligating it to provide service to all within its franchise territory.

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Wisconsin pioneered this model in 1907 and virtually every state adopted it shortly thereafter. Municipal “public power” was the main competitor to the IOU model. Under the municipal public power model, municipalities would establish and finance a utility to provide electrical power to its citizens. Wisconsin adopted the IOU model to settle the political conflict between public power advocates in Milwaukee, Wisconsin’s largest city, and the local electrical power company. In this struggle to control the benefits and cost of the emerging electrical power sector, municipalities largely lost their legal authority and administrative capacity to establish public power systems, which some cities are struggling to regain as they attempt to meet their climate governance responsibilities. The Wisconsin model supported large, centrally controlled fossil fuel plants which served geographically and jurisdictionally dispersed customers. Unique to the electrical power industry, generation and loads (supply and demand, in economic discourse) must be perpetually and instantaneously matched. Under the Wisconsin model, the utility had the primary responsibility and control over this matching, which it did by increasing generation in response to increasing loads. Utilities accomplished this matching with leading-edge (at the time) econometric modeling, sophisticated marketing, and pricing strategies to induce load building, and precision monitoring. The “flow” of electrical power was from utility to customer and the gird was “unidirectional.” Governments (but not utilities) largely took load (or customer behavior) as a natural manifestation of autonomous consumer behavior, and thus not a subject of governance. The emergence of renewable technologies, in conjunction with the urgency of mitigating greenhouse gases, has distressed the Wisconsin model and opened new opportunities for electrical power generation and use. The imbrication of these technologies into the energy system presents a variety of governance challenges to the green, resilient cosmo-polity.

The Smart Grid, Distributed Energy Resources, and the City Distributed energy resources (DER) disturb the grid’s unidirectionality. Rather than “production,” these new technologies are “resources” and rather than “central” they are “distributed” and operate at the “grid edge.” DER include, inter alia, fuel cells, demand response (receiving payment for curtailment of demand during peak hours), combined heat and power systems, thermostats, batteries, solar PV, micro-wind, electrical vehicles, smart meters, and a variety of devices in the built environment (such as LED light bulbs) which reduce energy consumption in the domicile or place of business. Especially given the constraints of the grid, DER manifest a paradigmatic big data, machine learning coordination problem. Besides having different spatial and temporal locations in the grid, DERs have different technological attributes and greenhouse gas emission profiles. Solar PV produces only when the sun shines, which may or may not be contemporaneous with the use of electrical power. Batteries are both load and production, and some DER are not also non- or low-carbon emitters. Coordinating the variegated spatial, temporal, environmental,

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and technological attributes of DERs to sustain grid reliability while meeting mitigation targets and maximizing capital efficiency is conducive to monitoring and machine learning (or perhaps surveillance and control). The transition to DER, then, is the physical and technological manifestation of the digitization of the economy generally, and of the electrical grid specifically. For instance, the New York Public Authority (NYPA) has partnered with GE to self-consciously become the “world’s first fully digital utility” (https://www. businesswire.com/news/home/20171025006089/en/New-York-Power-AuthorityNYPA-GE-Partner). The project will monitor all utility assets along a range of variables (e.g., temperature and vibration) and NYPA is constructing a facility specifically to analyze the data flow (https://www.utilitydive.com/news/nypa-con tinues-digital-transformation-as-new-york-funds-grid-upgrades/521259/). This monitoring of grid typology should allow for enhanced capital and technological efficiency, prevent blackouts, and increase grid resilience. Smaller systems, such as the micro grid at Gordon Bubolz Nature Preserve in Appleton, Wisconsin have tailored a general software package (SaaS) to coordinate the contributions of solar PV, a micro turbine, a battery, and hydrogen fuel cells so as to minimize power imports from the grid. As an example of the challenge of incorporating DER into the “old” Wisconsin model, due to the absence of state-level policies coupled with utility reticence, the Nature Preserve is able to take only partial economic advantage of its contributions to the grid and the greenhouse gas benefits of its micro grid remain uncredited. Blockchain is frequently mentioned as a software solution to coordinating and crediting the diverse DER contributions to the grid from entities with varied economic interests. Blockchain’s distributed and digital ledger allows for the trusted transfer of digital coin between unknown parties. It also allows for the creation of digital currency representative of a variety of underlying values. Hence, many have noticed that it can be used to create an exchange platform for digital coins representative of underlying energy values, such as a “renewable kilowatt,” which otherwise anonymous parties could exchange. As compared to a centralized, perhaps stateadministered clearinghouse, however, blockchain has yet to be recognized as the superior solution to this coordination challenge. Blockchain is itself energy intensive and the energy needed to maintain the digital ledger might vitiate the environmental benefits of the DER it supports. Incorporating DER into the grid offers a once a century opportunity to reallocate the ownership (and hence the economic benefits) of generative assets and make an important contribution to unwinding economic inequality. Many DER have convivial characteristics, empowering their owners and users toward participation and selfdirection (in contrast, for instance, to nuclear power). When accompanied by intentional and vigorous policy support to remove barriers to access, the local and convivial characteristics of some DER provide the opportunity for economic rejuvenation. Under a regime of energy democracy, the ownership of the DER will be widespread and diffused, allowing a large number of stakeholders to enjoy the benefits of the transition to the renewable energy economy. That ownership might be individual (rooftop solar), through a collaborative association (a coop or

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not-profit) or through the municipality. Various pilot projects have settled on the “neighborhood” as the optimal social unit from which to build up the green, resilient cosmo-polity. Oakland’s Ecoblock aims to enhance social and economic inclusion, while reducing its biophysical impacts to near zero (https://ced.berkeley.edu/eventsmedia/news/cities-of-the-future-harrison-frakers-ecoblock-project-to-make-oaklandneig). The neighborhood scale allows for the digital coordination of DER’s stochasticity over a variety of loads while taking advantage of economies of scale without surrendering the appropriation of the system’s surplus value to a third party. However, as yet another type of sensor, DER are data source which powerful actors can manipulate for their own benefit. According to Zuboff, for instance, the terms-of-service and end-user licensing agreements for the Alphabet-owned Nest “smart” thermostat “reveal oppressive privacy and security consequences in which sensitive information is shared with other devices, unnamed personnel, and third parties for the purpose of analysis and ultimately for trading in behavioral futures markets, an action that ricochets back to the owner in the form of targeted ads and message designed to push more products and services.” (Zuboff, p. 237) The convivial characteristics of DER, then, are only preserved against such intrusion when citizens use law and policy to restrain profit-maximizing actors. Moreover, if not accompanied by countervailing policies, the introduction of technologies will perpetuate or exacerbate existing social and economic inequalities at both the local and global scale. In the case of DER, the threat is of energy apartheid, under which presently favored economic classes invest in both DER and their enabling physical and social infrastructure. They “defect” from the grid and others are left dependent upon an aging and financially unstable fossil grid. The presently existing built environment is not designed to support DER and thus provides the inertial conditions for the development of energy apartheid. Roofs do not necessarily face towards the azimuth, renters do not have control over the structure of their dwelling, and while economically competitive on a 7–10 year time frame, the initial DER investment is substantial from the point of view of the individual household’s budget. The “neutral” market reflects this same bias. One study from Professor Reames’ Urban Energy Lab found that census tracts with low-income, low-mobility, and high percentages of minority groups lack access in the private market to the basic DER of LED lightbulbs. The image of the illuminated Goldman Sacks building amongst another otherwise blacked out Manhattan in the wake of Superstorm Sandy is a representative image of energy apartheid.

Conclusion The incorporation of DER, then, offers both promise and peril for smart cities aiming at a green, resilient cosmo-polity. At best, they can help meet local and global mitigation commitments while supporting energy democracy and convivial technology. At the other end of the spectrum, they threaten energy apartheid and intrusive surveillance. The green, resilient cosmo-polity transcends nation-states, but the successful incorporation of DER (and other smart technologies) into the

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municipality depends upon the reorientation at every level of governance towards greenhouse gas mitigation, equitable energy access, and the support of social and environmental conditions required for human welfare. Without this reorientation, the imbrication of digitally connected technology, such as DER, into the city will remain nonproductive, or even retrograde.

Summary What exactly a smart city looks like varies. This introductory chapter identifies key fundamental characteristics of a smart city. First and foremost, a smart city is based on humans and human interactions for a common purpose. These interactions coalesce in institutions with smart cities characterized by the synergies enabled through university-industry-government-citizen collaborations. A distinguishing feature of smart cities is the unprecedented amount and types of data and information that can now be collected through diverse types of digital technologies. Smart cities have a multi-faceted relationship to climate and environment: they rely on physical and energy resources from the environment; they also affect their environment. To thrive and flourish, smart cities must be designed so as to take into account societal changes hailed through climate change and human movement (migrations and immigration). This introductory chapter has been formatted in such a way as to indicate that the five areas of smart city research and applications might be considered in isolation from one another. The section on climate and energy has provided some linkages by applying significant and recurring themes within the energy framework. Finally, it may be useful to suggest thematic similarities between the areas themselves; these themes can be used to guide decision-making and planning in a variety of smart city developments. The cascading effect within systems is central to smart city development. A cascade occurs when actions taken in one venue will have a continuous impact on other venues. This can be positive or negative; due to the connectivity of the smart city platform, it is certain to occur. The ability to not only mitigate cascade effects but also to use them to advantage is characteristic of a resilient system. It seems useful to address smart city developments from a system thinking perspective, applicable not only to the IoT technology and network connections but to the connectivity between actors and across subsystems. Technology allows the city to prioritize interactions and establish dynamic relationships, allowing a continuous identification and resolution of decision-making challenges; the data collected from edge computing arrays creates an increase in information pathways with the potential for spontaneity. This is countered by the structuring of data through deep learning which allows for pattern creation and reduces the reactionary behavior at the edge to predictive planning for the future. From a transactional perspective between actors, responses in one interaction can lead to multiple ripple effects in other portions of the network. Smart city development necessarily involves new types of relationships across levels of communities, within communities, and across individual agents. The first

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case, the establishment of relationships across levels of communities, represents a new opportunity for explanatory and predictive analysis. Up until now the ecological inference fallacy, as the error of making inferences regarding one level taken from analyses at a different level, has limited predictive capacity. With big data that is structured and aggregated, one can combine both individual and group level data to enable us to make inferences that circumvent this fallacy. Within communities, technology and data can also permit us to create institutional relationships (as compared to institutional structures) that can dynamically organize change agents and stability providers as needed. Finally, recognizing that agents are not fixed in a smart city network, there is more incentive to use collaboration rather than competition. The latter process is divisive and isolating, contrary to the assumptions of a network. This has implications that immediately confront existing governance institutions, social forms, and moral boundaries. It is certain that there will be destabilization/reconfiguration of existing systems, with either good or bad impacts. For example, the intertwining of previously separate institutions, as represented by the helix model, presents a new relationship that will require different types of decision-making, levels of transparency, and balancing of individual versus group interests. Finally, there are two areas of thematic inquiry that are most directly related to the concept of flourishing introduced earlier in this chapter as an important consideration in the design of any smart city development project. They are the moral questions of privacy/security and the political questions of civic engagement and participation. A critical examination of the impact of smart city technology on individuals must not be ignored. Wellbeing can be considered through the efficient delivery of services, but it also must include issues of privacy, security, individuality, and objectification. Citizen participation, and consent, in data collection, technology use, and artificial intelligence must have priority in order to ensure the flourishing of society. Data as management threatens to become surveillance and the sculpting of behavior so as to become profitable to the public or private surveyor. A related concern addresses issues of equity and inclusivity. Technology can increase access for citizen engagement and participation in decision-making by reducing barriers generated by disparities in income, education, and digital literacy. However, technology is not an end, it is a means. Equity must not be promised to residents as equality of opportunity, which would introduce the potential ethical problem of financial burdens necessary to take advantage of the opportunity. Rather, it should ensure equality of condition so that any smart city development always includes citizens at the core.

Glossary 5G Fifth Generation Cellular technology that will enable faster and more reliable data and signal transmission at near real time. Considered a necessity for smart city development, 5G allows for connected vehicles and mass sensor deployment. Actuator A device that causes a machine or other device to operate.

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Artificial Intelligence Activity that mimics human thinking. A broad term which encompasses both machine and deep learning. The latter works most effectively with large amounts of data. Barriers to Access Physical, legal, or sociological structures which hinder access to otherwise appropriate technology and resources, especially for low-income individuals and communities. Batteries Used for Grid energy storage to store energy for use at peak demand. Big Data Large amounts of data that, due to size and complexity, is difficult to process using traditional methods. Using the cloud as a relational platform, new technologies, such as Apache Hadoop and NoSQL, can structure and analyze big data. Biophysical Resilience The biophysical approach to economy and society incorporates the first and second laws of thermodynamics into its inquires. Biophysical resilience is the ability of a social or biological form to return to that form after an exogenous perturbance. Bitcoin A type of digital currency in which a record of transactions is maintained and new units of currency are generated by the computational solution of mathematical problems and which operates independently of a central bank. Blockchain A data structure composed of cryptographically connected records. Transactions can be transparent, but it is virtually impossible to modify the data within a block. First used for bitcoin trade, it has expanded into global economic transactions and information exchange. Blockchain removes the need for a middleman in transactions and can make transactions near instantaneous across borders. Built Environments Human made space that includes buildings and greenspace. Capital Efficiency Also referred to as return on capital employed (ROCE), this measures the ratio of profit to capital invested. Because the collecting and analyzing of big data allows cities to target services more effectively, capital efficiency can be increased. The rate at which fixed, physical capital is in use as measured against its technologically optimal usage. Carbon Budget The amount of greenhouse gases (usually designated in tons CO2e) which may be emitted into the atmosphere before a biophysical threshold is exceeded, such as global average temperature or atmospheric concentration of CO2-e. As a policy instrument, a carbon budget can be allocated to jurisdictions. The jurisdiction must then undertake measures to ensure it does not exceed that budget. Many think that “carbon budget” should be replaced with “carbon debt” as humans most likely need to remove carbon dioxide from the atmosphere and are therefore “over budget.” Of the Carbon Neutral Alliance member cities (2019), 6 are located in Europe, 7 in the United States, 3 in Australia, 2 in Canada, 1 in Japan, and 1 in Brazil. Carbon Dioxide Removal A process which removes carbon dioxide directly from the atmosphere. In 2018 Tampere Finland was one of the first cities to use this process and use the CO for regional heating, thus becoming a carbon negative city.

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Centrally Controlled A business or governance structure under which processes and operations are under the direction of a single entity. Typically contrasted with a networked structure. Climate Disasters Disasters attributable to the new climate regime. Typically, a climate disaster appears as an ordinary “natural disaster” but differentiated by its increased intensity or increased frequency. Climate Governance The set of policies, strategies, and implementation processes responsive to global warming. Typically, these include mitigation, drawdown, adaptation, and, recently, damage and loses. Urban areas can be primary sources of threats to climate change, but the nature of the challenge is such that multilevel and multisector approaches are required to succeed in climate governance. The nonhierarchical multilevel approach is well suited to the regional and state institutions of Europe but may be more challenged in a federal system such as the United States and Canada. Cloud/Cloud Computing Cloud computing is an information technology (IT) model for enabling ubiquitous access to shared pools of data and computing resources, typically over the Internet. Cobots Collaborative robots specifically designed to work cooperatively with humans in a work environment. Combined Heat and Power Systems (CHP) The thermodynamic process of cogeneration by which “waste” heat is used to produce electrical power. Uses otherwise wasted heat generated through electricity production to provide thermal heat energy. Communication Technologies It includes all mediums that are used to process and communicate information. Used in combination with information technology (ICT). Convivial Technologies or Tools Originally developed by Ivan Illich, convivial tools empower individuals to work with independent efficiency. As developed by Adrea Vetter, convivial technologies enhance relatedness, adaptability, accessibility, bio-interaction, and appropriateness. Cyberspace Interconnected digital technology (internet) that allows for communication and information transfer. Data Quantitative or qualitative measures of variables in an unstructured format. Structured Data is information. Decentralized Network Distributed nodes or groups of nodes that can operate independently from each other, but can also share information. Typically contrasted with centrally controlled systems. Deep Learning An artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision-making. Demand Response An economic relationship between energy producers and consumers which pays consumers to reduce consumption during peak demand so as to enhance overall capital efficiency. Demand response is a distributed energy resource. Digital Addiction Increasingly dependent relationship with digital devices.

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Digitization The transfer of information into digital formats (computers and network). Digitization increases efficiency of information storage, transfer, and analysis. In political economy, digitization characterizes the economic trend of business revenues originating from the sale, use, and manipulation of digital data vis-a-vis the production of material goods. Distributed and Digital Ledger Database that is shared and synchronized across multiple sites, transparent, with no central manager or controller. Blockchain and Ethereum are distributed ledger systems. Distributed Energy Resources (DER) Energy resources that can be used individually or combined into the larger electricity grid. DER’s can be a combination of energy sources integrated or supporting the larger electrical grid when necessary. Dynamic Network Slicing software that optimizes 5G performance by partitioning portions of the 5G network according to dynamically changing use cases. Ecoblock: An urban community design which manages biophysical systems (energy, water, waste) so as to minimize impacts on the surrounding environment. Ecoblocks exist in Oakland California and Quingdao China. Edge Computing A distributed computing architecture in which data processing is performed on a network of devices or nodes know as edge or smart devices rather than taking place in a centralized location like a cloud or server. Edge computing allows for real time response, even while providing data to a central source. Electrical Vehicles A vehicle which uses one or more electric motors for propulsion. Energy apartheid The inequitable access to energy and energy resources which perpetuate and are an aspect of global or local exploitation. Typically contrasted with energy democracy. Energy Democracy Equitable decision-making and ownership of productive energy resources. Consumers are also producers, innovators, and decisionmakers in planning energy creation and distribution. Typically contrasted with energy apartheid. Equity/Equitable The absence of avoidable or remediable differences among groups of people, whether those groups are defined socially, economically, demographically, or geographically. An equality of condition, rather than opportunity. Smart city planners can use technology to include marginalized populations in areas such as public health, transportation, and biometric identity cards for the homeless. Flourish The ability to attain and achieve a complete and sufficient good. Fuel Cells A renewable and clean technology that produces energy outside of the main electrical grid but can be linked to the grid. Energy is produced from a supply of oxygen and hydrogen. Greenhouse Gas Emission Profiles A description and classification of an entity’s or technology’s greenhouse gas emissions. A greenhouse gas profile provides data about the origin of greenhouse gas emissions with the aim of identifying abatement policies. Greenhouse Gas Inventory A type of emission inventory developed for a variety of reasons. Greenhouse gas inventories typically use Global Warming Potential

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(GWP) values to combine emissions of various greenhouse gases into a single weighted value of emissions. Grid Resilience The degree to which an electrical grid can withstand disruptive threats to power distribution (either natural or man-made). Often used interchangeably with grid reliability or grid hardening. Grid Typology Grids are composed of generation, transmission, distribution, and distributed energy resources, each of which, besides their electrical power characteristics, have environmental and financial characteristics. Grid typology is the multidimensional description of these characteristics, sometimes visual. Human-Machine Interaction Design and use of computer technology for a cooperative and seamless relationship with users and machines. Inclusivity The practice or policy of including people who might otherwise be excluded or marginalized, such as those who have physical or mental disabilities and members of minority groups. Institutional Logics A core concept in sociological theory and organizational studies. It focuses on how broader belief systems shape cognition and behavior of actors. Internet of Things (IoT) The network of physical devices that are connected to the Internet and the communication that occurs between these objects and systems. Investor Owned Utilities (IOU): A for profit enterprise that acts as a public utility. IPCC 1.5C Report Intergovernmental Panel on Climate Change report (2018) on the impacts of global warming of 1.5C above pre-industrial levels https://www. ipcc.ch/sr15/ LED Light Bulbs Bulbs that utilize LEDs (light-emitting diodes) to produce light. LED light bulbs are a more environmentally friendly alternative to incandescent bulbs. Media Richness A quality of a communication medium that refers to its ability to use multiple opportunities to provide meaning to the users. Mesh Network A local network topology in which the infrastructure nodes (i.e., bridges, switches, and other infrastructure devices) connect directly, dynamically, and nonhierarchically to as many other nodes as possible and cooperate with one another to efficiently route data from/to clients. Microgrid A group of interconnected loads and distributed energy resources within clearly defined electrical boundaries that acts as a single controllable entity with respect to the grid. Micro-Wind Residential-based wind turbine that can link to a larger electrical grid or stand-alone off grid. Mitigation Pathways A depiction, typically graphic, of a future abatement curve for reducing greenhouse gases on a timeframe which will meet a particular carbon budget goal. Neural Network A set of algorithms, modeled loosely after the human brain that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling, or clustering raw input. Patterns are numerical, contained in vectors, into which all real-world data, be it images, sound, text, or time series, can be translated.

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Public Power Not for profit utilities operated by municipal state or national governments. Public Private Partnerships (P3) A cooperative arrangement between two or more public and private sectors, typically of a long-term nature. Related Concept in in Australia and the UK- Private finance initiative (PFI). Quadruple Helix A set of interactions between academia, industry, governments, and citizens to foster economic and social development. Resilience The ability of an organism or entity to recover to its original state after an external, disruptive impact. Robots A machine that can be programmed to perform a series of complex tasks. Sensor Array A sensor is an electronic component, module, or subsystem whose purpose is to detect events or changes in its environment. Smart City Urban development that integrates information and communication technology (ICT) and Internet of things (IoT) technology in a secure fashion to manage a city’s assets, deliver city services effectively, efficiently, and equitably. Smart city uses information and communications technology (ICT) to enhance livability, workability, and sustainability. Smart Meters An electrical meter which provides a temporally indexed record of two-way flows of electrical power, often transmitted to the central controller and aggregated for the purposes of optimizing the electrical power system of which it is a part. Typically, transmissions are done at least once a day, sometimes more often, enabling dynamic adaptation to energy production, consumption, and distribution needs. Social Cohesion A societal condition that works toward the well-being of all its members, fights exclusion and marginalization, creates a sense of belonging, promotes trust, and offers its members the opportunity of upward mobility (rising from a lower to a higher social class or status). Social Resilience The ability of a group or community to adapt to change, take advantage of opportunities, and become less vulnerable to disruptive events. In social resilience, this includes the ability to adapt, even while maintaining the core identities and values of the group. Socially Constructed The provision of meaning by society. Socially constructed meaning will vary depending upon region, culture, and organizational systems. And, an urban environment based on integrated technology can socially construct a different idea of “city.” Philosophically, the social reconstruction of the city and its citizens has implications for the objectification of an individual, and its functional relationship within a managed system that might be terms a city. Software as a Service (SaaS) A software distribution model in which a third-party provider hosts applications and makes them available to customers over the Internet. Solar PV A power system designed to supply usable solar power by means of photovoltaics (PV). Technological Determinism A theory that suggests technology can shape individual and societal values and behavior.

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The Fog A computing architecture that allows for the performance of intermediate computing, networking, and storage, connecting data centers and edge devices. Advantages of using the fog as an intermediate network are low latency (quicker response time) and less bandwidth (dynamically aggregated use of information sources). Thermostats A device for sensing the temperature of the surrounding environment and turning on a heating or cooling system to retain the surrounding environment at a designed temperature. Smart thermostats are connected to the Internet of Things so become a device for the sensing and digitization of events. Triple Helix A set of interactions between academia, industry, and governments, to foster economic and social development. Can contain Private Finance Initiatives (PFI) and Public Private Partnerships (P3s).

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Ravani, S. (2019). Oakland committee approves ban on facial recognition surveillance. San Francisco Chronicle. https://www.sfchronicle.com/crime/article/Oakland-committee-approvesban-on-facial-14050026.php. Accessed 8 July 2019. Raven, R., Sengers, F., Spaeth, P., Cheshmehznagi, A., & Xie, L. (2017). An institutional perspective on smart city experimentation: Comparing Ningbo, Hamburg and Amsterdam. Presentation at ECOCITY World Summit, 12–16 July 2017, Melbourne. Roland Berger. (2019). Navigating complexity: The smart city breakaway. Think: Act Magazine. https://www.rolandberger.com/publications/publication_pdf/roland_berger_smart_city_break away_1.pdf Sadowski, J., & Pasquale, F. (2015). The spectrum of control: A social theory of the smart city. First Monday, [S.l.], June 2015. ISSN 13960466. https://firstmonday.org/ojs/index.php/fm/article/ view/5903/4660. https://doi.org/10.5210/fm.v20i7.5903. Date accessed 1 July 2019. Shachtman, N. (2008). 26 years after Gibson, Pentagon defines ‘Cyberspace’. Wired. https://www. wired.com/2008/05/pentagon-define/ Speck, J. (2013). Walkable city: How downtown can save America, one step at a time. New York: North Point Press. Tanaka, W. (2012, April 10). Cities of the future: Songdo, South Korea-Education. https://news room.cisco.com/feature-content?articleId¼776668. Accessed 8 July 2019. The Threat Lab. (2019). The history of cellular network security doesn’t bode well for 5G. https://www.eff.org/deeplinks/2019/06/history-cellular-network-security-doesnt-bode-well-5 g?fbclid¼IwAR0RK0s_HtDaUXsR2Vcz0eFFdgEYI3fqhxxa6aHIPpTGbUOt8Ii4Z1ZWlsw. Accessed 8 July 2019. Townsend, A. M. (2013). Smart cities: Big data, civic hackers, and the quest for a new utopia. New York: WW Norton and Company. Waal-Montgomery, M. D. (2016). World’s data volume to grow 40% per year & 50times by 2020: Aureus. https://e27.co/worlds-data-volume-to-grow-40-per-year-50-times-by-2020aureus-20150115-2/. Accessed 8 July 2019 Weisburd, D., Mastrofski, S. D., Greenspan, R., & Willis, J. J. (2004). The growth of Compstat in American Policing. Washington, DC: Police Foundation Reports. https://www. policefoundation.org/publication/the-growth-of-compstat-in-american-policing/ Zikopoulos, P., Parasuraman, K., Deutsch, T., Giles, J., & Corrigan, D. (2012). Harness the power of big data the IBM big data platform. Emeryville: McGraw Hill Professional.

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Smart Cities Can Be More Humane and Sustainable Too Eduardo M. Costa

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . More Humane and Sustainable Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Live-Work-Play in the Same Area! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sidewalks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bike Lanes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Light-Engine Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Public Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Listen to Citizens’ Wishes, Interests, and Needs! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Deindustrialize your Mind! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 a.m. to 5 p.m. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Schools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tech Parks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Work and Employment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Car . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross Reference and Major Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Demographics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diversity and Priorities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Special Needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Socialization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

The term “smart city” has been used extensively by technologists and the media to describe a place where modern technologies, mostly information and communications technologies (ICTs), are widely used by local governments, institutions, E. M. Costa (*) LabCHIS – Humane Smart City Lab, Federal University of Santa Catarina (BR), Florianópolis, Brazil Knowledge Engineering and Management Dept., Federal University of Santa Catarina (BR), Florianópolis, Brazil e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_3

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and citizens. Technology is always seen as a way forward. “The more, the better. They will be beneficial to citizens eventually, in one way or another.” Or will they? Well, maybe, but not necessarily. This chapter argues that the humane side of urban planning should be taken into account first. Humane is not only one of the dimensions of a proposed solution. It is the dimension to guide all the others. In fact, every city project should start with a clear definition of what is the actual citizen’s problem that is being solved, and its results should be measured against that goal. For instance, a control center is a costly and useful tool to measure and orientate car traffic in the city. But its cost should be evaluated and compared against other solutions that improve mobility of citizens in town, not mobility of cars. There is a subtle difference here. Mobility of car drivers is not a measure of citizen’s mobility as a whole. A proper bike lane or an improvement in the public transportation system, for instance, may offer much better and cheaper solutions when the focus is changed from the car to the citizen. Once citizens’ wishes, interests, and needs are clearly identified, technology will be, of course, part of the solution. It is just a question of resetting priorities: people and the environment first; then, comes everything else.

Introduction Let us start in the medieval villages, in the fifteenth century. People lived in small areas, in villages (up to 10,000 people) or cities (more than 10,000 but less than 100,000). Cities were many times enclosed by a defensive wall, and most of the daily activities were conducted in the house (Gies and Gies 1969). Women bought foods and spices at the local stores, cooked and worked on household chores, sewed clothes, and raised children, with or without the help of house maids. Peasant men went out of the walls for crops and hunting. Military men left their villages for battles. And clergy and noble men ruled the place. Two characteristics of the medieval village are important for our study. First, the cities were small, up to an approximate circle of 1-mile radius, for the very simple reason that the water that was used for everything in the house came from the well, generally a single structure in the central square or commons. Water was carried in heavy buckets made of iron and steel, and, when loaded with water, these buckets could not be carried far. The second characteristic is that people lived, worked, and played in this small area and did not have to go anywhere. Except for merchants and people in battles, their whole life was lived in the city. Now let us fast forward to Paris in 1852. Louis Napoleon, a nephew of Napoleon I, had just become Emperor Napoleon III, after a meteoric rise from elected member of the National Assembly of the new republic in 1848; first president of the new republic in the same year; a coup d’état in 1851, which gave him a 10-year term as president; and a plebiscite in 1852, when the French voted to become an empire again, away from the short-lived first republic. The new emperor had had plans for

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some time for the new redeveloped capital of his empire but lacked someone to execute them. He then found the mayor of Bordeaux, Georges-Eugène Haussmann, a long-term political ally. The two men were responsible for the rebuilding of the city center, the opening of new boulevards and plazas, and the new residential and commercial buildings and for the development of new financial mechanisms (Kirkland 2013). In an amazingly short time, they transformed Paris into the most beautiful city in the world, starting with the 4 arrondissements (first, second, third, and 4th) in the city center and then expanding it to the 20 arrondissements that exist still today. One of the most striking characteristics of these boroughs is that, in each one of them, one could live, work, and play, as if they were back in the medieval times. And, coincidence or not, all of these boroughs have circa 1-mile radius each! Paris influenced the redesign of cities all over Europe and in other places in the world, including Rio de Janeiro, in Brazil, and Buenos Aires, in Argentina. Unfortunately, this period didn’t last long, as the industrial revolution, which had already started, had a profound impact on the urban planning of cities in Europe and elsewhere. The industrial revolution set up its factories in the city (Roberts 1980). But in a short time, they discovered that the industry was messy, dirty, and polluted the city with smoke. Henceforth, planners began to segregate the daily functions of living, working, and playing into separate portions of the city (Mumford 2018). This movement was later facilitated by the invention and popularization of the car (principally Ford Model T). People, at least those who had a car, could move from one function region to the other, using the new machine. Urban planners adapted quickly to the new idea, and the city became hostage to the presumption that what was good for the car (new and larger roads, viaducts, bridges, etc.) was also good for the citizen, an equivocal concept that is prevalent still today. Jane Jacobs (1961) was a lonely voice in the 1960s against the segregation of daily functions and an early apologist of the need for diversity in the city, and maybe that is the reason her book The Death and Life of Great American Cities became a classic reference. The initial phrase of the book sets the tone: “this book is an attack on current city planning and rebuilding.” But even with the repercussion of her ideas, the city-for-the-car planning became dominant all over the world. The results of this deliberate choice are there to be seen: traffic congestion everywhere, street violence, anxiety, depression, etc. The number of annual deaths in the world related to traffic accidents amounts to more than 1,350,000! It is by far the major cause of nonnatural deaths in the world. And we hear, in the nightly news everywhere, that there was an accident in a remote mountain that killed five people. We feel sorry for them and their families. However, traffic accidents, at the same time, kill circa 4,000 people a day! And what do we do about it? Maybe a campaign against drinking and driving (a major cause of accidents) here and there, and not much else. Besides this horrible death toll around the world, traffic accidents disable or injure another 50 million. Since these terrible numbers are concentrated in a few countries, they are equivalent to the death rates of civil wars. And we only pray not to be part of these gruesome statistics! It is high time we do something against this fact. Now.

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For instance, limit the speed within a city to, say, 40 miles/h, not a sign here and there or a bumper before a school crossing but everywhere in town. Another measure could be directed to car manufacturers: in many countries, the maximum speed limit in any road is 70 mph. How do manufacturers produce cars with a maximum speed of 110 mph? Isn’t this a violation of the law or, at least, the spirit of the law? In recent years, a myriad of tools and services to help in the city are being developed and offered in the market. They do help city managers to take care of their cities and also help connected citizens to move about and use city services. Some of them will be described in this book. Unfortunately, though, most tools and services are offered to help the driver. Cities are places for cars, right? So, tools and services should help drivers and traffic managers. It is only logical. It is also a mistake. Tools and services should help citizens to opt for mass transit, shared cars, biking and walking, and other active transportation alternatives, and not only help the ominous driver. This move requires a change in attitude, a cultural change – naturally difficult but has got to be tried. In the beginning of this century, a movement started in Europe to give more attention to the citizen instead of to the new technology. Many people in the world adopted the concept, including this author. They called their movement the Human or Humane Smart City (Costa and Oliveira 2017) and later coined by the author Humane and Sustainable Smart City (HSSC) (Costa and Pacheco 2020). The movement is not against technology, of course. It is just a reminder of the main focus we are all after: the citizen and the environment. There are successful projects to study such as in Bilbao (Azua 2006), Melbourne (Yigitcanlar et al. 2008), and Copenhagen (Gehl and Svarre 2013). They all have in common the citizen, the user, and the environment – not technology, or cars. A good way to start is to follow three basic concepts of the more HSSC: Livework-play in the same area, which means a new attitude toward urban planning; attend to the citizens’ wishes, interests, and needs, asking them to participate actively in the urban planning; and go through a process of mental deindustrialization. The following sections detail this action plan.

More Humane and Sustainable Smart Cities Smart cities and knowledge cities are quite well-known (Yigitcanlar and Lee 2011; Chang et al. 2018; Lara et al. 2016; Jones et al. 2019), and some of their characteristics are detailed in this book. Knowledge cities have been defined first, before the arrival of modern technology (Carrillo 2006, 2015). The concept reflects the need to characterize the place that is suitable for the development of the new society where knowledge – and therefore people – are the main means of production. Then came the idea of a creative city (Yencken 2013) that was capable of attracting the creative class of people (Florida 2014) who would develop the creative economy (Howkins 2001, 2013) of the future: a recognition of the powerful transformational characteristics of sectors such as advertising, gastronomy, architecture, design, fashion, video,

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Internet publications of all sorts, photography, computer games, music, performing arts, publishing, research and development, software, electronic publishing, arts and crafts, and entertainment as a whole. Another concept that is related to our studies is the resilient city (ICLEI 2019) that refers to the city that is flexible enough and prepares itself to face natural disasters, wars, or economic downturns (The Rockfeller Foundation 2013). Here we introduce the humane and sustainable dimensions that are to be added to the smart city concept in order to transform the city in a way that is relevant to the citizen, and not, for instance, to some smart city ranking (of which there are so many). In particular, one has to be careful of the already more than 100-year old phenomenon that Sam Schwartz baptized motordom: an unrelenting devotion to the kingdom of the private car (Schwartz 2015). Consider, for instance, that there is a travel book entitled “the ten widest freeways in the world to drive before you die.” Imagine the indescribable thrill of driving on Katy Freeway in Houston with its 26 lanes! I can hardly wait. A city that wants to be more humane and sustainable has to focus its urban planning, projects, and expansion on the citizen and the environment (Yigitcanlar et al. 2018). This is not to go against technology: it is to set priorities right. Technology is undeniably smart and useful if, and only if, we can use it in this right direction. A few examples might help explain the idea. Florianópolis, a state capital in the south of Brazil, is an island with circa 400 km2. It is connected to the continent by three bridges. The old bridge is being repaired, and the two more recent bridges carry the traffic across the 500 m channel. Traffic on both bridges is terrible with frequent congestions throughout the day (Yigitcanlar et al. 2018). Because of this, the city has been considering, for some time, where to build a fourth bridge, in order to alleviate the traffic congestion. It is only logical, isn’t it? Yes, if you answer the wrong question of how to improve the traffic conditions between the island and the continent. Wrong, if you answer the right question of how to improve the mobility of people across the channel. To this right question, the answer might be a passenger ferry boat service – several times cheaper! Janette Sadik-Khan, former transport secretary of New York under Mayor Bloomberg, planned to re-urbanize Broadway Avenue. Had she limited the scope of her project idea to the car traffic in the large avenue, she would be able to propose a very limited intervention (Sadik-Khan and Sollomonow 2017). Instead, she surveyed the daily movement of people in the avenue, including pedestrians. To everyone’s surprise, four times as many people used the sidewalks than the road! She then convinced the mayor (she recalled Bloomberg’s mantra in office “In God, we trust. Everybody else, bring data!”) that that precious public space should be occupied accordingly, giving more space to sidewalks and bike lanes. Bloomberg approved the plan in stages, changing a few blocks at a time. Today, Broadway is a boulevard with large sidewalks, bike lanes, and less space for the cars. And everyone loves it! Note that the change did not necessarily involve technology, but it helped transform the city into a more humane and sustainable place. Focus on the citizen, not on cars or technology, is clearly the lesson learned here.

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Live-Work-Play in the Same Area! Most of our cities grew enormously in the 1900s. The trouble is that the twentieth century was exactly the time when our planners embraced the wrong idea that the daily functions of living, working, and playing should be conducted in different parts of the city, segregated from one another. And transport from one segregated part to the other would be performed by car. It did not work, of course, firstly, for people who did not own a car. Later on, and still today, it does not work for anyone. It is probably better not to try and analyze why we went on that route. Let us just recognize that it did not work and that from now on we must do something different. Starting with the city code: in most cities, the segregation is frozen in the local regulation. For each space in the city, the code establishes what is the allowed use of the area: commercial, residential, industrial, entertainment, and such. One might think that some of this regulation is needed. For instance, you don’t want a bar cluster next to a secondary school or a hospital. But the trouble is that the code is much more divisive. For instance, who would not want a bakery on the corner of a residential block? City codes do not allow this arrangement in many places. “Residential area only” used to be a plus for a house value, within or outside a gated community. This means you need the car to buy a match box or a pain killer pill. Is that really desirable today? City codes should allow and even give incentives for residential, entertainment, and light work licenses in every region of the city (Glaeser 2011). A good area to limit a region is the 1-mile radius from medieval villages, a size that was followed in Paris many centuries later in their arrondissements (Deutschmann 2017). In each of these 1-mile radius regions in the city, people would be allowed to live, work, and play (Larson 2012). And how would they go about within the region and move from one region to the other? By foot, by bike, light-engine vehicles, and public transport. Let us take them in turn (Fig. 1).

Sidewalks Sidewalks are probably the most neglected urban equipment in most cities today. They were, at first, the citizen’s main means of transport. Then they lost space (and area) gradually to new car lanes and parking spaces. In many towns there are public parking places downtown that are used all day long by single cars. Each car takes one person only (in general) to work. Even so, and worst of all, some of these parking lots are free! While the cars sit there all day long, using public land, hundreds, maybe thousands, of people go about their businesses in very narrow sidewalks. It is simply insane! The existing sidewalks, besides being very narrow, are kept very poorly, provoking daily accidents, breaking limbs, and spraining ankles, especially for women in high heels. Since it is so bad, how does it go on and on? It is difficult to tell. My hunch is that sidewalks, in many towns, are to be used by poor people, so authorities are not that much concerned with their opinion.

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Fig. 1 Live, work, and play in the same area

Funny enough, tourists flock to Las Ramblas, in Barcelona, and to ChampsÉlysées, in Paris, and marvel at their width and beauty. But then they return home and approve the enlargement of an existing avenue in detriment of the existing sidewalk. This trend must be reversed. Several cities, even in North America, have put their streets on a diet to get thinner. One may think of Portland in the USA or Vancouver in Canada, well-known for their modern attitude toward urban planning. But what about Oklahoma City? It used to be the eighth most obese city in the country up to 2007, when Mayor Mick Cornett launched his re-urbanization plan that included 36 miles of new sidewalks and 35 miles of bike paths and walking trails. The active mobility plan was detailed in a website named thiscityisgoingonadiet.com. He even lost 40 pounds himself in order to set the example. In their studies of walkability, they identified that sidewalks are better used when they connect to good public transport, so the plan provided for a new modern streetcar system set on the grounds of a former road that run right through the city. One can also start small. A good guerrilla tactics to enlarge sidewalks in your city is the concept of a parklet. In the parklet, two car park spaces by the curb are occupied by a wooden or steel deck that is decorated with small flowerpots, benches, and tables and chairs, extending the serving area of a restaurant or a café. Once parklets are applied in a few places along a block, it is much easier to enlarge the sidewalk in the full extension of the block. The parklet idea, in use in many towns today, might require some change in the city code, but it is well worth the effort. The importance of good sidewalks cannot be over emphasized since they are so neglected today. Politicians like to inaugurate new and large avenues, viaducts, and bridges (named after someone they need to honor for some reason). But they don’t inaugurate a sidewalk (and they should!), so why bother?

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In order to start interventions of the street diet kind, it is important to bring merchants and shop owners on board. Their first impression is that if you reduce the number of car lanes in the street, their existing business would receive less people. It is exactly the opposite. It is not difficult to figure out that pedestrians and bikers are much more likely to stop by and buy something than passing cars. But it takes talking and convincing. Better show good examples and reliable data about the results of the interventions through simulation, a handy tool that is available to predict the ultimate results of change.

Bike Lanes Bikes are a wonderful means of transport for small distances. Bikers do exercise in the open air, leave their cars at home, and do not pollute the atmosphere. They depend on weather conditions, of course. But consider, for instance, Copenhagen: even with the severity of the winter there, 40% of the workers in town commute daily on their bikes. The same is true in Amsterdam. In the developing world, bikes used to be very important vehicles for daily transport as well, but unfortunately, bikes are being replaced by cars, a sign of modernity and of status. Cities should encourage bikers with the construction of a network of bike lanes everywhere in town. But the whole system in the city must be adapted so that bikes are more useful: there must be safe bike parking lots in town, showers in commercial buildings, mutual respect education campaigns in order to avoid collisions between cars and bikes and to increase safety for all, etc. And it is relatively cheap, compared to, for instance, a new viaduct or underpass, projects that are never questioned by the population, still in adoration of motordom. A smart idea was implemented in Paris and in many other towns in the world – the concept of a bike sharing system. You get the bike here and return it there, either in pre-assigned bike parking lots or, in some cases, anywhere within a defined delimited area. Bike sharing may be free to the user (maintained by the local administration) or may cost a small monthly subscription fee. In Barcelona, for instance, the largest cost of the system to the administration is to carry and redistribute the bikes at the end of the day, since Barcelonians that live in the hills take the bike in the morning going down and commute back by bus in the evening. One of the reasons the bike sharing system can be very cheap is the use of the actual bikes for advertisement of large corporations or specific marketing campaigns, in any case, a good system to implement anywhere. With the adoption of bikes by young professionals, many cities created bike lanes painting lines to limit them in existing streets. This clearly does not work! It is better not to do anything! Painted bike lanes are a constant source of collisions between cars and bikes and should be avoided. Bike lanes must be clearly segregated from the streets in order to make the arrangement safe for both bikes and cars. There are even cheap rubber block separators that may be used to segregate them, as used, for instance, in Barcelona.

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Light-Engine Vehicles In recent years, many cities all over the world are using light-engine vehicles for city transport. Scooters, electric bikes, electric skates, Segways, toy scooters, and all kinds of variations of these are in the streets today, mostly powered by electricity. They are wonderful means of short-distance transport, and many companies are renting them on a shared basis, charging monthly or by the hour. They are dangerous too, of course. And as they are already in service, they will require some kind of regulation, now, maybe even segregated lanes. Thsese new vehicles should not be demonized, banned, or dismissed, though, for being dangerous: we must regulate all vehicles according to their perilously. Otherwise, we would add an additional problem with the new vehicles to our already dangerous transport system.

Public Transport There is a chicken-and-egg dispute between cars and public transport. Some cities don’t invest in public transport because everyone, and their dog, has a car. The flip side is that people say they need a car because there are no, or very few, public transport options. This stalemate must be broken by the public authority. And it should be in favor of the public transport, by all means. There are so many possibilities of public transport today that one or many of them will be suitable to any given city – all sorts of buses, underground metro, light rail, BRTs (bus rapid transit), and such. As the new generation of the millennials takes over, it will be imperative for modern cities to provide good-quality transport in order to attract these youngsters. They are not so keen on cars (thank God!), and many don’t even bother to apply for a driving license – new world! In order to be more successful and attractive, cities must assume that they are in direct competition, with other cities in the same country or in other countries, in order to attract talent. The alternative is to face decay. A good way to sell the investment in transport idea to “old-timers,” even to the ones that live in the green suburbs, is that we need to prepare the city with good public transport for the new generation; otherwise our children and, God forbid, our grandchildren, will go live somewhere else.

Listen to Citizens’ Wishes, Interests, and Needs! Citizens, in this context, are not only the persons who live in the city but also people who work or go there on a regular basis, like weekday workers or regular tourists, for instance. In short, people who are direct users of the city. Think of Manhattan, for instance, where the number of workers that commute into town is the same as the number of actual residents (Morse and Qing 2012): it would make no sense to plan the city for its residents only.

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And no, it does not happen naturally. You hear all and every elected official say that they are there to serve the citizen (that is why they are called civil servants). But they don’t. Reasons are many. But once invested in a commanding post, they know what to do. Maybe because of a common mistake, we tend to think we know what other people want. It is even worse when you have things in common with that group. Let us say a graduate student thinks he knows what other students think and want; an elected official thinks he knows what is better for the town since he is also a resident. The popular project development tool called design thinking deals exactly with this problem. It helps developers put themselves in the users’ shoes. Walt Disney Jr., who created the Disney empire, is reported seen squatting several times in the internal roads and looking pensive during the construction of the first Disneyland Park: he was trying to look at the place from the perspective of and with the eyes of a child. We need to observe, study, measure, and feel what are citizens’ main wishes, interests, and needs. Frank Gehl, a Danish specialist in urban design, suggests several forms to study the city before doing anything (Gehl and Svarre 2013). Observers should watch the city in order to determine what the citizens do, how often, and at what times, and who they are. And the observer may assume different roles as she counts, analyzes, interprets, and tries to make sense of this complex system that a city is (Fig. 2). A common mistake is to develop a new city plan and then conduct a public hearing in order to invite the locals to contribute to the final details of the project. Eduardo Paes, former mayor of Rio de Janeiro, planned a new cable car for Rocinha (the largest slum in town, with more than 100,000 people) and went there for a public hearing: to his astonishment and dismay, people said they did not need or want a cable car there. They needed a sports facility, a new sewage system, a good school, and many other things first, and then, they would consider a new cable car. The lesson was learned by the administration, and, in their next major project the “Marvelous Port,” the redevelopment of the old harbor, they partnered with the population from the beginning, and the project was an astounding success. Fig. 2 Observe citizens’ wishes, interests, and needs. (This Photo by Unknown Author is licensed under CC BY)

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Mouraria (Moorish quarter) in Lisbon is a good example of a successful redevelopment of a place achieved in close proximity with the locals. Set at the back hill of Castelo São Jorge, a national monument and former royal residence, it was forgotten in the twentieth century and became a dangerous district with drug dealing and prostitution activities all day long. It was also a cheap place to live, so it housed many immigrants from Portugal’s colonial past. In the last two decades, with the direct involvement of the European Human Smart City movement (Oliveira and Campolargo 2015), it was transformed completely, building up from the strength of the local communities. And now, besides being the home of the fado (most traditional Portuguese music), it is a place for diversity in Lisbon with all different kinds of cuisine, art, and culture and a tourist attraction that is a testimony of the city’s rich historical and cultural tradition. In some cities all over the world, City Hall launched an initiative of a “participatory budget.” It is a good start. In the process, city officials talk to and listen to local neighborhoods’ demands and make them vote on the better use of the money (ultimately their money) originally allocated to that district. It evolved from Porto Alegre in Brazil and has shown interesting results all over the world. A recent World Bank report (United Nations 2019) describes these cases but also demands more participation of the very poor and the young in the process. One of the benefits of this talking and listening to the population directly (Nelson 2006) is a movement against clientelism and wrong decisions by the administration. For instance, some smart city solutions are easy to sell to city mayors with or without corruptive practices: new technology looks good, is shown in the media (“a new camera surveillance system is being installed in the walking precinct downtown. . .”), and makes the city top on comparative rankings, but it may be irrelevant for the citizen. China has pioneered a guideline toward a more sustainable environment making it compulsory for cities which want to get finance from the China Development Bank (Huang et al. 2015). The 12 Green Guidelines point to the relevant measurements to be taken into account in the city and clearly put technology as a tool to achieve better livability. This study followed the earlier eco-city idea from the green movement (Register 2006). Citizens are opinionated people. Give them a city topic in a local café, and they will have an opinion about it. Most of these opinions, though, are ill-informed, shortsighted, and mixed up with ideology. The antidote is data and good governance (Almeida et al. 2018). It is all too important to collect and make available reliable data about everything in town, or in the area of a proposed project (Aravena 2018). This is where technology can be of good use, as will be shown later in this book.

Deindustrialize your Mind! Yes, we have to face it! We are addicted to industry. We only think of private companies as if they were industries. We prepare our kids at school and at universities to work in industries. And we prepare our cities as if the daily commuters were going from the residential areas to some industry somewhere, with its rigid working

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Fig. 3 Deindustrialize your mind

hours. It is high time we deindustrialize our minds and recognize that we have lived for a long time already in a postindustrial era, the knowledge era. Let us analyze a few examples and relate them to our cities and our urban planning mindset (Fig. 3).

9 a.m. to 5 p.m. In a normal industry, every single worker of a typical assembly line must be there at the same time since they are part of a process where one worker depends upon the completion of the work of the previous worker in order to fulfill his/her task. At 5 p. m., the assembly line halts (or there might be another shift of workers), a loud whistle is blown, and everyone leaves the premises and goes home or elsewhere. There is nothing wrong with this routine except for the fact that only one in four or five workers in OECD countries still works in industry. And our cities are prepared and planned to tackle only this kind of commuter and this particular pattern. Even when you take the group of industrial workers, a large proportion is working in comfortable offices downtown, not in the assembly line outside the city. So, a question pops up: why do we all work from 9 to 5? Daylight might be an excuse, but even that would suggest a different pattern based upon the season and the latitude of the place. In fact, it is because of our industrial mentality which we should get rid of as soon as possible. The majority of our workers in the city work in the services and financial sectors and need not follow this rule. In many companies today, they do not have to work together or in the same place at all. This fact explains the success of the new co-working facilities that sprang up in every city. Workers there (one in five works for large companies) mingle, discuss, and enjoy meeting people from other companies, not their own mates. In this era, since knowledge actually grows when it is shared, co-working spaces are ideal to share experiences and grow professionally.

Schools Schools are in trouble. Students aren’t happy. There are interesting experiments here and there, but the old joke unfortunately is closer to reality today: an engineer, a surgeon, and a teacher were frozen 100 years ago and come back today. The first two

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cannot understand and grasp all the changes that happened; the teacher walks into the classroom and starts giving his lecture as usual. There are two major problems with this reactionary behavior in the schools. Firstly, the teacher is not the only holder of knowledge anymore, and it does not make sense to keep the one-to-many format of a classroom. Knowledge is also available in students’ hands on their smartphones. In many instances, they even challenge what the teacher says. Secondly, and this is much more serious, the schools prepare our children to work in the industrial society, and the education process is an assembly line! Sir Ken Livingston, a brilliant English educator, bashes our school systems in his popular TED talks, which have been seen by millions of people already. With a lot of humor and wit, Livingston points out that kids go through an assembly line, receiving the same components at every stage of the line. Parents who have two or more children are always surprised by how different their children are, even having been brought up in exactly the same way. But not in the schools. There, they are all the same and must behave likewise. Livingston compares the school system with a prison, where the inmates also have prescribed times for lunch, recreation, sunbathing, etc. and must follow strict rules of behavior. Any brakes in the code of conduct are punished severely. No wonder studies have shown that creativity, a basic soft skill for workers in the new knowledge era, actually diminishes with every year in the school system! A good start to change is to flip the classroom. Make the students work their way through the learning process. Transform the teacher into a facilitator that has some knowledge about the subject (not all) but that can help curate the content that is available online. Trouble is, of course, the teachers! Some are not prepared or willing to change the way they have taught the same subject for decades. And they might feel insecure with new technology.

Tech Parks Tech or science parks are a “must” in any modern city. It is a place where you congregate modern companies for the “new millennium.” Most of them are built by a roadside far away from the city, and companies which go there receive tax incentives from the local government. Most of them are also empty. Why? You guessed it. Our industrial mindset thinks of a tech park as a new modern version of an “industrial district,” a popular city project of the second half of the last century. In that case, it made sense to have industries gathered by a major road to receive and distribute supplies and products. And tax incentives were crucial for the established industries. But is it still relevant today? In the knowledge era, the main production means is, of course, knowledge. Knowledge, contrary to any other production means, actually grows when it is shared. So, tech parks or any other innovation hub should be established and planned with the objective of facilitating the exchange of ideas and the direct contact between workers from different companies. Modern tech parks should look more like a large

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co-working facility, compact, with lots of cafés and amenities for people to meet and talk to other people. And they should not be placed away from town. They should be placed downtown or as close to the city’s coolest areas and universities as possible. Let me just stress this point once more: Tech parks are not modern versions of industrial districts!

Work and Employment Almost every day we hear and read studies about the “end of work” and the millions of unemployed people that will follow this disruptive trend. A recent cartoon depicts a modern industry that has only two creatures inside the factory: a dog and his keeper. The keeper is there in order to feed the dog. The dog is there in order not to let anyone, including his keeper, mess up anything. So yes, industrial employment is decreasing. In fact, any menial job that can be executed by a machine or robot will eventually be so. As for the millions of unemployed? Not so sure. Employment contracts are indeed being transformed and will look different. Labor laws were enacted in the industrial era (again) and were meant to protect the “poor” industrial worker from the “greedy” capitalist owner of the factory that wanted to exploit him/her to the limit. These laws do not make any sense today and are being revised everywhere. Trade unionists march in protest, but the trade unions themselves do not make a lot of sense today. Think of a software startup company or an IT export company (Costa 2001). It has to install modern and cool facilities with jukeboxes, table tennis, and free lunch in order to attract the best possible talent (Florida 2005, 2014). Do these workers need a trade union? Do they even know that they belong to one (when it is compulsory)? Work will change, definitively. Employment with a contract will probably diminish also. But will that mean the end of work, or the end of work as we know it today? My hunch is the second. The United Nations study on future work estimates that half the students entering school today will work in their adult life in some profession or occupation that does not exist today. How then can we predict that work will end? In occupations that we do not know of, today?

The Car And yes, back to the poster child of the industrial era, the automobile or, simply, the car; we think of industry the whole time, as we have seen, but we just love the car. Cars are useful machines to take you from one place to another, at your own time and will. They are nice toys too (remember “the difference between men and boys is the price of their toys. . .”). And they are very pricy too – to the owner and to society. Let us first calculate the cost to the buyer. Car prices have been kept more or less constant through the decades. You get more stuff in today’s car obviously, but the final price to the buyer is more or less the same. The buyer, when making his purchase decision, decides if she will pay in cash, installments, or lease. If she can

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afford that, fine. She drives out the car shop door, onto the road. But in most cases, there was no calculation of what economists like to call TCO (total cost of ownership). Take this list of hidden (or forgotten at purchase time) costs of your car just sitting still, parked in your garage, no consideration of running costs, at this moment: insurance at, say, US$1500 a year (average in the USA); road tax at, say, 5%; depreciation at 10% a year; opportunity cost (what you would get if you invested the money instead of buying the car) at, say, 5%; and garage costs (if you own your garage, maybe you could rent it or do something else with it) at say US$1000 a year. If the purchase price is around US$25,000, our estimate of these hidden costs would amount to an additional US$7500 a year! That is a staggering 30% a year or circa double the original price in 3 years. And that figure doesn’t take into account the running cost of the car – fuel, tolls, maintenance, parking, cleaning, etc. – a very costly toy, if you ask me. Better use Uber or your other favorite app for shared transport. Nobody likes to see these numbers, because people love their cars. Or have we been brainwashed by marketing campaigns to love the car? Since the car industry is in deep trouble today, faced with what amounts to a “perfect storm” of unforeseen radical changes (electric, autonomous, and shared cars), they are pushing their wares even more strongly through all kinds of ads. In fact, there are so many car ads on television today that the ad campaigns lost creativity and became more or less standardized. Car ads go like this: a nice looking young professional enters her keyless car and drives happily on a nice city street (no traffic), to a winding road (sunny day), and then, suddenly, she turns into a tarmac road where the car performs nicely (seen from the outside, not if you are in), crosses a small river stream, drives through meadows and flowery fields, and ends up in a swirl that raises dust beautifully on top of a mountain during sunset. Have you seen it? Me too – several times and from different manufacturers. A car ad campaign in Brazil tried to break away from this homogeneousness and produced this novelty which managed to be even more stupid: a nice looking young professional enters his keyless car and drives happily on a nice city street (no traffic), to a winding road by the sea (sunny day), and then, suddenly, he sees a stunningly beautiful mermaid swimming in the distance. He parks the car, swims there, kisses the mermaid, and then poof, she drives away in his car leaving him there, astounded, in the water. This is a real ad; believe me! One wonders: what is the message? Buy this car, drive to the sea, and you will see a mermaid that will fool you and run away with your new car? Or is it buy this car that even a mermaid with a long tail is able to drive? Now let us study the cost of the car to society. Most cities have a traffic department that takes care of the flow of vehicles in town, mostly cars; a road and transport department that builds and maintains viaducts, streets, and roads; a public parking (sometimes free) facility; and many other operations related to the car. They are all very costly, but the services are free to their users, the car owners. For some reason, every infrastructure demanded by cars is free. Schwartz (2015) suggests there was an orchestrated lobby of car manufacturers and other interested parties (tire industries, insurance companies, etc.) in the USA in the beginning of the 1900s that

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set the scheme, but the fact is it is supposed to be free almost everywhere. Few cities have a bike and pedestrian department. It is as if our cities were built for the car. And its infrastructure cost is huge. Now think of the cost of car accidents. Besides the personal and family tragedies involved in these accidents, there is the additional cost of treatment for the more than 50 million people that are disabled or injured in the world every year. Some, perhaps most, of these costs are borne by society as a whole. Add to that the cost of air pollution and all sorts of illnesses caused by it, and also the cost of the disposal of tires and old parts, or even entire old vehicles, a large number that nobody talks about. And what is the actual solution? If we do deindustrialize our thinking and take the car not as a deity but as a means of transport, rational conclusions emerge. For instance, clarify the costs of cars to the owners through public campaigns. Charge the car owner the full cost of his piece of equipment. We charge air travelers an airport tax to use the infrastructure, don’t we? Why not charge the car for its use of the public roads, parking lots, and such? Some cities (Singapore, London) did just that, charging tolls for cars going to congested downtowns on weekdays. It is a good start.

Cross Reference and Major Challenges Change is always difficult – cultural changes, even more. In order to improve our cities, we must face many challenges, such as these depicted in the corresponding chapters of this book.

Demographics As the percentage of senior people in society grows, the city needs to prepare itself for the different needs of an aging population. There will be many active old people who will want to be in contact with other people, young and old, and not to be segregated away from society and into old people’s homes. And the city will need to tend to the special needs of old people in terms of mobility, exercise, and health care in and outside the home. Technology can certainly help here, and a number of startups are now focusing on this growing segment of the market.

Diversity and Priorities Cities that are more humane, sustainable, and smart value diversity in terms of the use of the space, in terms of gender, sexual orientation, and age and in terms of income levels. But this diversity also brings with it a new level of difficulty in setting up priorities. A new kind of governance must be in place, where citizens or citizens’ councils meet frequently to discuss and reach consensus on the best ideas and projects to be prioritized by the administration.

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Originally this challenge was mostly related to poor people in big cities of developing countries. But with the recent wave of lawful and unlawful immigration of all kinds of different people from poor countries to richer ones, the developed world and their major cities must now tackle this challenge head on.

Special Needs It is surprising the difference in cost to have a ramp installed in a building for wheelchairs when 1) it is done at first, during construction, or 2) it is done later as an afterthought or something determined by legislation. Inclusive design is this new trend where architects and engineers think of all kinds of possible users of a new facility before it is built. Many of the users will have special needs and must be accommodated in the project from its conception. This challenge is also related but not limited to the demographics challenge. It involves people who have some kind of disability but that want and deserve to have a life with as much activity and work opportunities as everybody else’s.

Socialization With the new media and social networks, it is much easier to be in electronic contact with lots of friends and acquaintances. Eventually, it is also much lonelier. The new tools are fine but there is still need for physical contact and face-to-face meetings. Technology may be a distraction but may also help in the organization of networks of people that resemble the old neighborhoods and invite them to interact in person as well. The European Union My Neighborhood Project (Oliveira and Campolargo 2015) presented several different experiments toward that goal. It is interesting to realize that some of the problems of the cities today (violence, pollution, no safe parks, segregation, etc.) compound with the excessive use of the social networks in order to isolate us and our kids (Harari 2015). But before we start planning for the next “tech detox spa,” let us use the power of technology for something that brings people closer, in the old village sense – a major challenge indeed, in an era when we tend to have lunch alone and with our smartphone next to the plate, which is even worse for one’s health than the old TV set on the kitchen table.

Conclusion The first step to improve livability in our cities is to recognize that the situation is not good. Even in the star cities that attract the best talent available in the world (New York, London, Hong Kong, Vancouver, Lisbon, San Francisco, etc.), there are vexing signs of instability: gentrification, unaffordability, segregation, and inequality, in the words of Florida (2017). Not only that, the situation is getting worse,

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particularly in the developing world. It is important to open people’s eyes to the possibilities of more humane interventions in their cities. From the amazing achievements of Emperor Napoleon III and Mayor Haussmann in Paris, during the nineteenth century, and its side effects in Europe and other cities in the world, we went through a series of mistakes in the twentieth century that led to the situation we are in today. And considering the dislocation of people from rural areas to cities that is happening now, and will continue in the future, in the developing world, past mistakes must now be corrected. China alone will move a large population, the size of one entire USA, to cities, in the next 12 years. It is just impossible (in economic terms) to do this with the model of “cities for cars.” There must be another way. In order to implement change, we must first go through a process of deindustrialization of our mindset, including seeing the car for what it is, not as a necessary key component of our daily life. Perhaps, in our process of deindustrialization, this is the most difficult step. But once we realize the amount of brainwashing about cars that we have been submitted to, it is doable. For the reader who gets interested in the subject and wants to do something in his/ her own neighborhood, we suggest a list of books for further reading. And as a way of starting immediately, we suggest an acupuncture intervention in his/her city. Choose a borough or region of the city that is rich in diversity that carries a history or narrative worth telling other people and that is significant to the city: start there, and the results will showcase the wonderful possibilities open to the whole city. Cities can be more humane and sustainable, using technology, being smarter. And it does not depend on the mayor, the local council, or anyone else. It depends upon you.

References Almeida, V. A. F., Doneda, D., & Costa, E. M. (2018). Humane smart cities: The need for governance. IEEE Internet Computing, 22(2), 91–95. https://doi.org/10.1109/MIC.2018.022 021671. Aravena, A. (2018). Interview to Arch Daily. Available at https://www.archdaily.com/906076/ alejandro-aravena-shares-the-foundational-philosophies-at-the-core-of-his-socially-consciouspractice. Accessed 1 Apr 2019. Azua, J. (2006). Bilbao: From the Guggenheim to the Knowledge City. In J. Carrillo (Ed.), Knowledge society: Approaches, experiences and perspectives (pp. 97–112). Oxford: Elsevier. Carrillo, J. (Ed.). (2006). Knowledge society: Approaches, experiences and perspectives. Oxford: Elsevier. Carrillo, J. (2015). Knowledge based development as a new economic culture. Journal of Open Innovation: Technology, Market, and Complexity, 1, 15. Chang, D. L., Marques, J. S., Costa, E. M., Selig, P. M., & Yigitcanlar, T. (2018). Knowledge-based, smart and sustainable cities: a provocation for a conceptual framework. Journal of Open Innovation: Technology, Market, and Complexity, 4(1), 5. Costa, E. M. (2001). Global e- commerce strategies for small businesses. Cambridge: The MIT Press.

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Costa, E. M., & Takahashi, E. (2020). Humane and sustainable smart cities. London: Elsevier. To be published. Deutschmann, M. (2017). The one mile radius. Advantage, Charleston, NC. Edition. Oxford: Oxford University Press. Florida, R. (2005). The flight of the creative class: The new global competition for talent. New York: Harper Collins. Florida, R. (2014). The rise of the creative class. New York: Basic Books. Florida, R. (2017). The new urban crisis. New York: Basic Books. Gehl, J., & Svarre, B. (2013). How to study public life. Washington, DC: Island Press. Gies, F., & Gies, J. (1969). Life in a medieval city. New York: Harper-Collins. Harari, Y. N. (2015). Sapiens – A brief history of humankind. New York: Harper-Collins. Howkins, J. (2001). Creative economy. New York: Penguin Books. Howkins, J. (2013). Creative economy: How to make money from ideas. New York: Penguin Books. Huang, C. C., Busch, C., Dongquan H., & Harvey, H. (2015). The 12 green guidelines. China Development Bank Capital. https://energyinnovation.org/wp-content/uploads/2015/12/12Green-Guidelines.pdf. Accessed 15 Mar 2019. ICLEI. (2019). Resilient cities report 2018. https://resilientcities2018.iclei.org/wp-content/uploads/ RC2018_Report.pdf. Accessed 19 Mar 2019. Jones, P., et al. (2019). Smart cities: Overview and glossary. In J. C. Augusto (Ed.), Handbook of smart cities. Cham: Springer. Kirkland, S. (2013). Paris reborn: Napoleón III, baron Haussmann and the quest to build a modern city. New York: St.Martin’s Press. Lara, A. P., Costa, E. M., Furlani, T. Z., & Yigitcanlar, T. (2016). Smartness that matters: towards a comprehensive and human-centered characterization of smart cities. Journal of Open Innovation: Technology, Market, and Complexity, 2(1), 1–13. Larson, K. (2012). Brilliant designs to fit more people in every city. TedX talk, Boston. Available at https://www.ted.com/talks/kent_larson_brilliant_designs_to_fit_more_people_in_every_city Morse, M. L., & Qing, C. (2012). The dynamic population of Manhattan. https://wagner.nyu.edu/ files/rudincenter/dynamic_pop_manhattan.pdf. Accessed 29 Mar 2019. Mumford, E. (2018). Designing the modern city: Urbanism since 1850. Oxford: BW&A Books. Nelson, A. C. (2006). Leadership in a new era: Comment on “Planning Leadership in a New Era”. Journal of the American Planning Association, 72(4), 393–409. https://doi.org/10.1080/ 01944360608976762. Oliveira, Á., & Campolargo, M. (2015). From smart cities to human smart cities. In 48th Hawaii international conference on system sciences, Kauai, HI (pp. 2336–2344). https://doi.org/10.1 109/HICSS.2015.281. Register, R. (2006). Eco-cities: Rebuilding cities in balance with nature. Canada: New Society Publishers. Roberts, J. M. (1980). The pelican history of the world. London: Penguin Books. The Rockfeller Foundation. (2013). 100 resilient cities. https://www.rockefellerfoundation.org/ourwork/initiatives/100-resilient-cities/. Accessed 19 Mar 2019. The United Nations. (2019). The future of work. Available at https://www.un.org/pga/73/event/thefuture-of-work/. Accessed 19 July 2014. Yencken, D. (2013). Creative cities. In Space, Place and Culture 2013. Victoria: FutureLeaders.com.au. Yigitcanlar, T., & Lee, S. (2011). Moving towards a Knowledge City?: Brisbane’s experience in knowledge-based urban development. International Journal of Knowledge-Based Organizations, 1. https://doi.org/10.4018/ijkbo.2011070102. Yigitcanlar, T., O’Connor, K., & Westerman, C. (2008). The making of knowledge cities: Melbourne’s knowledge-based urban development experience. Cities, 25(2), 63–72. Yigitcanlar, T., Marques, J. S., Lorenzi, C., Bernardinetti, N., Schreiner, T., Fachinelli, A., & Wittmann, T. (2018). Towards Smart Florianópolis: What does it take to transform a tourist island into an innovation capital? Energies, 11(12), 3265.

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Further Reading Costa, E. M., & Oliveira, Á. D. (2017). Humane smart cities. In R. Frodeman, J. T. Klein, & R. C. Pacheco (Eds.), The Oxford handbook of interdisciplinarity (2nd ed.). Oxford: Oxford University Press. Glaeser, E. L. (2011). Triumph of the city. New York: Penguim Press. Jacobs, J. (1961). The death and life of great American cities. New York: Vintage Books. Sadik-Khan, J., & Sollomonow, S. (2017). Street fight: Introduction for an urban revolution. New York: Penguin Books. Schwartz, S. I. (2015). Street smart: The rise of cities and the fall of cars. New York: PublicAffairs. Yigitcanlar, T., Kamruzzaman, M., Foth, M., Sabatini, J., Costa, E. M., & Ioppolo, G. (2018). Can cities become smart without being sustainable? A systematic review of the literature. Sustainable Cities and Society. https://doi.org/10.1016/j.scs.2018.11.033.

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Smart Energy Frameworks for Smart Cities: The Need for Polycentrism Joseph Nyangon

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Climate Change and Urban Energy Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Nature of the Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Grid and the Future of Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Emerging Models for Urban Energy Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distributed Energy Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Energy Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Microgrids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Demand Response and Energy Management Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Measuring Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Harvesting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Green Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . From Robustness to Resilience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Polycentric Approach to Smart City Energy Governance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Rapid growth in megacities has prompted deep transformations intended to change sociotechnical systems, deep social and institutional practices, and scientific inquiries to better understand energy and material flows of cities. Typically, these processes are defined by sociotechnical experimentation and purposive reshaping of the synergies between jurisdictions, sectors, and technical solutions required to optimize resource management and improve institutional diversity and its configurations. This chapter studies features of smart energy frameworks

J. Nyangon (*) Center for Energy and Environmental Policy (CEEP), University of Delaware, Newark, DE, USA e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_4

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for smart cities leadership in an attempt to ignite transformations in energy business models for sustainability systems from the bottom up. Following this polycentric approach, the chapter documents seven emerging models for smart city energy governance, namely distributed energy resources development, energy storage, microgrids, demand response and energy management systems, smart measuring systems, energy harvesting, and green technology innovations. One observation is that while Singapore and Shanghai are a product of advanced polycentric strategic planning, the urban developments around the greater Jakarta area is an outcome of gradual alignments and reconfigurations of urban design toward the polycentric goal. In addition, energy systems and utility business models are changing simultaneously in several cities with respect to institutional contexts, urban planning, and customer choice. A key message of this chapter is that capturing the impacts of these urban transformation across the quartiles of energy resource development, technological progress, and policy stringency requires the design and implementation processes that simultaneously promotes polycentric authority and contributes to informed understanding of the scale and consequences of these transitions.

Introduction Rapidly rising populations in cities, urbanization and economic development have prompted the emergence of megacities, i.e., urban agglomerations with populations exceeding 10 million inhabitants (Kennedy et al. 2015). Because of their sheer size and complexity, megacities present epic social, economic, and environmental challenges. In the last three decades, major cities in the United States and Europe have been prioritizing new forms of sustainable urban development, notably new urbanism, compact city models and smart urban growth through transit-oriented development to counteract these challenges (Ewing et al. 2017; Noland et al. 2017; Kim and Larsen 2017; Chhetri et al. 2013). Although these models have different origins and objectives, they generally seek to improve energy and material flows toward reduced energy- and water-use intensity, increased adoption of mass transit systems, controlled growth and expansion, mixed and diverse land-use development, and stronger urban sensibility. In the global south such as in many African and Asian cities like Cape Town, Bangalore, Bangkok, Beijing, Chennai, Guangzhou, Hong Kong, Lagos, Mexico City, Nairobi, Nanjing, and Shanghai, however, a variety of smart urban energy solutions have mainly focused on alleviating environmental pollution and the decreasing density (or, alternatively, urbanized area per capita), in part, owing to rapid population growth and rural-urban migration (Chiu 2012). In 2014, China reported the largest urban population globally of 758 million as well as six megacities, i.e., Shanghai, Beijing, Chongqing, Guangzhou, Tianjin, and Shenzhen, and is projected to add one more megacity (Chengdu) and six more large

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cities (i.e., Wuhan, Dongguan, Hong Kong, Nanjing, Foshan, and Shenyang) by 2030 (United Nations 2014). This remarkable rapid growth in the size and number of megacities upends a range of social-technical transitions, institutional change, economic innovations, and scientific inquiries. The frameworks adopted to understand energy and material flows of cities, in particular, the nexus between urbanization, resilience, and resource efficiency and related synergies between jurisdictions, sectors, and technical solutions required to optimize resource management and improve institutional frameworks for effective service delivery have focused on top-bottom strategies. (Clark et al. 2019; Sircar et al. 2013). As a result, these strategies have been criticized for being ineffective, inflexible, less transparent, and inadequate in mediating the effects of socio-environmental inequalities in cities. For instance, unlike most American cities, Chinese cities are high-density areas integrated via transit-oriented development (TOD) (with high levels of mixed-land use configurations around public transport stops), making them ideal for the compact city model. The new urbanism concept thus is not ideal for the progrowth ethos of most Chinese megacities and other densely populated cities in the developing world, but rather less populated European cities which tend to emphasize a more compact urban form and smart growth as an antidote to the ills associated with urban sprawl (Wey and Hsu 2014; MacLeod 2013). Transit-oriented development model, however, is more suitable for cities like Berlin, London, Madrid, Milan, New York, Paris and others, which have a long history of implementing mass transit systems, because it maximizes integrated access to residential, business and leisure activities within walking distance of near-excellent public transport (Noland et al. 2017). Smart, networked cities increasingly require polycentric governance of socio-technical systems that together form the elements of their energy frameworks in order to foster smart growth, accelerate low-carbon transitions, and lessen fragility concerns that emanate from a troika of rapid population growth, urbanization, and climate change challenges. With respect to climate change, the cost of urban infrastructure damage is rapidly rising. For instance, in August 2017, Hurricane Harvey cut a destructive path across Texas and the Gulf of Mexico leaving thousands without electricity, while in 2012, Hurricane Sandy caused extensive destruction and damage to energy infrastructure across several northeastern states (Maryland, Delaware, New Jersey, New York, Connecticut, Massachusetts, and Rhode Island) due to high wind and coastal storm surge. The U.S. National Oceanic and Atmospheric Administration (NOAA) estimates the total damage from Harvey and the 2005 Hurricane Katrina at $130 billion and US$168 billion (2019 consumer price index cost adjusted value), respectively, making these two events the costliest U.S. weather and climate disasters on record since 1980 (Smith 2019). As acknowledged by the Intergovernmental Panel on Climate Change (IPCC), energy infrastructure (as well as other high-quality urban infrastructure-based networked systems like transportation) is increasingly confronted with a series of grand challenges – rapid population growth, urbanization, and climate change (Revi et al. 2014). This situation is compounded

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by the fact that these critical urban infrastructure systems constitute the backbone of a networked city, are largely inflexible to changes in utilization and external conditions due to the spatial alignment and coevolution of their elements, are underfunded and often poorly maintained, and are increasingly complex and interconnected across several functions. (The problem is particularly acute in urban areas, where growing populations stress society’s support systems and natural disasters, accidents, and terrorist attacks threaten infrastructure safety and security.) Diversifying energy systems – through the interlinked mix of clean energy technologies, institutional change, user-centered design practices, and market and regulatory innovations – can help cities achieve their low-carbon objectives, promote greater energy security and electric grid stability, and improve access to modern energy services as new energy demand is projected to take place in rapidly urbanizing metropolitan regions and megacities (Taminiau et al. 2019; Nyangon and Byrne 2018; Byrne and Taminiau 2018; Taminiau et al. 2017). Therefore, the investment decisions cities make today in high-growth intensive sectors especially infrastructure such as roads, electrical power systems, sewers, and buildings will influence the evolution of urban spatial structure and their socio-environmental dynamics for several decades. Different types of urban systems, for example, compact, well-connected cities versus sprawling, car-dependent urban locations, can act as straightjackets for smart cities of the future by providing integrated and resilient energy frameworks and metrics for addressing known and unknown challenges. Such efforts entail increasing momentum of niche innovations, weakening existing legacy systems, and strengthening exogenous trends and developments, which when aligned can destabilize the existing system to create processes that yield breakthrough innovations (Nyangon 2017). While the potential benefits of smart energy networked cities exist in smart investments such as energy management services, energy storage, distributed energy resources, demand-side management, and automatic measurement and verification, poorly managed urban growth does have social and economic costs. This chapter proposes reorienting the principles and tenets of energy business models and frameworks toward a polycentric approach by focusing on six key imperatives: (a) stakeholder-driven approach, (b) enhanced accountability and legitimacy, (c) inclusivity and equitability, (d) adaptive management, (e) shared learning, and (f) continuous improvement to promote integrated energy governance and material flows in cities. Polycentricity can result from advanced planning or self-organization. For instance, while polycentric cities like Singapore (Field 1999) and Shanghai (Ziegler 2006) emerged from an advanced strategic planning, the urban development around the greater Jakarta area is a result of gradual alignments and modifications in planning to explore the potential of interactive technologies and systems, toward the polycentric objective (Hudalah and Firman 2012). Conversely, Shenzhen and Guangzhou are a product of special urban policy (Wu 1998), while London, Amsterdam, Paris, Frankfurt, and many European cities are a product of both self-development and planning (Hall and Pain 2006).

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Climate Change and Urban Energy Infrastructure “How does a lively neighborhood [city] evolve out of a disconnected association of shopkeepers, bartenders and real estate developers? How does a media event take on a life of its own? How will new software programs create an intelligent worldwide web?,” Steven Johnson writes in Emergence (Johnson 2001). According to Mumford (1938), the city is “a point of maximum concentration for the power and culture of a community.” Cities are shaped by policies, regulations, and formal written development plans as well as spontaneous, unpredictable self-organizing individual elements that give rise to intelligent and sophisticated systems working on their own prescribed logic. Consider control processes such as traffic lights and their control coordination mechanisms, or the socioeconomic characteristics and attributes of heating and cooling in buildings which influence household energy consumption, or even waste collection and management processes, every city model falls somewhere along a continuum. These self-organization phenomena involve large-scale systems where no single activity or individual exerts control over the processes. It also provides a fruitful source of inspiration for understanding the elements of smart networked cities: how they emerge, deliver societal functions such as personal mobility, and implement niche technological innovations such as piped water infrastructure, heated buildings, pedestrian streetscape facilities, as well as cultural, political, economic and behavioural changes, in a manner often known as “combinatorial innovation” – the ability to combine novel technologies together to create a new wave of discoveries (Youn et al. 2015). (Combinatorial innovation process exhibits two key characteristics: “exploitation” (i.e., continuous refinements of existing combinations of technologies) and “exploration” (i.e., the development of new technological combinations) (Youn et al. 2015).) Today, the narrative of the low-carbon transition puzzle in cities, the ascent of energy-related infrastructure challenges in metropolis, the demands for quality livability standards, and the entire unfolding urban sensibility is fundamentally intertwined with climate change. It is also embedded in politics, urban policy, and polycentric governance efforts. The rapid spread of the COVID-19 pandemic is a grim reminder of how global natural disasters exacerbated by unsustainable resource and energy use practices like climate change respect no boundaries.

The Nature of the Challenge Dynamism is an abiding feature of smart cities. The dynamic nature of cities is characterized by rising quest for accumulation, of creative destruction and of growth and dislocation spurred on by technological advances which have become symbolic with rapid urbanization and the ascent of megacities. However, climate change now threatens this dynamism and its configurations of urban sensibility and urban resilience. Often characterized as a “super wicked” problem, meaning that its impacts are global, complex, and urgent, climate change is, in part, “driven by policies and technologies that created a path-dependent reliance on high carbon

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fossil fuels” paradigm, implying that a robust climate solution for governing smart cities should nurture “countervailing policies that trigger path-dependent low-carbon trajectories” (Levin et al. 2012). The impacts of current and anticipated climate change on urban systems are substantial, disrupting energy provision, food distribution, water supply, waste removal, financial markets, and increased susceptibility to pandemics (Agbemabiese et al. 2018). For instance, solar photovoltaic (PV) cells generally work optimally at low temperatures. Climate-induced temperature increase affects the conversion efficiency of a PV cell (Emodi et al. 2019). Increased intensity and frequency of storms disrupt wind power generation, with higher waves lessening electricity production of offshore wind turbines (de Jong et al. 2019). Offshore oil and natural gas facilities and low-lying coastal infrastructure in port cities are equally vulnerable to climate-induced impacts as sea levels rise and wind storms increase in severity and frequency (de Jong et al. 2019; Emodi et al. 2019; Cortekar and Groth 2015). As urbanization spreads up, high unemployment and social unrests in cities, rising competition for resources such as water and food, widening inequality gap, and environmental degradation also threaten urban development. Climate change exacerbates these threats, forcing city authorities to explore explicit sociotechnical interventions to mitigate these threats as well as support economic growth and sustainable low-carbon development. Attention must thus be broadened toward interactions between climate, energy, and other socio-technical systems. Aging urban infrastructure increases severity of climate risks. Storm-related power outages and direct physical damages from climate-induced natural disasters increase operations and maintenance costs as well as capital investment in energy infrastructure (Markolf et al. 2019; Miller and Hutchins 2017). Furthermore, increased frequency and duration of extreme weather and storm-related power outages result in prohibitively expensive insurance premiums for cities and utilities. A case in point is California’s PG&E bankruptcy filing in 2019, citing billions of dollars in liabilities stemming from wildfires in 2017 and 2018 (Blunt and Gold 2019). Due to these geophysical and climatic disasters, cities are increasingly exploring a host of adaptation and mitigation strategies, especially adaptive smart policies, energy frameworks, user practices, programs, and technical solutions to actively phase out existing technologies and systems that lock in institutional and behavioral systems for decades (U.S. Department of Energy 2015). Table 1 summarizes direct physical impacts from extreme events on urban infrastructure systems and potential smart grid solutions.

Smart Grid and the Future of Smart Cities Given the centrality of technological innovations in supporting polycentric energy governance efforts related to climate change, water and wastewater management, mobility, economic competitiveness, and a range of other material flows, it is not surprising that cities are expending considerable capital in developing evolutionary business models for explaining innovation, consumer acceptance, and multi-level energy frameworks to better understand socio-technical regimes of

Water and sewerage systems, e.g., water supply, flood management, etc.

Transportation systems, e.g., roads, bridges, rail, airports, etc.

Urban infrastructure Energy systems, including energy storage (e.g., flywheels, compressed air, pumped hydro, and batteries); energy generation, transmission and distribution systems; energy efficiency (e. g., air-conditioning systems); smarter grids (e.g., smart street lighting, grid management, intelligent loads, and traffic signal)

Climate risks and vulnerabilities Reduced power plant efficiency Increased variability, power outages Reduced capacity utilization of energy assets Thermal expansion joints beyond design capacity Rising electricity demand structural instability of gas pipelines Reduced transmission and distribution capacity Flooding of railways, highways, and low-lying infrastructure Reduced structural integrity of gas pipelines, pavements, etc. Road and bridge washouts Cancelled or delayed flights Traffic disruptions, vehicle overheating, and tire deterioration Damage from increased thaw cycles More debris in stormwater management systems Rising operations and maintenance cost Smart measuring Alter design storm criteria Green infrastructure and technologies

Green infrastructure and technologies Resilience Relocation of roads and infrastructure

Potential smart solutions District heating and smart grids Microgrids Energy storage Harvesting Robustness and resilience

Table 1 Summary of city infrastructure disruptions related to extreme weather events

(continued)

Nyangon et al. (2017a), Hakelberg (2014), Bulkeley and Castán Broto (2013), and Bartos and Chester (2014)

Markolf et al. (2019), Clark et al. (2019), Underwood et al. (2017), Rattanachot et al. (2015), and Taylor and Philp (2015)

Sources Taminiau et al. 2019, de Jong et al. (2019), Burillo et al. (2019), Emodi et al. (2019), Nyangon and Byrne (2018), Agbemabiese et al. 2018, Nyangon (2017), Taminiau et al. 2017, and Cortekar and Groth (2015)

3 Smart Energy Frameworks for Smart Cities: The Need for Polycentrism 61

Green infrastructure systems

ICT infrastructure

Urban infrastructure Waste management and disposal systems

Table 1 (continued)

Climate risks and vulnerabilities Increased cost of repairs and maintenance Flooding of underground waste infrastructure systems Signaling and control disruptions Performance-reduced functionality of on-demand services Interaction complexity Need for security and amount of data Increased runoff due to vegetation loss Increased wildfires Rising energy costs Green stormwater infrastructure Permeable pavements Green roofs

Green infrastructure and technologies

Potential smart solutions Green infrastructure and technologies

Burillo et al. (2019), Clark et al. (2019), Agbemabiese et al. 2018, Nyangon et al. (2017b), and Nyangon (2014)

Maki et al. (2019) and Duncan (1995)

Sources Choudhary et al. (2019) and Miller and Hutchins (2017)

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transitions and the momentum for renewable energy innovations such as solar PV, wind, and bio-energy. In recent times, cities such as New York City, London, Shanghai, Philadelphia, San Francisco, and Tokyo have adopted smart energy roadmaps, including energy storage, demand response, and smart measurement and reporting, to accelerate their transformation toward clean energy economies. Besides climate-related concerns, energy systems are experiencing several challenges, including lagging investments, energy efficiency gap, diversification of energy generation assets, optimal deployment of expensive assets, and demandside management. Yet, majority of the urban electricity infrastructure is outmoded, stressed, aging, and incapable of responding to these critical issues (ASCE 2017; U.S. Department of Energy 2015; Fox-Penner 2010). In addition, the existing grid is unidirectional and hierarchical and consists of mostly centralized generation assets, meaning it converts only half of the fuel input into electricity without recovering the waste (heat). In this framework, transitioning to a smart grid system addresses this major shortcoming of the power grid, as well as optimizes energy asset utilization and operation efficiency while facilitating roll out of new energy products, services, and platforms. A smart grid is an “intelligent” electrical grid – uses digital, multidirectional communications; provides multiple customer choices to improve reliability of electricity supply, system operating efficiency, and energy services; and consists of mostly distributed generation assets which reduce operating costs while maintaining power grid flexibility and use of pricing models applications. The unfolding New York energy transition, for example, involved diversifying energy generation mix, through solar PV, wind and bioenergy technologies. The goal is to improve electricity choices for customers as well as enhance the resiliency and flexibility of the electricity, transportation, heating, and industrial systems against possible direct impacts from climate risks. Nyangon and Byrne (2018) used a combination of business model innovation, simulation, and Gary Hamel’s business concept innovation framework to study the ongoing reorganization of the New York energy market under the Reforming the Energy Vision (REV) process. Expanding on these concepts of diversified power generation mix and intelligent grid infrastructure solutions (DeRolph et al. 2019), this chapter proposes that incorporating elements of smart energy business models such as strategic resource management, revenue model, customer interface, and value propositions, in addition to flexibility and agility, may help animate high levels of reliability and resiliency of urban infrastructure systems, as illustrated in Fig. 1. Furthermore, as the existing urban energy infrastructure continues to age, a new window of opportunity for smart grid applications is emerging. Elements of this smart grid regime include technologies and strategies such as distributed energy resources, energy storage, microgrids, demand management technologies, smart measuring, harvesting, green technologies, and resilience (or robustness). Most cities in the developed world are already implementing a variation of these smart grid systems, through an assortment of technological innovations, principally by incorporating new technologies and assets into old operations and existing infrastructure. First, at its core, the smart grid implementation is a lateral integration and careful overhaul of the existing grid through information technology, circuit

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Fig. 1 Common interconnections of critical urban infrastructure systems. (Source: Author’s illustration)

infrastructure, and communication applications. However, because of the electric power sector’s tangled economic and regulatory structure, the implementation of smart grid in cities may take the form of “part destination” and “part vision” (U.S. Department of Energy 2015). As such, evolution of the smart grids will be dependent on several factors, notably innovation in technology, energy investment and market structures, policy, regulatory jurisdictions, and a city’s needs and requirements. Second, the rising demand for a decarbonized, distributed, and digitalized electricity landscape creates technical and business process challenges for power operators. These challenges include transitioning to a smart grid future, at the highest possible return on investments, as soon as possible, at the minimum cost, without endangering critical energy services in their jurisdictions. Utilities in the developing world have fewer legacy systems and have a clear advantage over their counterparts in the developed world (Farhangi 2010). This is because most cities in developing counties have minimal requirements for backward compactivity with their existing systems, and moving forward investment can be directed toward cleaner, sustainable energy alternatives. Furthermore, cities in the developed world make smart grid investments in a highly regulated environment compared to their counterparts in developing countries. As Fig. 2 demonstrates, a typical smart grid pyramid consists of several technologies, with asset management occupying the base of smart grid development. Decomposing smart energy systems into implementation components such as energy efficiency, demand-side management programs, energy storage, and microgrids provides cost-effective solution to mitigation and adaptation (Pallonetto et al. 2019; Oprea et al. 2018). Accomplishing each task requires deployment and

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Fig. 2 Components of smart grid pyramid. (Source: Author’s illustration)

integration of various technologies to climate-proof the electric grid. Which style of smart grid innovation is right for smart cities? Implementing the smart grid vision involves weaving an array of technologies into the city infrastructure, including predictive analytics, the Internet of Things, big data, and artificial intelligence. In terms of specific projects, examples of smart grid milestones include advanced metering infrastructure (AMI), advanced distribution operations (ADO), advanced transmission operations (ATO), and advanced asset management (AAM). The IBM Smarter Cities Challenge program, for example, promotes a systematic data collection approach and strengthens sustainability planning and urban governance (Alizadeh 2017). AMI comprises of smart meters, communications networks, and information management systems for processing vast amounts of new data. AMI networks enable utilities to collect meter data remotely, facilitate customer participation in demand response and energy-efficiency improvement, and support the evolution of tools and grid management technology that will drive the smart grid future, including outage restoration and integration of electric vehicles and distributed generation. Furthermore, AMI supports practical application of time-varying rates, resulting in peak demand reduction in household energy consumption, in

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certain cities, by almost 30% (Wang et al. 2019). While smart meters offer substantial benefits to utilities and consumers, new challenges are being upended such as the need for continuous improvement of interoperability and embedding of AMI architecture systems to address cybersecurity and privacy concerns (Lightner and Widergren 2010). In light of this, the AMI smart grid electricity infrastructure should be scalable, be capable of adapting to changes, and include open-standard technology architecture to enable interoperability among several applications in order to support a wide array of city operations. For distribution networks, implementing ADO, particularly distribution management system; automated fault detection, isolation, and restoration (FDIR); and distribution automation technologies – i.e., energy management system (EMS), supervisory control and data acquisition (SCADA), distribution management system (DMS), and outage management system (OMS) – could provide increased granularity of and access to smart control mechanisms needed for an adaptive and “selfhealing” distributed grid, improving reliability and climate resilience (Pérez-Arriaga and Knittel 2016). On the other hand, ATO improves transmission reliability and congestion management on the transmission system by integrating the distribution system with regional transmission organizations (RTO) and market applications. Finally, AAM improves the utilization of transmission and distribution assets at the operational level and supports effective management of these assets from a life cycle perspective. AAM includes equipment health monitors and synchrophasor systems – consisting of phasor measurement units, communications networks, and data visualization systems. On the grid network, a key distinction is that whereas transmission and consumption are essentially passive elements of the power grid, generation is dynamic. For cities, identifying and addressing decarbonization, decentralization, and digitalization challenges, require investment in a smart grid to facilitate systematic deployment of energy assets from the outset. Furthermore, characterizing the deployment challenges to establish if they are technological, behavioral, or structural provides a good starting point. In addition, integrated assessments, foresight, and scenario facilitate imagination of urban innovation futures, including diversified generation assets, economic transformation, and policy innovation. Therefore, the sequence of knitting in smart energy solutions might vary significantly across cities and regions, and utilities should approach this transition based on a holistic assessment of their assets and the existing regulatory environment. For instance, to advance decentralized energy governance approach, cities such as Songdo and Masdar have adopted a sequence that follows the following strategy to improve resilience and robustness: implement AMI to establish physical communication infrastructure to the energy generation assets, followed by ADO to assure selfhealing of the distribution system, and then ATO systems to address congestion concerns on the transmission system. Finally, implement AAM to support “smart” real-time transactions, with predictive asset-modeling capabilities built on real-time data (Lee et al. 2016). As James et al. (▶ Chap. 1, “Smart Cities: Fundamental Concepts”) highlight in the introduction chapter, climate change adaptation measures, including distributed

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energy resources, provide double dividend benefits such as emission reduction, energy savings, and operational improvement. In broader terms this enables cities to improve their energy security, grid reliability, and demand-side management. To realize this goal, technology, market, and policy-oriented strategies for smart grid such as energy storage, microgrids, demand response, and distributed energy resources are discussed to explain their role in optimizing existing energy assets and mitigate climatic extremes. In addition, the modernization of the electric power grid policy framework is increasingly not an optional add-on for utilities but an essential planning component of urban energy infrastructure.

Emerging Models for Urban Energy Transformation This section discusses some of the components and elements of a smart grid in Fig. 2.

Distributed Energy Resources Distributed energy resources (DERs) enable active participation by consumers in the power grid. DERs include PVs, small wind power plants, small natural gas-fired generation and combined heat and power (CHP) technologies, energy efficiency, electricity and thermal storage, demand response (DR), heat pumps, and electric vehicles (EVs). (While wind power systems are often connected at distribution voltages, they are a mature technology and rarely deployed at customer sites.) All of these technologies have unique characteristics and sometimes complex interactions with the distribution grid. For example, while rooftop and ground-mounted solar PVs and wind power systems are fueled by non-dispatchable sources of energy and therefore have variable energy output, electricity and thermal storage and fuel cells provide more flexibility and reliability to the grid (Nyangon 2017). On the other hand, energy efficiency, DR, EVs, and heat pumps are customer dependent and therefore behavior- or participation-centric. In this regard, city planners could assess the potential of the DERs from two main perspectives: accommodating DERs, which implies that their implementation may create adverse impacts on the electric distribution network, and integrating DERs, which means that they may mitigate grid constraints such as limited hosting capacity and unbalanced power flow (Trencher and van der Heijden 2019; Eid et al. 2016). The transformation from consumers to prosumers – active energy market participants who consume less bulk kilowatt-hours from the grid due to energyefficiency improvements while producing more through small-scale distributed generation – is one of the most exciting research areas of DERs and grid service development. (The term prosumer refers to energy consumers who are also producers.) For urban areas where conventional CHP plants are available, DER installations can be used to improve power systems restoration after power outages, improve frequency stability, and reduce blackouts. DER electricity development in

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cities, particularly solar PV and wind power systems, can be traced to favorable support policies and incentives by national and local governments, private sector financial investments, and technology and market improvement (Nyangon et al. 2017b). DER can help cities lower and stabilize household electricity costs, which are passed down to consumers, and improve grid flexibility because they are far more flexible to site and operate than fossil-based technologies. At the transmission level, flexible alternating current transmission system (FACTS), phasor measurement unit (PMU), fault current limiters, and synchronous switching devices provide instantaneous voltage support during extreme weather events and storms (FoxPenner 2010). In addition, they also enhance power quality, balance reactive power, and improve reliability and efficiency of bulk power shipment over long distances during power outages. At the distribution level, high-speed transfer switches and dynamic volt-amperes reactive (DVAR) support load isolation, improve grid reliability, and minimize power quality events. This makes a combination of DERs and smart grid investment a cost-effective strategy for improving grid flexibility to mitigate against climate-induced impacts. Furthermore, because DERs accommodate all generation sources particularly intermittent, non-dispatchable renewable energy sources, storage options, and low-carbon renewable natural gas-fired generation systems as well as cogeneration, they sustain a clean energy economy and urban infrastructure development. DERs also offer cities opportunities to reduce their near- and long-term greenhouse gas emissions through “solar city” strategy and economics (Byrne and Taminiau 2018), thereby mitigating climate impacts by reducing total GHG emissions. DERs perform twin functions: (1) adaptation and (2) mitigation of climate impacts. For example, combined cooling heat and power (CCHP) and cogeneration systems are a form of an integrated DER energy system, which delivers both heat and electricity, as well as improve system efficiency (Prinsloo et al. 2016; Eid et al. 2016). Such DER technologies can be paired with information communication technologies (ICTs) in cities to enable communication and control of the DER resource of interest. ICTs can also improve local system signaling and reliability of electrically constrained portions of the grid, thus providing critical system resiliency during widespread outages caused by extreme weather events and other disruptions.

Energy Storage Utility-sited energy storage provides the needed integration with variable renewable energy sources to mitigate supply-demand imbalances. Previously, pumped hydropower plants had been the only known grid-integrated technology for delivering significant flexibility to the power grid (Beires et al. 2018). Common electricity and thermal storage technologies include electrochemical or physical (e.g., compressed air) mediums, ice storage, molten salt storage, and others. In recent times, the growth of large- and small-scale battery storage has supported stabilization of power grids in cities. Electromobility is the main technology driver of the growth of battery

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storage, and as Hoarau and Perez (2019) show solar PVs, lithium-ion batteries, and electric vehicles (EVs) are emerging as the three disruptive innovations in power grids. Lithium-ion battery technology has very good power and energy density at high frequency and robustness which makes it suitable for consumer electronic devices (Nykvist et al. 2019). Another new factor is that advances in smart grid technologies make deployment of energy storage to integrate high amounts of renewable electricity systems possible. This necessitates maximizing locational value of DERs to deliver reliability services in locations where networks are frequently constrained (Burger et al. 2019). To minimize network supply-demand imbalances and integrate a growing share of variable power into the grid, investment in distribution network assets is necessary. Additionally, to improve electric grid resilience in cities, on-site renewable energy systems can be combined with energy storage (i.e., batteries, ultracapacitors, and flywheel energy storage), as well as other auxiliary equipment and services. When paired with energy storage, and facilitated by smart grids, these systems provide a reliable backup power in the event of a blackout, as well as ultra-clean power needed for sensitive industrial processes. Apart from batteries, utilities can deploy vehicle-to-grid (V2G) distributed storage devices to support grid balancing and enhance peak-shaving capability in cities. Noel et al. (2019) analyzed willingness-to-pay attributes for EVs in Norway, Iceland, Denmark, Sweden, and Finland and found that V2G capability is significantly positive. With V2G and smart grids, municipal utilities can flatten their daily consumption load curves, optimize grid management, and improve system flexibility (Pérez-Arriaga and Knittel 2016) with significant benefits to the environment, urban air quality, energy security, and ecological integrity.

Microgrids Microgrids are self-contained, self-sustaining grids, operating in a small geographical region, often powered by DERs, and can operate in both grid-tied and islanded modes (Hussain et al. 2019; Farzan et al. 2013). They are a potential solution to climate-induced power disruption events due to their islanding ability. Urban infrastructure such as hospitals, schools and universities, data centers, and municipal facilities and offices are examples of facilities that require unusually high levels of reliable electricity and can benefit from microgrid deployment, operating either as a stand-alone system or in conjunction with the municipal utility system, to guarantee proactive scheduling, feasible islanding, and outage management and reduce the impact of major disruptions. A notable microgrid project in the United States is a Hurricane Sandy star, the Princeton University campus. The Princeton microgrid consists of 15 MW CHP plant, a 5 MW PV array, and a load prioritization strategy during islanding. (Two years after Hurricane Sandy, recognition of Princeton’s microgrid still surges https://www.princeton.edu/news/2014/10/23/two-years-afterhurricane-sandy-recognition-princetons-microgrid-still-surges) Microgrids provide reliable onsite power supply with fewer outages, and self-healing power systems,

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through the use of digital information, automated control, and autonomous systems (Farzan et al. 2013). By enabling integration of multiple DERs with advanced controls and communication platforms, Washom et al. (2013) explain that microgrids offer significant system operational benefits and ameliorate constraints associated with the centralized electric grid.

Demand Response and Energy Management Systems Adaptation measures in the power sector, to implement climate change resilience, are best done at supra-local level by county and regional governments, because of their broad legislative powers. Near-term demand management measures (e.g., smart meters, new tariffs, and intelligent load management) provide cost-effective mitigation strategies as well as enhance flexibility and grid management solutions for reducing carbon intensity in the electricity sector. In this regard, demand management and demand response offer two strategies to even outpeak power demand: (1) load shedding and (2) load shifting. According to the Federal Energy Regulatory Commission (FERC), demand response refers to “changes in electric usage by demand-side resources from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized.” (Federal Energy Regulatory Commission. Reports on demand response and advanced metering http://www.ferc.gov/ industries/electric/indus-act/demand-response/dem-res-adv-metering.asp) Demand response is a subset of end-use customer energy solutions known as demand-side management (DSM). Besides demand response, DSM includes energy efficiency programs. Various entities including transmission and distribution system operators, utility companies, and end-use consumers can all benefit from demand response, either in the form of price-driven or incentive-based demand response programs. Load shifting tries to smooth the power demand away from peak periods through price incentives thereby improving power efficiency and optimizing resource allocation to achieve efficient electricity use (Kuiken and Más 2019; Varma and Sushil 2019; Wang et al. 2018). Load shedding, on the other hand, is a form of targeted blackout where utilities enter into agreement with large electricity consumers such as industries or universities to reduce their consumption during peaking crises in return for discounted rates. This is a form of incentive-based demand response program and is triggered either by high electricity prices or a grid reliability problem. Dynamic pricing can dramatically reduce energy demand swings and increase overall generating efficiency. Demand response as a proactive measure can be implemented both manually and automatically. When fully automated, human intervention is removed and demand response is initiated at a home, building, or facility through receipt of an external communications signal that shifts the load, thus reducing peak and total energy demand (Fox-Penner 2010). The manual demand response entails controlling the use of certain appliances, for example, dishwashing machines, in different time

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periods of the same day. Semi-automated demand response, on the other hand, involves some form of human intervention whereby a pre-programmed demand response strategy is initiated through a centralized control system. The success of dynamic pricing methods nevertheless depends on consumer behavior. The current price-driven demand response programs include time-of-use (TOU) pricing, critical peak pricing (CPP), and real-time pricing (Yan et al. 2018; Faruqui and Leyshon 2017). Besides dynamic pricing, cities are engaging investorowned utilities (IOUs) in their territories to implement smart building automation and control solutions to improve provision of energy services. Advanced metering, time-varying rates, dynamic market-based prices, and energy management systems (EMSs) have the potential to reduce uncertainty in electricity prices than ever before. Without these smart controls, the problems of the grid will worsen, and critical operations in cities will be severely affected. Siemens has observed that investments in smart grids – resulting in increased usage of load shedding and load shifting – could reduce national electricity needs by nearly 10%. (Smart infrastructure business update: https://assets.new.siemens.com/siemens/assets/public.1557934458.69c056d92369-49ddba5e-1a519a71049e.dgcustomersummit2019.pdf)

Smart Measuring Systems Significant progress has been made in improving measurement, reporting and verification (MR&V) system for urban energy as well as other performances like air, environment, water, waste management, transport and mobility. However, the indicators used for measuring these performances and smartness rarely consider a holistic approach that goes beyond one component (economy, environment, mobility, people, governance). Additionally, a lack of standardized common metrics for MR&V adds complexity in governance and information management. Under these circumstances, energy performance measurement through data-driven powered insights and peer-city benchmarking strategies have emerged as viable solutions for improving the understanding of the complexity and dynamism of urban energy transition – from fossil fuels to renewable power (Hiremath et al. 2013). As far as the building energy dimension is concerned, polycentric frameworks based upon people-centric and smart measuring strategies, particularly common indicator approaches, could be deployed to improve both the measurement of energy supply and demand metrics in cities. In this framework, these efforts could focus on the linkages between multiple innovations and sociotechnical systems like integrated district heating systems in which electricity grids are coupled with gas networks, rooftop solar PV systems for electricifying residential buildings, V2G configurations, integrated urban planning and transport systems via TOD, and intermodal transport to facilitate efficiency of transport links, nodes, and the provision of services within and across cities (Pires et al. 2014). By adopting a common indicators approach, cities can also improve reporting of energy project performance on an ongoing basis. Smart, automated performance measurement and control opportunities will arise in many ways:

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• Intelligent monitoring and control of energy-consuming devices to reduce performance variations, engage energy users in new decisions and action that reduce energy, and improve energy savings guarantee. • Foster better understanding of the drivers of energy-efficiency improvement and changes in the social, natural, and built environments. • Use targeted incentives and rewards to increase participation and commitment to energy efficiency actions. • Any governmental program providing a subsidy for clean energy projects can require reporting as a condition. • Leverage technology to promote smart metering of generation resources, transportation system efficiency, appliance labeling, building codes, and energy savings performance contracts (ESPCs). Standardization of ICT interfaces for smart cities will also support the New Urban Agenda (Habitat 2019) and a specifically Urban Sustainable Development Goal (USDG) as part of the United Nations 2030 Agenda for Sustainable Development, encourage continuous learning and improvement, promote accountability, and identify performance gaps (d’Alençon et al. 2018). Under a common framework of MR&V, learning, cooperation, and emulation among different cities with common smart city objectives and characteristics can be enhanced to promote smart mobility, smart urban infrastructure, smart economy, smart energy, and smart urban governance. An example of a comprehensive framework for measuring economic, environmental, and social performance of cities is the release of the Sustainability Tools for Assessing and Rating (STAR). The STAR framework offers a menu-based system for enabling cities to build inclusive, equitable, and accountable development (STAR Communities 2017). It is a leading framework for assessing and promoting sustainability performance of cities at different scales. With the STAR framework, cities can evaluate their performance across different goal areas covering the built environment and climate and energy (STAR Communities 2015), discover “best practices” that move the needle toward the identified outcomes, and communicate their progress to the stakeholders. However, cities and their measurement metrics are network phenomena and cannot be studied in isolation. If the measurement indicators fail to improve or fully capture the resource and material flows in cities, the performance evaluation of the city’s assets could be considered incomplete or undervalued (Pires et al. 2014). In addition, without fully understanding the energy and material flows, systematic evaluation of the measurement indicators could be problematic thus undermining long-term planning and development goals. To address these concerns, a smart measurement framework should incorporate three main guiding principles: bottom-up stakeholder-driven approach, consensus-based process, and inclusivity. Stakeholder-driven process fosters synergy among different city agencies and entities, thereby improving decision-making process. Consensus-based process promotes transparency and accountability, offering immense opportunity for deeper engagement on smart development. Stakeholder-driven and consensus-based approaches together foster improved qualitative assessment of urban complexity

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and dynamics. On the other hand, inclusivity refers to the balance between rigor and comprehensiveness of the measurement metrics in order to address the full range of measurement, reporting, and verification systems.

Harvesting Rapid urbanization, population growth, climate change, and high cost of maintenance of urban infrastructure projects have pushed city planners, engineers, and scientists to look for alternative and sustainable methods of harvesting energy such as solar, wind, hydropower, and geothermal. Solar PV panels absorb sunlight as a source of energy to generate electricity in the form of direct current (DC). Solar cells are made of semiconductors, such as wafer-based crystalline silicon cells or thin-film silicon cells. Solar energy harvesting occurs when electrons in the PV cells are freed upon after being struck and ionized by photons from the sun to power electrical devices. Examples of harvesting include the following projects: • Solar and wind energy: harvesting both bulk and distributed solar and wind power, from sparsely populated regions with low electrical demand outside the city and transmitting it to urban areas where electricity is needed. • Biofuel or bioenergy: sustainably harvesting bioenergy from crop residues, crops, wood, or wood waste using methods that do not contribute to emissions. • Stormwater mitigation: promoting green infrastructure such as green roofs and urban rainwater harvesting for use in landscape irrigation or interior building applications, which would reduce water consumption. Rainwater harvesting also saves energy incurred in municipal networks for transportation and distribution.

Green Technologies Despite documented compelling benefits of green technologies – e.g., addresses climate change adaptation and mitigation, sustains economic growth and investment, improves energy utilization efficiency, and promotes substitution of fossil fuels with clean energy in production – pragmatic investments in these technology solutions remain limited, uncoordinated, and ineffectual relative to demand and the climate challenge (Weina et al. 2016). A review of heterogeneous effects of green technology innovations across cities with different income levels shows that these investments can stimulate total-factor carbon productivity as well as human capital (Du and Li 2019) in several ways. First, the applications of green technology innovations improve climate change adaptation and mitigation, thereby promoting health and well-being of existing residents. Second, green technologies advance energy innovations and utilization efficiency. Third, green technology innovations can promote the substitution of fossil fuels with low-carbon energy in production, supporting industry upgrade which in turn spurs economic growth. However, in practice,

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investments in green technology applications face barriers such as financing obstacles as well as market and institutional complexities. This is compounded by uncertainty and risks associated with new green technologies – smart street lighting, lithium-ion batteries for EVs, solar PVs, offshore wind power, biofuels and bioethanol, and cost-effective green vehicles – with respect to regulatory structure, financing barriers, and systemic institutional gaps (Kanger et al. 2019; Nykvist and Nilsson 2015; Lam et al. 2010). Unfortunately, this affects the diffusion of these technologies in cities. Second, difficulties in administering huge capital investments due to legal and administrative challenges, complex investment instruments, and lack of specialized expertise restrict the level of infrastructure-scale investments at the city-level because of higher initial cost. A key element of green technology innovations is that most investment decisions target a specific sector (e.g., energy, utilities, manufacturing, etc.), a specific asset class (e.g., fixed income, equities, infrastructure, etc.), or a specific city (e.g., cities in the developed world or the developing world). While there is significant investment potential in many of these areas – particularly in renewable energy, energy efficiency, and decarbonization technology – the growing investment gaps and the tightening of capital in the global banking sector mean that investors often weigh investment decisions against risk profiles of a city, and not merely on the merits of the green technology innovations alone. To generate new sources of revenues to fund green technologies, cities should expand the share of green financing, issue municipal-backed “green bonds” to promote socially responsible investments, pursue new international sources of climate funding, reduce taxes on green products, and promote new cooperation on green technologies with other cities through forums such as C40 Cities, Global Covenant of Mayors, Cities Alliance, and ICLEI – Local Governments for Sustainability. Table 2 summarizes risk factors focusing on the green technology aspects of a smart city programs.

From Robustness to Resilience Robustness and resilience are two complementary concepts applied in various energy studies, including energy security assessment (Martišauskas et al. 2018), building energy design and retrofits (Ascione et al. 2017), grading building energy performance (e.g., energy use intensity, total energy, peak power) (Papadopoulos and Kontokosta 2019), and energy installation (e.g., heating source, ventilation system, status of refurbishment) (Pasichnyi et al. 2019), to deal with the increasing uncertainties and meta-complexities that characterize energy systems in cities. Resilience refers to the ability of a system to “rebound” or withstand initial shock (interruptions) (Hughes 2015), while robustness is the capacity to maintain functions of a system (policy, political system, organization, or institution) in spite of uncertainty (Capano and Woo 2017). The nonlinearity and spillover effects associated with the complexity of climate hazards, urbanization, and unforeseen population and demographic shifts, combined with widespread and systemic environmental damage, aging infrastructure, pollution, and mounting health costs in cities, require a

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Table 2 Risk factors associated with smart city solutions Risk-tier Technical risk

Energy resource risk

Extreme weather risk

Construction risk

Offtake risk

Environmental risks

Risk events Performance of technology, technical availability, technical lifetime, equipment defect/degradation, grid connection failure, inability to fulfil warranties and guarantees, technological change, etc. Variability and quality of technical potential, e. g., solar irradiation data, wind speed data, simulation model Extreme wind conditions, heatwaves, flooding, thawing, and snowstorm Cost overruns, completion delay, noncompletion, project quality, abandonment, force majeure, natural disasters, political risk, land availability

Credit risk, large level of investment/long tenor of return, additional equity required later, commitment, misalignment of investors’ objectives Risks related to the location and surrounding environment of the project, impact on local residents, weather, and environmental opposition

Mitigation strategy Proven technology Quality components correctly dimensioned Manufacturer warranties and performance guarantees O&M guarantees Take-and-pay power purchase agreements (PPA) Use of proven databases with wellcorrelated theoretical and empirical data On-site measurements Use of technical protection measures Site selection

Risk taker Manufacturer, engineering procurement, and construction (EPC) contractor, O&M contractor

Fixed time and budget turnkey contract (EPC) completion guarantees Monitoring reports Performance reports Penalty clauses Cost contingency funds Long-term offtake agreement (PPA) takeand-pay, credit enhancement, accelerated taxes, regulations, etc.

Manufacturer, EPC contractor, sponsor

Environmental impact assessment Risk of incurring fees, fines, or withdrawal of license

Developer, sponsor

Developer, consultants

Designer, EPC contractor

Sponsor, offtaker

degree of flexibility in policy and governance systems. Indeed, it necessitates combinatorial innovations in technological exploitation and exploration (Youn et al. 2015). These adaptations also demand application of a resilience-based approach rather than robustness per se to address these complex, emergent, and

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evolving threats to urban energy systems (Nyangon et al. 2017b). Is robustness synonymous with resilience? How can cities advance flexibility, agility, and overall resilience to address technological and institutional “lock-in” effect inherent in our urban infrastructure systems? These are vital questions for city authorities and can be resolved by implementing incremental adaptations in public policy and policy design to mitigate potential risk of system lock-in effects for new technological solutions and corresponding institutional path dependency that can prevent these transitions from taking place. In addition to robustness, cities can address uncertainties and complexities of energy networks by integrating complex adaptive systems, dynamic planning, flexibility, and agility in order to advance greater resilience. As a result, contemporary urban studies are increasingly emphasizing resilience of the socioeconomic and built environment and technical functions of cities by including these additional elements in urban policy formulation in order to strengthen and fortify city assets in the face of increased risk of failure. As Spaans and Waterhout (2017) explain, “resilience includes not only the shocks (such as earthquakes, fires, and floods), but also the stresses that weaken the fabric of a city on a day-to-day or cyclical basis. By addressing both these shocks and stresses, a city becomes more responsive to adverse events, and is overall better equipped to deliver its functions in both good times and bad, to all populations.” Similarly, Davoudi (2012) explains that “resilience is defined not just according to how long it takes for the system to bounce back after a shock, but also how much disturbance it can take and remain within critical thresholds.” Notable methods for improving resilience in energy governance include diversifying energy supply (e.g., solar PV, wind, biomass, if available + fossil especially gas-fired generation and CHP) (Nyangon and Byrne 2018) and improving socioeconomic metrics through enhanced choices for electricity services for customers and developing cost-efficient mini- and microgrid networks. Such diversity allows smoothing of daily electricity load patterns and shifts electricity load to locations with greatest demand, thereby increasing cost-efficiency and network management. Bisello and Vettorato (2018) offer a seven-part multiple benefits approach as a paradigm for evaluating smart urban energy transition – smart economy, smart governance, smart built environment, smart mobility and connectivity, smart community, smart services, and smart natural environment – and its positive impacts on resilience of energy infrastructures, well-being, health, indoor comfort, property value increase, and competitive advantage on the smart city. Energy is the cornerstone of these components. With the rapid progression of climate change, advances in technological innovation, and urbanization shifts, energy systems, and by extension these components, will likely become more complex and interconnected. As a result, a better understanding of interconnectedness and the resulting indirect vulnerabilities of urban energy systems is necessary to mitigate increasing risks. In doing so, cities ought to advance a nexus thinking and integrated urban design, planning, and management rather than a sectoral line (“silos”) approach, meaning municipalities can appropriate synergistic benefits of better integrated resource management.

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Finally, recourse to moving beyond robustness (toward resilience) is an essential component of smart city energy networks. In particular, peer-city benchmarking is vital for understanding when robustness is particularly suitable and when it is not based on the complexity of smart cities. Regular peer-to-peer comparison and evaluation of the foundational measurement metrics (e.g., smart growth, environmental integrity, healthy communities, and social inclusion) are desired to gain a better idea of specific actions that can enhance flexibility, agility, responsiveness, and inclusive decision-making. Finally, quantitative and qualitative methods for benchmarking energy strategies and energy protocols toward smart resource use are needed to foster resilient urban energy frameworks for better decision-making in cities.

A Polycentric Approach to Smart City Energy Governance The above discussion highlights several key elements of the smart city energy governance: (a) networks (e.g., the existence of networks for facilitating mutual learning processes between cities to deliver quality urban services, promote effective urban governance, and improve management structures); (b) scales (e.g., connecting and aligning several scales, actors, and responsibilities rather than containing efforts to one scale;) (c) polycentric energy systems (e.g., thinking of solutions in context, notably developing numerous smaller, modular rooftop solar PV plants located closer to consumers); and (d) the common pool issues of energy access, rebound effects, energy justice, and inequality for greater acclimatization of the benefits of electricity decarbonization in cities. Polycentricity refers to decentralized governance systems encompassing several independent centers of decision-making often performing function in a coordinated fashion across sectors and scales (Aligica and Tarko 2012; Ostrom 2010). The emerging “polycentricity” paradigm and thinking or “adaptive regulatory framework” in smart city governance enables a salient conceptualization of citywide transformations. Notably, governance for transformations, governance of transformations, and transformations in governance (Burch et al. 2019) resulting in significant environmental improvements (such as waste reduction and air-pollution abatement), social progress (e.g., social relations and growth of green jobs), and economic benefits (e.g., reductions of energy costs). The nonlinearity and complexity of smart grid challenges, especially climate risks, urbanization, demographic shifts, systemic environmental change, and energy infrastructure investment deficit facing many cities, can be addressed by a “polycentric” strategy that incorporates shared learning, adaptive management, civil society strategies, and creative experimentation to support existing transformative innovations and empower local energy development. Initially proposed in the 1960s and 1970s (Aligica and Tarko 2012), polycentric strategy has been applied to evaluate “solar city” economics (Byrne and Taminiau 2018), climate justice (Fischedick et al. 2018; Martinez-Alier et al. 2016; Ostrom 2010), urban energy planning for 100% renewability in Frankfurt and Munich cities (Radzi 2018), alleviating urban traffic congestion (Li et al. 2019), assessing energy efficiency

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gap (Zou et al. 2019), and evaluating new capacities for transformative climate governance in New York City (the United States) and Rotterdam (the Netherlands) (Hölscher et al. 2019). In terms of smart energy infrastructure governance, polycentric policy underscores the elements summarized in Table 3 – transparency, inclusivity, accountability, and responsive network practices. Jones and Kammen (2014) posit that urban development and suburbanization have created key questions: what is the degree of change of each urban energy process, for example, in energy cost savings, mitigation of greenhouse gas emissions, and material flows in cities? How are cities reshaped by governance processes as they grow? Polycentricity offers a promising strategy for addressing these questions. It provides a viable strategy to rethink energy infrastructure investments with a view to implementing smart energy systems for residential homes, smart buildings, and increasing energy security through energy analytics and artificial intelligence applications. It also resonates with the concepts of regime complexity (Keohane and Victor 2011), institutional fragmentation (Zelli and Asselt 2013), and experimentalist governance (Jordan et al. 2015), allowing for flexibility, agility, social learning, systems-oriented approach, and changing course when new information becomes available. In essence, a polycentric approach engages multiple stakeholder groups in the design, implementation, and management of smart energy future in cities. As a result, a polycentric system spans across multiple scales. For example, both centralized and decentralized electricity networks serving a city’s jurisdiction such as the New York City metropolitan area or London metropolitan belt are a part of city or regional or national grids.

Table 3 Key elements of polycentric policy Elements Stakeholder-driven approach Enhanced accountability and legitimacy

All-inclusive and more equitability Adaptive governance system

Shared learning More robust

Polycentric discourse applications Polycentric policy designs emphasize community ties and collaboration among various agencies and civil societies Project sponsors of polycentric policy designs support internal and external transparency and accountability, for instance, in the planning and management of resources, to cater for the growing urban complexity and dynamics Polycentric systems address a full range of environmental, social, and economic issues as well as involve a diverse number of stakeholders “distributes decision-making powers across the system and ensures coordination through an overarching system of norms and rules that defines the logic of interactions between actors” (Biesbroek and Lesnikowski 2018) to encourage polycentric innovation across scale, place, and time Emphasize creative experimentation, trust building, and problem resolution Ability to address grand challenges of governance in megacities through continuous improvement and steady accumulation of marginal changes across sectors and scale – if one approach or domain fails, others can step in, hence, greater resiliency

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Many smart city developments already exhibit elements of a polycentric governance approach. For example, Ruhr in Germany, Stoke-on-Trent in the United Kingdom, and the San Francisco Bay Area in the United States all have complex coordination arrangements across sectors and scale, involving different stakeholders at multiple levels. Additionally, a number of cities are already implementing decentralized and “smart” infrastructure solutions, including rooftop solar technologies which provide parties with multiple simultaneous roles as both producers and consumers – prosumers of energy. In addition, these plans address unique challenges of the city, for instance, by establishing “smart” solutions that support short- and long-term operational risks, as well as incentives. Cities can formalize these requirements into (smart grid) technical standards in order to institutionalize the polycentric smart energy framework. Together, these plans and strategies, supported by “orchestration” of cooperation involving diverse actors and technologies, could phase in an integrated smart city framework (see Table 4) that is viewed much more as part of a long-term solution to pervasive global changes in human societies, including urbanization, climate change, urban inequality, and digitization agendas.

Conclusions What can a polycentric paradigm offer energy infrastructure governance in cities? In this chapter, a polycentric strategy, which connects and align scales, responsibilities, and actors, has been described and proposed as an alternative pathway for energy governance in smart cities Historically, cities were mostly centrally governed, but rising contemporary challenges highlighted in this chapter have upended the need for multiple domains of authority governing. This new mode of governance is characterized by adaptive management, new dynamics of techno-economic networks and multilevel, polycentric and multi-layered governance of energy decisions. A key message of this chapter was to analyze the significance of socio-technical systems for deep decarbonization in a way that simultaneously promotes polycentric authority. This approach promotes accountability, inclusivity, innovation, trustworthiness, bottom-up learning, adaptation, and multiple levels of cooperation across sectors and scales. It also embeds flexibility, agility, cultural discourse and social acceptance, systems-oriented design, and equity at multiple scales, all which are fundamental to operationalizing socio-technical energy transitions. While a polycentric perspective is a offered to rethink the governance of urban energy infrastructure, the existing energy systems and utility business models are changing simultaneously in several cities with respect to diversity, planning, and customer choices. As a result, this requires careful political attention to the technological, regulatory, infrastructure, user-design practices and markets, maintenance and supply networks, and social consequences of the energy transitions. Climate change, demand for modern energy services, outmoded and aging power grid, and rising cost of energy are some of the drivers of this transformation. As a major source of carbon emissions, the electricity sector is also facing regulatory pressure to transition in a manner that limits stranded assets and risk of locked-in technological

Power Communications infrastructure

Applications and technologies

Transmission Wide area network – backhaul network between the field assets and the utility

Utility control and load monitoring for EV and V2G applications Integration of utility systems into consumer business processes

Smart charging of EVs and V2G Customer solutions

Generation Local area network

Visibility and control systems for distributed assets

Peak load management and control AMI, MDM, CIS, outage detection, billing Visibility and control systems for DER assets

Utility system EMS, SCADA, DMS, and OMS

Microgrids

Distributed generation

Demand response AMI

Smart energy strategy Distribution automation

Table 4 Framework for the integrated smart grid

Application data flow to/from enduser energy and building management systems Substation/distribution Home area network (links load devices and appliances for greater utility and consumer control)

Application data flow for EVs and V2G devices

Application functionalities FDIR, remote switching, voltage control, substation automation, grid asset protection, power quality management, automated feeder configuration Acquisition of energy data; load forecasting and load shifting Remote meter reading, theft detection, customer prepay, and real-time pricing Monitoring, dispatch, and control of DER assets such as renewables, CHP, and energy storage devices Aggregation of supply and demand resources into a network that is either grid tied or islanded (e.g., microgrid)

Home or building Communications infrastructure

Home/building portals, online billing, and pay/prepay; TOU pricing data

Customer usage and revenue for demand response activities; customer and utility loads; impacts to peak and non-peak Vehicle load; storage capability

Data and visualization of energy enduse Power outages, consumption (voltage and current readings) DG load generation capacity and performance data

End-user data Point of consumption voltage

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systems. A polycentric smart energy governance promotes a bottom-up continuous incremental change, in which lock-in problems are reduced because outlived technologies are phased out in a manner that complements model-based analysis with socio-technical policy-oriented solutions. The approach to urban energy system governance described here acknowledges the role of smart grids in fostering climate resilience and robustness in cities. This strategy, in part, incorporates “hardening” of urban infrastructure to withstand extreme weather events as well as in certain circumstances the option to relocate certain infrastructure services to less vulnerable locations. Moreover, advances in smart grid networks and energy infrastructure systems, including smart meters, DER generation, EVs, demand response, energy storage, and V2G technologies as well as integration of these solutions across urban sectors and scale, mean that the number of actors involved is likely to increase, and so does the complexity of regulation and governance system. Such complex systems call for shared learning, experimentation, information sharing across scales and jurisdictions, greater accountability and transparency, and flexibility in order to deal with uncertain, unpredictable, and nonlinear forms of economic, social, and environmental interruptions. Additionally, adapting and developing smart grid systems in cities require reliable and state-ofthe-art energy supply and demand datasets and related infrastructure services – transportation, housing, water, mobility, and ICT services. Although full adaptation is an ongoing challenge for city management, public and private sector service providers, local businesses, residents, and other stakeholders, deepening and aligning polycentrism across sectors and scales underpins the development and implementation of smart energy frameworks that are sociopolitically acceptable, cost-effective, and coevolutionary with technologies and societal development.

Cross-References ▶ Smart Cities: Fundamental Concepts

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Urban Computing: The Technological Framework for Smart Cities Mélanie Bouroche and Ivana Dusparic

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Sense-Analyze-Actuate Paradigm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Optimizing the Use of Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case Study: Optimizing Urban Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Urban Sensing Modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Networking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Urban Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Analyzing: Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prediction of Urban Resource Supply and Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Decision-Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ethical Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Actuating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Human Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Consumer Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Robotics/Autonomous Actuation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Increased urbanization is putting a strain on the limited shared urban resources, for example, road space, energy, and clean air and water. Smart cities leverage technology to manage such shared resources more efficiently, thereby improving citizens’ quality of life. This chapter introduces and discusses technical challenges in managing city-scale resource infrastructures and potential solutions. M. Bouroche (*) · I. Dusparic (*) School of Computer Science and Statistics, Trinity College, Dublin, Ireland e-mail: [email protected]; [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_5

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We frame the discussion within the Sense-Analyze-Actuate paradigm, a model leveraged by most smart city solutions. The Sense step entails gathering data from existing or newly deployed dedicated sensors, owned by public agencies and businesses, as well as contributed by citizens. In the Analyze step, these disparate and often unreliable sources of data are fused to improve the authenticity of data (improving detection, confidence, reliability, and reducing ambiguity) as well as extending its spatial and temporal coverage. In this step, optimization techniques, and artificial intelligence in particular, allow to reason on the data, making resource management decisions in a centralized or decentralized approach. In the Actuate step, the results of the analysis can either be presented to human operators, i.e., visualized for decisions support, or used to directly actuate the changes, adapted to the specific urban resource.

Introduction As the proportion of people living in cities increases, shared urban resources, such as road space, energy, and clean air and water, come under increasing strain. Smart cities leverage technology to manage these shared resources better (Manzoor et al. 2014). As cities are large-scale, loosely structured, dynamic systems of systems, such urban computing needs to adopt a multidisciplinary approach to consider the multifaceted impact of technology in cities and ensure that it ultimately improves citizens’ quality of life.

The Sense-Analyze-Actuate Paradigm Definition Urban computing has been defined as “a process of acquisition, integration, and analysis of big and heterogeneous data generated by diverse sources in urban spaces, such as sensors, devices, vehicles, buildings, and humans, to tackle the major issues that cities face (e.g., air pollution, increased energy consumption, and traffic congestion)” (Zheng et al. 2014). While this definition encompasses a lot of the concepts associated with urban computing, the acquisition, integration, and analysis of data is not in itself sufficient to address urban challenges. Indeed, some actuation, potentially in the form of visualization to support human decision-making, is required to close the feedback loop and effect changes in a city. This can be represented by the Sense-Analyze-Actuate paradigm (see Fig. 1). While this concept is very close to the Map-Analyze-Plan-Execute (MAPE) loop of autonomous computing (Jacob Fig. 1 The Sense-AnalyzeActuate paradigm

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Fig. 2 Smart streetlight in the Sense-Analyze-Actuate paradigm

et al. 2004), and even some older concepts such as Stability Augmentation Systems in control theory (Markland 1970), its simplicity and wide applicability make it particularly suitable to describing urban computing.

Example Consider, for example, a smart streetlight, a very simple urban computing application. In its simplest version, a smart traffic light would Sense the current light level, Analyze this by comparing it to a threshold, and Actuate by setting the light intensity level. A more elaborate version might Sense nearby pedestrians and road traffic in addition to current light level, in which case it might Analyze by also computing the expected paths of the actors (see Fig. 2).

Optimizing the Use of Resources Shared urban resources can be classified into several domains: mobility (road space), energy (electricity, gas), water, pollution (air quality), waste, health, etc. Each domain presents its own set of challenges. For example, mobility needs to address traffic congestion and the provision of appropriate transport means at the right place, at the right time, and for the right price, in terms of both public and shared transport. Similarly, the water domain investigates the provision of clean water when and where it is needed, at a sufficient pressure, despite potential leaks in the network. Traditionally, each type of urban resources has been managed independently, sometimes even within the same domain. Indeed, in many cities, buses are still operated independently of metros or tramways. Different resources, however, interact with each other, as buses, tramways, and cars often share some of the same road space, as well as potentially having partially overlapping demand. There are also some cross-domain interactions of resources, such as increased car traffic increasing pollution. These interactions can be positive (an increase in bus frequency might decrease private car traffic), negative (an increase in motorized traffic decreasing air quality), or mixed (an increase in the use of electric vehicle potentially improves air quality but also increases energy demand). For these reasons, smart cities need to break down existing application or domain silos and adopt a system of systems approach to integrate interdependent public and private systems (Naphade et al. 2011).

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Overall, optimizing the use of urban resources can be modelled as matching the supply and the demand of the resource, in real-time. Both the supply and demand of resources can vary over time. For example, in the mobility domain, the road traffic (demand) will vary over time. The road space (supply) typically only varies very slowly over time (as new roads or lanes are planned and built), with the exception of reversible lanes. Both the demand and supply of public transport vary over time (e.g., bus frequency and capacity). Matching supply and demand can be achieved by a combination of three approaches: (a) Adapting the supply to the demand: regulating the amount of the resource available, for example, scheduling more buses. (b) Demand-side management (or adapting the demand to the supply): incentives (positive or negative) can be used to modify the consumers’ demand, for example, by providing cheaper metro tickets at off-peak times. (c) Storage: some resources can be stored when the supply exceeds the demands and released when the demand exceeds the supply, for example, water can be stored in water towers.

Case Study: Optimizing Urban Energy This section illustrates the concepts presented above in the domain of urban energy. The energy demand of a given household varies over time depending on the time of the day, the weather, and the presence and occupation of its inhabitants. For example, a typical weekday usage of a household exhibits low demand during the night, with two peaks: the morning one when inhabitants are up and getting ready for work and the higher evening peak, when they arrive home from work. This pattern is illustrated in Fig. 3, which shows the daily usage of a typical Irish household recorded during Irish smart meter trials. The amplitude of these variations is exacerbated by electric vehicle uptake, as they tend to be plugged in at around the same time (when people come home from work) and draw a large amount of energy as soon as they are connected until they are fully charged. The supply of energy typically also varies significantly over time, especially with the use of renewable energy such as solar panels or wind turbines, the outputs of which are weather-dependent. In the case of urban energy, the three approaches to matching supply and demand correspond to: (a) Adapting the supply to demand: generators can be powered or shut down; hydroelectric dams can generate extra electricity by releasing water when required. (b) Demand-side management: financial incentives in the form of scheduled or realtime varying pricing are commonplace in the electricity market, for example. (c) Storage: energy can be stored both in consumer-owned devices, such as storage heaters or electric-vehicle batteries, and on the grid, for example, by pumping water into storage when the electricity supply exceeds the demand. These can be enabled in 15 s and recover about 80% of the energy.

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Fig. 3 A typical daily residential energy demand pattern

The remainder of this chapter includes a section for each of the Sense, Analyze, and Actuate steps.

Sensing The first step of the smart city loop relates to capturing and gathering urban data. A significant challenge is to find suitable data in terms of content, accuracy, and frequency. To address those, section “Data Categories” presents the different data categories available in smart cities, and section “Urban Sensing Modes” discusses the different sensing modes. Sections “Networking” and “Internet of Things” discuss the gathering of the data focusing on communication technologies and the Internet of Things, respectively, and section “Urban Platforms” urban platforms, which can store and combine this wide array of data.

Data Categories There is a wide range of data available in smart cities, including maps, listing of restaurants, traffic counts on specific roads, pollution levels, mobile phone traces, etc. This data can be classified in terms of its spatial and temporal properties. Spatially and temporally static data includes points, such as points of interests, e.g., cafes; lines, such as rivers or pipelines; and graphs such as road networks. Spatially static and temporally dynamic data relates to a specific point or area but varies over time. For example, the temperature at a given location or the traffic count in an area.

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Spatially and temporally dynamic data include moving objects and their trajectories, for example, plane or individual bus tracking.

Urban Sensing Modes Sensing in smart cities can be undertaken in four modes: • Traditional sensing uses sensors dedicated to a specific application, for example, a particulate matter monitor for air quality monitoring (e.g., Paprotny et al. 2010). • Passive crowdsensing leverages existing infrastructures to passively collect data generated by crowds, for example, using public transport ticketing information to monitor and predict people’s movement (Bagchi and White 2005). • Opportunistic sensing (or active crowdsensing) exploits information from users’ own sensors, in a purpose other than the one originally intended, such as their GPS data to estimate congestion when using a navigation app (e.g., D’Andrea and Marcelloni 2017). • Participatory sensing (or crowdsourcing) relies on users to generate information, for example, by taking a photo, pressing a button, or writing a report (e.g., Manzoor et al. 2014). In addition, such sensors can be static or mobile. Information can typically be obtained using several of the sensing modes. For example, the congestion levels in a given street could be assessed via traditional sensing using loop sensors buried in the road, by passive crowdsensing using mobile phone traces to estimate the number of cars and their velocity, by active crowdsensing using phones’ GPS readings, or by crowdsourcing written or photo reports from users. Each sensing mode has different advantages and drawbacks. Traditional sensing typically offers very accurate information, as sensors can be chosen specifically for the task at hand. It is, however, typically very costly to deploy and maintain such sensors (e.g., loop detectors get damaged during road works and the road needs to be dug up if they need to be repaired or replaced). Passive crowdsensing on the other hand does not require any additional investment but can only gather data for which sensors are available, and the resulting data is typically less complete and/or accurate. Similarly, opportunistic sensing can only use sensors that are already owned by users, and when these are mobile, extra sensing might have (battery or data) cost implications for users. In addition, privately owned sensors raise specific challenges both in terms of trust (Manzoor et al. 2012) and privacy (e.g., Qin et al. 2014), and citizens might need to be incentivized to cede their privacy (Connolly et al. 2018a, b).

Networking The wide array of sensed data often needs to be shared or gathered to extend its authenticity and availability. Smart cities can use a variety of communication technologies, which can be split according to their range.

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Short-range technologies can be based on: • The 802.11 standard, also known as Wi-Fi (range ~100 m) – thanks to its large bandwidth and low cost, it can be used both to transport sensed data (e.g., Kanhere 2011) and to monitor urban mobility (e.g., Chon et al. 2014). • The Bluetooth and Bluetooth Low Energy (BLE) standards (range 50–150 m) – present on a wide array of devices, they can be used, for example, to monitor mobility and interactions at large-scale events (Stopczynski et al. 2013). • The IEEE 802.15.4 standard, which specifies the physical layer and media access control for low-rate wireless personal area networks used by Zigbee (10–100 m), for example, its low energy consumption makes it particularly suitable for cheap, potentially remote, sensor platforms, such as precision agriculture (Morais et al. 2008) or environment monitoring (Haefke et al. 2011), potentially at very large scale (Tennina et al. 2011). • Near-field communication (NFC) – its very short range (4 cm) makes it suitable for applications where close contact is guaranteed, such as payment, ticketing, advertising, and indoor navigation (Pesonen and Horster 2012). • The 802.15.6 standard – its very short-range communication is used for body area networks (BANs) and wearable computing. Long-range technologies include: • Cellular network technology, including its fifth generation (5G) (~500 m range), which, thanks to its very high bandwidth, coverage, and reliability, can be used for a wide array of applications such as traffic management (Marinescu et al. 2012) and smart grids (Accenture 2017). • Low-power wide-area network (LPWAN), which is divided into licensed (LTE-M, NB-IoT, and EC-GSM) and unlicensed (Sigfox- and LoRa-based standards – up to 50 km) – their high range and low energy requirements make them suitable for large-scale monitoring applications such as river (Guibene et al. 2017) or building (Pasolini et al. 2018) monitoring. These technologies, however, often need to be combined depending on the range, bandwidth, and power consumption requirements, to ensure scalability (e.g., Morris et al. 2017).

Internet of Things The Internet of Things, or IoT, is the interconnection, using the networking technologies discussed above, of devices in our environment such that they can interact with each other without requiring human interaction. The Internet of Things has a wide array of applications, including in: • Consumer applications, such as in smart homes or for elderly care • Commercial applications, in healthcare and transportation, for example

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• Industrial applications, e.g., in manufacturing and agriculture • Infrastructure application, such as energy management or environmental monitoring The Internet of Things has been mostly characterized by its explosive growth and current scale: it is expected that by 2020, over 20 billion devices will be connected. Managing this scale is particularly challenging, in particular in relation to addressing, discovery, composition, standardization, architecture, interoperability and integration, management, etc. (Razzaque et al. 2015; Colakovic and Hadzialic 2018).

Urban Platforms Once the data has been gathered, it needs to be integrated in common platforms. These platforms can offer either data-only services or a full integrated platform.

Data Services Datastores and marketplaces provide access to urban data, typically in raw form, in a common, open publishing format, as an input into wider services innovation. A recent example is the London Datastore (https://data.london.gov.uk), established by the Greater London Authority. On top of that, a number of platforms offer urban data platform-as-a-service, providing a service for the sale, purchase, and sharing of a wide variety of data from multiple sources between citizens, city government, and businesses in cities. An example of this is the City Data Exchange, established within the City of Copenhagen (https://www.citydataexchange.com). Integrated Urban Platforms Fully fledged urban platform, also referred to as City Operating Systems, goes further than just providing data services, by • Being open and programmable, offering an open application development environment where third-party developers can create new smart city applications and services on the city’s ICT infrastructure • Being technology agnostic, supporting a wide variety of devices and technologies from Internet-of-Things (IoT) sensor to wireless, wired, and cloud computing infrastructure • Abstracting underlying resources, by hiding all the technology details of the underlying ICT infrastructure and providing simple programming interfaces • Integrating with legacy systems, already running in cities Such platforms can be: • Centralized, which is easier to manage but creates a potential bottleneck and single point of failure

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• Fully distributed, which is more resilient, offers potentially faster reaction times and higher privacy but is challenging to maintain and scale • Hybrid, based on co-locality, and exploiting edge computing to maintain privacy, reduce bandwidth requirements, and improve timeliness when required While a number of urban platform proposals exist, such as the open-source FIWARE (https://www.fiware.org), CityOS (https://cityos.io), and CityVerve (https://cityverve.org.uk), there is currently no standard platform, and fervent efforts are being put into research and deployment activities by academia and industry, fueled by the perspective of a global market of $755 million by 2027 (Navigant 2018).

Analyzing: Intelligence With the increased amount of information from sensors, an opportunity is arising to enable more fine-grained management of resources for greater efficiency. This requires novel algorithms for the management of large-scale resource networks, which are able to utilize this information and make use of it both for long-term planning and decision-making in real time. In this section, we present the main developments in the algorithms for management of smart city resources. We first discuss algorithms for prediction of demand, which can better inform decision-making algorithms, and then discuss resource-allocation algorithms, before finally discussing their ethical implications.

Prediction of Urban Resource Supply and Demand Abundant data gathered from traditional and IoT sensors can be analyzed to predict future resource demand and supply, with the two main aims: to improve long-term network capacity or plan network structure and for real-time resource allocation decision. For example, an open dataset containing New York City taxi trips (NYC Taxi 2019) has been used both to plan the size of the taxi fleet required (Yang et al. 2019) and to position the available vehicles in real time in the areas where higher demand is expected based on historical patterns (Gueriau and Dusparic 2018). For resources where, in addition to the demand, supply can also vary dynamically, data can be used to predict resource supply as well. For example, in balancing energy usage, the prediction of the demand, which varies based on the time of the day, season, or day of the week, is required as well as the prediction of renewable energy supply, which varies based on weather (Dusparic et al. 2015, 2017). The techniques used range from well-established traditional statistical techniques such as ARMA, ARIMA, neural networks (Marinescu et al. 2013), custom dynamic hybrid models (e.g., Marinescu et al. 2014) to novel approaches based on deep neural networks (DNNs), for example, DNNs for traffic flow prediction (Yi et al. 2017) and ridesharing demand (Wang et al. 2017). The type of techniques used varies based on

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numerous data characteristics, as well as the scale on which prediction is performed (Cavallo et al. 2015). Results of the prediction are then incorporated into decisionmaking processes, which are optimized for the predicted demand or supply at any point in time. Furthermore, they can monitor the resource usage in real time, and if the current demand differs from the predicted one, changes in the resource allocation algorithm are made in real-time to optimize it to the new demand pattern. Example applications of this synergy between prediction and dynamic real-time optimization in the smart city context can be found in demand-side energy management (Marinescu et al. 2017) or urban traffic control (Salkham and Cahill 2010).

Decision-Making Decision-making at city scale requires multi-objective optimization and coordination of a large number of heterogeneous geographically dispersed entities, based on historical, current, and predicted information about the demand and supply of a given resource. Take the example of optimizing urban traffic control; traffic lights need to optimize their own local throughput, waiting time, as well as prioritizing public vehicles and serving pedestrian requests. In addition, they need to coordinate with their upstream and downstream junctions to enable steady flows of vehicles, implement so-called green waves, and ensure city-wide traffic optimization. As discussed in section “Optimising the Use of Resources,” the supply of the resource (road space) can differ throughout the day (e.g., rush hour restrictions for private vehicles, road closures due to accidents) or between days (e.g., road maintenance). The demand on the road network also differs throughout the day, both spatially and temporally (e.g., rush hour vs nighttime), between days (e.g., weekend vs weekday), or depending on the weather or season (e.g., increased demand on rainy days). Similar patterns can be observed in domestic energy use (as discussed in the previous section), or in taxi/ride-sharing, where the demand for shared vehicles increases on a rainy day, at rush hour, or on weekend nights. To address all the requirements and constraints of optimization in such large-scale heterogeneous systems, a wide range of algorithms is being investigated. They can be broadly divided into centralized and decentralized approaches, depending on whether they are managed in a distributed fashion or a central management entity has a view of the overall system (Bennati et al. 2018), as well as into exact (guaranteed optimal) or heuristic algorithms. Due to the scale of the system, the complexity of exact centralized algorithms is often prohibitive in city scenarios, so a variety of decentralized artificial intelligence approaches are being explored, such as multiagent systems, game theory, and cooperative intelligent agents. In the remainder of the section, we provide an overview of the main types of approaches rather than a fully exhaustive list, but we do aim to present examples of approaches with different characteristics and those that currently have the most applications in smart city scenarios. We observe that a particular algorithm can be suitable for a range of smart city applications (due to their similarity in terms of characteristics and scale) but also that a particular smart city resource can be managed by a wide variety of

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algorithms, depending on the desired focus of the management strategy (e.g., privacy, fairness, multi-objective optimization, etc.).

Control Theory Control theory is concerned with regulating a system, while satisfying its operating constraints, and taking into account available resources (Crisostomi et al. 2016). This maps to the requirements of a city resource management, with a significant difference being that classical control theory is concerned with regulating a single system (and having a complete view of the system’s state), while at the city scale that is often not feasible. Numerous extensions to classical control theory approaches are being investigated to enable their applications in smart city domain. Model predictive control (MPC) has, for example, been used to manage urban drainage systems (Lund et al. 2018) to prevent flooding, thereby enabling protection of human life/ health, property, and environment. Another example of MPC application is in the energy domain, where it has been used for demand response in a multi-zone building to optimize energy consumption, cost, and comfort (Lauro et al. 2015). The fuzzy logic approach has been applied in urban traffic control, to model traffic flows and control the duration of the green signal at a particular traffic light (Eze et al. 2014), as well as in parking allocation to optimize travel time/distance to the parking location (Dahiru 2015). Proportional-integral-derivative (PID) controllers have been applied in smart city domain in energy management (Harris et al. 2014) and in autonomous vehicle tracking (Alonso et al. 2013) and control (Monteil et al. 2016). Exact Optimization Algorithms Exact optimization algorithms ensure the optimality of the solutions provided (i.e., guarantee finding the one that minimizes or maximizes a given objective function) but at the expense of high computational complexity for solving larger problems. Therefore, as already mentioned, such approaches are not feasible for city-scale realtime management, as decisions need to be made frequently and quickly. However, exact algorithms can still be utilized either to study a smaller computationally feasible subset of a problem and for longer-term planning. Examples of use of exact optimization algorithms are the use of the Dijkstra’s path finding algorithm in vehicle routing (Nha et al. 2012), integer linear programming for optimization of waste collection route (Bueno-Delgado et al. 2019), and classical AI planning for urban traffic management (McCluskey et al. 2017). Heuristic Algorithms and Artificial Intelligence In contrast to exact algorithms, heuristic methods do not provide guarantees of optimality but generate good enough (and sometimes optimal) solutions in a reasonable computational time, enabling their applications in real-time to city-wide dynamic systems. Often these systems are implemented as collections of intelligent agents that optimize their own performance but also cooperate and coordinate to ensure satisfactory overall system performance. While in isolation, optimal behavior of a single agent can often be guaranteed to converge to optimal performance, the lack of guarantees in multi-agent systems stems from agents operating and actuating

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in the same environment, thereby influencing each other’s performance. In the smart city resource management scenario, individual local resource managers, consumers, and providers are modelled by intelligent agents, and the overall system is modelled as a multi-agent system. In traffic management, agents can be individual vehicles or traffic lights; in ride-sharing scenarios, agents are again either vehicles or passengers/ consumers; and in energy demand response scenario, devices that use, store, or generate energy can be modelled as intelligent agents. There is a wide variety of algorithms that define the behavior of these intelligent agents as well as the techniques in which they cooperate (or compete, in the case of game theory) or achieve system-wide optimization. Ant Colony Optimization (ACO). This family of optimization algorithms is inspired by the behavior of ants in an ant colony. When searching for a food source, ants in a colony converge to moving over the shortest path, among different available paths, when moving between their nest and the food source. This behavior is realized by ants depositing a substance called a pheromone on the path which attracts further ants. Trips over shorter paths get completed more quickly, causing more trips to be made on those routes and therefore more pheromone to be deposited on them. This behavior is naturally suited to routing applications in smart cities, and as such has been applied, for example, in the optimization of traffic routes and achieving a citywide balance of vehicles (Rehman et al. 2018) or optimal path planning for waste collection (Bueno-Delgado et al. 2019). Particle Swarm Optimization (PSO). This approach is a self-organizing optimization technique inspired by the flocking behavior of birds. Each particle (a bird) in a swarm (a flock or a population) represents a potential solution and moves through the search space (possible set of solutions) seeking an optimal solution. Particles broadcast their current position (i.e., the quality of their current solution), and each particle then accelerates its movement toward a function of the best position it has found so far and the best position found by its neighbors. In the smart city domain, this approach has been applied to improve quality of riot video footage retrieval (Ramyam et al. 2017) and to optimize wind turbine energy performance (Abdullah et al. 2018), for example. Evolutionary Algorithms (EA). EAs are a family of optimization algorithms inspired by biological evolution. The initial population of solutions is created randomly, and through the evolutionary processes of selection, crossover, and mutation, the most suitable solutions are found after a number of generations. By mimicking the biological evolutionary process, EAs are able to self-adapt, i.e., they can evolve and tune their own parameters. EAs have also been applied in routing for waste collection (Karadimas et al. 2007), traffic assignment in traffic modelling (Bazzan et al. 2014), and device scheduling in energy demand-side management (Mellouk et al. 2018). Game Theory. Unlike in the other approaches presented, in game theory, multiple agents compete against each other while improving the overall system performance. Such approaches have been applied in traffic optimization, where game theory has been used to incentivize users to change travel routes and models to provide balance in the system (Mei et al. 2017) and to resolve water allocation conflicts (Jhawar et al. 2018).

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Reinforcement Learning (RL). RL is an unsupervised learning technique in which an intelligent agent learns how to meet its goal by receiving feedback on its actions from the environment in the form of a reward (e.g., a traffic light agent is “rewarded” by the low waiting time of the cars). Recently, RL has been combined with deep artificial neural networks, into deep RL, which is able to address more complex environment-state inputs than conventional RL, expanding its use in smart cities. Both conventional RL and deep RL have been extensively applied in the smart city domain, with the former, for example, applied in traffic management (Dusparic and Cahill 2016) and energy demand-side management (Reymond et al. 2018; Diddigi et al. 2017) and the latter in achieving zero-energy status in energy communities by energy sharing (Prasad and Dusparic 2019) and in optimizing ride-sharing request allocation (Al-Abbasi et al. 2019).

Ethical Implications With the increasing automation of decision-making in all spheres of our lives, including smart cities, there are growing concerns about the lack of transparency and accountability of AI-based algorithms. As AI-based decisions are often based on enormous amounts of data and simulation, the solutions provided are often “black box,” i.e., they offer no explanation as to why did an algorithm makes a particular decision. This opens up a potential for both unconscious and malicious introduction of bias and discrimination within AI-based applications (O’Neil 2016). For example, one of the most controversial AI-based technologies currently on trial in smart cities is the use of facial recognition, for policing in particular. There are growing concerns about the accuracy of such technology, arguing that its use is premature as it is not technically ready (Page 2018), as well as concerns about citizen privacy advocating that such technology should not be deployed regardless of its technical readiness. As a result, several cities have legislated against the use of facial recognition technologies, which as of this year is illegal in San Francisco and Oakland, California (Osborne 2019). Other examples of technologies the ethical implications of which are being particularly discussed are predictive policing (Asaro 2019), where algorithms are used to predict which individuals will re-offend; self-driving cars, which will, for example, need to make decisions on who to potentially hurt when accident is unavoidable (Borenstein et al. 2017); and in-home smart devices (Sivaraman et al. 2018) and assistants which have the ability to listen to inhabitants conversations and derive information about them from their voice. Ensuring ethical and legal implementations of technologies in cities is being addressed through legal frameworks (e.g., the German government is proposing rules on how autonomous vehicles should behave in the case of an accident (Lutge 2017)), technology solutions (Zambonelli et al. 2018), as well as proposing guidelines and standards that ethical, transparent, and accountable systems should follow. Some of the most prominent design standards proposed are developed by bodies such as Atomium – the European Institute for Science, Media and Democracy

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(AI4People 2018), the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems (IEEE 2016), and the European Commission’s High-Level Expert Group on AI (EC 2019).

Actuating Cities effectively become smart when they act upon the data that they have sensed, gathered, and analyzed. Actuation can include humans in the loop (decision support) which requires the visualization of the data and features derived either on a screen or on other interfaces or be fully autonomous.

Data Visualization Data visualization is “the study of transforming data and information into interactive visual representations” (Liu et al. 2014). It is essential to enable humans to make sense of the large amount of data present in smart cities. The techniques depend on the category of the data represented (Zheng et al. 2016). Temporal data can be represented by line graphs (see Fig. 4). Spatial data can be represented by point-based visualization (see Fig. 5), but those do not scale well when the number of items to be represented increases. In this case, heatmap-based visualization (see Fig. 6) is more appropriate. Spatiotemporal data can be represented by space-time cubes (see Fig. 7) or by a combination of images (see Fig. 8) or an animation (Zheng et al. 2016).

Human Interfaces Smart cities should be ultimately at the service of their inhabitants, in their multiple roles, and this requires appropriate interfaces: Number of trips per time 150

Num Trips

125 100 75 50 25 0

21:33 May 01

01:20 May 03

05:06 May 04

08:53 May 05

Fig. 4 Taxi trips over time for three different areas (Ferreira et al. 2013)

12:40 May 06

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Fig. 5 Point-based visualization of taxi pickup and drop-off points (Ferreira et al. 2013)

Fig. 6 Heatmap-based visualization of number of vehicles at a given time (Liu et al. 2011)

• As consumers or residents of the city, for example, a smart home management system. • As information recipients, such a real-time passenger information for public transport. • As participants for example contributing data or participating in citizen science. • As testers, notably by providing feedback on new proposals, for example, in validation workshops. • As managers, by providing key insight on current urban conditions and supporting human decision.

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Fig. 7 Visualizing earthquake events in a spacetime cube (Gatalsky et al. 2004)

Fig. 8 Crime rates in each US states over the last 40 years (Andrienko et al. 2010)

• As makers/creators, for example, by designing and implementing community/ neighborhood apps, engaging in civic hacking, etc. The scope, nature, and stage of the interaction between citizens and urban projects are crucial to ensuring a meaningful participation of citizens in cities and realizing truly citizen-centric cities (Cardullo and Kitchin 2018). Indeed, new technologies such as smart phones and associated apps can contribute to the further individualization and liberalization of urban society, though they could also potentially be

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harvested to create an open, democratic “community of strangers” (de Waal 2014), with more collaborative models of smart city governance (“smart cities 2.0” see (Barns 2018)). This highlights the importance of designing appropriate interfaces for citizens, catering for their wide range of ages and abilities.

Consumer Interfaces A wide array of public-facing interfaces are available in smart cities. Some interfaces are available in public places, ranging from screens displaying the time of the next bus, for example, to more abstract ambient displays, potentially facilitating local engagement, such as results of votes on local issues (Koeman 2017). Other interfaces are available only in private spaces, such as smart home control systems, or even serve a single user at a time (e.g., a mobile app for travel support). These different types of interfaces exhibit different challenges, ranging from the ability to capture and maintain user interests for public displays, recognizing users, learning their preferences, and preferred modality of interaction for private space and devices interfaces (see also Augusto et al. 2013).

City Dashboards City dashboards are performance management tools, allowing quick access to key performance indicators via data visualizations and simple metrics. They typically focus on revealing data relevant to a city’s operation via simple data visualizations, widgets, and analytics. These provide dynamic and/or interactive graphics, maps, and 3D models to display information about the performance, structure, pattern, and trends of cities (Kitchin and McArdle 2016: 2) (see Fig. 9). Such dashboards can be designed and managed by the cities themselves or by other organizations, e.g., universities (e.g., see the University College London’s “City Dashboard” (Pettit et al. 2017)).

Robotics/Autonomous Actuation The development of smart cities is also marked by the more extensive deployment of different types of robots, with their use being extended from the home, hospital and factory use out into the cities and their integration with urban spaces. Smart cities are seeing the deployment of both mobile and stationary robots, ground, aerial and marine ones, and humanlike robots (Tiddi et al. 2019). Example uses include robot taxis, smart chairs, and social robots/translators (Fourtané 2018). Humanoid police officer robots are being trialed, with Dubai hoping to replace its police force with police robots by 2030 (Kovacic 2018). The European Robotics League (ERL 2019) is supporting the developments of urban robots by organizing yearly competitions for smart city robots, in which they compete in a set number of smart city tasks, such as serving products in a shop, delivering coffee shop orders, taking the elevator, opening the door, and delivering aerial emergency pill. Waste collection in urban

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Fig. 9 Four examples of urban data dashboards (Barns 2018)

environments has been demonstrated in the DustBot project, where robots were deployed to both autonomously clean pedestrian areas and perform door-to-door collection interacting with householders (Reggente et al. 2010). City traffic is being improved by the use of unmanned aerial vehicles (UAVs) for traffic management, such as accident reporting, flying speed cameras, flying dynamic traffic signals, flying speed cameras, etc. (Menouar et al. 2017), as well as for more efficient parking, in which a robot acquires, lifts, and transports vehicles to a parking spot in a garage structure (Smart City Robotics 2019). Health applications of robots in cities include an ambulance robot, which comes equipped with an AED to be used in cases of cardiac arrest (Samani and Zhu 2016). As all of these robots require city and citizen data for their correct operation, as well as to make automated decisions, they are subject to the same privacy, data protection, and ethical concerns discussed previously in section “Analyzing: Intelligence” (Torresen 2018).

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Conclusion This chapter provided an overview of the technological framework for smart cities. It first presented the Sense-Analyze-Actuate paradigm and how it can be used to optimize the use of shared resources in smart cities. The supply and demand of given resources can be matched by a combination of three potential approaches: adapting the supply to the demand, demand-side management, and resources storage. This was illustrated on a residential energy case study. The chapter then presented an overview of the challenges and existing work related to each of the Sense-Analyze-Actuate steps. Urban data can be arranged into three different categories, in terms of its spatial and temporal properties. This will affect how, where, and how often it should be collected. Four modes of sensing are available in smart cities: traditional, passive crowdsensing, opportunistic sensing (or active crowdsensing), and participatory sensing (or crowdsourcing). A wide range of networking technologies, both long and short range, can be exploited to gather the sensed data. The combination of connected artifacts makes the Internet of Things, the scale of which is growing exponentially. Finally, the data is typically connected to urban platforms either providing only data services (datastore or marketplaces) or a fully integrated platform. The Analyze step exploits algorithms to optimize the use of resources. These algorithms can be exploited not only to allocate resources, but also to predict both the supply and demand in real-time, as an input to such algorithms. Prediction algorithms include well-established traditional statistical techniques, custom dynamic hybrid models, and novel approaches based on deep neural networks. Resource allocation mechanisms can be centralized or decentralized and exact or heuristic. Automating decision-making raises a plethora of ethical questions, especially surrounding the lack of visibility as to how an outcome is reached. Actuation closes the feedback loop of the Sense-Analyze-Actuate paradigm. It can be undertaken via visualization to support human decision-making or visual analytics, via other smart city interfaces with its wide range of users, or directly, exploiting robotics, which have been applied to a wide variety of urban domains.

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Smart Cities Data: Framework, Applications, and Challenges Muhammad Bilal, Raja Sher Afgun Usmani, Muhammad Tayyab, Abdullahi Akibu Mahmoud, Reem Mohamed Abdalla, Mohsen Marjani, Thulasyammal Ramiah Pillai, and Ibrahim Abaker Targio Hashem

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Data Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensor Network Databases and Data Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . City Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quality and Veracity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . General Data Protection Regulation (GDPR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Data Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Government and Governance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Social Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mobility and Transportation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Privacy Challenges in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Security and Privacy Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Privacy Threats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Privacy-Enhancing Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Privacy in Data Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Privacy and Availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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M. Bilal (*) · R. S. A. Usmani · M. Tayyab · A. A. Mahmoud · M. Marjani · T. R. Pillai School of Computer Science and Engineering, Taylor’s University, Subang Jaya, Malaysia e-mail: [email protected] R. M. Abdalla School of Hospitality and Tourism, Taylor’s University, Subang Jaya, Malaysia I. A. Targio Hashem (*) Future Technology Research Center, National Yunlin University of Science and Technology, Douliu, Taiwan e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_6

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Abstract

Recent technological developments and the availability of enormous amounts of real-time data have played a vital role in the expansion, evolution, and success of smart city projects. Smart data can be used in a variety of smart city applications, but difficulties in managing such data are pushing smart cities toward the adoption of data management frameworks. Many studies have brought into focus the importance of these frameworks as they combine data collection, processing, analysis, management, and visualization and provide privacy and security features for different smart city applications, i.e., transportation, to promote a better quality of life. This chapter highlights key components of the data management framework, reviews various smart city applications, and discusses privacy and security challenges associated with smart city data. From the perspective of data frameworks, it is seen that the data used in smart city applications is unstructured coming from heterogeneous sources, i.e., sensors and social media, besides others. Therefore, the collection, processing, analysis, management, and visualization of such data are challenging. To perform these tasks, recent technologies, i.e., Internet of Things (IoT), sensor networks, machine learning, etc., have been used. Moreover, the use of smart data for smart government and governance provides several facilities for the public and business. The smart data is revolutionizing the daily communication of users along with their mode of transportation by introducing Social IoT (SIoT) and autonomous vehicles. Lastly, the challenges related to privacy and security of the data in smart cities that needed to be addressed are highlighted. This chapter will guide academics and enterprises to progress in data management framework and its applications in smart cities in the near future.

Introduction Big data is a massive flow of data produced by the digital world such as the Internet of Things (IoT), multimedia, and social media that can be analyzed for more accurate business decisions and strategic moves. The organizations continuously capture this rapidly increasing volume of detailed data from the Internet of Things, multimedia, and social media. The total amount of big data is beyond imagination as it is increasing at a rapid pace around the globe. People are exchanging information, ideas, and data on web application all the time. Moreover, this big data has enormous potential in the utilization of services in smart cities. In smart cities, a significant role is played by information and communication technology (ICT) as it makes data available, which is collected from the digital city. The information and communication technology (ICT) is also known as the Internet of Things. Smart city associates a city with the digital city, and it links them via the Internet of Things. The smart cities sensors capture data through the IoT devices from various smart city gateways and resourcefully process it to execute it in a specific region. Data and smart cities have made life more comfortable around the globe by creating better cities. The smart city and IoT have helped the government of China

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in creating new traffic routes to avoid congestion. The smart city and IoT have cut cost in their road construction. Nanjing information center has installed one million sensors into private cars and 10,000 into taxis and 7000 into busses to collect and analyze traffic data. After processing the data, they send updates via smartphones to the commuters. In Italy, sensors are installed in the trains, and the major rail operators get the real-time messages about the mechanical condition of each train. This data has helped the officials to prepare a course of action for any unfortunate event by providing a process for better maintenance predictions. The systems and services are reliable due to this innovative technology and prevent cities from major interruptions. Los Angeles (LA), USA, is switching to new light-emitting diodes (LEDs) and replacing approximately 4500 miles of streetlights. These LEDs are connected with the smart devices that will update the officials about the status of each bulb in the city. This data will help the team to repair any malfunction in the LED. LA is planning to use these lights as a signal to warn citizens about the precarious conditions in the future. The city is thinking of changing the colors of the LED or maybe making them blink which will solve the problem. The population of urban areas and smart cities are ever rising. Smart city sensors monitor almost everything. Such innovation will not stop until they can monitor each and everything from sources of energies, to road constructions, to trash cans and streetlights. This data comes with challenges like effective management of data so it can be accessed, analyzed, combined, and used across departments and organizations. A smart city should have the ability to share data in real time so that the private and public sectors can work seamlessly together, which poses a challenge of integration between these sectors. Furthermore, smart cities deploy different types of sensors, and each sensor usually requires a new database, triggering a procurement process. The high cost of storing big data reflects on the cost of the smart city, adding to the financial backing needed upfront (Deren et al. 2015). Moreover, it is difficult to do knowledge mining in big data as big data contains rules and knowledge associated with data. These data rules and knowledge are obtained by conducting in-depth data mining and analysis. However, the fundamental properties of big data automatically make it difficult to process and to analyze the smart city data especially dataset containing spatial information (Li et al. 2001). This chapter consists of five sections. The section “Smart Data Framework” gives an overview of key components of data management frameworks for smart cities, and the section “Smart Data Applications” describes the application of smart data in smart cities. The section “Privacy Challenges in Smart Cities” discusses the privacy and security challenges regarding smart city data, and lastly, section “Conclusion” concludes this chapter.

Smart Data Framework In this section, each component of a data management framework for smart cities are discussed. Figure 1 gives a graphical illustration of the data framework for data management in smart cities.

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Fig. 1 Smart cities data management framework

Sensor Network Databases and Data Management Sensor network databases involve a blend of sensor and stored data. Sensor network is a set of sensor devices (nodes) with resources, which are connected to each other wirelessly and installed in an area to collect environmental elements such as humidity, temperature, light, gas density, motion, pressure, and so on (Plageras et al. 2018) and process the data to store them in database (Changbai et al. 2008). Sensor network allows the user to remotely monitor the physical information of the environment (Küçükkeçeci and Yazıcı 2018). Sometimes sensor database is identified as in-network sensor query processing systems (ISQPS), which had been designed to collect, process, and aggregate data from sensor network/wireless sensor network (Luo and Wu 2007). In recent years, with the rapid development of information and communication technologies, the sensor network is collecting a massive amount of environmental data based on query processing construct. The challenge nowadays is how to reduce the volume of data collection and to transfer them from the node to the base location. Due to this issue, (Changbai et al. 2008) developed a new query language construct called SNQL, for dealing with large sensor database. The result indicated that the developed SNGL database increases the efficiency of the network and query flexibility. A study by (Plageras et al. 2018) has proposed a sensor management system for collecting tremendous data generated from sensors installed in the smart buildings. This proposed system found to be a solution for accumulating and handling sensor’s data in a smart city. The sensor network is one of the advanced technologies for smart buildings. It collects several types of data from various sources. It is essential to consider the reduction of energy consumption and operating expenses. Owing to these reasons, (Azri et al. 2019) carried out research and proposed 3D geo-clustering techniques/algorithm that assists in organizing information of sensor network stored

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in a database. The result shows that the algorithm can stabilize the node energy utilization and extension of the network. Network power consumption needs to be reduced when the database was queried, which aided in decreasing the traffic. The research conducted by (Tsiftes and Dunkels 2011) proposed an Antelope database management system for network sensor devices. This result shows the proposed system enables it to reduce congestion for network power consumption. In order to improve power consumption, it is vital to understand the physical data and query processing between network layers and applications. The research was carried out by (Sudha and Nagesh 2018) that listed the most important query, which includes queries on multidimensional ranges, queries on historical data, long-time continuous queries, snapshot queries, and event-related queries. Similarly, investigation of the query layer design for sensor network and interaction between the query layer and in-network aggregation was studied by (Yong Yao 2012). Incremental time slot algorithm was proposed to explore how to record the data transmission between nodes. The result showed the successful performance of the proposed algorithm. The SINS database system was developed by (Dekkers et al. 2017) using a network sensor, which was installed in five different rooms to capture daily activities in a home environment for 1 week which included 16 activities. The sensor network comprises of 13 nodes. The purpose of the study was to investigate the activities carried out in the house using the benchmark system. The normalized confusion matrix was used for analysis. The result showed that the best performance was found in the hall and the worst in the bedroom.

City Analytics The world is rapidly urbanizing, with future global population growth projected to occur mostly in cities and towns, and the environmental impression of cities encompasses beyond what is sustainable (Moglia et al. 2018). These developments provide some innovations such as economy creativities, the transformation of economy innovations into solutions, and a safe haven for the functional development of urban cities. Moreover, information and communications technologies (ICTs) are major tools that facilitate the developments and configuration of urban devices (Valls et al. 2018). Users must know how to use and operate the technologies in an urban environment, which will aid their adaptability to the environment. With the expansions in smart computing and mobile technologies, the collection of datasets in urban cities is improving geometrically that capture the pulse of urban life (Galbrun et al. 2016). These publicly available datasets have created opportunities for both the government and other authorities to make use of them to improve the quality of life for the people living in the city. With recent trends and development of low-cost sensors, miniaturization of computing and electronics, actuation and control systems, nanotechnology, and wireless communication have contributed to emerging research areas in urban computing with overlapping themes and challenges. These technologies enable urban computing research to be deployed in the wild, in the real context of cities

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as the living laboratory, situated within public spaces, facilitating open interactions with individuals, groups, and communities. Recently, the nature of pervasive computing technologies has made our lives to revolve around smart objects/things that are always connected to the Internet, which have changed the way we live, communicate, and work in a smart urban environment (Salim and Haque 2015). However, this has created both technological and interactional opportunities for citizens in smart cities through urban-computerized projects. Studies by (Krieg et al. 2018) have shown that urban data gathered can be used to develop a parking system to reduce traffic congestion in cities. In this regard, the authors developed and implemented SmartPark in San Francisco cities. The system depends on pervasive Wi-Fi and cellular infrastructure, which is capable of providing drivers with real-time parking availability information. In the city of Montreal, (Malandra et al. 2018) used LTE embedded into a web-based application to support a huge amount of machine-to-machine (M2M) traffic communication model. The model provides the precise location of different sets of machines such as traffic lights, smart meters, bus stops, etc. It also enables the study of the traffic produced by realistic M2M components in smart cities environments. Recently, (Honarvar and Sami 2019) used real urban dataset collected and extracted from multiple sources in the city of Aarhus, Denmark, to develop a prediction of particulate matter model in the city. The data collected are related to urban buildings, road traffic, air pollution, weathercasts, and points of interest (POI). The model is based on transfer learning and is validated using RMSE and MAE. The urban big data supported by the IoT are progressively becoming related entirely through regularly and automatically sensed data, especially in smart sustainable cities. The IoT and ICT tools are used for generating the datasets using routine and automatic sensing, which replaces the conventional approach (Bibri 2018). Besides, ubiquitous sensing is the main feature of smart sustainable cities of the future, which typically rely on the fulfillment of several ICT visions of ubiquitous computing, particularly the IoT. A smart, low-cost, static, acoustic sensing device based around consumer hardware was implemented by (Mydlarz et al. 2017) in New York City (NYC) using microelectromechanical systems (MEMS) microphone in order to generate consistent decibel levels. The NYC is known as an urban sound environment having the following characteristics loud, disturbing, exciting, and dynamic. The urban sound environment has an intense influence on the quality of life of the city’s inhabitants.

Deep Learning Bu, Wang, and Gao (2019), the authors, presented a multi-projection deep computation model (MPDCM) to generalize DPDCM for smart data in the Internet of Things (Bu et al. 2019). MPDCM maps the input data into multiple nonlinear subspaces to learn the interacted features of IoT big data by substituting each hidden layer with a multi-projection layer. The used learning algorithm is based on backpropagation and gradient descent that are designed to train the parameters of the presented model. Finally, the authors conduct an extensive experiment based on the

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two representative datasets, i.e., Animal-20 and NUS-WIDE-14, to verify the presented model by comparing with DPDCM. Chenhui, Shuodong, Zhuo, and Peng (2019) presented a deep learning model for potentially diagnosing gallbladder stone with big data from the medical Internet of Things (Yao et al. 2019). Gallstones can be classified into four types, i.e., cholesterol stones, bile pigment stones, mixed stones, and other rare stones, based on the chemical composition of gallstones convolutional neural network to learn the features of the collected data. The authors used a convolutional neural network model to learn the features of the collected imaging data of the gallstones. Moreover, the authors have described an effective learning approach for training the developed convolutional neural network. Leyi et al. claimed to accurately predict protein subcellular locations (Wei et al. 2018). The authors have proposed a deep learning-based predictor called DeepPSL by using stacked autoencoder (SAE) networks. The authors claimed that the predictor automatically learns high-level and abstract feature representations of proteins by exploring nonlinear relations among diverse subcellular locations. Experimental results evaluated with threefold cross-validation show that the proposed DeepPSL outperforms traditional machine learning-based methods. It is expected that DeepPSL, as the first predictor in the field of PSL prediction, has great potential to be a powerful computational method complementary to existing tools. The authors initially used an unsupervised approach to automatically learn the high-level latent feature representations in the input data and initialize parameters and then use a supervised approach to optimize these parameters with the backpropagation algorithm. Using the computational power of graphical processing units (GPUs) and CUDA, they have trained the deep networks efficiently. The authors also considered two well-known feature representation methods. The first one is based on physicochemical properties of proteins, while the other is based on adaptive skip dipeptide composition. Both features have been proven effective in multiple bioinformatics problems. Finally, the authors claim that by fusing the above two feature types, they yielded a total of 588 features (¼ 188 + 400) as the input of deep network (Wei et al. 2018). The proposed DeepPSL achieved satisfactory overall performance, obtaining 37.4% in terms of overall accuracy (OA) for the ten-class subcellular localization prediction. Sannino and De (2018) have proposed a novel deep learning approach for ECG beat classification (Sannino and De Pietro 2018). The proposed approach has been developed using the TensorFlow framework, the deep learning library from Google, in the Python programming language. A deep learning technique is introduced in this work to meet the challenges faced by classifying the ECG beats. The authors used the dataset for each subject of the MIT–BIH database; they have computed four preprocessing steps. Furthermore, they have removed from the dataset the last 14,828 items. Additionally, the authors claimed it was necessary in order to balance the dataset due to the fact that the classes were imbalanced, namely, we had too many normal beats (N) compared to abnormal ones (V, S, and F). In fact, in these cases, conventional algorithms are often biased toward the majority class because their loss functions attempt to optimize quantities such as error rate, not taking into

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consideration the data distribution. In the worst case, minority examples are treated as outliers of the majority class and are ignored, the learning algorithm simply generating a classifier that classifies every example as the majority class. To avoid these problems, the authors decided to select only 2288 items representing the normal beats class (N) from the initial 66,750, randomly selected from all the subjects. Therefore, the final dataset was composed of a total of 4576 items, 2288 representing the normal beats class (N) and 2288 representing the abnormal beats class (A).

Smart Visualization Visualization of data is the process in which the data can be visualized, and more information can be shown with the help of pictures, graph, and charts. This technique helps in deciding on challenging issues in the data visually. When it comes to big data visualization, it becomes more challenging because of its characteristics. Application of big data in smart cities makes it more complex and challenging as the data is coming from several sources, and it also has a significant impact on decisionmaking. However, with the development of virtual reality (VR), augmented reality (AR), mixed reality (MR), and Google Maps have changed the practical efficiency of smart city application (Hashem et al. 2016). In smart cities, data is collected with the help of different sensors automatically, and then this data can be used for long-term analysis. In order to make long-term decision depending upon the data, several tools can be used. Usually, in smart cities, a benchmark approach is used aimed to have a better result while comparing the performance and data usage between cities (Osman 2019). Similarly, data now in smart cities is available to the habitant of the city via the Internet using different visualization methods like dashboard, etc.; this smart dashboard may be a compromise of fact and figures in the form of chart and statistics that explain how much affects are there on citizens from such policies (Osman 2019). For example, noise value can explain the effects of noise pollution on citizen and others as well. Visualization of data via dashboard can have multiple view categories like data streams, format of data, and resources of data that involved key challenges and processes (Ben Sta 2017). In order to display the data in a specific format, first data should be in a standardized format that includes visualization and understanding (Lim et al. 2018; Rathore et al. 2016). Visualization and understanding can also be made easy with the involvement of graphs and plots and different pictures. While in a phase of development of the dashboard, there may have several data resources that have to be considered. In the smart city project, data may come from different (1) experimental setup like temperature, sound, barometer, and others and (2) third-party data like project partner. Firstly, data is fully controlled by the project manager and member of the project and then send it to the database in a particular format. These preprocessing methods are easier as the data is under a controlled environment and continuously under critical monitoring phase so that all the bugs and other mistakes can be fixed at this stage (Lee et al. 2014; Pan et al. 2016).

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Preprocessing will guarantee that the values sent to the database must be correct and dependable. Secondly, for privacy reasons, there exist some degree of uncertainty in the data as it is hard to know what the team member has implemented on his behalf. For example, one may use a different file like JSON file with values and time stamp, but on the other hand, the team members may have the complex format of CSV file with time stamp and other parameters. Special care may be needed for external and legal data. For this reason, first, the data need more care for retrieval from sources to be processed and converted to a standard format so that it can be inserted into the database. The data is now ready for visualization with the help of frontend. Concerning the frontend, one of the major challenges was to settle the color palettes because it helps the user while navigating the database. Chart, graph, and other visual basic can be done with the help of JavaScript add-on called Chart.js, which is an open-source and free to use package. This package helps to build smooth, simple, and very informative charts. Visualization of data is always a challenging task, and it depends upon the data and constraints.

GIS-Based Visualization Geographic information system (GIS)-based visualization is now widely used for analyzing and decision-making for spatial data. It has earned a high level of popularity in urban planning, traffic data monitoring, environmental decision, and modern mode of transportation. Visualization in a smart city context is challenging as it provides an interactive and easy-to-use environmental tool for users (Hashem et al. 2016; Pan et al. 2016). Such an environment can integrate 3D touch screen integration with smart city application. These integrations can enable policy-maker to translate data into knowledge or information, which is the most critical in quick response or fast decision-making platform (Hashem et al. 2016). The information extracted from different platform and environment will be used to represent information based on the requirement of the user. GIS-based visualization will create efficient and flexible devices for smart city toward realizing the vision of a smart environment.

Quality and Veracity In modern time, cities are expanding more and more, and almost half of the world’s population lives in developed cities according to the environmental statistics with more than six devices connected (Habibzadeh et al. 2019) to the Internet. This concludes that there exist billions of devices connected to the Internet, namely, smart light, traffic road signals, pedestrian management system, smart security cameras, smart monitoring room, and control rooms and smart healthcare. Furthermore, smart homes and the devices connected to them are also part of smart cities (Habibzadeh et al. 2018a, 2019). Moreover, application connected to the smart cities has benefits for both citizens and the underlying environment (Habibzadeh et al. 2018a). Similarly, smart cities include smart economy, smart governance, smart

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people, smart mobility, smart environment, smart security system, smart surveillance, and smart living standards (Appio et al. 2019). With all the advantages of smart cities, one of the integral components of real-world smart cities includes data management. Data management consists of data acquisition and processing, and it relies on quality and veracity of data. A smart city collects data from heterogeneous IoT devices, such as pollution, noise, weather, and traffic among others. The quality of smart city data depends on three factors, i.e., (1) precision of collection devices or measurement errors, (2) quality of data communication and environmental noise, and (3) level of detail of the measurements and observations in temporal and spatial dimensions (Barnaghi et al. 2015). The quality of information issues become more prominent when different data with varying quality has to be integrated for use in smart city applications that use data with high dynamicity, velocity, and volume. Smart city applications include futuristic applications such as Ambient Assisted Living (AAL), which helps the elderly to live independently for as long as possible (McNaull et al. 2012); smart parking, which helps drivers to find empty parking spaces; smart environment, which help in conserving energy by adjusting temperature in fully automated workplaces and homes; and smart transportation, which issues bad traffic condition warnings to drivers (Habibzadeh et al. 2018b). These applications are reliant on the veracity of smart city data. The veracity of smart city data depends on the precision of data collection devices, measurement errors, quality of communication, and environmental noise. The veracity of smart city data can be assured by using trustable resources or using a combination of resources to verify the data.

General Data Protection Regulation (GDPR) In 2016, the European Union (EU) introduced a law to protect the data and privacy of all individual citizens of EU and European Economic Area (EEA). This law is known as the General Data Protection Regulation (GDPR). The primary goal of GDPR is to give its citizens the control to their own data (Team 2017). As discussed in previous sections, smart cities collect and use an overwhelming amount of personal data. As smart cities collect more and more data of the citizens, the concerns about the security of smart city data protection measures become more noticeable, especially prominent in cases of data breaches in private companies like Yahoo (Trautman and Ormerod 2017). These privacy flaws are highlighted by the EU’s GDPR as it helps in securing the huge amount of data collected and stored by smart city technologies. Many smart cities were unprepared for privacy practices introduced with GDPR, for they are not looking for solutions in the private sector. The major sticking point of GDPR for businesses and organizations is the requirement for the appointment of a data protection officer, whose role requires dual skills in data protection laws and IT. Apart from the data protection officer, the compulsory implementation of a local cyber security plan is also a big concern to smart city data protection operators. Local cyber security plan is in place to secure the storage of data, but it will increase the

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cost of smart cities initiatives. Hence, in the implementation of GDPR, a substantially stricter form of personal data protection in the EU and EEA, the question arises: Will GDPR slow down the development of smart cities? GDPR will certainly affect the development of smart cities, but it shouldn’t be seen as a hurdle in its development as it will help in building trust with the citizens as they will reduce the fear of possible abuse and they will have control over their information and privacy in smart city models (Vojkovic 2018).

Smart Data Applications Smart Government and Governance In the public sector, the promising transformation has been observed over the past few years. Cities are being turned into smart cities by governments around the globe to address challenges (Allwinkle and Cruickshank 2011). This gave birth to a new phenomenon known as “smart government.” Previously, the term smart government was used to refer to a government that is aware of its social roles and performing its tasks effectively by using its capabilities (Kliksberg 2000). Development projects like public administration and e-government were initiated to meet the needs of individuals and companies which also motivates governments to become smart (Schedler et al. 2004; Schedler and Proeller 2010). The smart government can be viewed as an effort of using the latest digital innovations to achieve promises that have not yet been achieved in previous development initiatives, i.e., e-government (Guenduez et al. 2017). In addition to few new features, i.e., data-based decision, creativity, resilience, etc., most of the features, i.e., sustainability, integration, effectiveness, efficiency, public administration, etc., known from the literature of e-government were also listed as the smart government features (Schedler et al. 2004; Gil-Garcia et al. 2016). The smart government is still a fuzzy concept as in the literature there are only a few definitions of smart government, and none of them are widely accepted (Harsh and Ichalkaranje 2015; Mellouli et al. 2014; Scholl and Scholl 2014; von Lucke 2016). This makes it difficult for the implementation and governance of smart government initiative. The smart government can be referred to as “using advanced technologies to improve the effectiveness of public services, establish a commercial setting for companies and start-ups, and reduce both expenditure and energy utilization.” Emerging technologies such as the Internet of Things (IoT), machine learning, cloud computing, and sensor networks have enabled objects to connect, interact, exchange, and process data in smart cities (Schedler 2018; Paola and Rosenthal-Sabroux 2014). Thus, smart cities tend to enhance financial and political effectiveness, enable sociocultural- and industry-driven growth, and solve social, financial, and environmental issues (Hollands 2008; Townsend 2013). IoT also offers unique possibilities for people to engage and impact smart city policies, create, and test them (Viale Pereira et al. 2017). IoT-enabled artificial intelligence-based solutions are being used as key areas of smart government to enhance governance

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effectiveness and the living standards, i.e., energy management (Chatterjee et al. 2018; Axelsson and Granath 2018). It can be assumed that a smart government will generate cooperative environments and foster cooperation between government and nongovernment organizations (NGO) besides citizens (Nam and Pardo 2014). Whereas, smart governance is usually described as the ability to use digital technologies and smart information processing and policy-making practices (Scholl and Alawadhi 2016). Rational, political, cultural, and institutional perspectives have been used for understanding smart governance. The rational view perceives governance as the results of the rational study. The political view takes governance as the consequence of a tradeoff between various significant values. The key idea of the cultural perspective is that governance is primarily meaningful among stakeholders. Whereas, the outcomes form the combination of past practices, principles, standards, procedures, and conventions form the institutional perspective (Meijer and Thaens 2018). A study also differentiates smart city governance perspectives including smart government, smart decision-making, smart management, and smart communication (Meijer et al. 2016). The notion of smart government has a vital role in the increasing smart city debate and grows alongside other smart city aspects including smart environment, smart economy, smart mobility, smart living, and smart people (Pereira et al. 2018). Smartness in these fields occurs in the domain-specific assessment and by combining huge volumes of structured and unstructured data. This allows self-learning algorithms to produce more accurate predictions about certain facts, communities, and individuals which enables a far more efficient and user-friendly way of automating or executing certain tasks (Guenduez et al. 2019). Governments and authorities lack thorough knowledge of success factors for smart government, i.e., in Switzerland, numerous municipality governments are adopting a smart city strategy, some are in their inception, whereas others are very developed (Hollands 2008). This study has demonstrated on how smart public projects can be introduced by a recent study (Guenduez et al. 2017). They concentrated on technology, big data, algorithms, and individual participation. However, in smart cities, public authorities are still at the initial stage of the journey to the smart government (Mettler 2019). Currently, the most serious challenge in exploring the potential of emerging technologies in smart cities is probably not realizing what needs to be done for smart governments (Praharaj et al. 2018). The important success factors for smart government projects were reported as institutional, organizational, and leadership (Guenduez et al. 2018). There are already many successful examples of this transformation toward smart cities. Artificial intelligent bots in France are helping and advising the individual in searching for jobs. Analysis of traffic data in Los Angeles improves road safety. Big data-based surveillance of fishing quotas paves the way for evidence-based decisions in Germany. Automatic data retrieval in Sweden is saving user’s time. Moreover, government agencies and real-time data make rapid, focused, and even preventive police operations possible in Estonia (Kankanhalli et al. 2019; Ruhlandt 2018). Initiatives for real-time monitoring of water quality and flood detection using sensor networks

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can be used address prevailing situations of water crisis (Bilal et al. 2019a). The change toward smart government is not easy as the present institutional, organizational, financial, and technical barriers present significant difficulties for government authorities (Schedler et al. 2017).

Social Networks A “social network” is a digital space where people share opinions and ideas, connect and communicate with individuals, and create a sense of virtual community (Clemons et al. 2007). Online social networks (OSNs), i.e., Facebook, Twitter, Pinterest, and Instagram, are extremely popular, and more and more people are using the OSNs to connect with their friends and acquaintances. OSNs altered the means by which individuals connect and triggered a lively debate on whether the affordances of such OSNs will also change the means by which individuals communicate (Kumar et al. 2010). Social networking platforms produce an enormous quantity of information on a regular basis, and the social network analysis research is increasing exponentially due to the diversity, volume, and complexity of data (Eirinaki et al. 2018). Social networking sites have provided individuals with access to the huge source of data with little or no restrictions (Pang and Lee 2008). The OSNs have become a major source for the acquisition and distribution of data in various fields such as commerce (Beier and Wagner 2016), entertainment (Shen et al. 2016), technology (Chen 2016), and contingency planning (Stieglitz et al. 2018). Online social media data has the ability to predict user profile attributes (Kosinski et al. 2013). It is essential to provide the consumer with the information they are searching because of the rapidly growing data. In order to profile user interests, social recommendation systems have been implemented (Jamali and Ester 2010; Tang et al. 2012). Researchers have been using social media data to predict the outcome of the elections, political debates and its influence on the individual, and perspectives into reactions to health and disease outbreaks and spread of news via social networks (Bilal et al. 2019b; Nawaz et al. 2017; Hermida et al. 2012). Social network analysis (SNA) incorporates network and graph theory methods to study and explore social interactions. Within social networking sites, people, users, items, or objects are regarded as nodes, while relationship, interaction, and association are depicted as edges (Otte and Rousseau 2002). Web 2.0 has empowered people to interact efficiently, establishing networks with mutual interests, sharing the information, and posting huge volumes of valuable, user-generated content (Tan et al. 2011). Moreover, the application program interface (API) provided by social media platforms can be used to crawl and retrieve data. “Data analytics” can be used to derive perceptions, relations, patterns or behaviors, and insights from these tremendous amounts of data from OSNs (Bendoly 2016). SNA is considered as the most common and very well-established data analysis sub-domain. It provides a broad range of tools, techniques, methods, and principles for collecting, processing, and analyzing huge amounts of social media data that contain valuable information (Wasserman and Faust 1994; Gandomi and Haider 2015). Machine

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learning (ML), natural language processing (NLP), and text mining are among the most popular methods for sentiment analysis, SNA, and data mining (Stieglitz and Dang-Xuan 2013). It is a common perception that the social relationship usually influences communication between smart devices. This indicates that social networking theory can be utilized to boost the quality of service for those social connections. Furthermore, the key concepts of social networks, i.e., centrality and community, were studied in order to efficiently understand the recent architectures of the wireless network. A study provides a detailed overview of social networks and reviews their applications in wireless communication (Jameel et al. 2018). Presently, the focus of researchers on SNA is rapidly increasing, and several studies have explored various characteristics of social networks. The application of social networks in the wireless network has been widely studied. The social network can aid healthcare services by providing location-based facilities and monitoring individual behavior (Falk 2011). A study used one of the renowned social networking websites Vk.com to search and collect public data related to the user in a systematic manner (Bagretsov et al. 2017). An SNA-based rising star forecasting model was reported to produce the best results when compared with baseline models based on other approaches (Ning et al. 2017). On the basis of social media profile and social relationships, one can accurately predict an individual’s personality. A number of studies had explored language variation from the perspective of demographic and psychographic characteristics (Bamman et al. 2014). Various recommendation systems, such as recommendations for movies, recommendations for friends, etc., use enormous social media data to find trending topics and friends recommendation (Jiang et al. 2016). Different features extracted from social media data were used to develop machine learning algorithms to identify the missing link between individuals (Fire et al. 2013). Content-based fitness and health assessment were carried out by using Twitter data (Kendall et al. 2011). A proposed system introduced comprehensive geographical features based on topics discussed on Twitter and maps consumers’ geographical preferences (Vosecky et al. 2013). The social network of influential individuals and their followers can be identified with SNA. SNA is also used to study user behavior to assess its causal relationships on the network as a whole. Social media firms have acknowledged the significance and role of influencers in the purchase or replacement of products. The content of social networks has been used in the information systems to identify and to analyze information dissemination (Zhang et al. 2016). The businesses use data from social media to target audiences, to identify preferences of clients, and to get feedback of product or services. Social networks have many advantages along with some disadvantages (Wendling et al. 2013). It is also necessary to study and to analyze events such as spreading fake news and rumors along with the reputation of the user across social networking sites (Qin et al. 2015). With the increasing user-generated content across social networks, it has become increasingly important to profile consumers by extracting information shared on OSNs. Social profiling is an emerging approach to address the challenges faced in meeting user demands. A study reviewed and classified social profiling research, describing methods, sources of data, limitations, and open challenges (Bilal et al. 2019c).

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Social media analytics is generally described as a complex process. Therefore, the entire method and steps involved need to be standardized. Recently, the Internet of Things (IoT) has been seen as an efficient technique for improving asset management (Lee et al. 2015). The IoT connects computers, devices, sensors, and individuals, and it is anticipated that this technology will connect billions of devices in the near future. However, it is very difficult to use traditional techniques for integrating and maintaining a huge network of these devices. Social networks are capable to connect and maintain communication with billions of people using social interactions. This leads to an emerging field of Social IoT (SIoT) that connects and maintains billions of devices in IoT networks using principles of social networks (Thangavel et al. 2019). The concept of SIoT resulting from the integration of social media in IoT has been introduced in fields such as management of product lifecycle, vehicle tracking, and employee assistance (Cai et al. 2014; Schurgot et al. 2012; Kranz et al. 2010). Twitter has provided users with an API that can be used by users and applications to post messages and manage the user account. The efficient communication between devices can be achieved using such APIs, and this leads to the rapid implementation of IoT solutions. Twitter can assist the devices in the IoT network to interact and communicate with inter-network devices, intra-network devices, and people, thereby increasing the strength of the IoT as a whole (Ortiz et al. 2014). But with such a broad user network and their related information, Twitter also attracts spam or fraudulent users who foster their illegal activities or attempt to deceive users and influence the feelings of specific social communities (Schulz et al. 2017).

Mobility and Transportation One of the twentieth-century significant socioeconomic transitions was the widespread use of automobiles (Geels 2012). There is an ongoing global discussion on how new technologies, e.g., automated cars, communication applications, and IoT, will improve mobility for individuals and groups. Furthermore, it is stated that “smart mobility” transformation, which combines these emerging technologies to improve the organization and operation of the transportation system, has already started. Like any socio-technical transformation, the questions of how the change will be handled and how the advantages and disadvantages will be managed are important (Docherty et al. 2018). Smart mobility is mostly described as a transition of equal reach with respect to automobility, centered on a variety of positive developments in how individuals travel. The smart mobility was described as a future vision in which transportation will be presented as a service accessible on request, with people having immediate access to a clean, renewable, effective, and convenient transportation system (Wockatz and Schartau 2015). Followed by the extensive adoption of integrated and autonomous vehicles, it was argued that somehow the smart mobility will offer potential benefits in safety and fewer travel expenses by efficient use of transport infrastructure and automobiles. These new frameworks with shared ownership of mobility resources and real-time data

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integration will also reduce the hold of big companies on transport supplies (Fagnant and Kockelman 2015). The capacity for autonomous cars to decrease journey times for a diverse range of trips will have a much greater impact on individuals and the economy rather than only saving time (Wadud et al. 2016). There are some key characteristics of smart mobility that are being discussed and common among all feature perspectives (Kuosa 2016). Mobility is taken as a service in which companies replace individual ownership of automobiles, i.e., the capacity of the individual to buy transport facilities package operated by certain operators. This is supported by the embedded aggregator and payment systems capable of processing huge volumes of real-time data to meet user demands (Thakuriah et al. 2016). The user’s decisions of mobility and non-mobility are being influenced by real-time crowdsourced user-generated content (Toole et al. 2015). Smart infrastructure, such as connected automobiles, uses individual operational data and gives realtime feedback to monitor the behavior of travellers and enhance system efficiency (Alam et al. 2016). Nowadays, automobiles are being electrified using renewable energy from batteries, hybrid, and other new technologies. The use of smart power grids in electric cars can be emission-free as well as provide a solution for the use of renewable energy (Bakker et al. 2014). The autonomous vehicles enable all passengers in a vehicle to perform their task during travelling where no user is required to drive the vehicle (Fagnant and Kockelman 2015). The recent trend toward the installation and use of vehicle automation and communication systems (VACS) in automobiles is due to significant advances in ICT and sensor networks. VACS provided users with comfort and safety along with controlling traffic and emissions for connected automobiles (Diakaki et al. 2015). The volume of VACS-equipped connected autonomous cars will rise quickly over the next decade. In addition, human-driven vehicles still retain their place in global markets. However, the roads will soon be shared by both human-driven and autonomous vehicles (Levin and Boyles 2016). It is essential that we know how users adapt to the existing smart transportation system, given the advantages of a connected environment. Vehicular social networks (VSNs) inherited features make it difficult to enable smart mobility and efficiency in data transfer. The Internet of Vehicles (IoVs) has emerged where automobiles operate as sensing terminals to collect data of in-vehicle and smartphone devices and then release it to users. VSN is a new framework that attracts scholarly and industrial attention, but the integration of social networks with IoVs is in its early stages. Incorporating smart controllers and connectivity techniques in smart cities create a whole new domain for IoVs as automobiles have considerably transformed (Rahim et al. 2018). There are still many obstacles, i.e., the distribution of messages and analysis of big trajectory data, trust, security, and anonymity, to use VSN for enabling smart mobility. The literature related to overcoming these challenges faced by VSNs is very limited until now. Therefore, it is necessary to develop social trust-based techniques for secure and reliable connections in VSNs (Rahim et al. 2018). There are many examples of smart mobility in various smart cities throughout the globe. London ranked second in the world’s smartest city ranking, according to the Cities in Motion Index (CIMI). London’s transport system integrated number plate

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identification for managing traffic flow to effectively reduce traffic jams during rush hours. It also involves Wi-Fi accessibility, smart roads, and bike share programs (Berrone et al. 2016). The green city index of the United States and Canada recognized San Francisco as the greenest city. The San Francisco transport authority adopted the approach to substitute single-occupant automobiles with shared electric, connected, and automated vehicles which solved many problems with the timeconsuming and costly transport (Silva et al. 2018). One of the potential problems faced by the upcoming transportation systems is developing smart mobility governance techniques that use wireless communication to accomplish worldwide roaming. In addition, it is necessary to integrate and interoperate advanced mobility governance techniques in heterogeneous networks to integrate prospective wireless systems in smart cities (Yaqoob et al. 2017). With the evolution of smart mobility, the key system components will be reconfigured resulting in different outcomes of mobility, i.e., patterns of land use, jobs, housing, etc. (Kim et al. 2015).

Smart Environment Smart environment means the living standard, living style, and the things around the smart city, not the actual environment of the city. It aims to provide the basic necessity of life and provide a better interaction between the citizen and their surroundings. The smart environment is provided with the help of artificial intelligence and machine learning, thus creating a responsible, adaptable machine into the environment (Jain and Nagarajan 2016; Augusto et al. 2013), for example, data collection from different microphone sensor and cameras located in the city and applying different machine learning algorithms to detect emotions and gesture recognitions, especially in case of smart classrooms, where the instructor and students both adjust their learning with the help of visual aids. In a smart environment, data can be from different nodes with different format because of the diverse nature of devices connected in a particular environment, which further generates the ranges of senses like communication, data, and security of data (Jain and Nagarajan 2016; Sheu et al. 2016).

Smart Streetlights Smart streetlight is one of the most adaptable applications in smart cities. Smart streetlights can help in energy consumption optimization. This can be done with the help of sensor nodes as well as monitoring with smart cameras. The saved energy can be supplied to that area where the energy is needed. Sensor nodes are efficiently deployed to monitor the streetlights. These nodes also have a camera for the visual insight of the location so that action can be taken as per need. The smart light system can also be found in the literature review, like Veena et al. (Gharaibeh et al. 2017) has proposed the smart streetlight system for smart city, in which the hardware application is capable of taking video as an input with the help of a camera and detect the movement of vehicles and pedestrian to switch on or off streetlight. This feature will optimize energy as well as the consumption of energy in an efficient manner. Sheu

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et al. (Sheu et al. 2016) have introduced the light-emitting diode (LED) for streetlight with multiple colors, using high-power integrated circuit (IC) and high-quality image processing for accurate decision. In case there is fog or high rain, the system immediately generates the alert to activate the LEDs with the help of power IC so that with the help of multicolor, pedestrian and drivers can recognize the exact path.

Smart Homes and Smart Building Inside the smart environment, there are two different applications named as smart homes and smart buildings. These applications can ensemble different sensors and actuators that are deployed in homes and buildings to improve the energy efficiency (Bellido-Outeiriño et al. 2016; Collotta and Pau 2015) and consumption of utilities (Crowther et al. 2012; Daher et al. 2017) and ensure security (Zeng et al. 2017), which connect smart homes to homes and overall smart applications like smart grid (Zhang et al. 2015) and smart health management system (Zhang et al. 2015) for citizens. Smart Surveillance in Smart Cities It is the most challenging part of the smart city application for the past recent years, mainly due to the improvement and advancement in image processing and its application. In previous, IBM Db2 (van Zoonen 2016; White 2001) and IBM WebSphere (White 2001), IBM smart Surveillance Systems (S3) can generate the system alert and security alert with the extraction of information and detection of a vulnerability in the system.

Privacy Challenges in Smart Cities Smart cities are developed that reforms the society and quality of life through several features like digital connectivity, digital transport system, smart health management, and increased inefficiency and accessible in cities. Similarly, the interest of smart cities has been increased up to a certain threshold with the deployment of information and communication technology (ICT). Long-term objectives of smart cities are organized in order to enhance the quality of services provided to the citizen, and that will ultimately improve the lifestyle up to mark (Khatoun 2017). One of the basic features of smart cities is the development of infrastructure, construction of road, and introduction of smart health. Without an efficient transport system, the concept of the smart city will not be fulfilled. Intelligent transport system (ITS) has been known as one of the primary building blocks for a smart city. Indeed, road infrastructures have been benefiting from ICT for a decade (Menouar et al. 2017). Although the advanced level of ITS has been deployed to update, the technology is continuously evolving. By the symmetry of continuous inventions, next-generation ITS technologies like smart health card, smart vehicles, either finished or about to complete toward largescale worldwide deployment. The concept of smart cities will be demolished if the citizens are reluctant to participate and involve in the construction of smart cities. However, by incorporating benefits, on the other hand, it also opens for security and

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privacy challenges in smart cities, along with the people living in these cities (Menouar et al. 2017; Braun et al. 2018). Maintaining user privacy and ensuring data security are one of the challenge tasks in smart cities, especially for those scenarios where the public is involved directly like the health system, transport system, communication system, and other critical systems. These challenges may include privacy preservation with high-dimensional data, securing a network with the large surface attack, establishing reliable data sharing practices, properly utilizing artificial intelligence, and mitigating failure cascading through the smart network (Braun et al. 2018).

Security and Privacy Challenges As a core concept, security is not absolute, but, in fact, it is dynamic, a basic and phenomenal method to prevent attacks on smart cities and its inhabitants. These can be directly or indirectly related to the citizen through digital or physical connections. So, security challenges will always be the most abundant opportunities for security risks in a smart city environment. Specifically, while taking privacy into account, Elmaghraby and Lasovio’s are the first two principles that help regarding the preservation of privacy and cyber privacy. These principles state as (1) “activities within the home have the greatest level of protection” and (2) “activities that extend outside of the home depend on reasonable expectations of privacy” (Elmaghraby and Losavio 2014).

Privacy Threats Due to the nature of the interconnectivity of smart cities, data will be manipulated throughout the processes, with multi-access among multiparties. This property makes the data open for the vulnerabilities. An attacker can get access from any point of entry into the system and get the most secret information of the citizen. Furthermore, since every stakeholder of the smart city have different priorities and will have exits gaps between intermediate and other stakeholder’s privacy standards. Privacy threats are prevalent in public sector organizations, such as hospitals and transportation authorities that provide essential services to citizens (Braun et al. 2018; Ijaz et al. 2016). Unlike the private sector, public authorities will have more scope to ensure the privacy protections as it should not be like its funding, and livelihood may be affected while achieving its goals. In smart cities, this gap in the protection of privacy will have a higher stake as compared to other cities. Roughly, the health system may involve public and private partnership while achieving the desired goals in terms of maintaining privacy and security (Khatoun 2017). While in public sector, hospitals may be on administer care and be under central decisionmaking authority so that the distribution of patient and medicines can be achieved efficiently through public and private partnership (van Zoonen 2016; Elmaghraby and Losavio 2014; Khokhar et al. 2016). In this way, the critical and sensitive

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information can be taken by the respective authorities for a decision regarding the transferring of patients, treatment schedule, and home address.

Privacy-Enhancing Technologies Indeed, cities are developing to become “smart cities,” where the applications suffer a severe concern regarding security and privacy of user’s data. In the paradigm of new information and networking, a smart city should have the following properties to maintain or to be declared as a smart city. These are the properties like information from unauthorized resources, disclosure, modification, inspection, disruption, and annihilation. The most common security properties that the smart city should have in order to provide secure information, communication, and physical world are confidentiality, integrity, non-repudiation, availability, scalability, access control, and privacy (Salas Mccluskey 1988). Besides all the general and basic concern, still smart cities are facing numbers of security challenges. Smart cities, on the one hand, collects sensitive data and information directly from lives of citizens and manipulates the collected data in respective scenarios and influence citizens accordingly. This unique characteristic of data opens many security loops.

Data Privacy in Data Sensing The data is processed after it is successfully collected and transmitted over the network; therefore, it creates loopholes for an attacker to inject vulnerabilities into the data to manipulate and misuse the data. This privacy concern in the smart city may be compromises of user’s identity, location, health reports in the healthcare system, lifestyle inferred from intelligence, smart energy, home, and society even in the community so on and so forth. It would be a very large security damage if such information can be stolen from the smart city system. To overcome and address this issue of privacy and security of data, some off-the-shelf security and privacy techniques can be applied. These techniques may include encryption, anonymity, and access control (Khokhar et al. 2016; Salas Mccluskey 1988). Martinez et al. have proposed a set of privacy and security concept for general privacy requirements for smart cities and their applications. This privacy may include the identity, query, location, and biometric prints like footprints. After that, the owner is identified and provides the basic idea to overcome the general problem. However, still, there exists some portion of private information leakage that can be treated as a strong concern in this era (Jones et al. 2015). Similarly, in smart cities, especially in the smart home, a surveillance camera is used to detect abnormal behavior or theft. This act of taking information from home may acquire secret information of smart home, and it is prejudicial to the privacy of home (Elmaghraby and Losavio 2014; Salas Mccluskey 1988). To overcome such intruders, many application or existing security and privacy protection measures are taken into account. However, the potential attackers like an agent, a security guard, and an employee of the smart home who can have

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access to the surveillance record or security record may take the private information and leave it to the attacker (Cherdantseva et al. 2016). Moreover, the data in a smart city are on the highly granular scale as it comes from diverse types like the privacy requirements which may differ for different types (de Bruijn and Janssen 2017). It is still a challenge to develop a phenomenal mechanism that can balance between the efficiency and privacy of smart cities.

Privacy and Availability The smart cities have comprehensive and remarkable benefits of using the cloud server to provide services to the citizen as well as data storage and information. It creates a security threat for smart cities due to the untrustworthy nature of cloud servers. In case, if the data is not protected and it is saved in plaintext into the cloud, then it can quickly reveal to many attackers especially if the cloud admin itself revealed the data (van Zoonen 2016; Braun et al. 2018). To overcome this problem, the other way to save user data is to encrypt the data and save it in the form of ciphertext so that server admin can see user data (Baig et al. 2017; Amin et al. 2014; Aldairi and Tawalbeh 2017). In this, the cloud server admin can see the encrypted data and cannot perform any kind of operation over encrypted data of applications of smart cities. Furthermore, the use of a fully homomorphic encryption scheme to protect the data in the cloud can improve the security of data in the cloud. However, on the other hand, this method also allows operation on encrypted data like summation and comparison. So, this also has opened a new way for researchers to dig it out more, and still it is a challenging work for researchers, especially in smart cities where there is already massive data. Similarly, data sharing and access control are also a challenging issues in smart cities, where the data is being shared to another point for a particular operation such as in healthcare, the patient data is shared with a doctor for analysis or in traffic data where the data is collected from a smartphone, a surveillance camera or GPS in a crowdsourcing way. For all over the globe, yet it is a challenging and security risk to define the common policy for data sharing and access control (van Zoonen 2016; Martinez-Balleste et al. 2013; Lacinák and Ristvej 2017). Data sharing and access control policy for homomorphic encryption are still open for research.

Conclusion With the increasing research, advancement in technology, and attempts toward the transition of cities into smart cities, the concept of smart cities is becoming more complex and ambiguous. To overcome this challenge and grasp the various characteristics of smart cities, this research organized the current literature from the perspectives of smart data framework, applications, and challenges. The increasing volumes of data being generated by the sensors network have been collected, processed, and organized in a variety of ways using deep learning techniques and

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smart network databases. The datasets generated form IoT, and ICT tools are used for routine and automatic sensing, which replaces the traditional approaches. However, the communication of user-rich and private data over public networks also leads to several challenges, i.e., privacy and security. The proper and secure use and analysis of this rich data collected by using advanced technologies, i.e., IoT, WSNs, VSNs, etc., lead to a wide range of services. The most prominent applications of using smart data include smart government and governance of smart cities, smart mobility, and smart communication. However, the change toward smart government is not easy as the present institutional, organizational, financial, and technical barriers make it difficult. From the perspective of social networks, SIoT is an emerging field as it connects and maintains billions of devices in IoT networks using principles of social networks. Similarly, several solutions have been proposed for smart mobility and transportation, which introduce mobility as a service in which a user can request a transport service provided by different companies. The introduction of autonomous vehicles is also revolutionizing the domain of smart mobility to a great extent. The open challenges faced by the current research on the provision of services in smart cities and data management are also highlighted. This study will help practitioners and researchers to grasp concepts, characteristics, and current state of literature for smart cities data and how emerging technologies are being used to manage such huge volumes of data in real time.

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Smart Institutions: Concept, Index, and Framework Conditions Hans Wiesmeth, Dennis Häckl, and Christopher Schrey

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Selected Literature on Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Institutions in the Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Working Definition of a Smart Institution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Case Study on University Hospitals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . General Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Review of the Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . An Index for Smart Institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Framework for Smart Institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Framework Conditions for UML and SSMU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Framework Conditions for UML in 2009 and 2017 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Other Framework Conditions of Relevance for a Smart Institution . . . . . . . . . . . . . . . . . . . . . . . . Smart Institutions in Various Sectors of the Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

For a city to turn into a smart city, suitable framework conditions have to enable and enhance the required creativity of the inhabitants. If new technologies are meant to establish fast connections between citizens, also in order to H. Wiesmeth (*) Graduate School of Economics and Management, Ural Federal University, Yekaterinburg, Russia Faculty of Business and Economics, TU Dresden, Dresden, Germany e-mail: [email protected] D. Häckl · C. Schrey WIG2 GmbH, Wissenschaftliches Institut für Gesundheitsökonomie und Gesundheitssystemforschung, Leipzig, Germany e-mail: [email protected]; [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_7

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strengthen channels for innovative activities, then smart institutions should operate under framework conditions, which integrate the employees to allow an optimal functioning of these institutions with respect to the societal goals of a smart city. This chapter introduces a concept and an indicator of smart institutions, focusing on research-oriented public institutions. These concepts are discussed by means of case studies involving institutions providing maximum medical supply and access to high-performance medicine. Framework conditions, raising the degree of smartness, are analyzed as well. Some remarks on smart government and smart institutions in other sectors of the economy are included as well.

Introduction The concept of a “smart city” has gained increasing attention over the last years. More and more cities from countries from all over the world consider themselves “smart” in one way or the other and use this seeming mega trend as a marketing device: the “Smart City Strategy Index 2019” (SCSI 2019) identifies 153 cities around the world with an official smart city strategy. A closer look, however, shows that there is no clear and unique definition. This is understandable, if one accepts that each city faces its own challenges on its way towards sustainability, a concept which is often closely related with a smart city (cf., e.g., Hollands 2008; Barrionuevo et al. 2012; Mori and Christodoulou 2012; Turcu 2013; MarsalLlacuna et al. 2015; Yigitcanlar and Lee 2014; Albino et al. 2015; Huston et al. 2015; Hajduk 2016; Ahvenniemi et al. 2017; Martin et al. 2018; Jones et al. 2019) and which is itself a concept depending substantially on various local and regional framework conditions (cf., e.g., Holman 2009; Turcu 2013 for the context considered here). In fact, according to SCSI 2019, smart cities “tend to have one thing in common – a sound strategic approach” (SCSI 2019, p. 6), and Jones et al. concede “. . . that smart cities are diverse in their planning, applications, and values” (Jones et al. 2019, p. 2). Such a situation inspires and requires reviews of the literature, and there exist quite a few of them on smart cities and related concepts. Albino et al. (2015) provide an interesting survey on the various definitions of a smart city, which have been proposed in the literature in recent years. Anthopoulos, among other issues, discusses smart city conceptual models (Anthopoulos 2017, p. 9f), and Neirotti et al. (2014) explore the “diffusion of smart city initiatives” in order to understand the role various variables have in planning a smart city, and Wilhelm and Ruhlandt (2018) mention in their literature review that research on smart cities lacks a systematic understanding of the different components of smart city governance. Thus, it seems that not too much has changed since Hollands (2008) complained about the sparse knowledge on smart cities. Interestingly, important concepts refer to, as already indicated, sustainability and connect the development towards a smart city in some sense to a sustainable development (Barrionuevo et al. 2012; Kourtit et al. 2012; Thuzar 2011).

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In view of this literature, applications of new and advanced technologies, in particular, information and communication technologies (ICT: cf. Glossary in Jones et al. 2019), to cities are still important for a smart city (cf. Frost and Sullivan 2019; Jones et al. 2019), but the concept itself seems to go far beyond technical and technological issues. It is understood that a smart city is obviously dependent on its inhabitants, in particular on their willingness to accept these new framework conditions, on their willingness to bring themselves in with their creativity, to contribute towards the goals of a smart city, a sustainable development, for example. This points then to the creativity of a city, possibly to the existence of a “creative class,” and then also, of course, to “enablers” (SCSI 2019, p. 7), to the framework conditions enabling and enhancing this required creativity, which is recognized as a key driver of a smart city (Albino et al. 2015; Komninos 2011; Thuzar 2011). It is here that the concept of a smart city touches or, maybe, even comprises the concept of creativity: implementing a smart city necessitates a climate for establishing a creative class (Florida 2005, 2012), without, however, disempowering and marginalizing other citizens (cf. Martin et al. 2018). There is, thus, an interesting constellation: according to quite a few core publications, the concept of a smart city refers first of all to sustainability, which is also the ultimate goal of a circular economy (Korhonen et al. 2018). Nevertheless, Martin et al. (2018) point to certain tensions between smart city visions and the goals of a sustainable development, and Ahvenniemi et al. (2017) study differences between smart and sustainable cities. Next, there is the relationship of a smart city to creativity – with a creative class considered necessary for preparing the path towards a smart city. In conclusion, a smart city, sustainability, a circular economy, and the existence of a creative class are – in the literature – to some extent considered related, dependent on each other, enhancing one another. The important question that remains is to get this process, the development towards a smart city, started. One should not expect that there is a natural evolvement of all relevant societal issues towards such an outstanding state of the society, also characterized by public goods, nor should one expect that the provision of advanced digital technologies alone will initiate a revolutionary development. No doubt, they are important, and they can surely accelerate the required development, but more needs to be done, in particular regarding fundamental changes in the mind-set of the people. Without support from a tangible part of a city’s inhabitants, the intended sustainable development risks to fail. In particular, in view of the important role of ICT for smart cities, privacy and security issues must not be forgotten (cf. Jones et al. 2019, p. 7). This issue will be briefly addressed in section “Smart Institutions in Various Sectors of the Economy.” In SCSI 2019, the role of the “stakeholders” is indicated pointing to “citizen acceptance” and “partnerships” (SCSI 2019, p. 7). A weight of 7.5% assigned to the stakeholders in the Smart City Development Index seems, however, little, given the importance of a support of a smart city strategy through the citizens. A recent study, conducted by ATG Access, found that 68% of UK respondents do not know what a smart city is or how the concept can benefit urban residents (cf. https://www. governmenteuropa.eu/smart-city-investment-report/92975/). This points to the

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necessity of a more careful integration of the citizens, including the companies and institutions. That’s exactly the point, where “smart institutions” come into the picture. If new technologies are meant to establish and enable fast and reliable connections between the inhabitants of a city, also in order to strengthen existing and open additional channels for innovative activities, institutions, or, rather, smart institutions, should operate under framework conditions, which integrate the employees to allow an optimal functioning of these institutions with respect to the societal goals of a smart city, perhaps including a sustainable development and a development towards a circular economy. Smart institutions, to be more precise, thus motivate employees and enable the creative class and top researchers in universities, companies, and other institutions, to fully employ their capabilities – for their own goals – but thereby also raising the welfare of the society. These remarks provide a first characterization of smart institutions and help to prepare the working definition in section “A Working Definition of a Smart Institution.” Accordingly, a smart institution should react upon changing framework conditions, thereby encouraging innovative activities in support of developing a smart city. Obviously, research-oriented public institutions with malleable framework conditions seem to be perfect candidates for smart institutions. These considerations point to a in this sense perfect integration of relevant stakeholders. Clearly, a smart city depends on its smart institutions, and, vice versa, smart institutions are dependent on a creative framework, which flourishes best in a smart city. With smart institutions, the chapter also addresses contexts, which are often related to topics discussed in the literature on smart cities, namely, the careful utilization of relevant pieces of infrastructure for an optimal provision of services to the people in the city (Hall 2000; Harrison et al. 2010; Kourtit and Nijkamp 2012; Mardacany 2014; Marsal-Llacuna et al. 2015; Kumar et al. 2018). Some authors also consider the “built environment” (cf. Glossary in Jones et al. 2019) as a foundation of the smart city infrastructure in order to facilitate connectivity and provide networking across the community (Mardacany 2014). This chapter attempts to go beyond the perception of a smart city as a high-tech intensive city connecting people and thereby promoting a sustainable development (cf., e.g., Bakıcı et al. 2012) or “to deliver the new services in an efficient, responsive and sustainable manner for a large population” (Kumar et al. 2018). As already indicated, other aspects or framework conditions, incorporated in the concept of a smart institution, in particular creativity, are of relevance “to leverage the collective intelligence of the city” (Harrison et al. 2010). This emphasizes once again the conclusion reached above: smart institutions, occupying the built environment, are important for smart cities, not to say that smart institutions and smart cities are dependent on each other. It is the aim of this chapter to provide a working definition of a smart institution; also to introduce an index, which allows a comparison between smart institutions operating in related fields; and to investigate framework conditions of relevance for institutions to this regard. As the concept of a smart institution will, for good reason,

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focus on research-oriented public institutions, the relevance of smart institutions in the private sector and the government will also be addressed. After all, making better use of the creative potential of an institution is an issue for all sectors of an economy. This then points to the “quadruple helix model” (Jones et al. 2019, p. 20, Glossary), extending the “triple helix model” of university-industry-government relationships (Etzkowitz and Leydesdorff 1995, 2000) by additionally including the citizens as stakeholders of relevance for the developing a smart city. The analysis makes in particular use of case studies involving university hospitals and university medicine in Leipzig, Germany, and in Tomsk, Russia, on the one hand, and university medicine in Leipzig in two different periods of time, on the other. These cases provide sufficient insight into the intrinsic nature of smart (public) institutions, in particular the role of identifying and adequately integrating important stakeholders. The chapter is structured as follows: a short methodology section helps to explain the scientific procedure adopted in this chapter. The review of the literature in the next section turns first to publications considering certain aspects of smart cities, which are closely related to the concept of a smart institution adopted here. This review points to some characteristics, peculiarities, and framework conditions attributed to and associated with smart institutions. The next subsection addresses the literature on smart institutions, which is still manageable, probably due to the stronger focus on infrastructure in the literature in the context of smart cities. This stage introduces also some literature on stakeholder integration, which proves to be decisive for the concept of a smart city and which then allows, together with the other aspects collected, a working definition of a smart institution. A case study involving the medical universities in Leipzig and Tomsk mentioned above, demonstrates how different framework conditions can affect certain output parameters of such institutions, can affect the “smartness” of such institutions. These examples help to clarify the concept and to introduce an index of a smart (public and research-oriented) institution. This index is related to various existing indices in the context of smart cities, and its properties are briefly analyzed. By means of this index, it is possible to compare institutions, in particular those operating in the same field – as the university hospitals in Leipzig and Tomsk, for example. In a first approach, these differences will be related to differences in certain framework conditions – to ones which are exogenously given and to ones which are endogenously and which can be affected internally through the management of the institution or externally through public policy. Differences in innovation strategies of the participating countries will thereby play a role, revealing varying degrees of stakeholder integration. This latter aspect points to the relevance of the public administration in this context and, in particular, to “smart government.” Another case study referring to the university medicine in Leipzig will provide a more detailed insight into the relevance of certain endogenous framework conditions by analyzing the value of the index for two different periods of time: 2009 and 2017. This investigation demonstrates important steps of this institution on its way towards a smart institution. Moreover, it also points to possibilities for modifications of the internal framework conditions and the public policy to accelerate the development

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towards a smart institution. Again, the issue of choosing framework conditions to appropriately integrating interesting stakeholders to enable the creative potential of an institution will dominate the analysis. Thereafter smart institutions in other sectors will be briefly investigated. Also, the question of alternative indices for smart institutions, especially rankings, will be briefly addressed. Some final remarks conclude the chapter.

Methodology The extensive review of the relevant literature in the next section will motivate a working definition of the concept of a smart institution. According to the classifications of case study research designs, this design can be classified “no theory first” (Ridder 2017, p. 286), to capture the richness of observations without being limited by a theory. This research design will also play a role with respect to the introduction and definition of an index for smart institutions. Again, relevant properties for comparable indices are sampled from the literature and then combined with insight gained from the case studies, one of them taken from Wiesmeth et al. (2018). These case studies result from projects analyzing economic effects of the Leipzig University Medicine (UML) carried out by the authors in 2011 and 2019 with data from 2009 and 2017. The first case study helps to explain relevant features of these concepts. As already mentioned, the second case study involves only UML – with data from different years in order to investigate the changes from one period to the other. The research designs of these concrete case studies can be classified “social construction of reality,” with the case being itself of interest, not theory-building (Ridder 2017, p. 288). The formal methodology used directly in the case studies is characterized by an incidence analysis and specifies the Keynesian multiplier analysis in order to provide a framework for discovering and quantifying several regional economic effects. The quantitative analysis shows the importance of these institutions for regional economic development. Differences regarding the size of the various multipliers result from differences in relevant framework conditions, thus providing room for policy implications to be investigated thereafter.

Literature Review There is, for sure, a vast literature on smart cities, which will, for the purposes of this chapter, only selectively be reviewed in the following subsection. The focus is thereby on the role of certain components of smart cities, such as the physical infrastructure in its relation to the human capital, pointing to some aspects of smart institutions, in particular the requirement of an adequate integration of the population. The literature on indices measuring and aggregating various dimensions of smart cities will only be touched briefly.

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The literature on smart institutions and related concepts, including stakeholder integration, will be reviewed thereafter. It will also prove interesting to connect the concept of a smart institution with the “theory of institutions,” originating already in the 1960s.

Selected Literature on Smart Cities As already indicated, Hollands (2008), Nam and Pardo (2011), Neirotti et al. (2014), Albino et al. (2015), Hajduk (2016), Anthopoulos (2017), Fernandez-Anez et al. (2018), Yigitcanlar et al. (2018), and Jones et al. (2019), among others, provide extensive surveys on concepts or discuss relevant aspects of a smart city. Whereas Hall (2000) aims “to provide a preliminary critical polemic against some of the more rhetorical aspects of cities labelled as smart,” Nam and Pardo (2011) identify “a set of the common multidimensional components underlying the smart city concept.” Neirotti et al. (2014) strive to develop an integrated definition of the concept and explore “the diffusion of smart initiatives” by means of an empirical study, and Albino et al. (2015) aim to clarify the meaning of “smart” in the context of cities “through an approach based on an in-depth literature review.” Hajduk (2016) addresses urban planning as a crucial factor in urban management and refers to “adequate intellectual resources and proper institutions as well as developed infrastructure” as relevant for smart cities. Fernandez-Anez et al. (2018) turn to the issue of implementing a smart city, thereby addressing the importance of governance and stakeholders “in developing smart city initiatives and their capacity to face urban challenges.” Finally, Yigitcanlar et al. (2018) propose “identifying and linking the key drivers” for a smart city, thereby focusing on the literature aimed at a conceptual development and providing an empirical base. On the other hand, the literature referring to the role of institutions in the context of smart cities in particular is not very extensive. There is, of course, as already indicated, a vast literature emphasizing the role of the infrastructure and the role of the people, pointing to the necessity of connecting people, communities, and the industry (cf., e.g., Nam and Pardo 2011; Kourtit and Nijkamp 2012; Hajduk 2016). In particular Hajduk mentions trends of smart city initiatives referring to a different focus, also with regard to the physical infrastructure or the creative human capital: there is the “digital city,” the “green city,” and the “knowledge city” (cf. Hajduk 2016, Table 1). A smart city “exploits ICT to optimize the performance and effectiveness of serviceable and needful city processes, activities and services typically by joining up diverse components and actors into a more or less seamlessly interactive intelligent system” (Hajduk 2016, p. 37). There are various publications introducing, analyzing, and applying indices of smart cities. The approach of Holman (2009) is of particular interest in this context: the incorporation of governance into indicators in order to “aid the evaluation of policy.” Mori and Christodoulou (2012) discuss conceptual requirements for a “City Sustainability Index,” and also Turcu (2013) and Tran (2016) address various aspects of sustainability indicators. Lützkendorf and Balouktsi (2017) discuss

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“flexible indicator systems supporting the process of sustainable development,” whereas in SCSI 2019, an assessment of smart city initiatives is provided, based on 3 smart city dimensions, 12 criteria, and 31 sub-criteria. The resulting “Smart City Strategy Index (SCSI)” is then applied to reveal the leading positions of Vienna, Austria (cf. also Fernandez-Anez et al. 2018), London, the UK, and St. Albert, Canada. In the context of introducing a simple but nevertheless appropriate index of a smart institution, properties of these indicators and indices will be reconsidered. SCSI 2019 mentions 153 cities around the world, which “have published an official Smart City strategy.” However, out of these 153 cities, only 15 “have plans that demonstrate a comprehensive strategic approach,” and only 8 of these 15 “are at an advanced stage of implementation” (SCSI 2019, p. 2). This points to the decisive challenge of implementing a smart city. The crucial issue in this context seems to be to motivate citizens to accept the new environment, in particular the technological changes associated with a smart city, and to respond to changes in the environment. Again, the careful integration of the citizens, the integration of the relevant stakeholders, is necessary to leverage the creative potential. ICT can, of course, help to achieve this goal. There is a vast literature on stakeholder theory and managing stakeholders, originating with foundational publications of Freeman (1994) and others. Since then, stakeholder engagement has been addressed in a multitude of papers covering a broad range of areas. Interestingly, sustainability issues as well as issues of corporate social responsibility are often brought together with stakeholder engagement. Thus, Amor-Esteban et al. (2018) provide useful information for stakeholder engagement by proposing an industrial corporate social responsibility practices index. Reed et al. (2018) investigate the challenges associated with diverse and conflicting stakeholder and public priorities in environmental management, and Quan et al., while addressing a firm’s sustainable development, investigate the role of the government as the most influential stakeholder (Quan et al. 2018, p. 148). Moreover, Wiesmeth (2018) investigates stakeholder engagement and stakeholder integration in the context of environmental innovations, which are also of relevance for a smart city (cf., e.g., SCSI 2019, p. 7). In conclusion of this review of part of the literature on smart cities, what seems to be missing is a more extensive study of issues pertaining to the implementation of a smart city. In particular, the question arises to what extent the theory of stakeholder integration, originating and emerging in the management literature, could be of use in this context and, thus, also in the context of smart institutions. So far, in view of the literature, an analysis of framework conditions, which allow, for example, an efficient interaction of the physical infrastructure with the people working in it and with it, is not yet available. Such an analysis would add importance to the role infrastructure plays in a smart city. As smart institutions are considered an integral part of a smart city, implementing smart institutions is consequently of relevance to this regard and seems to be one of the first steps towards implementing a smart city. In addition, it allows a somewhat separate investigation of a critical part of a smart city, beyond the technical infrastructure, and the framework conditions supporting its operations within a smart city.

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Smart Institutions in the Literature As already indicated, there are only a few publications so far, referring directly to the concept of a “smart institution.” There are, however, various papers addressing smart institutions predominantly in a technical, less research-oriented context. Moreover, the review will also be extended to cover some aspects of the “theory of institutions” originating with the seminal publications of Coase (1937, 1960). To begin with some technical applications, Murphy et al. (2000) discuss the design of “smart” water market institutions, in order to integrate local climatic conditions into water markets. The idea thereby is to evaluate “proposed institutional changes to help facilitate a more rapid and smooth adoption of changes in the water system.” Similarly, the concept of a “smart” home is usually associated with the goal to “lead us to water and waste management, green building, safe and healthy living environment” (Gosh 2018). Moreover, Sai Sachin et al. (2018) identify “smart institutions,” which make use of a sophisticated energy management system. Their analysis refers to higher-educational institutions, which can keep the rising electricity bills in limits “by proper management of electricity distribution among various sections in the campus.” With his paper on “Smart Libraries,” Schöpfel (2018) connects smart cities with libraries. “How does the smart city impact the libraries as cultural and scientific assets? and how can libraries contribute to the development of the smart city?” are some of the questions addressed in his paper. The resulting concept of a smart library can then be characterized by smart services, smart people, smart place, and smart governance – with a particular emphasis on smart governance. Regarding these publications, in particular those of Gosh (2018), Sai Sachin et al. (2018), and Schöpfel (2018), it is important to understand the necessary and thoughtful integration of a physical infrastructure, even a building, and the people, associated with this infrastructure, using this infrastructure in one way or the other. Changes in the infrastructure, in the home, and in the library, for example, are meant to induce behavioral changes, of importance also for the implementation of a smart city. Referring again to Murphy et al. (2000), they explicitly stress a somewhat different context: in their case, institutional changes, not only changes in the physical infrastructure, should help to achieve a certain goal, of relevance first of all for water markets, but also for the design of markets for other environmental commodities. Especially this aspect is of interest with respect to the approach regarding smart institutions adopted here: institutional changes or policy changes should motivate and bring about behavioral changes supporting the development of a smart institution and a smart city. It is important to note that this literature brings stakeholder integration, addressed already above, clearer into the picture. Thus, as a first approach towards a definition, a smart institution should allow for framework conditions, which integrate relevant stakeholders in order to make optimal use of the existing physical and organizational infrastructure to the benefit of society. As already indicated, for a public institution, this refers to the “quadruple helix model” accentuated in Jones et al. (2019).

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It remains to clarify what is understood by “optimal use” of the given physical and organizational infrastructure. This issue will be addressed after the following review of another thread of literature on institutions and smart institutions. Additional and valuable input to these thoughts is provided through the extensive literature on “institutions,” originating with the seminal work of Coase (1937, 1960; cf. also Medema 1995), initiating with his ideas on the “nature of the firm,” on “property rights,” on “transaction costs,” and on various other issues on a broad theory of institutions, which has since then attracted and continues to attract many researchers. Consequently, a vast literature has helped to investigate and explain aspects of the economic role of institutions. This literature includes the well-known contributions of North (1990), Williamson (2009), and Ostrom (2010), among many others. According to North (1990), “institutions affect the performance of economies,” and institutions also “affect the differential performance of economies over time.” Williamson (2009) investigates the role of transaction costs in the context of institutions, and Ostrom (2010) argues that “a core goal of public policy should be to facilitate the development of institutions that bring out the best in humans.” Although investigating economic effects of certain institutions, the chapter does not directly address the issues of transaction costs, property rights, etc., which assume an important role in the literature on institutions. This contribution therefore continues in this framework with more or less straightforward economic effects of (smart) institutions. Nevertheless, the quote above taken from Ostrom (2010) characterizes a smart institution in the context of this approach quite well. Interestingly, Goorha and Mohan (2016) present an analytical approach regarding institutions, which “draws inspiration from control process engineering in the physical sciences.” In this context, they come up with the following characterization of “smart institutions,” which makes them different from “traditional” or “generic” institutions: they are assumed to be context sensitive, they are forward-looking in their operation, and they emphasize the role of information. Therefore, smart institutions “can be considered to operate in a closed control loop,” and they “envisage the management of smart institutions to involve feedback . . . to be drawn from members of the society . . .” (Goorha and Mohan 2016, p. 3f). This interpretation of a smart institution stresses in particular the importance of a feedback: a smart institution reacts in a certain way to changes in its environments and tries to adapt to new or changing framework conditions. Thus, in view of this literature, a smart institution is considered a living organism, which can be adjusted to respond optimally to changes in its environment or which reacts optimally upon changes in its environment. The questions that remain have to be asked with respect to the initiator of the changes or with respect to directions of the changes: where do the changes come from, respectively, what is the direction of the changes, and what does “optimal” mean in this context? These questions are, for example, addressed in Wiesmeth et al. (2018) for the case of research-oriented public institutions, for university hospitals operating in different countries under obviously different framework conditions. The focus is then on

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identifying the role of the varying framework conditions with respect to the general economic performance of these hospitals. Thereafter, changes in some of these conditions could be applied to improve this performance, of course. The above considerations lead, in the following subsection, to the working definition of a smart institution derived from and based on various aspects in the literature, thus mirroring the case study research design “no theory first.”

A Working Definition of a Smart Institution Wiesmeth et al. (2018) characterize smart institutions in a particular context, based on the results of a case study involving two university hospitals, one in Germany and one in Russia, as already indicated. In order to arrive at a working definition for a smart institution, the procedure in this case study is combined with various proposals provided in the literature reviewed above. From the literature on institutions, the basic view that “institutions affect the performance of economies” (North 1990) is important. Institutions are therefore of utmost importance for economies, in particular for highly developed economic systems. Part of the literature on smart cities addresses the role of the physical infrastructure in order to facilitate connectivity and provide networking across the community (Mardacany 2014) or “to deliver the new services in an efficient, responsive and sustainable manner for a large population” (Kumar et al. 2018). Turned somewhat differently, these opinions point to the integration of stakeholders by means of the built environment, thus opening a gate towards the literature on stakeholder engagement and stakeholder integration (cf., e.g., Wiesmeth 2018): for an optimal performance, a smart institution takes care of the adequate integration of the relevant groups of stakeholders. Moreover, respecting the societal goal “to leverage the collective intelligence of the city” (Harrison et al. 2010) means also stressing the role of creativity for a smart institution and the development of a smart city (Florida 2012). Goorha and Mohan (2016) point out that smart institutions are context-sensitive, forward-looking in their operation, emphasizing the role of information. Finally, Ostrom (2010) emphasizes that “a core goal of public policy should be to facilitate the development of institutions that bring out the best in humans.” Accordingly, these hints from the literature propose to introduce a working concept of a smart institution in the following way, stressing once again the relationship between a smart institution and a smart city: Definition: Facilitated through appropriate internal and external framework conditions, a smart institution adequately integrates relevant stakeholders to leverage the collective intelligence of the institution to bring out optimal results for society in all possible dimensions. Employment opportunities resulting directly or indirectly from the operations of an institution are an indicator of these benefits. After all, they point to the provision of goods and services for which there is demand in the society.

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Observe that this definition does not question the existence of a particular institution, nor does it refer to any plans establishing a particular institution. Rather, the definition focusses on an existing institution, investigating its operations and the framework conditions, which affect these operations. In particular, this definition postulates the context-sensitive development of an institution “to bring out the best in humans” (Ostrom 2010), to function optimally, by carefully adjusting the framework conditions. “Optimality” then refers to additional employment opportunities resulting from the operations of the institution. And, in view of the introductory remarks and the literature review, the definition seems to point to public institutions with research activities. The following sections and subsections discuss relevant aspects of this definition. In particular, the case study in Wiesmeth et al. (2018) will first of all help to explain the concept of a smart institution in more detail and then allow to introduce an index characterizing the level of “smartness” of an institution. Moreover, by means of this case study, it will be possible to at least partially reveal the effects of relevant and changing framework conditions, thereby also illustrating the role of the public policy – pointing to “smart government.” The relevance of smart institutions in other sectors of the economy beyond research-oriented public institutions will be discussed in section “Smart Institutions in Various Sectors of the Economy.”

A Case Study on University Hospitals This section discusses an example of research-oriented public institutions, which any major city must have: a hospital offering a maximum supply and access to highperformance medicine. With such an institution, the question arises whether existing framework conditions guarantee an optimal performance – not only regarding medical practices but also regarding research activities with further economic effects. The ensuing question is then whether modified framework conditions, resulting from public policy or managerial efforts, can gradually turn these institutions into smart institutions – given the above definition.

General Considerations Scientific institutions, such as university hospitals or, more generally, public institutions comprising the university medicine, can have significant impacts on the development and growth of regions. These impacts include economic and social impacts ranging from the offer of employments and trainee positions to the economy’s supply side with a qualified labor force, the provision of information and transfer of knowledge and technology, as well as cultural opportunities. This holds in particular for university hospitals with their wealth of different disciplines extending into other academic fields and the potential of attracting additional research institutes for intense collaboration and, in the end, for additional

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jobs. Considering this situation leads immediately to the question, how to make best or “smart” use of an institution, such as a university hospital, that a larger city needs anyway? In view of the definition provided above, the term “smart” refers to the benefits this institution is generating for society, measured through additional employment. However, before it is possible to provide a more detailed answer to this question, the various potential economic effects of such an institution on society have to be classified. There are, first of all, the so-called demand effects, pointing to resources the institution consumes, because it employs medical and administrative personnel, because it teaches and trains medical students, because it needs a large variety of medical supplies, and because it constantly needs to repair equipment and buildings or invest in new ones. At a first glance, the so-called supply effects seem to be more difficult to analyze. They refer in particular to the attractiveness of the institution – due to its research activities, or due to the quality of the students leaving the institution with an academic degree, and to other public or private research institutes settling in the neighborhood of the university hospital (“knowledge spillover”). However, most of these supply effects, for sure those which refer to the collaboration with other research institutes or other organizations or to third-party funded research projects, generate again demand effects through investments into the infrastructure or consumption activities of the employees. The analysis focuses therefore on the demand effects at large including those originating from these supply effects. By comparing these effects for university hospitals in different regions or countries, it is possible to get some insight into the framework conditions, which are of relevance for strong supply and, thus, additional demand effects. In this context, the required conditions for an “optimal” regional impact with outstanding innovation activities and substantial employment effects have to be investigated. This should motivate efforts or changes in the relevant framework conditions, allowing “smart” use of these university hospitals.

A Review of the Case Study The following example, taken from Wiesmeth et al. (2018), allows a more detailed view on the working concept of a smart institution. The analysis investigates the university hospitals or, rather, the university medicine, in Leipzig (UML) in Germany, and the Siberian State Medical University (SSMU) in Tomsk, Russia. Both institutions offer maximum medical supply and access to high-performance medicine and have a long history as research institutions. UML is larger in terms of the number of employees and the number of students; however SSMU serves a much larger area than UML. Moreover, these areas are different regarding climatic and geographic conditions and regarding the density of the population. In addition to that, access to these medical institutions is regulated differently in the two countries – with the possibilities offered by public and private health insurances playing an important role.

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Following the multiplier approach briefly discussed in the methodology section, a special impact model is adopted – an incidence analysis, which observes the flows of expenditures and their distributing impacts and which helps to determine direct income, consumption, and employment effects. As explained above, the model will be applied to study the demand effects at large of UML in Germany and SSMU in Russia – with data collected in close contact with the management of the hospitals. The respective institution is considered a consumer of various inputs in the model. These inputs consist of the expenses for construction, material, personnel, etc. within 1 year. The goal is then the analysis of the regional economic impact of these expenses. The calculated demand effects can be divided into direct, indirect, and induced effects. Although there are no consistent definitions in the literature, direct effects usually refer to primary income, consumption, and employment effects originating from the analyzed institution. Indirect effects arise from the university’s material and construction expenditures. Moreover, in order to capture the demand effects at large, the term is meant to comprise demand effects of people employed through third-party funds, in outsourced institutions, in research centers, and in spinoffs as well as students and visitors of medical fairs. Induced effects, for example, in form of increasing employment, result from this people’s consumption expenditures and the related demand of goods and services. Income effects describe direct, indirect, and induced incomes resulting from the existence of the analyzed institution. Consumption effects describe this people’s consumption expenditures separated into different sectors (cf. Wiesmeth et al. 2018 for further details). In the end, indirect and induced effects result again in demand effects, and direct, indirect, and induced effects comprise total demand effects, associated with this institution. In order to focus on the aspects of relevance for a smart institution, this analysis considers in particular “employment effects” that are again composed of direct employees (university hospital staff), indirect employees (university hospital staff paid by third-party funds, staff in supply firms, as well as staff in research centers and spin-offs), and induced employees, the latter being employed because of the staff’s consumption expenditures (cf. again Wiesmeth et al. 2018 for more details). The results based on total demand effects show that UML reveals an employment multiplier of 1.93, and SSMU of 1.56, implying that each full-time position in the hospitals leads approximately to an additional full-time position in the vicinity of UML and to an additional half-time position in the vicinity of SSMU. A more careful analysis shows that UML succeeds in attracting more additional research institutions, although SSMU supports more employees in the supplier industries. These core results are presented in Figs. 1 and 2 (cf. again Wiesmeth et al. 2018 for more details regarding this case study, in particular regarding the calculation of the multiplier). Summarizing, the analysis of these example institutions points to significant differences regarding supply effects and demand effects originating from and associated with these university hospitals. Thus, in view of the introductory remarks to this chapter, it should be the concern of “smart” cities to make “smart” use of their institutions, such as university hospitals, but also other institutions, which are

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Fig. 1 Employment multiplier and further results of the case study for UML. (Source: Own drawing after Wiesmeth et al. 2018)

Fig. 2 Employment multiplier and further results of the case study for SSMU. (Source: Own drawing after Wiesmeth et al. 2018)

dependent on modifiable framework conditions to leverage the creative potential. This is, by the way, the motivation for the particular case study approach adopted in Wiesmeth et al. (2018). Of course, a more detailed analysis regarding the reasons for the observed differences is required to provide further insight into the relevant framework conditions.

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Before the following short analysis of the framework conditions, an indicator for a smart institution is proposed. In the sense of the “no theory first” research design, this working definition is based on the results of the above case study, but also on various proposals in the literature. Thus, this index is applicable to other researchoriented public institutions. It mainly allows a comparison of institutions operating in more or less identical fields. The question of an appropriate indicator for smart institutions in other sectors will be addressed in section “Smart Institutions in Various Sectors of the Economy.”

An Index for Smart Institutions Mori and Christodoulou (2012) discuss “conceptual requirements” for a City Sustainability Index (CSI), thereby reviewing some existing indicators in this context. They refer in particular to the – regarding sustainability – necessary consideration of environmental, economic, and social aspects, of external impacts on areas beyond the city, and of applicability of the index both in developed and developing countries. Other contributions, just to mention two of them, propose a method for selecting a set of sustainable development indicators (Tran 2016) or explore “flexible” indicator systems for a sustainable development in cities (Lützkendorf and Balouktsi 2017). This literature on indicators for a sustainable development is certainly of relevance for smart cities in view of their sustainability goals. For this reason, these indicators might also be considered for this case of smart institutions. However, taking into account the somewhat smaller range of a smart institution and its specific tasks, a simpler index or indicator might do as well. As the “optimal” functioning of such an institution is of interest, the employment multiplier presented in the last section will be used as an indicator characterizing a smart institution. This implies, however, that this index is to some extent dependent on the context of public research-oriented institutions with a certain number of employees financed by the public administration of the city, the region, or the country. It remains to be seen how far this definition can be extended to include other smart institutions (cf. section “Smart Institutions in Various Sectors of the Economy”). Definition: The employment multiplier derived for a certain research-oriented (public) institution indicates in particular to what extent this institution succeeds – under the given framework conditions – to generate additional employment opportunities in the institution, in research centers and spin-offs, and in the supplier industries. This indicator is characterized by the following properties: first of all, although it is not really possible to use this index to compare fundamentally different organizations – the employment multipliers may fundamentally differ across economic branches – it allows the comparison of institutions, which provide similar services

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or goods. This is the result of the case analyzed in the last section: obviously different exogenous and endogenous framework conditions lead to different levels of the employment multiplier for UML in Leipzig and SSMU in Tomsk. Moreover, this index clearly focuses on economic aspects, and environmental and social aspects might, however, be indirectly included: if the activities of an institution spill over to environmental organizations, which profit from a corresponding collaboration and react with additional jobs, then this environmental aspect is also mirrored in the index. UML, for example, collaborates with the Helmholtz Centre for Environmental Research – UFZ (cf. also section “Framework for Smart Institutions” for more details). A similar consideration holds for social aspects: as soon as spillovers of an institution to the social agenda lead to jobs, they are “captured” by the index. In the context of the university hospitals, a cooperation with special care homes in the vicinity could yield such a result, for example. Also, the quality of services, for example, medical services, provided by an institution, is mirrored in this indicator: excellent services tend to attract thirdparty funds from the industry or invite research centers for collaboration – with further employment opportunities. Another issue addresses the regional impact, which is of relevance for the size of the index. The definition of this index refers, of course, to a certain region. In the case of the university hospitals investigated in the last section, the regions under study were the Free State of Saxony for UML and the Region of Tomsk for SSMU. However, different geographic delimitations of the areas under study are possible, to the greater city areas, for example, or to Central Germany instead of the Free State of Saxony, in order to better understand the economic importance of an institution for a city or the extended region. And, finally, the index can be applied to similar institutions both in the developed and the developing world, thereby possibly allowing interesting insights into the dependence of the index on the level of economic development. Once again, the results of the last section point exactly to such a situation: the differences regarding the employment multiplier for UML and SSMU are, likely, also a consequence of the differences in economic welfare – despite the fact that both hospitals provide maximum medical supply and access to high-performance medicine. This aspect deserves indeed some further analysis and is left for future research. In summary, this index is certainly not an index measuring sustainability of a (smart) city with all its pillars. Despite of its simple structure, it nevertheless allows interesting insights into the “smartness” of a public institution. It also allows, at least in principle, an investigation of the level of smartness depending on the relevant framework conditions. In this context, for example, it would certainly be interesting to understand in more detail the economic background of the different levels of the index for UML and SSMU discussed in the last section. It is also important that this index is incorporated into structures of local governance and that it therefore helps “to aid the evaluation of policy” (Holman 2009). A thorough comparison of the indices for various institutions enables policy makers internal and external to the institutions to adjust or redirect framework conditions –

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thereby attempting to raise the level of smartness of an institution. Consequently, this index allows a feedback loop in the sense of Goorha and Mohan (2016). This aspect will now be investigated in the following section. First, relevant framework conditions will be analyzed briefly in the context of the above case study, before a more recent case study with data for UML from 2017 will be investigated.

Framework for Smart Institutions In this section the effect of relevant framework conditions on the smartness of an institution measured by the index, the employment multiplier, will be investigated. The ultimate goal of this analysis is to understand these effects, also their strength, and their dependence on other economic variables, for example, GDP per capita. Some of these framework conditions are exogenously given, such as the density of the population; others, such as structural properties of the system of higher education, or the level of innovation activities in research institutions and companies, can, however, be modified, at least in the long run. This provides policy makers with some tools to raise the level of smartness of certain institutions and thereby that of the (smart) city in consideration. The following analysis first returns to the university hospitals UML in Leipzig, Germany, and SSMU in Tomsk, Russia, in order to gain some preliminary insight into this important context. These considerations help to learn more about how to make best use of this index. Thereafter, based on this additional information, the indices for UML for 2 different years, 2009 and 2017, will be investigated and compared, and relevant changes analyzed. The influence of completely exogenous factors, such as geography, demography, or modalities of access to the institutions, can thereby be neglected. Consequently, the focus remains on aspects of stakeholder integration by means of appropriately adjusting framework conditions.

Framework Conditions for UML and SSMU The “smartness” index reveals substantially different values for UML and SSMU. What are possible reasons? Which framework effects are particularly strong and induce these differences? Of course, owing to the high loss of information, the employment multiplier is only partially applicable as measure to evaluate the economic effects of an institution. In order to extract some additional information, in order to get a feeling for the differences regarding the values for the two institutions, it is meaningful to consider some aspects, which play a role in calculating the indices. This applies, for example, to characteristics of the affiliated research institutions, to the third-party funded research contracts, and to the service providers. In the analysis, the employment multipliers turn out 1.93 for UML and 1.56 for SSMU. The higher employment multiplier for UML likely points to a higher attractiveness of UML for public and private research institutions outside the

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university, for example, for the establishment of the Leipzig Heart Center. Although SSMU is responsible for a significantly higher number of indirect employees in the supplier industry due to large expenses for material and investments, the total number of indirect employments is lower. By additionally considering the thirdparty funded positions, UML is responsible for a larger share of indirect employment. Regarding induced employment, the support of the Leipzig Trade Fair through organizing medical conferences and congresses has to be mentioned. All effects together then yield a higher employment multiplier for UML (cf. Fig. 1 and Wiesmeth et al. 2018). Therefore, the question arises: what makes UML comparatively more attractive to third-party research funding and to outside research centers and spin-offs? Possible answers to this fundamental question could perhaps first be found in geographic differences: the index for SSMU is lower, although the area under study, the Region of Tomsk, is substantially larger, with a much lower density of the population in comparison to the situation of relevance for UML. However, as the economic effects of UML are restricted to the Free State of Saxony, considering a larger region, Central Germany, for example, would rather raise the employment multiplier of UML. Other potential framework conditions inducing this result could refer, for example, to the number of people treated in these hospitals in relation to the populations in the areas under study. However, one should expect that due to the fact that both hospitals are offering maximum medical supply, differences regarding these numbers should not have much consequences for the attractiveness to collaboration and outside research interests. In view of Ostrom (2010), there thus remains an analysis of the relevant framework conditions, which are meant to foster innovations in the medical sector, which, in general, “facilitate the development of institutions that bring out the best in humans.” A brief glance at the framework conditions, which affect supply of innovative commodities in the medical sector in Germany and Russia, will reveal some crucial differences between Germany and Russia. They are briefly documented in the following subsections, again pointing to the issue of a “smart government.”

Public Policy Supporting Innovations in Healthcare in Germany With its “High-Tech Strategy” (HTS), the German government wants to focus “research and innovation policy activities on certain priority task areas and key topics of relevance to society” (BMBF 2018, p. 11). The goal thereby “is not merely to generate technological innovations, but also to set processes of social change in motion, at the same time developing and spreading service innovations and social innovations” (BMBF 2018, p. 13). “Healthy living” is one of the current priority tasks. The government considers personalized medicine and digital networking as key drivers of progress in patient care, with the digitalization of healthcare constituting “one of the greatest challenges facing the healthcare sector in the years ahead” (BMBF 2018, p. 18). The telematics infrastructure shall be extended, and nursing care shall be updated to make more efficient use of information and communication

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technologies. To accelerate deployment of new knowledge and innovative products, scientists from university and non-university research facilities shall collaborate with other research institutions in the area of healthcare. Moreover, a specialized program on medical technology shall help the highly SME-based sector developing viable innovations for the benefit of patients. Thus, this policy of the federal government is certainly meant to further develop institutions such as university hospitals in this sense of smart institutions. The main goal of these activities is to integrate all relevant stakeholders in an appropriate way: researchers at universities and in other research institutions, the industry, and, in particular, the SMEs focusing on innovations in medical technology. This, again, is in agreement with various recommendations to governments and city planners on how to develop a smart city: think integrated, and involve all stakeholders (cf., e.g., SCSI 2019, p. 16). As already indicated, this allows to talk about “smart government” as a facilitator through adjustments of the regulatory framework (Gil-Garcia and Aldama-Nalda 2013). BMBF (2018) considers this approach as a means to attract “brilliant minds” and “creative thinkers” and “to open up new creative forms of collaboration to spur the transfer of ideas into innovations and the implementation of research findings into applications” (BMBF 2018, p. 1). It remains to be seen whether this approach is visible in the development of UML between the years 2009 and 2017.

Public Policy Supporting Innovations in Healthcare in Russia The percentage distribution of gross domestic expenditure on R&D by sector of performance in 2014 shows that in Russia the government sector accounts for 30.5%, in comparison to 15.1% in Germany. The business sector in Russia is with 59.6% not too far below that of Germany with 66.9%. A larger difference results for the higher education sector with 9.8% in Russia compared to 18% in Germany (Russia 2016, p. 254f). In addition, there are various large-scale research institutes such as ROSNANO, ROSATOM, and the Federal Space Agency in Russia, which receive a substantial part of the federal research funding (http://minfin.ru/ru/budget/federal_budget/index. php). These data point to one essential difference between the public research policies of Russia and Germany: universities with their large number of researchers play a more important role in Germany than in Russia. Moreover, SMEs seem to be better integrated into the research activities in Germany, which results, of course, also from the – due to historical reasons – larger share of SMEs in Germany. Therefore, in view of the important aspect of stakeholder integration, relevant stakeholders, in particular researchers at the universities and SMEs, are of higher relevance in innovation policies in Germany. In conclusion, this stronger “integrating” policy in Germany might help to explain the significant number of research contracts and collaborations of UML, in addition to the research proximity of the Leipzig Heart Center. The following section explores in more detail the development of UML on its way towards a smart institution.

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Framework Conditions for UML in 2009 and 2017 The comparable analysis of UML with data from 2017 leads to the following main results graphically represented in Fig. 3 (corresponding to Fig. 1) and Fig. 4. First of all, Fig. 4 reveals with 2.18 a significantly higher employment multiplier for UML in 2017 in comparison to 1.93 in 2009. A closer inspection of the results for the 2 years shows, in particular, that there is, first of all, an increase in the number of direct employees between 2009 and 2017. This larger number of direct employees in 2017 is associated with an even stronger increase regarding the numbers of indirect employees. Accordingly, UML continued to attract additional research activities – first through a larger number of third-party funded research projects. Also, various research centers in the vicinity of UML responded with an increase in the number of employees. This is true for the Leipzig Heart Center, in particular. In addition to that, the number of medical conferences and congresses at the Leipzig Trade Fair has increased substantially between 2009 and 2017, raising consumption effects, thereby inducing further employment effects. What are major reasons for this development? Some of the changes in relevant framework conditions since 2009 will be addressed in the following subsections.

Outstanding Academic Performance of UML Between 2009 and 2017, the number of direct employees (full-time equivalents) increased from 3.604 to 4.139. The more than 500 new positions comprise more than 110 additional physicians, who help to extend the research activities of UML and attract new research projects. In particular, the establishment of the Clinic of Angiology and the Leipzig University Cancer Center has attracted additional renowned researchers.

Fig. 3 Employment multiplier and further results of the case study for UML in 2009. (Source: Own drawing after Wiesmeth et al. 2018)

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Fig. 4 Employment multiplier and further results of the case study for UML in 2017. (Source: Own calculations and drawing)

Various reasons were, of course, responsible for this increase in the number of direct employees at UML. However, one of them was the extension of the medical supply, at least for the years to come, into important and promising fields, such as angiology and cancer research. Consequently, the number of third-party funded positions in UML increased as well from 339 in 2009 to 462 in 2017. The number of scientific publications in renowned journals increased similarly and accompanied the institutional development. This number increased from 1.326 contributions in 2009 to 1.572 in 2017, which is well above the average within the university medicine in Germany (cf. [in German] http://www.landkartehochschulmedizin.de). Of course, this positive development was fueled by the outstanding reputation, UML gained in the scientific community over the last decades. Thus, not surprisingly, an excellent academic performance, also resulting from a corresponding hiring policy, is one of the most important drivers or framework conditions for a successful further development of an academic institution towards a smart institution.

Openness of Academic Institutions for Collaborations Since 2009, UML has extended its relations and collaborations with various academic institutions in its vicinity, thereby again integrating additional stakeholders into its activities. The following information on selected collaborative research projects shows that the “openness” of the research institutions for an intense collaboration is crucial for this extension of the range of scientific activities: (a) Innovation Centre Computer Assisted Surgery (ICCAS): this is a research initiative at the University of Leipzig. It integrates researcher and surgeons from UML and further computer scientists and engineers from the University

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of Leipzig and the Leipzig University of Applied Sciences (https://www.iccas. de/?lang¼en). (b) Leipzig Research Center for Civilization Diseases (LIFE): there is a large number of internal and external research institutions linked together in the LIFE-network, among them are the Leipzig Heart Center, the Helmholtz Centre for Environmental Research – UFZ in Leipzig, the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, and various institutes of the University of Leipzig and the Leipzig University of Applied Sciences (http://life. uni-leipzig.de/en/). (c) Interdisciplinary Centre for Bioinformatics (IZBI): this institution is an interfaculty research unit of the University of Leipzig integrating researchers from UML and from other parts of the University of Leipzig and the Max Planck Institute for Mathematics in the Sciences in Leipzig (http://www.izbi.unileipzig.de). (d) Bio City Leipzig (BBZ): professors at UML are involved in research at the Bio City, supporting also young researchers (https://bio-city-leipzig.de/welcome). This continuously extended cooperation of UML with other research institutions, in particular with those located in Leipzig, induced additional research positions outside UML, but nevertheless closely associated with it. Moreover, younger scientists are integrated into these activities by means of Junior Research Groups, and further projects, such as the Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG), founded in 2018, will in near future contribute towards an even more intense collaboration of UML with other research institutions – in the region and beyond (https://www.helmholtz-muenchen.de/hi-mag/index.html). These positive results are a consequence of the “openness” of the research institutions enabling the integration of relevant stakeholders – thus a very important framework condition for the development of a smart institution.

Willingness to Cooperate Across Disciplines Clearly, the cooperation across disciplines in larger projects is often facilitated through personal contacts of researchers from different disciplines. Thus, an academic institution, a university, for example, should provide for appropriate framework conditions. This applies, in particular, to university medicine, which is often considered to engage more in applied research in comparison to the faculties in natural sciences, which are more often involved in basic research projects. This is true for UML, where third-party funding of (applied) research is dominated by the German Federal Ministry of Education and Research with approximately 28% of third-party funds in 2017 (UKL 2017). So far, UML collaborates with researchers from other parts of the university in various research centers, some of them characterized above. In addition, the Centre for Obesity Research integrates researchers of UML and University of Leipzig. This is a positive observation although there seem to be possibilities for intensifying these contacts. As it includes a further interesting potential for collaborative research activities across disciplines, this deserves a closer attention in the near

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future. Joint activities with researchers from biology, biochemistry, and bioinformatics seem to play a dominating role. Appropriate framework conditions could help to encourage further cross-disciplinary collaboration and help to exploit this potential for a smart institution. Thus, the German High-Tech Strategy (cf. section “Public Policy Supporting Innovations in Healthcare in Germany”) did not yet satisfactorily address this issue.

Support from Service Providers: The Leipzig Trade Fair Leipzig has always been known for its location at the crossroads of Via Regia and Via Imperii, two medieval roads with a large economic significance for interregional trade. This location helped to establish the traditional Leipzig Trade Fair some 850 years ago. After the German reunification, the fair gradually assumed its former role, meanwhile providing services for all kind of fairs. The medical sector has gained special importance in recent years, based on a closer and closer cooperation with UML. For this reason, expenses of exhibitors, guests, and visitors of medical conferences and congresses have almost doubled between 2009 and 2017, adding up to more than 14 million € in 2017 (cf. also UKL 2009, 2017 for more information on medical conferences and fairs). Moreover, the fact that many international conferences related to medical aspects, such as Arab Health (January 2019), Russian Forum on Prosthetics and Orthotics (June 2019), and Beauty and Health (October 2019), just to name a few, are scheduled each year encourages perhaps sustainable contacts between researchers from UML and from many other parts of the world. Thus, the increasing cooperation between Leipzig Trade Fair and UML helped, on the one hand, to develop an excellent infrastructure and perfect services and raised, on the other hand, not only the level of awareness for UML in Germany and abroad but probably also the attractivity of UML for researchers elsewhere. Consequently, the Leipzig Trade Fair has to be counted – as an important provider of services – among the important framework conditions with a positive effect on UML as a smart institution.

Other Framework Conditions of Relevance for a Smart Institution The above analysis of the development of UML from 2009 to 2017 has shown that this institution is on a successful track towards a smart institution. There are, however, as indicated above, a few issues, which need to be taken care of. Regarding the internal organization of UML, framework conditions should be adjusted to: (a) Encourage more direct cooperation between researchers at UML and researchers from other faculties of the University of Leipzig and other academic institutions. (b) Increase the share of third-party funding for projects with a stronger focus on basic research.

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Both issues will tend to raise the employment multiplier: in the first case, crossdisciplinary cooperation will eventually induce larger, externally funded research projects, and in the second case, the additional knowledge and competencies acquired through basic research will help to get additional funding for more applied projects. Regarding the external environment, the financial means for the Medical Faculty from public sources have to be considered more carefully. In 2010, the Medical Faculty received a grant from the Free State of Saxony of approximately 47.0 Mio. Euro (after 53.0 Mio. Euro in 2009) to cover running expenses. This grant was raised to 60.6 Mio. Euro in 2017. However, taking into account the rate of inflation (consumption price index), the Medical Faculty received only 55.4 Mio. Euro in 2017 – with the purchasing power of 2010 Euros. If one uses the rate of growth of the GDP of the Free State of Saxony as a deflator, then the grant provided in 2017 corresponds to the grant provided in 2010. Thus, between 2010 and 2017, the public grant did not rise faster than GDP. In view of the results obtained and discussed above, UML should be treated as a serious economic factor with a significant rate of return, measured by means of the employment multiplier. The final framework condition to be introduced refers therefore to finances from the public administration, acting as “smart government”: in the current situation of UML, additional public funds for attractive medical fields at UML will likely increase all kinds of research activities with a significant return for the medical sciences and for the medical supply of UML, but also for the economy of the Free State of Saxony. But, of course, the question where to put available, additional funds is also a matter of opportunity costs and cannot be answered without a detailed analysis of the regional economy.

Smart Institutions in Various Sectors of the Economy The empirical analysis regarding smart institutions so far focused on university medicine and on public institutions with research activities. This focus allowed the introduction and consideration of an index of smartness, the employment multiplier, with properties postulated in the literature. Nevertheless, smart institutions, operating under malleable framework conditions with a research-orientation and the goal to enable the creative potential of the employees, are also of relevance and importance in other sectors of the economy. This section briefly addresses smart institutions in the educational and health sectors, in industry, and in business, in addition to the important role the government is assigned in this context, pointing to the potentially increased vulnerability of (smart) cities through acts of terrorism and cyberattacks. Moreover, other potential indicators of smartness, such as rankings or any indicators based on profits of private business companies in a market economy, are investigated. In view of this more

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introductory approach, a more careful analysis of these and related questions pertaining to smart institutions in other sectors is left for further research. (a) Education: For public and research-oriented institutions in the educational sphere, the approach adopted here can be applied immediately. As all these institutions are dependent on framework conditions, which can be affected by the public administration, a feedback loop “to facilitate the development of institutions that bring out the best in humans” (Ostrom 2010), characteristic of a smart institution, is guaranteed. In case the institution is private, but not for profit, then the methodology remains in principle applicable, as long as there is a basic funding for personnel, material costs, and investments from the shareholders. However, it remains open, to what extent the shareholders are capable and willing to adjust the framework conditions to develop the institution towards a smart institution. Peculiarities of educational, research-oriented institutions, such as excellent performances in teaching and research, are respected in the smartness indicator, as they tend to attract further research institutes in the vicinity and/or the collaboration with other research institutes and the industry and third-party funding. The situation with private, for-profit institutions in education will be addressed below. (b) Health: Similarly, for public, research-oriented institutions providing health services, the procedure chosen here is again immediately applicable. Adjustable framework conditions allow the feedback loop, which is characteristic for a smart institution. Private, not-for-profit institutions can also be adequately respected, if the shareholders are willing to steer the institution towards a smart institution. An outstanding record regarding the provision of health services or research activities will affect the smartness indicator: other service providers such as special care homes or retirement homes will settle in the vicinity, or industry will look for collaboration. Again, the situation with private, for-profit institutions in education will be addressed below. (c) Industry and Business: In the assumed context of a market economy, the situation becomes somewhat different. For industry and business companies, also in the educational fields and the healthcare sector, profit orientation changes the picture. Of course, these institutions can react and do react upon framework conditions, and adjustments to changing market conditions are frequent. However, that’s the crucial difference to public institutions: these adjustments are typically initiated and governed by the motif to collect additional profit. This is, of course, absolutely justified in the context of a market economy, where business companies are free to choose their production activities and the public administration has only limited possibilities to affect the development of a particular institution towards a smart institution. However, as public goods tend to play role for a smart city, there might be a discrepancy between the operations of a private, for-profit institution and the focus of a smart institution.

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The consequence might be that business companies, on the one hand, offer their products and services for the development of a smart city, for example, in the fields of ICT, and claim even to be decisive in this context, but do not seem to get too much involved as a “smart institution.” Indeed, according to Frost and Sullivan (2019), who consider a smart city from a technological point of view, the smart city market is estimated to be at 1.56 trillion US $ by 2025. Smart metering, wireless sensor networks, and high-speed broadband, are, among others, the key building blocks of a smart city. Thus, it seems that the main task of industry and business consists in supporting the development of smart institutions and smart cities by means of technological innovations. There is, however, one interesting observation based on the brief analysis of public policy supporting innovations in healthcare (cf. sections “Public Policy Supporting Innovations in Healthcare in Germany” and “Public Policy Supporting Innovations in Healthcare in Russia”). Although in a free market economy the government can typically not interfere with the operations of a single private company, it can nevertheless attempt to steer a whole branch of the private economy in a certain direction. Among the prominent examples are healthcare and renewable energies. Thus, it seems to be possible to develop research-oriented private business into smart institutions. This requires again further analysis. The question regarding an appropriate indicator of smartness also remains to be investigated. Some remarks on indicators based on profit will be made below. (d) Government: As indicated occasionally throughout the text, public administration and government have an important role regarding the development of smart institutions. After all, it is their task to adjust the framework conditions for those institutions, which operate with public funds. Therefore, if they act as a facilitator through adjustments of the regulatory framework (Gil-Garcia and Aldama-Nalda 2013), thereby bringing out the best in humans (Ostrom 2010), they could be considered “smart government” or a “smart government institution.” There is, however, another aspect, which a “smart government” has to tend to: the increasing vulnerability of smart cities regarding terrorism and cyberattacks – also as a result of large investments in advanced infrastructure, in particular ICT. Urban security has to become top priority, although spending on public safety varies considerably among countries with the USA leading in terms of per-capita expenditure (Audier et al. 2017, p. 3). What needs to be done seems to resemble, even match the approach taken here: relevant stakeholders have to get involved to foster network collaboration. The government must promote collaboration among private security companies, business companies, and citizens “through information sharing, integrated tools (notably, to support command and control), incentives, and clearly defined roles” (Audier et al. 2017, p. 7). Thus, the government is obliged to make the required changes to the framework conditions in order to provide optimal protection, also through the expected reaction of the stakeholders upon these changes. This corresponds perfectly to the ideal of a “smart government.”

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The question of an appropriate indicator for the degree of smartness of an institution has already been addressed: for public, research-oriented institutions, the employment multiplier can be applied to measure smartness. However, for institutions such as universities in general, and research institutes in particular, there is also a multitude of rankings, evaluating their performance, in order to allow for national and international comparisons. Regarding universities, such rankings usually take into account performance indicators such as teaching, research, knowledge transfer, and international outlook with the weights for the various subcategories usually determined exogenously. Occasionally, also the opinion of peers is respected. Of course, these rankings measure important aspects of the performance of the institutions. Nevertheless, they do not seem to capture all aspects of relevance for a smart institution in the sense of the approach chosen here. For example, the methodology of the World University Rankings 2019 measures knowledge transfer mainly through industry income. Thus, research institutions settling in the vicinity of a university to profit from the proximity are not directly respected (cf. https:// www.timeshighereducation.com/world-university-rankings/methodology-world-uni versity-rankings-2019), although these “clusters” result from the research activities of the university and are of utmost importance of a smart institution. But, again, these rankings have a different focus and different goals and should, therefore, not be expected to function also as a primary indicator of a smart educational institution. As for-profit institutions operating in a competitive environment do not a priori count among smart institutions due to the feedback loop in a free market context focusing on profits and not directly on the requirements of a smart city, any indicator based on profits seems not appropriate as an indicator of smartness. To repeat and to emphasize, this does not mean that private business companies are not important for the development of a smart city or that the profit goal is not of relevance for these companies; it rather means that the profile of a smart city goes beyond private business interests. However, as indicated above, if public policy affects a whole branch of the private sector, then the question of an appropriate indicator comes up again and should be investigated. These are some preliminary reflections on smart institutions in various sectors of the economy. These thoughts clearly deserve and require further analysis.

Conclusion In this chapter, the economic performance of institutions, in particular researchoriented public institutions, was considered a significant cornerstone of a development of a city towards a smart city. The reason is that a smart city is dependent on the support from its citizens, in particular, from the creative class. This motivates the consideration of smart institutions, of institutions, which operate optimally under appropriate framework conditions. Smart institutions

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motivate employees and enable, in particular, researchers in universities, companies, and other institutions to fully employ their capabilities to the benefit of the society. Consequently, the chapter considers smart institutions in the context of a smart city. In view of the literature, these institutions adequately integrate – through appropriate framework conditions – relevant stakeholders to leverage the collective intelligence of the institution for the benefit of society. This characterization points first of all to public, research-oriented institutions. In order to allow a more careful analysis, case studies support and illustrate the theoretical concept of a smart institution and a corresponding indicator based on the employment multiplier. The case studies refer to particularly important institutions: university hospitals and medical faculties of universities offering maximum medical supply and access to high-performance medicine. The first case study compares UML with SSMU and allows some insight into the effects of differing framework conditions – some of them exogenous. The second case study considers UML at different periods of time. The ensuing analysis illustrates the role of various internal and external framework conditions in the context of a smart institution. Relevant, malleable framework conditions point to incentives for further collaborations, for integrating further researchers – internally and externally – requiring thus a certain openness of these institutions. Of importance is also the support of external service providers, the Leipzig Trade Fair in the case considered here. And finally, financing from public sources should take into account the economic impact of these institutions, which are often only considered as cost factors: they should provide their core services at minimum costs to the taxpayer. Finally, the context of institutions in various sectors of the economy is considered, regarding the potential for smart institutions. Whereas public, research-oriented institutions pose no problem, private companies in a market economy require special attention. The goal to generate profit need not always be in line with the provision of public goods also characterizing smart institutions and smart cities. A public policy addressing branches of the economy may, however, help to direct these institutions towards smart institutions. A brief discussion illustrated that indicators such as rankings or those based on profits are not really applicable as indicators of the smartness of an institution – they serve different goals. In summary, this chapter introduces smart institutions and demonstrates the dependence of the level of the smartness of an institution on framework conditions, which can be adjusted to increase economic performance. This fits seamlessly into the concept of a smart city. Further research should be directed to smart government and smart institutions in other sectors of the economy, especially to institutions in the private sector. Moreover, there is a need to introduce a more general indicator for smart institutions, which should, optimally, be compatible with the employment multiplier proposed here.

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Cross-References ▶ Corporate Social Responsibility (CSR): Governments, Institutions, Businesses, and the Public within a Smart City Context ▶ Smart Cities Can Be More Humane and Sustainable Too ▶ Smart Cities: Fundamental Concepts ▶ Smart City Wien: A Sustainable Future Starts Now ▶ Smart Energy Frameworks for Smart Cities: The Need for Polycentrism

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Part II Current Exemplary Smart Cities

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Smart City Edmonton Katie Hayes, Soumya Ghosh, Wendy Gnenz, Janice Annett, and Mary Beth Bryne

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Definition: A Smart City is a Healthy City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guiding Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart City Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart City Maturity Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart City Ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart City Achievements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Edmonton’s Smart City Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data and Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Accessibility and Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shareable Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future-Proofing Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inclusive and Accessible Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data and Technology Partnerships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Standards for Data and Technology Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Privacy, Security, and Ethics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Governance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Edmonton’s Open City Initiative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Security, Privacy, and Ethics Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Resident and Community Engagement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Engagement Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Engagement Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inclusive Engagement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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K. Hayes (*) · S. Ghosh · W. Gnenz · J. Annett · M. B. Bryne Open City and Technology, City of Edmonton, Edmonton, AB, Canada e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_17

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Abstract

As a world leader in leveraging data, technology, and innovation, the City of Edmonton (the City) recognizes the need for an approach that is communitydriven, evidence-based, and delivered through partnerships in order to achieve sustainable solutions. As a global leader in open data, open government, digital innovation, and being a Smart City, Edmonton demonstrates an unwavering commitment to being a progressive and collaborative learning organization. The City envisions a community that thrives and is united, not divided, by data, information, and digital technologies. Edmonton is a city for all, connected, and healthy. In order to achieve this, the City works with partners to break down traditional barriers to improving the development and delivery of policies, programs, and services.

Introduction Modern municipalities operate in a period marked by a rapidly changing business environment, a call for more open and interactive government, and an everincreasing need to work collaboratively to address the complex challenges of today and tomorrow. A smart city approach aims to achieve meaningful outcomes for residents by leveraging the fundamental benefits of data and technology.

Definition: A Smart City is a Healthy City Edmonton is currently undergoing a resident-led digital transformation. To continue to provide value to residents, the City recognizes it must be a nimble organization – continuously evaluating and embracing the endless possibilities that accompany change. As a result, the City created the Business Technology Strategy, the first of its kind in Canada, to guide the use of data, business solutions, and diverse technologies to improve life in Edmonton. As a digital city, Edmonton is embracing new ways of delivering programs and services to address the challenges of the day with residents at the core. Complementing the Business Technology Strategy is Edmonton’s Smart City Strategy – an innovation ecosystem of government, academia, residents, and industry – that follows the International Organization for Standardization Standard 37106 (2018). It is not just about the administration of municipal programs and services; it is about Edmonton as a thriving community. Edmonton is a creative community of changemakers and social innovators – where residents are engaged with their community and lead the charge for a better future. The City of Edmonton addresses today’s challenges and creates tomorrow’s opportunities through collaboration and innovation.

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For Edmonton, a Smart City is not just about technology. It is about creating and nurturing a resilient, livable, and workable city through the use of technology, data, and social innovation. According to the World Health Organization, urban populations experience some of the world’s most prominent health disparities. Residents are faced with increasing urban health hazards resulting from inadequate housing, transportation, food, and environmental systems including air pollution, unhealthy diets, physical inactivity, and isolation. Now, in the midst of the digital revolution that is transforming how individuals interact, communicate, and connect, cities are also faced with understanding the technological challenges affecting the health of residents and how to lessen the impact of the digital divide. Although technology is an integral part of building a smart and connected city, there are several nontechnical components that work together to complete a Smart City ecosystem and become catalysts for innovation. These components range from the creation of public spaces where residents can come together to gain a sense of community belonging to the partnerships that will continue to drive the transformation of today’s urban physical and digital environments. The City of Edmonton actively creates opportunities for diverse input and participation by inviting residents to play a larger role in shaping their community to enable social and economic growth and impact environmental and health outcomes. The City of Edmonton proposes that municipal-level interventions guided by residents will have a significant impact on building healthier cities and will improve the quality of life for residents today and into the future. Edmonton’s innovative Smart City approach to improve health through preventative measures addresses the true needs of the community through a collaboration between public and private sector organizations and residents. This approach, enabled by technology, analytics, and data, will ensure Edmonton is a place where all residents have equitable opportunity for healthy, safe, and joyful lives.

Guiding Principles The principles shown in Fig. 1 guide the ongoing development of Edmonton’s Smart City program. These principles emphasize the City of Edmonton’s commitment to building a healthier, more connected city with residents and partners, inciting innovation within the region and beyond. These guiding principles are directly aligned with the foundational principles of the City of Edmonton’s Business Technology Strategy – a City Council-approved strategy that enables a fully integrated approach to managing information, data, and technology. The City has a significant amount of valuable data, business solutions, and diverse technologies. To better leverage these assets, the application of the Business Technology Strategy increases internal and external data sharing, optimizes processes, and delivers quality service while managing costs effectively – all in partnership with stakeholders and residents.

Fig. 1 Smart City Guilding Principles

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Smart City Framework Edmonton’s Smart City Framework is a holistic approach to working collaboratively with residents and partners to optimize the use of data and technology and influence the development of policies, programs, services, and innovative funding models. Working with residents and partners, the City of Edmonton developed this approach to leveraging data, technology, and innovation in order to provide an exceptional quality of life for residents. This framework is the foundation for Edmonton’s phased approach to the building of a healthier, more connected city. This framework is the foundation for Edmonton’s Smart City approach to building a healthy, more connected city (Fig. 2).

Smart City Maturity Matrix The Smart City Program uses the following maturity matrix as a self-assessment to understand the current state of the organization and establish tangible goals for development (Fig. 3).

Smart City Ecosystem A city of the future – a Smart City – is a Healthy City. It is one where residents, industries, academic sectors, and government work collaboratively to learn about the

Fig. 2 Smart City Framework

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Fig. 3 Smart City Maturity Matrix

challenges the city is facing and create, test, and scale sustainable solutions. A Smart City identifies the transformational shifts required to boldly challenge the status quo and build an inclusive and digitally enabled community. Together with residents and partners, a Smart City creates and nurtures a resilient, livable, and workable community that rises to the challenges being faced today, enhances the vibrancy and diversity of the city, and embraces the opportunities of tomorrow. Cities are in the unique position of working directly with residents and the local built environment to use technology and innovation to revolutionize the urban setting and improve the health of residents. A city of the future – a Smart, Healthy City – recognizes this incredible opportunity to identify and intentionally advance transformative priorities with residents, not for them. Through established

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Fig. 4 Innovation Ecosystem

mechanisms and community partners, the City is uniquely positioned to understand the health of its residents and quickly test interventions at the neighborhood level, measuring outcomes and reporting results to inform decision-making in order to scale solutions effectively. The City of Edmonton continues to advance the development of the Smart City Ecosystem. This ecosystem comprises public sector organizations, private sector organizations, academic institutions, and residents. The ecosystem works collaboratively to improve the capacity of all partners while developing efficient and effective ways to provide meaningful services to residents. The growth of the ecosystem will continue by identifying partnerships, opportunities for innovation, and the means by which to improve the efficacy of programs and services (Fig. 4).

Smart City Achievements The City of Edmonton is a world leader in leveraging data, technology, and innovation to improve quality of life for residents. In addition to being named Canada’s Most Open City and a Top 7 Intelligent Community of the Year, Edmonton is the most recent winner of the Gold WeGo Smart Sustainable City Award, the first Canadian city to win the IBM Smarter Cities Challenge Award, and the first Canadian pilot of Johns Hopkins University’s Center for Government Excellence What Works Cities initiative. The following achievements and initiatives demonstrate the City of Edmonton’s readiness to work in partnership with community to continue to lead as a Smart City:

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• The City has led the country in understanding the value of combining open data, public engagement, and analytics. The Open City Initiative is a complex program of work streams and projects spanning all 30+ internal business areas and extending to external organizations through outreach and partnerships. The accomplishments of this initiative are internationally recognized. • With the development and implementation of Canada’s first measurable Open Data Strategy, Edmonton has shown its commitment to transparency and openness. Edmonton’s Open Data Portal was launched in 2010 and has grown to over 2,000 assets with more than 50 million annual transactions. Edmonton’s City Council was also the first in the United States and Canada to adopt the International Open Data Charter. This adoption again demonstrates the unprecedented commitment to accessibility and transparency by City of Edmonton leaders. • The City of Edmonton’s Analytics Center of Excellence (ACE) is worldrenowned for delivering complex and multidisciplinary projects. ACE has completed projects of global significance, including a contextual analysis of crime, development of a human trafficking identification tool, and an optimization model for snow plowing routes. The optimization and analytics models developed through these projects are made available to other municipalities under the Creative Commons license and open-source code. • The City was a finalist in the $50 million category of Infrastructure Canada’s Smart Cities Challenge, a competition open to all municipalities, local and regional governments, and Indigenous communities across Canada to define their future, using a resident-driven, smart city approach. The challenge was based on four guiding principles of openness, integration, transferability, and collaboration. • Edmonton was also the first Canadian city to win the Smart Cities Council of North America Readiness Challenge. More than 100 cities throughout the United States, Canada, and Mexico applied detailing the smart cities projects planned in their communities. Winners were selected based on inclusiveness, sustainability, and impact of their current and planned work. The City of Edmonton continues to deliver complex, multi-stakeholder, and multidimensional projects in partnership with all levels of government, industry, and residents – continuing to use proven mechanisms and processes for project excellence while demonstrating an unwavering commitment to Smart City growth, collaboration, and leadership.

Edmonton’s Smart City Projects Utilizing an extensive body of national and global subject matter experts, academic research, and thought leaders as well as ongoing, focused engagement with residents and stakeholders, the City of Edmonton identified two outcomes that would be the focus for Edmonton’s participation in Infrastructure Canada’s Smart Cities

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Challenge. The outcomes listed below remain relevant as the City continues to work toward achieving Vision 2050 and the goals outlined in ConnectEdmonton, Edmonton’s Strategic Plan 2019–2028. 1. To improve quality of life for residents 2. To transform how municipalities across Canada work with residents and partners to achieve excellence in data and technology Edmonton has made significant progress in achieving both outcomes, as shown through the project work described in Tables 1 and 2.

Table 1 Projects focused on improving quality of life for residents Project or initiative Health Hack Competition – March 2018

Grow with Google – September 2018

HackED 2019 – January 2019

You Can Benefit – ongoing

RECOVER – ongoing

Deliverable The Health Hack competition brought together the civic tech community to build solutions for a healthier city. Five finalists were selected on March 16, 2018, and their ideas included a buddy bench extension program, a cannabis ecosystem, a fitness app for nonathletes, an urban design and mental health app, and a wheelchair accessibility tracker Grow with Google is a series of community events that help Canadians develop the skills they need to prepare for a job, find a job, or grow their business. In this collaboration with the City of Edmonton, Google provided training to local educators, business owners, aspiring technology professionals, and entrepreneurs. Over 400 individuals were able to build community partnerships, learn valuable digital skills, and enhance their career potential through this initiative The City of Edmonton sponsored this student-led Faculty of Engineering initiative at the University of Alberta. Through this sponsorship and engagement at the event, the City of Edmonton raised awareness to 450 attendees for open data and Smart City initiatives. Participants in the hackathon created teams to design and build smart, innovative projects to solve problems important to the community. It is an excellent opportunity for learning through collaboration that leads to positive sociological and psychological outcomes The You Can Benefit online tool helps residents in Edmonton easily access information on municipal, provincial, and federal benefits. You Can Benefit provides Edmontonians access to more than 28 programs and 120 community services in one place, such as the City of Edmonton Leisure Access Program and Ride Transit Program, the Alberta Child Care Subsidy, and the Alberta Seniors Benefit. Several iterations have been introduced to provide better reliability and results RECOVER is a community wellness program developed for the City’s most vulnerable populations. Using a phased approach, the program aims to develop a new, fully integrated approach across a continuum of pre-crisis, crisis intervention, postcrisis, and transitional services

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Table 2 Projects focused on transforming how municipalities work with residents and partners to achieve excellence in data and technology Project or initiative Open Data Citizen Advisory Group – March 2018

Canadian Open Data Summit 2018 – Wendy Gnenz, Canadian Open Data Leader Award – November 2018

City Park Usage – Pedestrian Counter – ongoing

Open Science Partnership with the University of Alberta – ongoing

MetroLab Network partnership with the University of Alberta – ongoing

Deliverable The City, in partnership with the Open Data Citizen Advisory Group, shared insights and feedback on the functionality of the Open Data Portal. The group provided feedback on the look and feel of the tool and file structure allowing the City to make user-centric improvements The Canadian Open Data Summit jury recognized Edmonton’s successes and leadership in the Open Data movement in Canada under the strategic leadership of the City’s chief information officer, Wendy Gnenz. Wendy was the driving force behind advancing the Open City Policy, adopting the International Open Data Charter, and winning Most Open City in the Open Cities Index 3 years in a row Through the Smart Cities program, a prototype was developed for a park pedestrian counter. The prototype uses thermal sensing and image recognition to understand how parks or attractions are utilized. The sensors use a wireless data sharing network to transfer data. The pedestrian counter has gone through a variety of iterations to improve accuracy, and the code was shared through an open-source platform with municipalities globally The City of Edmonton works with researchers at postsecondary institutions to actively promote open data for research purposes. Edmonton’s Open Data Portal is regularly referenced as a source in academic publications. As an example, a University of Alberta professor in Earth and Atmospheric Sciences directed an entire class of graduate students to perform geospatial analysis using Edmonton’s open data. The students’ final work was presented at City Hall with viewers from City Planning Committees, City employees, and the public. The students contributed diverse research and analysis as well as requests for new datasets to be included in the Open Data Portal The MetroLab Network between municipalities and universities focuses on bringing data, analytics, and innovation to local government. These institutions partner together to tackle problems and share solutions and best practices for economic development, resiliency, social equity, transportation, and governance. This initiative aims to positively impact Edmontonians and strengthen the reputation of (continued)

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Table 2 (continued) Project or initiative

Developing Shareable Solutions – ongoing

Deliverable the City as a partner in innovative city-building. Edmonton is the first participating Canadian municipality in the MetroLab Network The City of Edmonton continuously works with partners in developing technology solutions to improve the lives of residents. These solutions are shareable with communities and can be applied utilizing local partnerships and data. Solutions include the Optimized Safe Needle Response and Emergency Operations Demand Dashboard

Data and Technology Progressive organizations around the world continually reimagine themselves through innovative digital tools, systems, and processes. In today’s dynamic environment, it is imperative for municipalities to understand the role technology plays in building smart, sustainable cities and addressing complex societal challenges in a collaborative setting. Urban planning needs to embrace the digital opportunities that contribute to the vibrancy and sustainability of places and refrain from taking a siloed approach to managing investments in connected technology and physical infrastructure. Through the use of data and connected technology, the City of Edmonton is rethinking the planning and development of urban landscapes and the delivery of services in order to avoid the inefficiencies of today and build a healthier, more connected city of the future. The built environment will influence health outcomes and impact the way residents feel, both physically and mentally. Data and connected technology will be used within the Smart City Ecosystem to improve well-being in Edmonton by creating spaces and solutions that are accessible, vibrant, and inclusive, that celebrate the unique features of the City and its residents, and that increase security and reduce isolation. Residents’ relationship with technology is constantly evolving. To meet the diverse and dynamic needs of residents and community, cities must build and strengthen internal and external collaboration and better leverage the data, business solutions, and diverse technologies that exist. Edmonton’s Business Technology Strategy provides a strategic framework to connect all of these pieces in order to transform Edmonton and the region it occupies into a place that meets the expectations of the modern world. The Business Technology Strategy is the foundation from which data and technology decisions for Smart City initiatives are made. Edmonton’s Smart City program also follows ISO 37106: 2018 Guidance on Establishing Smart City Operating Models for Sustainable Communities. The

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premise of the ISO framework is to lead the transformation of the traditional municipal operating model to empower the community through data. It will also break down silos that inhibit truly resident-centric service delivery and enable digital inclusion in ways that are not achievable through traditional technology approaches.

Data Accessibility and Sharing A foundational element of Edmonton’s Smart City program is to increase the capacity for data accessibility and sharing within the Smart City Ecosystem and in municipalities across Canada. Through this increased capacity, the use of data can be optimized to enhance the development and delivery of programs and services for residents, as well as enhance and animate the physical spaces they occupy. This consolidation of disparate data through partnerships also increases the ability for organizations and municipalities to identify gaps and biases and work together to resolve them. Information and data may be shared between partners; however, a formalized data sharing, information management, or research agreement must be in place prior to this occurring and a Privacy Impact Assessment complete, if required. In this event, the data is shared, retained, and refreshed at the source.

Open Data The City of Edmonton currently has over 2,000 data assets in the Open Data Portal. All of this data is publicly available and has been obtained and published under an established set of management controls and approval processes. The Open Data Portal also makes available certain datasets that are external to the City of Edmonton. The Edmonton Police Service, Edmonton Public Library, Alberta Environment and Parks, and EPCOR have all shared data to be used in this tool for residents and community. This data is anonymized and was made available to the Open Data Portal through data sharing agreements with the City of Edmonton.

Shareable Solutions As the data and technology landscape evolves, so does the ability for Canadian municipalities and governments to realize innovative opportunities through the sharing of their experiences and solutions. This begins with sharing code and best practices as municipalities are often striving for similar outcomes through building and procuring the same solutions. However, the truly transformative nature of data and technology is not achieved by simply sharing open-source code. It is done through building a network of cities and their respective innovation ecosystems that will extend and sustain the new digital products that residents expect. This is a new business model opportunity where the efforts of each city and their community partners are multiplied, rather than duplicated. The work of Edmonton’s Smart City

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program includes active engagement of the network and community around the analytic and digital products that are created through all areas of the program. Through the development of shareable solutions, the City of Edmonton ensures transferability and scalability of knowledge, experiences, processes, and solutions across municipalities. As such, solutions and learnings are open, integrated, transferable, and collaborative beyond the traditional municipal boundary and span of control (Table 3).

Future-Proofing Technologies As cities continue to embrace disruptive innovation and technologies, the challenge of future-proofing becomes increasingly complex. The City of Edmonton has developed effective processes to work with the community to identify and test new technologies to advance municipal programs and services. Through the City’s broad partnership base, extensive subject matter expert network, and ongoing engagement with residents, Edmonton has the capacity to recognize potential future-proofing issues and proactively make adjustments to the program in order to ensure ongoing success. With a focus on data sharing enabled through service-oriented and microservices architecture, the City of Edmonton will address the challenge of future-proofing. This approach places a priority on how the interface between systems is created rather than how the specific technology is being used. Systems will evolve and change, but the value created through data sharing is sustained.

Inclusive and Accessible Solutions Smart cities are inclusive and accessible. They develop and use innovative technologies to benefit all residents and create equitable opportunities to live healthy, safe, and joyful lives. The City of Edmonton prioritizes being inclusive and accessible. In 2016, the City of Edmonton’s Open Data Portal was awarded the Canadian Open Data Award for Accessibility by the Open Data Society of British Columbia and Open North. Throughout the development and implementation of technology solutions for Smart City initiatives, the City engages key stakeholders and subject matter experts to offer their perspectives regarding content, accessibility, and usability. The City also works directly with residents to ensure the identification and creation of inclusive technology solutions that are responsive to evolving needs. Understanding and mitigating data biases and gaps also contribute to the accessibility and usability of technology solutions. The City of Edmonton works with subject matter experts to understand how biases in data can be verified and what mechanisms can be put in place to overcome negative effects.

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Table 3 Shareable solutions Project You Can Benefit

Emergency Operations Demand Dashboard

Transit Security Deployment Model

Optimized Needle Response

Description You Can Benefit, a made-inEdmonton web tool, provides individuals, families, and community workers with information on available municipal, provincial, and federal benefits. Built using open-source content, You Can Benefit can be shared with other organizations and municipalities nationwide The wildfire that forced 88,000 people in Northern Alberta to flee their homes in 2016 required a municipal response that was nimble and adaptive. In an effort to support the evacuation efforts, City of Edmonton staff developed and deployed an analytic dashboard to monitor the ever-evolving demands. By consolidating real-time evacuee service reporting, the dashboard empowered municipal decisionmakers with the information necessary to make critical service delivery decisions in uncertain and ever-changing times Edmonton Transit Security adopted the Transit Security Deployment Model, an approach that optimizes the deployment of transit peace officers to trouble locations in a timely manner along the transit network. The Transit Security Deployment Model uses cuttingedge data mining algorithms to automatically analyze current transit incident data to deploy officers where they are needed the most. Since its introduction, Edmonton Transit Security has seen its number of proactive incidents go up by 159 percent, while reactive events have gone down by 52 percent The Optimized Needle Response solution overhauled the municipal strategy that was in place to manage discarded needles by way of leveraging data to forecast anticipated resident complaints and

Ease of replicability/transferability (5 (high) to 1 (low)) 5 – Municipalities who use this code require foundational open data and analytics. A strong and collaborative working relationship with benefit providers is an additional requirement in order to acquire the necessary data

5 – Municipalities who use this code require foundational open data and analytics

3 – Municipalities who use this code must be confident in their open data and analytics maturity

3 – Municipalities who use this code must be confident in their open data and analytics maturity

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Table 3 (continued) Project

Safety Code Inspector

Description incorporate route, needle box, and shift optimization. In addition to increased operational efficiencies, the Optimized Needle Response has resulted in a near $200,000 cost avoidance per year The City of Edmonton performs over 50,000 inspections a year on newly built houses. In order to reduce the burden of this workload while upholding the integrity of the inspections, the City developed a predictive analytics software. This solution is able to identify low-risk safety code inspections, freeing up resources to concentrate on higherrisk safety code inspections. Currently being piloted in Edmonton, the goal is to reduce annual inspections by 10% (5,000) per year

Ease of replicability/transferability (5 (high) to 1 (low))

2 – Municipalities who use this code must be advanced in their open data and analytics maturity

Data and Technology Partnerships As evidenced in Edmonton’s Business Technology Strategy, the City of Edmonton views partnerships as being critical to the advancement of a modern municipal corporation. Specifically, the City has developed strategic partnerships with technology collaborators who are essential to the continued growth of Edmonton as a Smart City. As the regulatory environment for data and technology evolves, the City of Edmonton expects data and technology partners to assist and respond to changing regulations.

Standards for Data and Technology Solutions The City of Edmonton recognizes the importance of understanding and incorporating standards into the development and implementation of data and technology solutions. This section outlines standards and strategies the City of Edmonton uses to ensure interoperability and replicability of all data and technology assets. Data and technology solutions are evaluated based on conformity to the following ISO standards: • ISO 37106 Guidance on Establishing Smart City Operating Models for Sustainable Communities • ISO 27001 Managing Information Risks

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• ISO 27017 Controlling Cloud-Based Information Security • ISO 27018 Protecting Personal Data In addition, the following standards, toolkits, and strategies are also considered as technology solutions are developed. This ensures continued interoperability between the technologies and other existing community systems and services. It also increases the opportunities for infrastructure replicability and scalability. • Report to the Clerk of the Privy Council: A Data Strategy Roadmap for the Federal Public Service • Government of Canada Digital Standards • Canada’s Spatial Data Infrastructure • Canada’s Digital Geospatial Metadata • Canadian Standards for Big Data Analytics • Cyber Security – Government of Canada • CIO Strategy Council • Smart Cities for All

Privacy, Security, and Ethics The quality, reliability, and integrity of information are critical to effective decisionmaking at the City of Edmonton. The City is committed to ensuring compliance with privacy and security standards for obtaining and using data as well as having mitigating controls in place to minimize risk. In addition, the City not only ensures compliance with controls but also prioritizes the ethical use of data. This section outlines how data is governed at the City of Edmonton and provides the framework for how data is managed throughout the implementation of technology innovation and Smart City projects. As an Open City, Edmonton is working to build new ways to share information with residents, find new opportunities for dialogue, and make programs and services easier to access – continuously enhancing the quality and increasing the quantity of information available through open data. By provisioning, delivering, consuming, and crowdsourcing data, the City, along with residents and partners, enhances services, stimulates economic opportunities, encourages innovation, and unlocks new social values. It is this approach that not only positions Edmonton as a leader in open government but allows the City to work collaboratively with other municipalities and communities to share resources and experiences that transform how governments interact with residents and partners.

Data Governance Data governance is a fundamental pillar in the success of digital transformation. The City is a recognized leader in the use of data as a strategic asset, and, from the awardwinning Open Data Portal to the innovative work in the Analytics Centre of

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Excellence, Edmonton has set the bar for municipal data governance considerably high. In recognition of these efforts, Edmonton was selected as the first Canadian pilot city for the What Works Cities Initiative, a program delivered by the Center for Government Excellence (GovEx) at Johns Hopkins University. As a result of this partnership with What Works Cities, the City of Edmonton developed a comprehensive Data Governance Roadmap to guide the work that will enhance the City’s ability to treat data as a strategic asset and lay the foundation for advanced data practices. This roadmap lists major milestones and associated deliverables, the majority of which are underway by a team of individuals dedicated to improving data management practices across the organization. This includes work in the areas of data quality and standards, prioritization for release, privacy and security, and data retention. Edmonton’s progressive data governance practices continue to support the advancement of the City’s open government initiative and leadership as a Smart City.

Edmonton’s Open City Initiative An Open City is a connected city. Edmonton is building an open and connected city, in which residents have the opportunity to collaboratively design, develop, and deliver innovative, inclusive, and efficient public programs, services, and policies. The City’s Open Data Portal was launched in 2010 and was followed by the Open City Initiative – a municipal perspective on the philosophy of open government – in June of 2014. The Open City Initiative guided the development of the Open City Policy which was adopted by Edmonton’s City Council on April 14, 2015. Since that time, the City has continued to progress in its open government journey. The basis of the City’s award-winning Open Data Portal and other Open City projects is that the City’s information is a public asset – consistent with privacy legislation, it exists readily in a portal that Edmontonians can easily find and use in ways that will help improve their quality of life. The City has established an Open Data Advisory Group with representatives from diverse business areas, including privacy advisors, legal advisors, and data stewards. The City has also established an Open Data Citizen Advisory Group where residents are engaged to provide their ongoing feedback and ideas. As an operational body, the Open Data Advisory Group also manages the open data lifecycle through robust data quality review and release mechanisms. In addition, Edmonton’s Open Data program established the Smart City Steering Committee with executive representation from across the City of Edmonton. The Committee oversees and supports the Open Data program as it achieves its goals and vision. By providing leadership support to the Open Data program, the Committee ensures value realization through an annual performance audit. As an Open City, the entire City of Edmonton organization is working to build new ways to share information with residents, find new opportunities for dialogue, and make services easier to access. Under the governance of the Open City Initiative and Edmonton’s Open Data Strategy, and with adherence to privacy and security standards that meet the expectations of regulatory bodies and residents, the

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application of the Smart City program will continue to demonstrate Edmonton’s leadership in the practice of open government and commitment to building a city of the future alongside residents and partners.

Security, Privacy, and Ethics Considerations The City of Edmonton recognizes the need for community and residents to retain control over sensitive and personal data and for them to understand how this information is being protected. The City is committed to ensuring compliance with privacy and security standards for obtaining and using data as well as having mitigating controls in place to minimize risk. Ongoing efforts are made to integrate security and privacy considerations raised by users, residents, and partners throughout project implementation. Individual project plans will have a privacy and security component that will be developed through ongoing consultation with residents and stakeholders to ensure their expectations are met and to further the collective understanding of ethical privacy and security measures. In addition, the City of Edmonton recognizes the importance of privacy to residents as technology advances and the use of big data increases. As such, the City is prioritizing the development of mitigating controls in partnership with residents as this field evolves. The City is focusing on assessing the ethical considerations that go beyond current legislation related to data usage and analytics. Researching and applying leading practices is prioritized, including referencing the Information Accountability Foundation’s Essential Elements of Accountability, the United Nations Global Pulse Risks, Harm and Benefits Assessment Tool, and the Open Data Charter 2019 Strategy: Bringing Power into the Open. To accommodate development and growth of Edmonton’s Smart City program, privacy and security are being approached through an ongoing, cyclical process. When new projects are identified or a change in direction of an existing project or initiative is deemed necessary, the project will be evaluated for privacy and security implications prior to any action being taken. Privacy and security will be considered throughout the lifecycle of all projects, and any new ideas, data, or changes in approach will be analyzed through a standard privacy and security assessment. Ongoing reviews of existing projects and initiatives will ensure the Smart City program is continuously meeting the privacy and security needs of residents and partners in the Smart City Ecosystem. This includes the completion of Privacy Impact Assessments and receiving their acceptance from the Office of the Information and Privacy Commissioner of Alberta.

Resident and Community Engagement Public engagement is a critical component of all decision-making, and the City of Edmonton has robust processes and standards to ensure engagement activities are meaningful and accessible. Edmonton is a city that enables and values the

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participation of residents to define and achieve a better quality of life. The City is committed to seeking diverse opinions, experiences, and information through inclusive public engagement practices. Active, engaged Edmontonians make for a more vibrant and connected city as they are contributing to the enhancement of the City’s policies, programs, projects, and services. The City of Edmonton’s Public Engagement Framework is part of the City’s overall commitment to open government – Edmonton is an open, innovative, inclusive, and engaged city. Building such a city takes foresight, planning, and active participation by residents. An Open City creates opportunities for diverse input and participation, inviting residents to play a larger role in shaping their community and enabling social and economic growth. The City of Edmonton, by applying diverse methods of engagement throughout the implementation of Smart City projects, will continue to ensure ongoing alignment between the program’s outcomes and the concerns and needs of residents and stakeholders. As projects are identified, engagement plans will be built in collaboration with residents and partners. This will allow for residents and partners to shape the activities to best suit the outcome of the project and to apply learnings from previous engagement activities. A component of the engagement plan for each project will include a change management approach – the steps that will be taken to gain acceptance and onboard residents and stakeholders throughout the project implementation and beyond. It will also include a comprehensive communications plan that identifies how residents and stakeholders will be informed of how their input influenced the development, implementation, and sustainability of the project. Whenever possible, the City will work internally to identify opportunities to collaborate on engagement activities with other projects and programs that seek similar outcomes, so as not to overwhelm residents and stakeholders with multiple activities or events on very similar topics. The City will also work with partners to identify other similar opportunities for collaborative engagement activities.

Engagement Tools Building relationships with diverse communities through public engagement is a priority for the City of Edmonton. In collaboration with residents, community leaders, and service providers, the City develops engagement activities to best suit the needs of residents and makes use of a diverse array of engagement tools to ensure a meaningful connection with residents and stakeholders. These tools can be adapted to target different population groups in order to encourage high participation. A sample of these tools is provided in Fig. 5.

Engagement Activities The development of Edmonton’s Smart Cities Challenge Proposal was informed by 16 months of intense, focused engagement with stakeholders to understand what

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Fig. 5 Engagement Tools

makes Edmonton a Smart and Healthy City. This specific engagement was built upon 11 years of previous engagement that shaped Edmonton’s 2009–2018 Strategic Plan and its subsequent initiatives, as well as the recent work that was done with the community to revise the plan for 2019–2028. To provide transparency to the development of this approach and encourage participation, the City: • Advertised in print media and through posters in libraries, community centers, social agencies, safe houses, and shelters • Used Twitter and Facebook to update progress and solicit ideas • Gathered 260 distinct viewpoints from more than 1,000 individuals in the newcomer, Indigenous, low-income, homeless, vulnerable youth, seniors, and LGBTQ2S+ communities through communication vehicles reflecting their preferences, including in-person workshops, interviews, and paper and electronic surveys

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Fig. 6 Results of engagement activities conducted as part of Edmonton’s final proposal in Infrastructure Canada’s Smart Cities Challenge

Figure 6 provides a summary of the activities completed between July 2018 and February 2019 in which residents and stakeholders shared their stories and ideas. It highlights the success achieved in applying this approach to resident and community engagement during the finalist phase of Edmonton’s participation in Infrastructure Canada’s Smart Cities Challenge. Throughout the course of the public engagement activities for the Smart Cities Challenge, the City of Edmonton received overwhelmingly positive responses from residents, service providers, academic institutions, and the private sector regarding the pursuit of building a smarter, healthier, and more connected city. Residents and service providers will be invited to provide ongoing input into the development and expansion of engagement activities as Smart City Edmonton progresses. The City will continue to engage with residents using approaches that are meaningful to them to facilitate and encourage broad participation so that Edmonton continues to be a community in which residents lead the development of the City’s long-term strategic priorities.

Inclusive Engagement The City of Edmonton is committed to ensuring all residents have the opportunity to participate in civic life. To help formulate the strategy for inclusive engagement, the City of Edmonton worked with the nonprofit and academic sectors to learn about meaningful engagement activities for newcomers to Canada; the urban Indigenous

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population, seniors, children, and youth; and those with experience living in poverty and homelessness. The City also met with subject matter experts in the nonprofit sector, government, and academia to discuss opportunities for future collaborations on engagement activities and potential partnership opportunities within the Smart City Ecosystem. All engagement activities and plans will continue to be developed by applying a diversity and inclusion lens through consultation with subject matter experts, community leaders, and service providers. As the program evolves and projects grow, engagement processes will be modified based on feedback from the community to ensure they remain relevant and reflective of the diverse needs and aspirations of residents. In order to mitigate the potential for unintentional effects or bias toward certain population groups to arise as a result of engagement, the City will work with community leaders to understand the diverse needs of individual groups and design plans and activities collaboratively. The City will ensure the community retains ownership over the information gathered throughout the engagement process and remains informed as to how the information is being used to inform, enhance, or build projects.

Conclusion Smart Cities hold the promise to create healthier urban environments where residents can live their best lives. An open, inclusive, and collaborative community is foundational to success. Edmonton, as an Open City, learns from and integrates aspects of other open government initiatives. The City is evolving to collect and share data that will influence how public services are designed and delivered globally. Through this mindset of continuous learning and evolution, the City of Edmonton is a collaborator and contributor to how other communities can increase their capacity for open government. This means reducing socioeconomic, physical, and technical barriers and creating accessible channels for delivery of effective programs and services. Smart and connected cities have vibrant public spaces, creative and diverse residents, opportunities for economic development, and smart technologies. Connected cities have inclusive and innovative spirits that challenge the status quo and overcome barriers collaboratively. They are the cities that are transforming the regions they occupy and influencing community development at a national and global level. Edmonton is one of those cities and recognizes the importance connected communities play in building connected regions and ultimately a connected nation. Edmonton is Canada’s Most Open City (Public Sector Digest, 2015, 2016, and 2017) and a Top 7 Intelligent Community (Intelligent Community Forum, 2017). It is a place where the community leverages data and connected technology to become more engaged with one another through social interactions such as volunteering, celebrating, or just being together in shared spaces. In Edmonton, progress is linked to and driven by community for community. Connecting with others – across cultures, age groups, geography, and communities of interest – is seen as essential for creating a vibrant, connected, engaged, and healthy community for all.

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From Invention City to Innovation City: The Case of Racine Wisconsin Peggy James and William Martin

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Racine, Wisconsin, Small Town USA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Advantages and Disadvantages of Smaller Urban Contexts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Importance of Strategic Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Importance of a Middleman in Public Private Partnerships . . . . . . . . . . . . . . . . . . . . . . . . . . . Establishing City Priorities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Community Wide Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Energy and Sustainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Mobility and TF Century Transportation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Priority of Inclusivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Racine, Wisconsin, formally declared itself to be on the smart city track in 2018. Only 2 years in, this is an examination of the political, economic, and social challenges that have faced the city to date, and the significant accomplishments it has logged along the way. And, as we narrate the process, the overwhelming objective reasoning (the social good) of the citizens will be highlighted as a dominant and leading force in the smart city imaginary of Racine.

P. James (*) Political Science, College of Social Sciences and Professional Studies, University of WisconsinParkside, Kenosha, WI, USA e-mail: [email protected] W. Martin (*) City of Racine, Racine, WI, USA e-mail: william.martin@widiversified.com © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_39

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Introduction As is evident throughout this book, smart cities can manifest in a variety of ways, depending upon the empirical evidence of the integration of technology and policy, and their inevitable interaction with the values of city policy makers. It has been suggested that the smart city is a sociotechnical imaginary (Sadowski and Bendor 2018) following the theoretical construct of Lacan, as it has a dynamic tension between subjective reason, represented by technology and its agents, and objective reason, the social good. Those who present this conceptualization critique the smart city as one that is dominated by the subjective, or individual interests, of an elite group (Cugurullo 2018; Sadowski and Bendor 2018). However, the theoretical conceptualization of the smart city imaginary is challenged by the empirical practices of its development. Taylor Buck and While (2015) argue “. . . the smart city discourse (including its critiques) is often rooted in the expectation of transformational systemic change that overlooks the roll out of the smart city through multiple incremental and smaller scale changes.” We interpret this as an acknowledgment of the pulling and hauling of the political decision-making process, where the decision context (actors, agendas, time constraints, learned behaviors, etc.) has as much to do with the final outcome as the original objectives. That is, the smart city imaginary can have many outcomes, and in order to understand how it got there, we need to be cognizant of the pulling and hauling throughout the process. With this in mind, this case study of Racine Wisconsin offers an insight into the very beginning of the smart city political process. Little work has been done on the political challenges that cities face at the zero point before they begin their smart city journey. At this zero point, there is no smart technology or information and communications technology (ICT) that dominates the decision-making agenda, only a goal to transform the urban landscape.

Racine, Wisconsin, Small Town USA Racine, Wisconsin, is a city transitioning from heavy traditional manufacturing to a postindustrial urban area. Situated on Lake Michigan between Chicago, Illinois, and Milwaukee, Wisconsin, it has seen declining economic development and population loss as many traditional factories have either closed their doors or moved away from the city into the more available farmlands to the west. Since 1970, the population has been slowly declining from a high of 95,162 in 1970 to 77,432 in 2018 (http:// worldpopulationreview.com/us-cities/racine-wi-population/). The population of the surrounding Racine County has seen modest increases in those years. With a poverty rate in 2017 of 20%, and a mean income of $31,111 in the same year, it has faced the daunting task of increasing the quality of life for its citizens by ensuring that those who live in the city are an employable workforce within new economic development. When compared to the notable smart cities in the United States, such as New York City; Boston, Massachusetts; Chicago, Illinois; Columbus, Ohio; Las Vegas,

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Table 1 Comparison of Racine to leading smart cities in the United States City Racine, WI New York, NY Chicago, IL Boston, MA Austin, TX Las Vegas, NV Columbus, OH

College graduate 16.1 27.4 38.4 48.5 50.4 23.9 35.7

Retail sales per capita 7944 11,067 8335 12,389 17,491 14,335 16,194

Poverty 2017 20 19 20.6 20.5 15.4 14.5 20.8

Population 77,432 8,398,748 2,705,994 694,583 964,254 644,644 892,533

Median family income 47,431 68,353 64,441 76,603 87,200 57,490 61,094

Source: US Census, https://www.census.gov/quickfacts/fact/table/US/PST045219

Nevada; and Austin, Texas (https://www.americancityandcounty.com/2019/05/08/ the-smartest-cities-in-the-u-s/), it appears to be an unlikely candidate for smart city development (Table 1). Easily the smallest city in terms of population, it also ranks the lowest in college graduate, retail sales per capita, and median family income. And, while the 20% poverty is slightly lower than three of the smartest cities, it is not balanced by any other income measure. Given that infrastructure (human and physical) and capital are two of the basic requirements to build a smart city, this puts Racine, and other cities like it, at an immediate disadvantage (Albanese 2018). Yet, with the estimate that 87% of the US population will live in urban areas by 2050, and the fact that only 349 of cities in the United States have a population of 100,000 or more (http://worldpopulationreview.com/countries/united-states-population/cit ies/), we need to be prepared for the urbanization of smaller towns and cities, with the accompanying need for efficiency and efficacy of services. Racine, and other towns like it, can be role models for the smaller urban areas. Chelsea Collier (2018) states: Some of the advantages that come with a smaller city include the ability to be more nimble. Overcoming the gridlock and regulatory red tape that plague many large metropolitan areas is easier when there are fewer people to slow down the process. Personal connections also come into play in a community where people know each other beyond titles. Being able to pick up the phone and discuss a new idea or project with someone you know often eases the difficulty of getting things done. Also in a less populated metropolitan area, it is easier to connect with the city’s residents. When designing solutions for friends and neighbors, it becomes more clear [sic] that smart cities is about people.

Racine, with a smaller population struggling with deindustrialization, can be considered a bellwether for many cities in the Midwest and across the country for the potential of successfully developing a smart city that couples strong economic development with public inclusivity and equity. Because of the qualitative relationship differences identified by Collier, there needs to be a communication strategy at the foundation of becoming a smart city. Racine Mayor Cory Mason began promoting his smart city vision with two basic messages: (1) an emphasis on a specific interpretation of a smart city as one that is inclusively responsive, equitable, efficacious, and organic

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with the objective of well-being for all citizens, and (2) connect the future vision of Racine with its past so as to demystify the smart city idea. The first message meant that all citizens, regardless of income, race, ethnicity, or education, could expect to benefit from a smart future. The second resulted in an intentional connection between innovation and invention. Racine is known as Invention City, due to the amount of products that emerged from the Racine in the first half of the twentieth century. Most of these inventions were directly related to the manufacturing industry and, as that declined, so did the reputation (Buenker et al. 1998). Tying the smart technological innovation to that past makes the step into the future more believable and acceptable to many of the long-term residents. Without certain events, however, it is unlikely that Racine would have stepped into this challenge. Economic activity in the area prior to 2017 was characterized by a struggling revitalization effort to recover from the 1980s when it lost 1000 jobs per year and the exodus of many downtown retailers (http://www.city-data.com/uscities/The-Midwest/Racine-Economy.html). But in 2017 Foxconn Corporation announced it would build its first facility in the United States, promising a 10 billion dollar investment over 3 years and the eventual creation of 13,000 jobs (https://www. theverge.com/2017/7/26/16034394/apple-iphone-manufacturing-foxconn-wisconsinplant-donald-trump). This announcement immediately generated a cluster effect of economic development in Racine County. Other corporations moved or expanded their activities in Racine and surrounding areas. Anticipating the needs of an increased population, roads were improved, health facilities were built, and housing developments began. The scene was remarkably similar to that which had occurred in the Reno-Sparks, Nevada community with the arrival of Tesla in 2014. Mike Kazmierski, president and CEO of the Economic Development Authority of Western Nevada (EDAWN), made these comments to Racine in 2018: • • • • •

Like Nothing You’ve Ever Experienced Before Unexpected 2nd Or 3rd Order Effects Massive Jobs Multiplier Effect – 2.84! Great New Secondary Job Opportunities May Feel Some Indifference: By The Rest Of The State: Many Not Actively Engaged; Some Will See This As Your Problem; Some Will Resent Your Success And Visibility • Surprising How Difficult It Is To Address Serious Issues Even After They Reach Crisis Mode And, one of the most important lessons was to recognize the importance of responding, rather than reacting; the need to develop technology that was not connected ONLY to the needs of the corporation. This is especially important in a smaller urban environment that can easily be overshadowed by corporate friendly technology to the point where the city loses its identity and the ability to follow its own agenda.

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Advantages and Disadvantages of Smaller Urban Contexts When we speak of the flexibility and adaptability of smaller urban contexts such as Racine, we are really talking about resilience (Bec et al. 2016; Zautra et al. 2008). Still, Bec and Moyle (2018) also find that there is a negative relationship between resilience and perceptions of change, suggesting that conservative reluctance to change the status quo can negatively impact the ability of a city to dynamically work with change. And, resilience is most effective in a smart city future when the model being used is based on a community city model. As James et al. explain in an earlier chapter, smart cities can follow a variety of pathways in terms of their relationship to technology. Small, postindustrial cities like Racine are best served by following a community pathway driven by social innovation that is geared toward meeting the already existing needs of its citizens. There are postindustrial cities in the United States that are struggling to meet the needs of their citizens. “Perhaps the most important common factor that many highperforming legacy cities share is an eye toward the future. Instead of trying to revive the industries that built them, the most successful cities are finding creative ways to reinvent themselves” (Boone 2017). For Racine, the priority of inclusive innovation in economic, political, and social development requires access, and collaboration. The groundwork for this has already been laid through an S.C. Johnson Foundation convening on Resilient Communities. S.C. Johnson and Son, Inc. was founded in Racine in 1886, and is currently in its fifth generation of family leadership. The Johnson Foundation, a philanthropic nonprofit trust established in 1928, is an educational center devoted to the facilitation of constructive and purposeful ideas. A forward thinking company, it removed chlorofluorcarbons (CFCs) from its products 12 years before the Montreal Protocol. Small urban contexts do have disadvantages, however, especially in the United States. Nationally, the United States is behind in smart city initiatives spending, accounting for only 4% of smart cities globally. Only four cities (New York, Los Angeles, Washington, D.C., and Chicago) are forecast to spend more than $300 million on smart city programs in 2020 (https://www.planetizen.com/blogs/105424us-falling-behind-smart-city-deployments-and-key-21st-century-infrastructure). The United States is also behind in 5G, considered as the backbone of any sustained and coherent smart city development. One of the significant obstacles to smart city advancement in the United States is the legal action filed in 2019 by over 80 cities against the Federal Communications Commission’s Accelerating Wireless Broadband Deployment by Removing Barriers to Infrastructure Investment rules (https://docs.fcc.gov/public/attachments/DOC-353962A1.pdf) which largely favored corporations over the local governments by removing decision making regarding the deployment of 5G cells. Small cities are even more vulnerable to this threat to local power, since they can be largely outmatched by corporate investors and business interests.

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Outside of the national context, local governments of small cities face the challenge of underdeveloped infrastructure and a lack of funds to invest in technology. This is especially true of the legacy cities, who are trying to recover from decades of a declining tax base, aging infrastructure, and governing that is largely based on crisis management rather than long-term strategic planning. Racine is a textbook case of just such a city. Henken and Amenta (2018) described the fiscal health of Racine: Its operations are heavily dependent on only two revenue sources – property taxes and intergovernmental revenues – both of which are severely constrained by external factors and neither of which has grown at the pace of inflation. . ..Overall, the City’s constrained revenue portfolio poses a significant challenge to its ability to maintain service levels and invest in quality-of-life and economic development initiatives.

The Importance of Strategic Planning Strategic planning to assign priorities of develop is vital in order to navigate the challenges small cities face and maximize opportunities. However, because of the dynamic shifting nature of opportunities and the fast pace of innovation, a master plan that is resistant to change may result in an inability to take advantage of shortterm developments. The City of Racine consciously eschewed a grand strategic plan in order to enable it to move quickly when the opportunity arose. Instead, the City encouraged the adoption of guiding principles that included (1) a realistic set of priorities, (2) a commitment to involve all stakeholders, including the private sector and the local universities, (3) prioritizing a 5G infrastructure, (4) creatively rethinking transportation. (Beyond the Hype Factory: 7 Steps to Make Cities Smarter. https://www.iotworldtoday.com/2017/10/05/7-smart-city-strategies-cities-across-world/)

Stakeholder Involvement Stakeholder involvement, so important in a small city, began with the successful collaborative effort to become the smallest designated smart city by the Smart City Council in 2019. In acknowledging the award, Mayor Mason recognized the important contributors to the proposal as Gateway Technical College, University of Wisconsin-Parkside, Racine County Economic Development Corporation, Racine County, and Foxconn Technology Group (https://journaltimes.com/news/local/govtand-politics/smart-cities-council-picks-racine/article_3654d5f6-4b4b-55c6-bdba-50 83083971e3.html). This initial core team represented private business, government, and university sectors, a combination that has long been considered essential to smart city planning and development. As stated earlier, Foxconn Technology Group was the stimulus to the smart city initiative, and remains a partner in the effort. The City of Racine continues to work closely with Foxconn Technology Group, which is investing $10 billion to build in the region the first-of-its-kind advanced manufacturing and research facilities and is opening an Innovation Center and other related facilities in the City’s downtown. However, it is important to note that Foxconn is not the sole driver of the private

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sector contribution. As a postindustrial city with a legacy of family-owned companies that still dominate the economic landscape, Racine welcomed the contributions of a number of corporations such as SC Johnson, CNHi, Twin Disc, and Insinkerator. Although this is a short list of private corporations and businesses, they are mentioned here because of their long-term participation in the Racine economy. SC Johnson and CNHI were founded in Racine in the nineteenth century, while Twin Disc and Insinkerator were founded in the early part of the twentieth century. As such, they represent an important and continuous link between past manufacturing and the future of Racine. Cluster development is also occurring; Foxconn suppliers in Racine County have been awarded 370 million dollars for construction work (Anderson 2020), and 23 new businesses in Racine’s downtown district in 2018 represented a 255% increase from 2016. Market rate housing with 850 units and an annual economic impact of 7.6 million dollars indicate that the city is benefitting from the Foxconn buzz as well as the smart city commitment from the Mayor’s office (Kruse 2019; Racine County’s 2020 budget is 167.8 million, https://www.racinecounty.com/government/finance/ finance-reports/executive-budgets, and the City of Racine’s budget is 215 million). The City of Racine is not the only government player in the smart city strategy; Racine County is a significant partner not only for the economic development potential but for its networking infrastructure that can be connected to the city. One thing that smart cities find to be challenging is the need to transcend traditional geographic borders using collaborative governance in the investment and application of technology (Soe 2018; Meijer and Rodríguez Bolívar 2016). ICT cannot do this automatically, especially in the historical context of smaller cities accustomed to competing with other local municipalities for dwindling funds from the state, federal government as well as a declining tax base. In 2019, Racine began leveraging state programs in the form of opportunity zones. In order to encourage economic investment and growth in distressed urban areas, the US 2017 Tax Cuts and Jobs Act established opportunity zones in all states (Congressional Research Service 2019). There are three tax incentives for investment in these zones: (1) temporary deferral of capital gains taxes on investments, (2) step up in basis for investments after 5 years, (3) exclusion of capital gains taxes after 10 years. In cooperation with Legacy Redevelopment Corporation, Racine is the first Wisconsin community to set up an opportunity fund to take advantage of the three zones in Racine. Investors can contribute to the fund with a minimum of $25,000.00 and the fund can be used for a variety of startups, development projects, or businesses. As stated earlier, no city government has the ability to engage in smart development, especially in ICT, without help from other sectors, and this is especially true of the smaller cities. Institutions of higher education (IHEs) and citizen groups are vital to support the process in the City of Racine. Even though it is a small city, Racine has the benefit of three IHEs within or on the edge of its boundaries. Two are 4 year Baccalaureate granting institutions, while the third is a 2 year technical college. The role of these institutions in the smart city process has not been the usual one many assume when thinking of universities as flagship research centers. These

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institutions have contributed to the functional needs of planning, workforce training, and citizen buy in which were so vital for the region. The relationship between the IHE’s government and the private sector has been through a process of knowledge creation, diffusion, and implementation. Carayannis and Grigoroudis (2016) provide an excellent conceptual and strategic discussion of how smart specialization can take place in this type of functional relationship, using a dynamic six-step process first described by the European Commission. The relationship is based on collaboration, planning, and functional feedback, rather than a series of discrete, possibly unrelated activities. An example of this collaboration is found in the development of a 2 year Associate Degree program by Gateway Technical College in Advanced Manufacturing, followed by a graduate certificate in Smart City Policy offered by the University of Wisconsin-Parkside. These complementary programs serve the needs of workers who need to upskill for industry 4.0 in robotics, machine interaction, networking, and intelligent automation; the graduate certificate provides the skills for the next level of company or government management by focusing on public private partnerships, smart city policy, and civic technology. Recall that one of the Mayor’s goals was to ensure that city residents could take advantage of smart city employment opportunities. These IHEs, smaller than flagship universities, had the ability to quickly develop and offer practical education packages that would help him achieve that goal. The fourth stakeholder in smart city development is the citizens. Often ignored in early smart city planning, we have learned that the citizens are vital for both success and sustainability. They are the group that transforms the triple helix into the quadruple helix. As we increasingly concentrate on inclusive access, it is not surprising that, once given access, citizens wish to participate in the decision-making process. Without their input, and their buy in, the city of Racine will not be able to sustain the smart city focus. Visioning Greater Racine is an example of such a group, tasking themselves to be a “networked-community initiative using the proven VISIONING process with the goal of transforming Racine into a flourishing place we are all proud to call home by 2030” (https://www.visioningagreaterracine.org/). Although formed prior to the smart city vision of Racine, they have embraced it as a necessary step toward accomplishing their mission. Members represent government, businesses, nonprofit organizations, and universities. As such is a thread that interweaves the quadruple helix. All of the above stakeholders, and more, were brought together in the “Smart City September” events in 2019 (https://racinesmartcity.com/#about). Three events were held. The first two allowed participants to work toward an understanding of smart cities, and engaged them in the practical applications of these concepts within the local context. Each of these events engaged over 400 participants, including leaders from the local universities, government, businesses, and nonprofit organizations. The events were absolutely essential for the involvement of citizens, and the collaborative efforts that were needed. Interestingly, these events would not have been nearly as impactful in a larger urban context, they would have been impossible to organize in a short period of time, and they also would not have been as needed.

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This is a smart city tactic for a small urban setting. The lesson here is that planning and politics is essential for successful launching in this type of urban environment. The third event was a celebration of Racine as an Innovation City, providing a connection to the city’s past, so as to make the future less daunting. The Racine Innovation & Technology Gala brought together the business community, government officials, educators, and nonprofit organizations to celebrate nominees for the Racine Innovation Awards and demonstrate how Racine is still shaping tomorrow. A second way to engage stakeholders, and to create new ones, was the use of tech prizes and competitions. Foxconn, partnering with the University of Wisconsin System, the Wisconsin Association of Independent Colleges and Universities, and the Wisconsin Technical College System, has sponsored two smart futures competitions. “The ‘Smart Cities-Smart Futures’ contest, a three-year competition that Foxconn said awards up to $1 million in cash and technical support to Wisconsin students, faculty and staff, last year drew 325 submissions representing 24 universities and colleges across Wisconsin, resulting in 12 final round winners” (https://www.jsonline.com/story/money/business/2019/09/10/foxconn-launches-sec ond-year-smart-cities-smart-futures-competition-wisconsin/2276843001/). In 2020, Visioning Greater Racine announced its own tech-prize competition (https://journal times.com/business/local/tech-contest-aims-to-make-racine-into-invention-city-again/ article_89b3e02b-1f62-51ca-8f65-9919bf444874.html).

The Importance of a Middleman in Public Private Partnerships Experience demonstrates that Public Private Partnerships (P3s) work best with the involvement of a mediator that has interests and connections in both the private and public sector. Dameri et al. (2016) cite the problem of coherence even within the triple helix partnership of government, private orgs, and universities. Without a central direction, coordinating the interests of all the key actors with the stakeholders expectations and needs, the smart city will remain an interesting innovative laboratory, but failing in creating public and private value for all in the long term. (Dameri et al. 2016, p. 2980)

As the triple helix model continues to evolve into a more collaborative, creative, and innovative partnership based on smart specialization, the role of the middleman becomes even more crucial (Poppen and Decker 2018). For small cities like Racine, which need to maintain decision-making autonomy in the face of private sector innovation, it is critical (Bielak et al. 2008). Middlemen can ensure that the partnership is a tool for better risk and cost allocation, not only a way to fill budget gaps, which is an ever present temptation for small city budget offices. As a nation, the United States lags behind most countries in the number of public private partnerships; enabling legislation in the states is lacking or incomplete, and there is little recognition of the need for a specific unit to ensure a successful partnership (Istrate and Puentes 2011). To compound the issue, in states with

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narrow enabling legislation, local municipalities have less experience structuring successful partnerships, since they have less ability to work within the enabled areas, usually transportation. In Wisconsin, legislation authorizes the state Department of Transportation to enter into build-operate-lease or transfer agreements with private entities for construction of transportation projects and for maintenance or operation of projects that are not purchased by the state upon their completion. An agreement may not be entered into unless the DOT determines that it advances the public interest and the private entity meets certain criteria. There is some good news, however; the National Law Review (2020) reports that 2019 was a banner year for P3s with a sharp increase in broader enabling legislation. And, with more cities pursuing this option, we can learn from the experience of others in this area. (Julie Kim and Mike Bennon (2017) have written an excellent case study and review of the Public Private Partnership process and structure. See P3 Project Structuring Guidelines for Local Governments.) Even though Racine is limited in formal arrangements, the City needed to establish a middleman to make the connections between all stakeholders, not only the private corporations. The City appointed its first Chief Innovation Officer in early January 2019 to facilitate and support Smart Cities initiatives, coordinate with partners, support the cross-functional team, work with stakeholders to develop a Smart Cities vision, goals and objectives, and action plan, and coordinate across the implementation of initiatives. Later that year, the City promoted the creation of Wisconsin Development Investments, LLC. The Innovation officer shifted his responsibilities to emphasize his role in the LLC as Executive Director. In this way a nexus was created between the city and private interests in economic development. The City of Racine has developed a number of informal public-private partnerships. At their core, those partnerships are based on several factors: shared public-private goals and objectives, unique private-sector capabilities or resources offered to enhance city services and outcomes for community residents and businesses, partner roles and responsibilities, delineation of public-private investments in initiative(s) and community, timeline or duration of the Memorandum of Understanding. These alliances represent a public private collaboration toward increasing economic competitiveness by leveraging the members’ assets as well as the city’s digital infrastructure.

Establishing City Priorities In keeping with the recommendation to keep strategy flexible and adaptive, Racine established priority areas, rather than develop a full strategic plan. There were problems to avoid, notably the intransigence of legacy governments, enclave development, and isolated projects. The last two were contrary to the city’s values of inclusivity, based on the practicality of ensuring that all residents could benefit from smart policies. As such, they were problems in the present and future; legacy governments represented the challenge of the past. Legacy governments represent an inheritance of particular behaviors and policies that worked well in the past, but may not be effective or equitable in the current day. The difficulty is that many

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residents, and politicians, still prefer to work within those behaviors, which can be characterized by power building, competitiveness, and opaque political dealings. None of these behaviors are useful in a smart city environment – worse they hinder technological progress. How does a city meet these challenges? In Racine, it began with a call for citizen engagement and broad stakeholder inclusion, short- and long-term planning, and an intimate understanding of the community needs and values (Heiskanen and Acharya 2017). Initiatives that appear to be complementary from a planning standpoint may not be compatible with public needs and will result in a rejection of technological improvements. For example, in Racine, public transportation is an identified challenge, yet public transportation and multimodal transportation, which is often in the private sector, are not easily integrated. Transportation is also one area where an enclave economy, the development of a business sector completely isolated from the local regional economy, will have a profound impact. Corporations setting up business on the fringe of an urban area may influence transportation lines that benefit them, but result in asymmetrical service provision for most of the population. This is a local concern in Racine, as many larger corporations seek to facilitate employee travel, but may be less concerned with the impact on Racine proper. Citizen engagement and stakeholder participation are important considerations in governmental decision making; in Racine, these actors also influenced the strategic choices made by the city and the agenda for technological development. Smaller cities need to take opportunities as they arise, even while maintaining their own decision-making autonomy. For Racine, three priorities emerged in 2019: (1) Community-wide Connectivity, (2) Smart Mobility and 21st Century Transportation, and (3) Energy and Sustainability.

Community Wide Connectivity Some efforts at smart city development have tended to focus on technology embedded in projects that have had high visibility, yet very little contribution to the overall well-being of the citizens. Any project undertaken must be assessed on two levels – its immediate value, and its contribution to the process, emphasizing characteristics related to its long-term vision, integrative capabilities, and the impact it will have on the population (Van den Bergh et al. 2018; Bilbil 2017; Letaifa 2015; Chatfield and Reddick 2016; Ramaprasad et al. 2017). Applying these principles to the prioritization of projects, many cities need to concentrate first on needed energy and digital infrastructure to support projects in the short and long term (Batty et al. 2012; Bresciani et al. 2017). And, infrastructure development should have the priority of avoiding enclaves, such as innovation districts, or urban islands, that are not connected to the rest of the urban population. As we noted earlier in this chapter, inclusivity is a priority of Racine, and is vital for citizen buy in, a population where education income and employment gaps are wide. Unfortunately, digital infrastructure is not a flashy attention getter, and the Mayor’s announcement of a partnership with US Cellular to install 5G capacity in

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Racine had a short-term public relations boost, but not a really lasting impact. Many in Racine are not sure what 5G capacity means; many do not have the ability to take advantage of a 5G connectivity. Still, a long-term vision required that this be a major priority early in the digitalization process. All City of Racine traffic signals and street lights are equipped with municipal Wi-Fi. Over 250 city-operated cameras are controlled through that system. Currently, the City of Racine has established 27.5 miles of conduit and fiber optic cabling. The conduit and the cabling are currently underutilized and can be a source of leasing income for the city. With limited additional resources, all cities need to be more cognizant of the resources that they do have that can be leveraged within public private partnerships. The City has also budgeted to expand the existing network, and has developed a plan to install additional conduit and fiber to this public infrastructure as the Department of Public Works makes road repairs and improvements. One of the functions of the expanded network will be to support one of the City’s primary Smart City initiatives – autonomous vehicle transportation testing, operation, and evaluation. The City’s fiber optic network will be integrated with small cells to enable low latency high-speed 5G wireless technology. The ultimate goal is to make this powerful new public infrastructure ubiquitous and establish communitywide connectivity. Municipal leaders are working closely with technology and higher education partners, including Foxconn Technology Group, University of WisconsinMadison College of Engineering – a leader in autonomous vehicle research and development, and Gateway Technical College – the nation’s first publicly funded institution for technical education, to define hardware needed and mitigate potential risks such as technological obsolescence. The Connecting Our Community project is vital to the future of the City of Racine and its economy. The City of Racine project aims to accomplish a number of important objectives: advance equity and inclusion; enhance the competitiveness of its human talent across all socioeconomic backgrounds and age cohorts; make city services more accessible to residents, businesses, and visitors; and accelerate the community’s economic growth by embracing high-speed ubiquitous 5G wireless technology installation and encouraging rapid adoption. The City’s goal is to extend that network throughout the municipality and integrate with it small cells, which will be mounted to its light poles and other available assets. Single units can provide service for upwards of 30 access points in high-density areas, and can extend service to access points anywhere from 10 m to over a kilometer away. Through a collaboration with the University of Wisconsin Parkside’s GIS Factory, maps indicated infrastructure suitable for providing the City of Racine citywide 5G Wi-Fi and identified gaps or areas beyond a potential coverage area provided by this infrastructure. As is often the case, private companies and public institutions have their own connectivity framework; the challenge is to be able to integrate their capacity into that of the city, and create a seamless network. In Racine’s case, connectivity sources included the City and County of Racine, Racine Unified School District, Midwest Fiber Optics, University of Wisconsin-System, WE Energies, and surrounding municipalities of Mt. Pleasant, Caledonia, Sturtevant, Franksville.

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The addition of small cells to the City’s existing public infrastructure will offer ubiquitous connectivity, creating the foundation for, and making possible, publicprivate Internet of Things (IoT) projects that cannot be imagined today. This new 5Genabling public infrastructure will support a new era of innovation in Racine, attracting those interested in leveraging these new tools of technology to innovate and advance quality of life for many more. The public infrastructure combining its fiber optic network with 5G-enabling small cells will be launched along the lakefront/downtown area to support the testing, operation, and evaluation of the City’s autonomous vehicle project. The plan is to expand that combined infrastructure into additional areas of the community to coincide with the expansion of the autonomous vehicle transportation service delivery routes. The City will work with Foxconn Technology Group, University of Wisconsin-Madison College of Engineering, and Gateway Technical College to ensure the small cell units procured meet optimal specifications for the testing and operations for autonomous vehicle transportation. The City also will work closely with the telecom industry and other sector leaders to determine devices needed to launch, operate, and maintain an effective 5G-enabled network. As Racine works to expand its digital infrastructure, it has also entered into a 5G expansion partnership with US Cellular. Enabling legislation was enacted by the State of Wisconsin in July 2019. “The bill creates a regulatory framework for the state and local governments for the deployment of wireless equipment and facilities, including in rights-of-way; the permitting process by wireless companies; regulation for access to government structures by wireless companies; and allows local governments to impose setback requirements for mobile support structures” (https:// www.bizjournals.com/milwaukee/news/2019/07/10/wisconsin-ranks-low-in-mobileinternet-speed-new.html). This can be considered a catch-up process for both the state and the city of Racine, since the push to provide this legislation and to enter into a partnership came from private sector needs for digital infrastructure to support AI, high definition resolution, and advanced manufacturing technology. In neighboring Milwaukee, which is part of the partnership with US Cellular, the impetus came from the Democratic National Convention, the nomination event for the party’s presidential candidate, coming to Milwaukee in summer of 2020. We think the external pushes are important to identify, as the process is not always about strategic planning – sometimes it is about seizing opportunities. The Cellular Telecommunications Industry Association estimates that installation of 5G-enabling technology and adoption of high-speed Internet access will result in an additional $118 million in GDP and 724 jobs for the City of Racine. The association estimates the increase in GDP to be $1.18 billion across Southeastern Wisconsin; over 7,200 jobs are expected to be created, all because of the integration and adoption of this powerful new tool (retrieved February 10, 2019, from: https:// www.ctia.org/the-wireless-industry/the-race-to-5g, CTIA, 5G Economic Impact by State: Wisconsin). Accenture, a multinational professional services company, offers similar estimates, indicating that small-medium-sized cities with population of 30,000–100,000 could see 300–1,000 jobs created as a result of embracing 5G technology (retrieved February 9, 2019, from: https://www.ctia.org/news/accenturesmart-cities-how-5g-can-help-municipalities-become-vibrant-smart).

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Energy and Sustainability An energy and fleet audit of Racine’s vehicles and city-owned buildings revealed 29,600 tons of carbon dioxide were emitted into the atmosphere in 2018. With this baseline, the city has prioritized opportunities for reducing its carbon footprint. One of the tactics to accomplish this has been to transition to electric public vehicles and buses. Illustrating the value of collaboration and partnership, Racine is a member of the Climate Mayors Electrical Vehicle Purchasing Collaborative, joining over 100 cities in 38 states. The Collaborative provides technical expertise to purchasers and a program that reduces the costs and barriers to electrifying fleets. In 2020, the City will partner with WE Energies on their SolarNow program, which will actually generate revenue for the City by creating solar panel arrays within the City limits. The energy company owns the solar panels and leases the city property; in return, the city will earn slightly over $2000.00 per month for 30 years. Aside from the monetary value, the arrays will encourage solar power in the city, as part of the Sustainability and Equitable Climate Action plan to implement an energy independence plan. Economic development in the downtown area includes a collaborative requirement to incorporate smart technologies, prioritize environmental sustainability, and LEED certification. (Leadership in Energy and Environmental Design (LEED) is a certification based on five areas: Building Design and Construction, Interior Design and Construction, Operations and Maintenance, Homes, Neighborhood Development. There are four ranking based on a point system – certification, silver, gold, and platinum.) In 2019, the Mayor created the Sustainability Task Force composed of representatives from IHE’s, local corporations, nonprofit sustainability groups, and city units. The task force provides continuity and coherence to sustainability projects, and is supported by the Coordinator of Sustainability and Conservation from the Mayor’s office. Originally the group was tasked with assessing energy usage of municipal buildings, but quickly extended its role to develop an assessment plan for the entire community. This will precede work on a sustainable action plan for the city, potentially using Detroit’s Sustainability Action Agenda as a template. Detroit involved multiple stakeholders in the development of the agenda, with 6800 citizens engaging in the process. The agenda holds the health and well-being of citizens at the center, focusing on equity and inclusivity as the core of sustainability, mirroring the goals of Racine. See Table 2 for a listing of Detroit’s Agenda Items.

Smart Mobility and TF Century Transportation Increasing mobility is a common priority for many cities in the United States. According to the 2019 Deloitte Mobility Index (https://www2.deloitte.com/us/en/ insights/focus/future-of-mobility/deloitte-urban-mobility-index-for-cities.html) all eight top performing cities for public transit supply were in Europe; four of the bottom seven cities for supply were in the United States. Why is this? Cities developed differently in the United States than in many other places across the globe.

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Table 2 Detroit Sustainability Action Agenda framework goalsa Healthy Thriving People

1. Increase access to healthy food, green space, and recreational opportunities 2. Improve air quality and reduce exposure to pollution 3. Advance equity in access to economic opportunity Affordable Quality Housing 4. Reduce the total cost of housing, including utilities 5. Improve the health and safety of existing and new housing Clean Connected 6. Transform vacant lots into safe, productive, and sustainable Neighbourhoods space 7. Reduce waste sent to landfills 8. Make it easier and safer to get around Detroit without a personal vehicle Equitable Green City 9. Enhance infrastructure and operations to improve resilience to climate impacts 10. Reduce municipal and citywide greenhouse gas emissions a

Source: Detroit Sustainability Action Agenda, https://detroitmi.gov/government/mayors-office/ office-sustainability/sustainability-action-agenda

Urban sprawl is much more pronounced resulting in lower density. The continued preference for driving one’s own car, and the affordability of doing so, results in less investment in public transport. And, even in those municipalities that create or maintain public transport, the systems are not integrated with population centers, meaning that many need to find transportation to the transportation. Racine has had a public transportation system since 1928 that has not been able to keep up with development patterns. Most of the service is close to the downtown, while residential and business development has increasingly used the space further west of the city. In 2013 a Southeast Wisconsin Regional Planning Commission, which included the County and City of Racine, evaluated the bus system and found unpredictable service, long waiting times, and, most importantly, a lack of access to many neighborhoods and businesses (see Map 1). From 2017 to 2018 all municipalities in Racine County increased in population with the exception of the City of Racine. According to the US Census, the 2018 combined population of these municipalities was 119,152 – all with little or no public transportation. Foxconn, along with dozens of other industries, is located outside the public transportation system, suggesting that employment opportunities provided by new or existing businesses are not available to Racine residents with no access to independent transportation. Many lower-income city residents do not have the personal transportation to reach the high-job areas outside the city’s corporate limits. Staffed bus routes to job growth pockets dispersed outside the city have proven cost prohibitive for relatively small ridership. Both the county and city of Racine are prioritizing a corridor to the larger industries to the west, but need to plan how this can be a platform for growing the transportation corridor to include other initiatives. Representatives from the Racine area, including government, university, and private sector members, worked with Kansas City to learn from their streetcar project. Kansas City put in a free 2.2 Mile streetcar supported by Wi-Fi along the

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Map 1 Public transportation coverage in the City and County of Racine

route, info on services and amenities in Kiosks, and integrated to other transit routes. They replaced water and sewer lines simultaneously and put in sensors for water management. The entire project incorporated citizen engagement; one of the consequences of this engagement was that each stop was moved at least once during the project, according to the input from the residents. Currently Kansas City is planning expansion of this route in two directions, linking to the University of Missouri-

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Kansas City and to the Riverfront neighborhood, long dormant and isolated, even though geographically close to downtown. Racine is learning from the Kansas City experience, ensuring that any transportation project takes advantage of the opportunities to include infrastructure improvements and embedded technology. Racine has largely rejected the expansion of public transport in terms of simply adding new routes, since that would be cost prohibitive, as well as continue the inefficiency of service. The development and expansion of an autonomous fleet of shuttles would support more flexible transit solutions; reduce the cost by operating small, driverless vehicles; attract new corporate partners desiring to participate in testing driverless technologies; and allow more city residents to gain those manufacturing job opportunities. City resident employment, earnings, and benefit coverage would increase, which would translate into greater economic impact and activity in the City of Racine. More families would be able to afford to buy and improve a home, rather than rent. In addition to increasing mobility and employment, and helping meet regional employers’ demand for the labor needed to continue competing and growing, such a project will augment the City of Racine fiber optic network. Just as roads, rail, ports, and utilities laid the foundation for future economic growth in earlier periods, the fiber optic network is the new public infrastructure that will fuel economic growth in the digital age. The City of Racine is soliciting corporate partners interested in leasing the expanded fiber optic network and/or attracted to expanding their business in the City of Racine because of the introduction of Smart Cities initiatives, such as the autonomous vehicle transportation and City-supported 5G integration. Besides the financial hurdles faced by this development, the city also must be a groundbreaker in the state of Wisconsin to promote enabling legislation that would permit a transportation partnership and allow testing on public roads. Part of the proposed test routing includes not only public city roads, but also state highways traversing through downtown Racine. The State of Wisconsin has been an early proponent of testing and advancing autonomous vehicle transportation, and the City of Racine is reaching out to the Department’s leadership in order to develop a partnership for the project. State statutes are silent with regard to the integration and implementation of autonomous vehicle operations. The City of Racine will need to take proactive steps to adopt new city ordinances permitting the operations of autonomous vehicles on public roads with the municipality’s corporate limits, and to integrate those vehicles with the county government.

Priority of Inclusivity The reader may have noted that all of the three priorities identified by Racine have been founded on the principle of inclusivity. Mayor Mason said at the very beginning of this effort that it was not worth doing if everyone in Racine would not benefit from the introduction of technology to the city environment. Technology is a neutral agent, and can be used to reduce gaps in opportunities or to widen them, depending upon the thoughtfulness of the politics behind the planning.

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When it comes to racial equity, the State of Wisconsin ranks at the very bottom (https://wallethub.com/edu/states-with-the-most-and-least-racial-progress/18428/). Significant gaps exist across racial and economic groups in education, income, and health. Residents have identified transportation, access to services, and a lack of information as barriers to decreasing that gap (Center for Urban Population Health 2015; Hess 2018). For city residents to benefit from a smart city, an infrastructure (physical and digital) needs to be in place to ensure equitable access to programs and opportunities. The National League of Cities (2017) identified action-oriented infrastructure development as one of the key tactics in a strategy to increase equity, efficiency, and community resilience. The logic is plain – equity is a relational and systemic policy demand; cities cannot address this through isolated, albeit well meaning, programs. Racine has made smart policy decisions to provide opportunities for an educated, mobile, and healthy workforce, integrating efforts of the private sector, universities, and government. Gateway Technical College, a 2 year institution, has initiated a degree in advanced manufacturing to enable graduates to increase employability in technology-based industries. The University of Wisconsin-Parkside has a Smart City Policy Graduate Certificate to train individuals in private public partnerships, civic technology, and smart policy making. Students will earn a graduate certificate emphasizing smart city policy making, the first of its kind in the United States. With smart cities more than doubling from 2018 to 2025, and smart city spending increasing to over 34 billion in 2020, effective city managers will need to manage the decision making and policy requirements of urban innovation. One of the ways to increase access to health care is to geographically decentralize service provision in both primary care and emergency services. Local, neighborhood-based services are more likely to be utilized as they are easier to get to, thus increasing the likelihood that residents increase their health literacy and practice healthy behaviors. In a move that builds upon Racine’s introduction of two community schools in the last 4 years, the city and county partnered with the private health sector, the Unified School District, and an IHE to establish a community health center at one of those community-based schools. “Instead of a family wondering, ‘Can I get to a hospital?’ Wondering, ‘Can I afford to set up an appointment for my child to get a physical?’ Now, I can walk from my house to the school,” says Shebaniah Muhammad, president of the Racine Community Health Clinic board of directors (Kraemer 2020). These equity targeted solutions, although they are not dominated by a technology component, are still smart city solutions. However, there are ways in which the digital infrastructure can contribute. The Smart Cities Council has optimistically suggested that the smart infrastructure might be more prominent at the nation level in 2020, with President Trump and the leading democratic presidential candidates all identifying it as a policy priority. Democratic candidates pledge to spend 1 trillion on infrastructure, as compared to the most recent expenditure of 459 billion in 2010 (Bane 2020). For example, Advocate Aurora Health and Foxconn Health Technology Business Group are partnering to transform health care in Racine by “enhancing preventive

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care and employer-based wellness programs; building a ‘smart city’ connectivity infrastructure; and investing in precision medicine and transformational training programs for a clinical team of the future” (Aurora HealthCare 2018). Increased connectivity between all levels of care will enable both providers and patients to benefit from more efficiency, allowing edge decision making throughout the sites, possibly enabling patients to make health-care decisions on their own. Besides increasing access across time and space, Foxconn’s technology will allow Advocate Aurora to utilize a predictive modeling platform that will enable more preventive care, reducing emergency room visits. The City government is also utilizing ICT to make its decision making more transparent and accessible to residents through a program called Connecting our Community, and to enable a more preventive approach to service needs. The City of Racine is making steady progress in this area, integrating multiple new technologies and making increasing use of existing platforms. For instance, the City of Racine has adopted CitySourced, a citizen engagement mobile application allowing residents to contact the City about a number of service needs, from garbage collection to code enforcement issues. CitySourced is an application specifically designed to support city-citizen interaction with data collection, analysis, and reporting as well as email and push notification functionality all built within the platform. The Racine Water Utility is currently working with the private sector to launch a mobile application that will allow residential and corporate customers to access data regarding their water usage. CitySourced will be included into the already existing Cityworks, which acts as a system of record and provides an ability to manage and schedule a wide range of city services. These Citizen Relation management tools increase responsiveness and communication with residents; the next step is to use the data collected at the edge to create a deeper understanding of community needs, and to predict where needs will emerge in the future. To that end, Racine is working with the national technology nonprofit, DataKind, as one of only six American cities to be awarded volunteer big data/data science services. DataKind is partnering with the City to analyze its neighborhood-based service utilization, which will inform internally developed, alternative service delivery recommendations so that the city can better serve or residents. Using AI pattern recognition methods to explore the spatial, temporal, and causal relations between city services, resources can be prepositioned and allocated more effectively with some problems being preempted altogether. Given the objective of increasing equitable and inclusive participation in Racine, government transparency and access is especially important to allow citizens to participate in the decision-making process. This is more than simple access to data or one-way communication; it involves the inclusion of the citizen voice in actual decision making (Kummitha and Crutzen 2018). This same consideration is important in providing ubiquitous access to 5G networks; citizens need to be able to access the network meaningfully, and defining that term within an equity framework may mean that different consumers participate at different capacities. For instance, civic technology can be provided, but citizens might not see the value of the technology for their own needs. Rolling out ICT needs to be accompanied by an information and engagement campaign to empower its users.

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The first step that Racine is taking toward that possibility is the use of electronic poll books in the 2020 elections. Only a handful of polling jurisdictions used e-books in 2012, the percentage increased to nearly 50% in 2016. E-books allow for shorter lines, reduce errors by updating voting records at the polling place, and can assist voters who may not be at the appropriate polling place in their jurisdiction. To do some or all of this, the software needs to be minimally connected to the election board’s network.

Conclusion As we said in the introduction, it is pretty much all about politics, and politics is based on values. Values often are battered by competing agendas, financial and regulatory constraints, and the fluidity of who is in power. Small municipalities are more vulnerable to these contexts than the larger megacities, so there exists an incentive to seize opportunities when they happen, resulting in what may look like a haphazard agenda. This is why it is preferable to speak about smart city priorities rather than a strategic plan. Still, due to the changing political landscape at local, state, and federal levels there is a concomitant need to institutionalize as much as possible, either through the political/legal system or through citizen support. No city can do become smart on its own – a small city will find the task impossible. Diverse and dynamic partnerships are essential. Especially in a small city environment, it is wise to not overly rely on any one source. Finally, we hope that this chapter will give some insight into the unique factors of an American City, as well as a look into the incremental decisions that need to take place at the very start of this transformation.

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Urban Innovation Ecosystem and Humane and Sustainable Smart City: A Balanced Approach in Curitiba Luiz Márcio Spinosa and Eduardo M. Costa

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Drivers for Smart Curitiba . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Humane and Sustainable Smart City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sustainable Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Urban Innovation Ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quadruple Helix as a Model to Bring Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Translating the Drivers into Policies and Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Policy-Mix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Curitiba 2035 Strategic Plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Translating the Strategies into Services and Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Cities Institute . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Curitiba Technopark and Vale do Pinhão . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Startup Movement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ICITIES and Smart City Expo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Urban Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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L. M. Spinosa (*) LabCHIS / Federal University of Santa Catarina (BR), Triple Helix Association (IT), Curitiba, Brazil LabCHIS – Humane Smart City Lab, Federal University of Santa Catarina (BR), Florianópolis, Brazil e-mail: [email protected] E. M. Costa LabCHIS – Humane Smart City Lab, Federal University of Santa Catarina (BR), Florianópolis, Brazil Knowledge Engineering and Management Dept., Federal University of Santa Catarina (BR), Florianópolis, Brazil e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_15

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Abstract

The need to foster more Humane and sustainable smart cities (HSSC) is a challenge in many cities all over the world. It is crucial for urban planners to take notice and to connect their projects to the HSSC concepts and also to the Sustainable Development Goals for 2030. This work explores a balanced approach observed in the city of Curitiba in Brazil, involving three leading components: (i) main conceptual drivers, (ii) a policy and strategic plan, and (iii) several projects under execution and already in place. A descriptive framework emerged from a triangulation method to organize the components. The main conclusions are: (i) there is a symbiosis between the urban innovation ecosystem and the HSSC implementation mainly involving the ICTs, (ii) there is a positive mindset for innovation in the city, (iii) the participation of the stakeholders in the innovation ecosystem and in the HSSC decisions facilitates the development of actions, (iv) the organized civil society plays a major role, and (v) co-creation and co-management based on a triple helix approach provide stability and reduce vulnerability. At the end, this paper presents some considerations about the framework to support the decision-making processes of innovation managers and urban planners.

Introduction The development of public policies and strategies driving the cities and regions to more satisfactory levels of intelligence and sustainability is a constant and imperative challenge for public managers and urban planners. This chapter focuses on this challenge and explores a balanced approach between an urban innovation ecosystem and a vision of a more Humane and sustainable smart city (HSSC). A HSSC is a foundation that joins concepts from citizen-centric urban development, smart cities, and sustainable development (Giffinger et al. 2007; Costa and Oliveira 2017). Innovation ecosystems inserted in the urban context are a great help for HSSC development, providing new solutions for common urban problems. To explain this balanced approach, this paper explores a set of initiatives occurring in Curitiba, a city in the South of Brazil. See Fig. 1 and Table 1. There is no official policy to transform Curitiba into a HSSC. Nevertheless, a descriptive framework arises from the current application of several concepts and models, the adoption of development strategies, and the execution of several actions. See Fig. 2. This framework emerged from an exploratory and descriptive research, combining triangulation methods (Minayo et al. 2016; Marcondes and Brisola 2014) with traditional approaches to social research (Bhattacherjee 2012; Gerring 2012). Three main levels describe the framework: (i) The conceptual drivers level, which introduces a theoretic background inspired by the areas of humane smart city, sustainable development, urban innovation ecosystem, and quadruple helix model,;

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Fig. 1 Curitiba’s location. Source: Google Maps(R).

(ii) The policy and strategic level, which translates the conceptual drivers into a policy-mix proposition and a strategic plan called Curitiba 2035 (2017), specifically designed by the Observatory of the Paraná Industry Federation (extract from http://www.fiepr.org.br/observatorios/) (iii) The implementation level, which translates the policy and strategies into information and communication technology (ICT)-based services and a portfolio of projects.

The Drivers for Smart Curitiba Humane and Sustainable Smart City The European Union approaches smart cities by six fields of study: smart living, smart people, smart governance, smart mobility, smart environment, and smart economy (Giffinger et al. 2007). Costa and Oliveira (2017) added to the list two more fields to apply the concept into emerging countries: smart social inclusion and smart safety place. It tackles poverty in cities, segregated in slums and ghettos, and the problems associated with rapid unplanned growth and geographical expansion.

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Table 1 Curitiba’s general information. Source: Curitiba 2035 (2017).

Territory Altitude Installation date Elected authority (2017–2020) Latitude Longitude Territorial area 2016 Population density 2016 Degree of urbanization 2010 Population Population 2016 Number of voters 2016 Geometric growth rate 2010 Senior index 2010 Dependency ratio 2010 Sex ratio 2010 Aging rate 2010 Human development index – HDI 2010 Education Nursery enrollment 2015 Preschool enrollment 2015 Primary school enrollment 2015 High school enrollment 2015 Higher education enrollment 2015 Illiteracy rate 2010 Health Health facilities 2015 Hospital beds 2015 Fertility rate 2010 Gross Christmas rate 2015 Mortality rate 2015 Public services Households 2010 Piped water households 2010 Household with bathroom or toilet 2010 Households with waste collected 2010 Electricity households 2010 Water consumption 2016 Electric power consumption 2015 Economy Establishments 2015

Curitiba

Curitiba’s participation Region Paraná

934 m 03/29/1963 Rafael Greca 25
250 4000 S 49
160 2300 W 435 km2 4.320 hab/km2 100%

– – – – – 5% – 94%

– – – – – 0,20% – 85%

1.893.997 1.289.215 1% 38% 38% 91% 8% 0,823

56% 59% – – – – – –

17% 16% – – – – – –

39.250 28.631 221.952 78.815 130.582 2,13%

67% 47% 51% 57% 93% –

22% 12% 15% 17% 33% –

5664 5580 1,58 children/woman 13% 6%

84% 68% – – –

26% 21% – – –

576.190 575.598 575.630 575.635 576.057 125.736.770 m3 4.733.290 Mwh

60% 60% 60% 60% 60% 63% 54%

17% 18% 18% 19% 18% 22% 16%

61.574

70%

20% (continued)

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Table 1 (continued)

Employment 2015 Establishments in activity characteristics of tourism 2015 Active age population 2010 Economically active population 2010 Occupied population 2010 Occupancy rate 2010 Gross domestic product – GDP 2014 GDP per capita 2014 Gross value added – VAB 2014 GVA agriculture and livestock 2014 GVA industry 2014 GVA trade and services 2014 Municipal revenue 2016 Municipal expenses 2016 ICMS 2015

Curitiba 914.006 5065

Curitiba’s participation Region Paraná 74% 29% 75% 25%

1.531.838 995.543 947.195 95% R$ 78.8 billions R$ 42.315 R$ 6.3 billions R$ 8.2 millions R$ 14.8 billions R$ 48.9 billions R$ 7.0 billions R$ 6.8 billions R$ 8.8 billions

58% 59% 59% – 59% – 59% 1% 50% 64% 68% 69% 57%

17% 18% 18% – 23% – 21% 0% 20% 25% 24% 24% 36%

These added dimensions are unfortunately becoming more important in European cities as well, as they face the influx of large immigrant populations. In these eight fields, there are good and bad examples to learn from, and cities are organizing themselves to exchange knowledge and share their experiences. Solutions to cities’ problems are inevitably interdisciplinary in nature. They involve the social sciences, with studies on people’s behavior in communities, urban studies of spatial distribution of people and functions, and studies of social networks and their use in the context of cities. These solutions also involve studies of computer technology, sensor technology and high-speed connections, electronic and participatory government, and big data and business intelligence.

Sustainable Development According to Van Bellen (2007), “despite the large amount of concepts and definitions, or perhaps exactly because of these, the exact meaning of the term sustainable development is unknown.” It appeared during a summit in 1987 with the publication of Our Common Future or the Brundtland Report (UN-WCED 1987). The Rio 92 Conference defined sustainable development as a policy that meets the needs of present generations, without compromising the ability of future generations to meet their own needs (UN-SD 1992; IUCN 1996). Sustainable development is an ongoing process, an evolution in which people act toward a development that satisfies sustainability requirements. Missimer et al.

Sustainable Development

Quadruple Helix

Fig. 2 The framework

Conceptual drivers

Urban Innovation Ecosystem

Humane Sustainable Smart City

Urban Planning and Management,

Safety

Policy and strategic

Health and Quality of Life,

Mobility and Transportation,

Socioeconomic Development,

Environment and Biodiversity,

Curitiba 2035

Policy-mix

Coexistence in a Global City,

Governance

City of Education and Knowledge

Implementation

Urban Projects

Curitiba Technopark and Vale do ~ Pinha o

Smart Cities Institute

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(2010) argue that “sustainability is a state, and sustainable development points at processes towards or within that state of being.” Sustainable development refers to meeting needs without overwhelming the rest of nature and society (Challenger 2013) and is the maintenance of certain desired and necessary characteristics of people, their communities and organizations, and the surrounding ecosystem for a long period (Hardi and Zdan 1997). Sustainability aims to maintain or increase human well-being. It is a balanced management of the relationships between people and the world around them. The idea is to foster the attention to the needs of people without undermining the world’s ecosystem. The Rio Conference also produced the Agenda 21 program (UN-SD 1992), which introduced a wide range of assumptions and recommendations on how nations should act to change their development and match acceptable sustainability levels. Agenda 21 is an action plan that covers the global, national, and local scopes adopted by governments and society. It embraces all areas where human actions affect the environment and seeks to guide a new pattern of development. This research assumes that sustainability and sustainable development involve a global awareness to preserve finite natural resources, reduce the emission of pollutants, search for social equality, and foster economic growth. All these efforts need to take place without degrading the environment.

Urban Innovation Ecosystem Scholars in the field of entrepreneurship have dedicated increasing attention to understanding innovation (Wright 2008; World Bank 2010; Kraemer-Mbula and Wamae 2010) and innovation ecosystems (IEs) (Gomes et al. n.d.; Nambisan and Baron 2013; Shaw and Allen 2016; EU-CoR 2016; Adner 2006; Hwang and Horowitt 2012; Jackson 2011). The term has partly replaced the “innovation regions,” “milieu innovateur” (innovative environments), and “clusters” established by Porter in the 1990s. Engel (2014) extended the concept of a cluster, incorporating relevant actors in a mature ecosystem to better represent IEs through their components and behavior. IEs are strongly associated with the “knowledge economy” and “knowledge society” and enable sustainable entrepreneurship and innovation (Lawlor 2014). According to Autio and Thomas (2014), an IE is a network of interconnected entities structured around an organization, which incorporates both production and user side participants and creates value through innovation. The term urban innovation ecosystem (UIE) derives from the previous definitions and adds an urban scope (Spinosa et al. 2015). The UIE describes the function or role of independent factors that act jointly, but randomly and spontaneously, to enable the actions of entrepreneurs and innovators to allow innovation to occur according to a sustainable process in a given territory. They are “competitive assets in the knowledge economy integrated with the urban and regional environment” (Spinosa et al. 2018). Studies on knowledge-based urban development (KBUD) (Knight 2008; Spinosa and Krama 2017; Yigitcanlar and Velibeyoglu 2008) seizes the main aspects of the

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urban dimension of the UIE. KBUD is urban planners’ response to the changes produced in the urban environment through technological, economic, and social advancements. KBUD intends to improve the city’s ability to attract, generate, retain, and foster creativity, knowledge, and innovation. The city is viewed as an integrated arrangement that combines physical and institutional science park functions with civic and residential functions, thus offering an effective paradigm for sustainable cities of the future (Yigitcanlar 2011).

Quadruple Helix as a Model to Bring Integration The quadruple helix model proposed by Carayannis and Campbell (2009) is based on the triple helix model initially developed by Etzkowitz and Leydesdorff (1995) and Leydesdorff (2013). They argue the need for an integrated effort of the academic, private, and government sectors to build innovation environments. The quadruple model adds a fourth dimension, the civil society, to consider a tighter interaction with the local community involved in the innovation process. Most actions in Curitiba devoted to foster innovation within the city consider the involvement of these four sectors. More, the quadruple helix model adds some advantages compared to the triple helix model: (i) enhances innovation processes based on cocreation, involving the four kind of stakeholders, (ii) highlightes the dynamics of open innovation, and (iii) solutions are designed considering regional and local contexts rather than external practices (McAdam and Debackere 2017). All those issues are suitable for urban planning.

Translating the Drivers into Policies and Strategies Policy-Mix Based on studies carried out in Curitiba (Spinosa and Krama 2017), a more harmonious option to turn around the current scenario is to adopt an extended policy-mix (Borrás 2008; Borrás et al. 2009; Borrás and Edquist 2013). The term policy-mix usually refers to the balance and interactions between monetary and fiscal policies. The extended notion adds a social development dimension, which gains significance in Brazil due to the need to include the lower-income population into the country’s development process. Also, this extended notion covers the need for a more equitable regional and urban development. A policy-mix essentially highlights the interdependence of policies and a more holistic perspective to understand the scenario that will change. Any intervention aimed at improving performance or change in behavior should be based on an understanding of how they will interact with existing agreements – for example, if they are complementary, neutral, or conflicting (OECD 2010a, b, 2011). The holistic

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perspective must cover the National and Regional Innovation Systems and consider them as innovation ecosystems as previously defined with a high degree of mutual interaction. The dynamics of the actors and factors of the ecosystems must be considered actual components of the innovation performance.

Curitiba 2035 Strategic Plan Curitiba 2035 (2017) is a project developed by the Curitiba’s local society to build long-term guidelines that will frame the city’s development policies in the next 20 years. The project is a prospective study to indicate a way to position Curitiba as one of the main innovative cities in the world. This prospective study was based on an organized process of collective reflection of diverse segments of society It prioritizes the themes and actions to the desired local transformation. The priority themes are (Curitiba 2035 2017): (i) city of education and knowledge, (ii) socioeconomic development, (iii) mobility and transportation, (iv) health and quality of life, (v) environment and biodiversity, (vi) coexistence in a global city, (vii) urban planning and management, (viii) safety, and (ix) governance. Education and knowledge are widely debated in the reengineering of the future of cities. In the context of Curitiba 2035, the this theme proposes a discussion about the city’s performance on the development of its people, so that they can understand, react, and intervene on the reality of the world around them. It also highlights the processes of creation, sharing and the use of knowledge within the municipality. Within this thematic area, issues such as management of education, new teachinglearning models, technologies and methods in education, knowledge, and others are addressed. Socioeconomic development is a field with a broad conceptual repertoire, involving the association of economic growth with the improvement of society’s quality of life. It proposes a new approach for the improvement of the living conditions of the population through the provision of new jobs and better income, as well as by the increase of productive capacity and circulation of wealth. It suggests a qualified development, with allocation of resources in different sectors of the economy, which has a positive impact on social indicators and the quality of life of the population in a balanced way with the growing of the economic indicators. Mobility and transport issues profoundly influence the planning processes of cities, giving rise to relevant debates on the present conditions and unfolding a series of challenges for the future, among which the environmental, economic, and social rights. Looking for answers to the environmental challenges, there is a tendency to strengthen collective public transport and non-motorized transportation, aiming at reducing greenhouse gas emissions. From the economic point of view, there is a search for a financial balance of costs, with appropriate, clear, and transparent political values. In the social logic, studies indicate a diversity of trends, such as the universalization of access, the adoption of vehicle sharing, and the greater security awareness.

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Health and quality of life are concepts with profound variations in the contemporary debate. In the scope of Curitiba 2035, the health theme proposes the discussion about services, management models, and technologies oriented to the prevention, promotion, and treatment of health-disease processes. The theme of quality of life covers issues such as humanization and modernization of urban spaces for the well-being of individuals, associated with aspects such as culture, sports, leisure, and other conditions. All efforts intend to the improvement of people’s lives in the physical, mental, social, and spiritual perspectives. Environment and biodiversity address the discussion about the relationship between society and nature in the urban environment, contemplating processes related to the conscious use of natural resources and biodiversity It emphasizes practices of preservation, mitigation, and environmental treatment, with the objective of reaching sustainable levels. The management of the urban-social-environmental system is an essential procedure to ensure that the next generations have adequate conditions of life in the long term. In this sense, measures to control, scrutinize, and protect the environment must be consolidated, along with programs that include basic sanitation, recycling and reuse of solid waste, correct treatment of waste, reduction of greenhouse gas emissions, and management of resources, among others. Coexistence in a global city proposes to discuss social interactions in a city that grows in the number and diversity of people. The theme has its relevance extended due to the simultaneity of cultural, relational, and identity phenomena that characterize the contemporary social structure of the city. The thematic area requires integrated thinking about social relations and urban space, with special focus on axes such as multiculturalism, diversity, equity, inclusion, vulnerability, and ethics. The theme urban planning and management has a wide interpretation in literature, covering a diverse set of meanings. In the context of Curitiba 2035, the discussion is about the social action impact over the city production, whether it is proposed by institutional action or by interventions made by the population. Regarding planning, the assessment refers to the future, seeking to elaborate plans or programs with the objective to coordinate preventive or necessary actions in the urban context. In the case of management, it refers to the present, aiming at the systematization of management practices related to the interventions of different agents in the city. Security is a field with a vast conceptual repertoire, commonly involving the set of political and legal processes aimed at guaranteeing public order and coexistence of individuals in society. The security theme addresses the issue of preserving or restoring social harmony in the city environment, allowing the individuals to enjoy their rights, exercise their duties, and live without disturbance or fear. Urban governance can be defined as the sum of the spheres in which citizens and public and private institutions plan and manage common land issues. The concept encompasses the many ways in which institutions and individuals organize city management, as well as the processes used to realize a short- to long-term development agenda.

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Table 2 Curitiba’s education, research, and innovation ecosystem. Souce: Curitiba 2035 (2017). Education, research, and innovation ecosystem – 2016 Establishments Search and development Education Total Employment Management and direction in search and development Researchers Engineers Total Actors Startups Coworking spaces Local movements Higher education institutions Investors (organized groups) Acceleration programs Supporting entities Incubators and technology hotels Total

Curitiba 6% 94% 100%

Participation of Curitiba PR Brazil 43% 2% 27% 1% 28% 1%

5% 7% 88% 100%

41% 54% 46% 46%

75% 5% 5% 4% 3% 3% 3% 2% 100%

36% 44% 22% 21% 86% 100% 18% 19%

2% 2% 3% 3%

For the achievement of all these themes, Curitiba 2035 specifies that the city holds three main assets: (i) the education, research, and innovation ecosystem (see Table 2), (ii) the public governance (see Table 3), and (iii) the citizenship profile (see Fig. 3).

Translating the Strategies into Services and Projects Smart Cities Institute The Smart Cities Institute (ICI – Instituto das Cidades Inteligentes, extract from https://www.ici.curitiba.org.br) is the main provider of services based on information and communication technologies (ICTs) to the city of Curitiba. The role of ICTbased services in a smart city is a well-known key point. The ICI is a nonprofit organization that operates close to the city hall, aiming at the integration, development, and implementation of solutions for public management. It attends more than 5000 daily calls or other demands from citizens and 9000 technical service desk calls. Some of the solutions provided by ICI are2:

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Table 3 Curitiba’s public governance. Source: Curitiba 2035 (2017). Public governance Transparency index – 2015

Tax management index – 2013

Municipal efficiency ranking – 2016

Position 4
7
12
18
20
23
41
80
84
94
99
138
181
16
36
53
77
86
121
146
196
239
275
346
350
– – 40
130
342
349
439
818
1.060
1.212
1.275
1.330
2.107
2.215


Capitals Brasília Curitiba João Pessoa Recife Rio Branco São Paulo São Luís Vitória Rio de Janeiro Fortaleza Palmas Campo Grande Porto Alegre Rio de Janeiro São Paulo Porto Velho Recife Rio Branco Campo Grande Fortaleza Belém Curitiba Porto Alegre Manaus Boa Vista Capitals mean Cities mean Vitória Florianópolis João Pessoa Aracaju Belo Horizonte Teresina Fortaleza Curitiba Recife São Paulo Salvador Rio de Janeiro

Grade 10 10 10 10 10 10 9,58 8,75 8,61 8,19 8,19 6,81 5,83 0,8169 0,7744 0,7579 0,7452 0,7399 0,7212 0,7126 0,6976 0,6877 0,6795 0,664 0,6636 0,6449 0,4545 0,597 0,576 0,55 0,549 0,542 0,52 0,509 0,503 0,501 0,499 0,473 0,468

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• The management+ administrative and financial set of services aims at meeting the needs and concerns of the public manager regarding the control of financial transactions, accounting entries, administrative routines, purchasing and bidding processes, transport fleet, and assets. This product line also offers the publication of official acts through an official electronic journal and the use of multifunctional smart cards. • Smart card integrates in a single card the functionalities that facilitate the management of city’s human resources, such as access control, frequency, transportation, and credit, among others. • Electronic buying provides to the city hall the administrative purchase processes in electronic auctions and price surveys; it enables the reduction of operational costs and manages the supplier registry. It allows the consultation, disclosure, and issuance of documents in public tenders, such as notices, regulatory laws and decrees, minutes, results, etc. It offers access to three types of users, according to the current legislation, restricting or releasing information and functionality according to each profile: general public, vendor, or administrator. • Urban maintenance manages the activities of preservation of the spaces and public services, offering better planning and administration, control, and saving of time and financial resources; the urban maintenance solution presents integrated and dynamic modules that offer the public manager more quality and dynamism to take strategic actions in the municipality. • The management + citizen provides a communication platform between the public administrator and the citizen. In order to carry out the management of a municipality, it is necessary to know the demands of the population and their expectations from public services, besides having reliable data to support decision-making. The platform addresses these needs and offers a comprehensive service including: 1. Virtual channel, a citizen assistance tool via automated or human chat to avoid waiting queues: it supplies services like generation and reception of documents from the citizens, detection of problems in public services, sending the demands to the responsible departments, and evaluation of the perception of the population about the quality of municipal services. 2. Business intelligence, which provides performance indicators related to the demand for services rendered to the community: managers have access to situations that require more attention. 3. Relationship center, which publicizes public administration campaigns and projects; monitors the degree of knowledge and approval of actions of the city hall; invites citizens to participate in inaugurations, debates, and public hearings; and builds a network of volunteers for social campaigns. • The management + education integrates the information of the municipal education network, providing the school manager with analytics to support decision-making. It automates the activities of the units and the school sectors,

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L. M. Spinosa and E. M. Costa Channels of social participation (3) - 2015 84% Social Accounts

78% 66% 63% 63%

General ombudsman 29% 10% Real time online service

5% 5% 57%

Online Reporting

51% 27% Municipalites+ 500 thousand

Capitals

Total

Municipalities' adhesion to social networks (3) - 2015 23% Blog

18% 9% 72% 70%

Twitter 13% 60% YouTube

73% 17% 84%

Facebook

78% 62% Municipalites+ 500 thousand

Capitals

Total

Forms of public consultaiton (3) - 2015

Online voting

20% 11% 8% 31%

Online Forums

24%

10%

28% 31%

Online public consultation 11%

63% Survery

36% 18% Municipalites+ 500 thousand

Capitals

Fig. 3 Curitiba’s citizenship profile. Source: Curitiba 2035 (2017).

Total

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organizing and expediting the attendance to the daily demands of the school routine. It organizes administrative and academic processes, generating specific and managerial reports for the global monitoring of the educational scenario. Some functions include registration of students, release of grades and absences, and supervision of school activities. The solution meets the needs of elementary and secondary education, special classes, accelerated classes, and early childhood education. It also supports the school administrators in the fulfillment of legal duties required by the Federal Ministry (legal and control office of the municipality). • The management + mobility offers to the public manager the Operations Center, through which is possible to manage traditional and electric-powered buses in order to provide urban mobility in an integrated way. The Operations Center was developed in partnership with the Center for Excellence and Innovation in the Automobile Industry (CEIIA, www.ceiia.com) of Portugal. It involves online monitoring and real-time dashboards for visualization of fuel consumption, remote intervention of the vehicle at any time, simulation of traffic scenarios, and so forth.

Curitiba Technopark and Vale do Pinhão Curitiba Technopark and Vale do Pinhão (extract from www.valedopinhao. agenciacuritiba.com.br) are the main projects of the city to consolidate its innovation ecosystem. They were created to organize urban infrastructure and services to foster entrepreneurship based on a national policy (Zouain and Plonski 2006; Zouain et al. 2007; Miranda and Negreiros 2011). The innovation ecosystem was created in 2008 by the municipal government to stimulate the development of the high-tech sectors. It is totally integrated into the urban environment and is not fully deployed in a single lot or plot. The innovation facilities are spread in several different city neighborhoods. The innovation ecosystem is composed of the typical and main quadruple helix stakeholders and additionally of the accelerators, incubators, investment funds, research and development centers, startups, cultural and creative movements, etc. In addition to the City Hall of Curitiba, other institutions also foster the ecosystem, among them the Paraná Micro and Small Business Support Service (SEBRAE-PR), the Federation of Industries of the State of Paraná (FIEP), and the Federation of Commerce, Services and Tourism of Paraná (FECOMERCIOPR). In the beginning, the total area of the Technopark was 90.000 m2, concentrating efforts to induce an innovation environment, knowledge transfer, and development of technology-based activities. Later on it spread through the city covering larger areas:

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(i) Logistics Ring: where the two main Universities’ campuses are located, the Federal University of Paraná (UFPR) and the Pontifical Catholic University of Paraná (PUCPR), besides Institute of Technology for Development (LACTEC) and the Federation of Industries of the State of Paraná (FIEP); (ii) Rebouças Sector: containing the Federal Technological University of Paraná (UTFPR) and a convention center; (iii) CIC North Sector: where a Software Park is located; (iv) CIC South Sector: where the Institute of Technology of Paraná (TECPAR) is located. Curitiba Technopark now covers the entire city and currently has several companies engaged in the innovation process in areas such as telecommunications systems, computer hardware: hardware and peripherals, computer services, and research and development; design; laboratories of quality tests; precision instruments and industrial automation; and new technologies (biotechnology, nanotechnology, health, new materials, and environmental technologies). Vale do Pinhão is a more recent development, which started in 2017 to promote Curitiba as a smart city. Its charter states that Curitiba should become an “Intelligent City that develops its economy while increasing the quality of life of its citizen and generating efficiency in urban operations. The program involves all the municipal secretariats and the innovation ecosystem of Curitiba. The Vale do Pinhão focuses on startup creation and support.

Startup Movement The precise date of the beginning of the startup fostering movement in Curitiba is uncertain. However, the movement became official with the advent of the Curitiba Technopark and intensified with the implementation of the Vale do Pinhão. The movement comprises a number of universities, incubators (UFPR Innovation Agency, Fiep System, Jupter, UTFPR Incubator, Hotmilk PUCPR, IBQP, Intec TECPAR) and accelerators (District, Fiep System, ACE, Hotmilk, Founder Institute, Orbital, Jupter, and Isae), mentors, mutual funds, and institutions that help to foster entrepreneurship. In 2018, the city won the first place in the Connected Smart Cities ranking (https://www.connectedsmartcities.com.br/o-que-e-o-ranking-connectedsmart-cities/), which mapped out the cities with the highest potential for technological development in Brazil. In 2018 and according to the 100 Open Startups Movement (https://www. openstartups.net), 10 among the 100 most attractive startups in Brazil are located in Curitiba. The startups and their ranking are: GoEpik (4th), Loox Studios (15th), Eruga (38th), Pipefy (41th), Beenoculus (43th), Vidya Technology (48th), Ubivis (51th), Send4 (68th), 33 Robotics (77th), and the Pollen (98th). Other internationally projected startups are MadeiraMadeira, Contabilizei, anothe startups, and Olist. Curitiba is currently organizing itself to publicize worldwide as a city that houses a startup ecosystem focused on smart city solutions. This is a brand strategy that

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takes advantage of the international projection that the city had during the term in office of former mayor Jaime Lerner. There are several events dedicated to promote the startups throughout the city, organized by the efforts of Vale do Pinhão.

ICITIES and Smart City Expo ICITIES is an initiative to develop culture and solutions for smart cities in Curitiba. It is undertaken by a group of entrepreneurs (a startup) who created the ICITIES company (http://icities.com.br). ICITIES is one of the first groups in Brazil to be organized around the subject of smart cities and is one of the most active startup in the city. ICITIES has developed a business based on connecting six major axes: entrepreneurship of high impact (startups), creative economy, sustainability, clean energy, technology, and connectivity. ICITIES are: (i) Smart City Brasil, which involves consulting for the construction of smart cities (ii) ICITIES KIDS ICITIES Kids, which promotes the culture of smart cities for children, simulating the environment of a smart city, where the children participate in ludic workshops about renewable energy, smart mobility, robotics, conscious consumption, and recycling (iii) Smart Neighborhoods, which involves implementing smart solutions in delimited areas of the cities to test new technologies and measure public acceptance Another action of the same group is the realization of the Brazilian version of the Smart Cities Expo (https://www.smartcityexpocuritiba.com), one of the most important events in the smartcity area worldwide.

Urban Projects Curitiba is internationally recognized for being a pioneer in the implementation of sustainable urban planning. Much of this recognition is attributed to the architect Jaime Lerner (https://pt.wikipedia.org/wiki/Jaime_Lerner), who was mayor of Curitiba in three periods (1971–1975, 1979–1984, and 1989–1992). Some urban projects implemented by Lerner and others are the following: • The Rua das Flores (Street of the Flowers) is the first pedestrian-only street in Brazil inaugurated in 1972. The street is in the center of the city and has 3.300 m of extension. Approximately 100 thousand people walk on the street per day. The street is defined by centenary buildings and townhouses, tourist bars, and flower beds throughout the pathway. Street performers such as clowns interacting with passersby, musicians, and statue people are attractions in this urban space. Recently, Rua das Flores has been converted to a smart street (https://www. curitiba.pr.gov.br/noticias/calcadao-da-xv-de-novembro-e-a-segunda-rua-inter

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•

•

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ativade-curitiba-conecte-se/48766). The work of a startup called MCities promotes interaction between the street and the people by two main technologies: (i) several buildings and facilities received QR code panels, which provide street and event information, suggestions of tours and experiences throughout the street and (ii) tiny location technology devices – called beacons – which transmit information about services and commerce on site to smartphones via Bluetooth. The bus rapid transit (BRT) (http://www.brtbrasil.org.br) is implemented by means of the project called Rede Integrada de Transporte in 1974. The BRT is largely documented and is a relevant solution to improve the mobility in large cities. BRT involves urban changes, with the premise of permanently replacing individual traffic with public transport, reducing CO2 emissions, and reducing traffic jams. BRT has become attractive due to its cost-effectiveness for public managers and urban planners. The Ruas da Cidadania (Citizenship Streets) were created by the Curitiba City Hall, aiming at decentralizing public agencies and facilitating the population’s access to various public services. There are currently ten Citizenship Streets around the city: Bairro Novo, Boa Vista, Carmo/Boqueirão (the first), Cajuru, CIC, Praça Rui Barbosa, Pinheirinho, Fazendinha, Santa Felicidade, and Tatuquara. Each street has a main office with approximately 20.000 m2 attached to a bus terminal, to which many bus lines converge. The citizens have access to services concerning health, justice, police, education, sports, housing, environment, urbanism, social service, supply, among others. The parks of Curitiba are composed of a set of urban planning solutions balancing environmental protection, leisure, tourism, and mainly civil security. The parks are a system to protect the city from high rainfall indices and storms. Some of them act to drain the water excess. Today there are 29 parks spread in the city, and the best known are Parque Tanguá (the most beautiful), Parque Barigui (the most known by citizens), Passeio Público (the oldest), Parque Tinguí (a tribute to Ukrainian immigrants), Bosque do Papa (a tribute to the Pope visit), Bosque do Alemão (a tribute to the German immigrants), and the Jardim Bot^anico (a botanical conservation unit and one of the most beautiful), among others. The Unilivre or Free University of the Environment is a university which stated Curitiba as the first city in the world to have a space for studies and transfer of knowledge about the environment and ecology to the population. Unilivre was inaugurated in 1992, with the presence of oceanographer Jacques Cousteau. It is installed in a 874 m2 building built with eucalyptus logs (from reforestation), surrounded by the Bosque Zaninelli, which has 37.000 m2 of dense native forest, housing several bird species. The Ópera de Arame (Wire Opera) is a theater made of an impressive array of steel pipes and metal structures, covered with transparent polycarbonate plates, in a circular shape. The theater is also in a park and surrounded by an artificial lake, so access to the auditorium is via a walkway over the water.

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Conclusions This chapter briefly presented the framework guiding Curitiba City toward a HSSC, based on an exploratory and descriptive research methodology. The main finding is a balanced approach between Curitiba’s urban innovation ecosystem and Curitiba’s strategies and efforts to transform the city in a HSSC. The main reasons for this balance are the following: (i) There is a symbiosis between the urban innovation ecosystem and the set of actions toward the HSSC implementation, mainly those concerning the ICTs. The urban innovation ecosystem is a platform for the development and adoption of urban technologies. In fact, several of the identified technologies came from the need to implement the HSSC. The implementation carries out experimentation by the citizens that in turn provides new demands for the urban innovation ecosystem. New technology-based businesses are nurtured and oriented to solve urban problems. (ii) There is a positive mindset for innovation in the city. Curitiba’s citizens are proud to consider the city as a locus for innovative urban solutions. In fact, there is a constant appeal for innovation to the public planners and managers. This mindset also fosters the development of the urban innovation ecosystem. A common belief of citizens and stakeholders is that Curitiba is a living lab for urban innovations. (iii) Several stakeholders participate at the same time in the urban innovation ecosystem community and in the smart city community. Such context allows the perception of common problems and thus facilitates cooperation and coordination. Despite the inexistence of official governance for the entire process, the participation of stakeholders in both sides helped in the decisionmaking about what needs to be done. The construction of Curitiba 2035 is a major example. (iv) The balanced approach is mostly obtained by a development agenda supported by the organized civil society of Curitiba. There is no official statement or legal structures comprising the whole approach. However, there are specific laws and projects in the city hall focused on some components of the urban context, which help the balance. (v) For almost all the stakeholders, the development agenda for the city must be cocreated and co-managed (at least) by the government, academy, enterprises, and nongovernmental organizations. This perception guarantees more stability of the agenda and reduces the vulnerability that naturally comes when the substitution of public managers occurs due to new mayors or change of public policies. The three main components of the framework that structure the balance are organized in a deployment sequence that requires an analysis from effectiveness viewpoint. The main considerations are:

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(i) Despite the existence of the conceptual drivers inspired from the areas of humane smart city, sustainable development, urban innovation ecosystem, and quadruple helix model, there is no tight relationship among them. The apprehension of the drivers by the stakeholders is mainly tacit, and explicit references are few. The common sense among stakeholders is more important than formal references. A shared vision on how the city needs to be in the future is more powerful than documents. The common sense and the shared vision observed in Curitiba have been enough to provide the basis for policy and strategic definitions. (ii) The unfolding of the conceptual drivers in a policy-mix is also tacit. One of the main guidelines of the policy-mix concerns the fostering of innovation in all dimensions of sustainable development. Again, the common sense among the stakeholders allows enough security and stability for the transformation actions. The policy-mix and the high common sense came about in well-defined and detailed action plan entitled Curitiba 2035. This plan is explicit and cocreated, involving stakeholders, policy makers, users, and several other representatives of the city. (iii) Curitiba 2035 led the perception of delegates from the quadruple helix approach in a coherent way. Two present challenges are defying the implementation of the Curitiba 2035 plan. First is the difficulty to establish a governance system to handle the plan. This imposes several additional actions (workshops) to the planners to get the engagement of leaders from the city hall, industry, and academies. Second, federal economic policies in Brazil aren’t yet clear enough to support the investments. The city planners are working with public-private partnership models to replace the lack of investments. This research was only possible thanks to the financial and institutional support of the Higher Education Personnel Improvement Coordination (CAPES) of the Brazilian Ministry of Education, through the process N. BEX 6555/14-4, Senior Internship.

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Contents The Strategy of the Taipei Smart City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Build a Smart City Ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Establishment of Smart City Management Office . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Establish a Smart City Operation and Promotion Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Strengthening the Linkage of International Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Taipei Smart City Achievements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Government . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Social Housing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Transportation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Health and Care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Payment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Start-Up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Future of Taipei Smart City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . New Promotion Framework for Taipei Smart City with 1 Core+ 7 Key Directions . . . . . . . Continue to Promote Innovation Culture to Public Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Establish Sustainable Smart City Implementation Mechanism and Specification . . . . . . . . . Improve Public-Private Partnership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Strengthen PoC Effectiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Broaden Collaboration and Construction Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Establishment of GO SMART . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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C.-Y. Lee · Taipei Smart City Project Mangement Office (TPMO) (*) Taipei, Taiwan e-mail: [email protected]; [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_22

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Abstract

TPMO, as the bridge between city government and industry to improve Smart Taipei. Taipei City has a vision of livability and takes promoting smart city as a driving force. Therefore, “Smart City” is not a noun but a verb and is seen as a method to solve city challenges, meet the needs of citizens, and improve the quality of municipal services, to make Taipei City develop toward a livable city. The promotion of the smart city in Taipei City is in accordance with the policies and objectives of the city’s governance strategy. Through the innovation of mechanism, with citizens as the main part and combining the power of citizens and communities, together with the opportunities opened up by the public sector, information technology and innovation will be brought into the public sector, so that departments of Taipei City, private enterprises, start-ups, academic institutions, communities, and citizens can participate together and keep communicating with stakeholders and choose appropriate technology service solutions. Taipei City therefore will be enabled to make continuous progress under rapid environmental changes (Fig. 1).

The Strategy of the Taipei Smart City In the strategy of promoting smart city, both the local development and linking up with the world are taken into account. Taipei City takes “from public to private, from internal to external” as the policy and sees “the government as a smart city platform, the city as a living lab” as the main spirit of strategy. It is expected to facilitate the public, private, and people to form a partnership, cooperate in the promotion of the smart city, and improve the “Smart City Ecosystem” through engaging, encouraging, enabling, empowering, etc. Thus, Taipei City proposes three main development strategies, including establishing a smart city management office, setting up a smart city operation and promotion mechanism, and reinforcing the cohesion between smart cities globally. When promoting smart city, Taipei City not only considers the background of its population, geographical environment, historical context, etc.; it also carries out related work in line with international standards. Following the standardization framework developed by the international standardization organizations in the field of smart city, Taipei City hopes to interact with other international smart cities in a common language to address the same city issues and to think and develop technology solutions together. The concept of promoting smart city in Taipei refers to PAS 181 of British Standards Institute and ISO 37106 of International Organization for Standardization. Taipei City believes that smart city is people-centered, digital, open, and cooperative and that smart city services innovation and transformation are led and promoted by stakeholders. Therefore, in 2015 Taipei City launches promotion framework of creativity, innovation, and entrepreneurship based on “Open Government,” “Citizen

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Fig. 1 Taipei at a Glance. (Source: TPMO)

Participation,” “Public-Private Partnership” and seen as the core spirit, and further promoted the two major development axes of “Open Matching Platform” and “Service Innovation & Transformation.” The government is a smart city platform to promote public-private partnership, implement Smart City solutions, and drive the transformation for industries. In promoting smart city, Taipei City combines the vision of sustainable development with the UN Sustainable Development Goals and ISO 37120 Sustainable Development of Communities. ISO 37120 is divided into 17 themes, including Economy, Education, Energy, Environment, Finance, Fire and Emergency Response, Governance, Health, Recreation, Safety, Shelter, Solid Waste, Telecommunication and Innovation, Transportation, Urban Planning, Wastewater, Water, and Sanitation, about 2 to 10 indicators per theme, with a total of 100 indicators, which measures the performance of city services and quality of life and presents the city’s social, economic, and environmental development and performance. In 2015, Taipei City joins World Council on City Data, WCCD, and receives the Platinum certification in 2016 and 2017. Taipei City uses ISO 37120 as the KPI of Taipei City Strategy Map and sustainable development indicators. Taipei City uses indicators to observe the impact of information technology on citizen’s lives and social development and therefore to conduct expert assessments to help the department review implementation strategies. Taipei City continues to make connection with the industry through the Department of Information Technology of Taipei City Government to help other departments better use the information communication technologies to accelerate and innovate the promotion of city policies.

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Build a Smart City Ecosystem Instead of the government-led way of promoting the smart city, the Taipei City, on the other hand, promoted it through engaging, encouraging, enabling, empowering (4E), etc., to facilitate the public sector, the private sector, and people to form a partnership (4P). All three parties jointly participate in the promotion of the smart city and build a “4P smart city ecosystem driven by 4E.” The Taipei City opens up the experiment fields by public sector, in order to provide opportunities for the private sector to verify innovative smart city solutions, facilitate the partnership between each party, and develop a smart city innovation model. In the process of promotion, Taipei City encourages and engages to change the culture of the public sector. By enabling the public officials to take risks, they are willing to accept innovation projects which are brought up by the private sector. Furthermore, by the help from the departments of the city government to empower cooperated private sectors, strong and firm support will be provided for the innovation projects. All projects are expected to bring more smart services to the public; therefore, adjustments will be made through the feedback after the experience. After that, competent authorities will evaluate the projects and try to convert proof-of-concept (PoC) projects into policy’s direction.

Establishment of Smart City Management Office The fields of smart city are diversified, and the development of smart city must integrate industry, government, academia, and research institutes. Therefore, the “Smart City Committee” has been established by the Taipei City Government since 2015. The mayor serves as the chairman and appoints professional leaders in industry, politics, and academia as the committee members. The committee acts as a platform for the communication of policies between the private sector and the government and to assist public sector and private enterprises become smarter and more efficient. In 2016, the Department of Information Technology (DoIT) of the Taipei City Government establishes the “Taipei Smart City Project Management Office (TPMO),” which serves as the bridge between the public and the private sector and carries out the concept of “the government as a smart city platform, the city as a living lab.” On the one hand, with TPMO’s expertise in the field of information communication technology (ICT), TPMO provides consultation and suggestions on smart city issues for the government as the reference of “Top-Down” policy planning; on the other hand, TPMO adopts the “Bottom-Up” model and serves as a communication and matching platform for the public and private sector and promotes the Public-Private-People Partnership (4P); the government therefore can introduce innovation and resources of the private sector through the program named “Taipei Smart City Industrial Field Pilot Program.” Smart technology application solutions from tech companies or start-ups can become a part of the policy, truly solving city problems. By doing so, Taipei City

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Fig. 2 Taipei Smart City Project Management Office LOGO. (Source: TPMO)

becomes a “living lab” of the smart city and improves the quality of living for the citizens (Fig. 2).

Establish a Smart City Operation and Promotion Mechanism Under the existing Top-Down mechanism, the Taipei City Government promotes the smart city in the direction of the municipal development; meanwhile, it emphasizes the driving force of Bottom-Up mechanism, allowing all departments of Taipei City Government, private enterprises, academia, and citizens to participate. In the promoting practice, the Top-Down and Bottom-Up mechanisms do not operate separately but will be integrated into the process and then developed into a different collaboration mechanism. At this stage, the promotion of smart city has been gradually moved from the phase “establishment” to the phase “rolling-wave planning.” It is hoped to promote the smart city through the “Public-Private Partnership” method, letting the departments of Taipei City proactively cooperate across different units, and the practical solutions for cities can be implemented worldwide.

Top-Down: Private Sector Operating Mechanism The Top-Down mechanism can be divided into four steps according to the development of the city government policy: filtering, drafting, scenario setting, and implementing. In the filtering stage, Taipei City starts from the strategy map and assigns the department which should be responsible for it and introduces appropriate PoC projects into the public sector. Next, in the drafting stage, DoIT and TPMO assist departments of Taipei City Government to formulate and recommend “Smart Elements,” to applicate of innovative technologies such as IoT, AI, etc. Then, in the scenario setting stage, DoIT, TPMO, and the authority concerned jointly develop a “smart city plan and proposal” based on the actual demand and the feasibility of the solution provided by the industry. Finally, the PoC project will step into the implementing stage. Promoting smart city needs the participation of all departments of the Taipei City Government to formulate smart city-related policies with their expertise and resources. In order to make each department understand its role as a smart citypromoting unit and be willing to get involved into the collaboration mechanism,

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DoIT and TPMO organize several educational training courses and workshops with the help of international think tanks and academia so that the public officials can have a clear understanding regarding the connection between their own business and smart city in a practical experience lectured by professionals. In addition, through these educational training courses, each department can be able to recognize what assistance DoIT and TPMO can provide while implementing smart city policies. Therefore, when facing new Top-Down projects in the future, each unit can fully judge whether the projects can connect to other resources in the government. The core function of the Top-Down mechanism is to assist departments in “Smart Elements” designing and planning. When conducting policy planning on smart city, departments often have doubts about which Smart Elements or services should be included and hence consult DoIT and TPMO. With abundant case studies and international resources, TPMO first prepares a draft of Smart Elements or services that can be imported for the project based on the development of the trend of smart cities. Then, DoIT not only provides service content suggestions regarding technic and standardization, to assist departments in policy planning, but also acts as an ICT consultant for the follow-up project implementation. In the past, the government frequently faces the lack of industry information when drawing up the service requirement, which makes it difficult to carry out the project smoothly. Through TPMO’s close connection with the private sector, smart city solutions, technical standards, and other information can be easily obtained. As a consequence, DoIT and TPMO can be able to assist other departments in updating their industry and technology knowledge, provide commercial feasibility suggestions, and optimize the design of project services (Fig. 3).

Bottom-Up: Private Sector Operating Mechanism In response to the policy of “from public to private, from internal to external,” the Bottom-Up mechanism enables innovative technology from the private sector to be demonstrated in the public sector’s experiment field, which accumulates experiences by continuously reviewing and revising; the innovative technology eventually becomes a solution suitable to solve city problems, so as to improve municipal services and meet the citizens’ needs. For the purpose of letting the private sector have an opportunity to cooperate with the public sector in making innovative ideas and shaping a new type of smart services, the Taipei City Government launches “Taipei Smart City Industrial Field Pilot Program,” providing opportunity for entrepreneurs. Therefore, each stakeholder can follow the regulation as a basis to conduct PoC projects with the Taipei City Government. Once TPMO accepts the proposal from the private sector, the Bottom-Up mechanism will be initiated. The proposal then will be reviewed based on innovation, viability, the extent of public welfare, and legibility, and TPMO will have an overall discussion with the proposer. The proposal with the potential to develop will enter the scenario planning stage. At this stage TPMO discusses with the proposer regarding the application scenario of the solution, the design of scenario from the aspect of stakeholder, and the appropriate public experiment field. Then, with

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Fig. 3 Top-Down Projects. (Source: TPMO)

the assistance of the DoIT and legal advisors of TPMO, the relevant stakeholders who are responsible will sign a memorandum of understanding (MOU) of the pilot project and step into the implementing stage. The Taipei City Government will provide necessary administrative support during the period, and it is hoped that the project can be copied to other places with similar demands in Taiwan, or even be exported to cities abroad. Therefore, the city government will work with the central government in order to help in the business model development and promotion problems in the process of paradigm reproduction and marketing (Fig. 4).

Public-Private Partnership From the perspective of demand, the Top-Down approach comes from policy-driven needs, and the Bottom-Up approach comes from industries. Some innovation plans are in line with the Top-Down policy requirement and the Bottom-Up proposal, so the Public-Private Partnership is formulated. The partnership mechanism includes matching departments’ demands and private sector’s solutions and opening departments’ experiment fields. The former is that the department has a clear need to seek innovative solutions from the industry. The latter is the way to shape policies through demonstration in the experiment fields, and usually, those projects are more forward-looking or integrated smart services. At present, the themes of opened experiment fields include smart wards, autonomous vehicles, smart parking, smart street lightening, etc. The development of related smart services involves emerging technologies and covers issues such as hardware equipment, software application, and maintenance. Thus, it is necessary to coordinate companies from various industries. Through the departments’ opening up the fields actively and investing relevant expertise, the partnership can be seen as a platform for cultivating innovative public services for citizens and allows industries,

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Fig. 4 Bottom-Up Projects. (Source: TPMO)

academia, and research institutes to develop their own solutions, so as to lead the direction of smart city policy services in the future (Figs. 5 and 6).

Strengthening the Linkage of International Smart Cities Taipei City is actively linking up with global smart cities, by participating in international expositions, exchanging visits, holding cooperation workshops and video conferences, and establishing cross-city PoC project exchange platform. Taipei City strengthens the link between Taipei City and the international community and creates opportunity to cooperate with each other, so as to create commercial opportunities for smart city-related industries. Currently, Taipei City has contacted up to 15 countries, with 27 cities worldwide, including Amsterdam, Eindhoven, Boston, Kansas City, Barcelona, Greenwich, Fukuoka, Seoul, Selangor, Singapore, Tampere, etc.

International Expositions Nowadays, all countries are committed to the development of smart cities and their applications. In addition to planning domestic smart services, plenty of smart cityrelated expositions have also been developed, hoping to drive interaction between cities. Therefore, participating in relevant expositions is one of the measures that Taipei City promotes itself to other cities. In recent years, the Taipei City Government has successfully promoted its achievement through exhibitions. Aside from the Smart City Summit & Expo, which is held in Taipei every year, the government also proactively participates in important expositions, such as the Smart City Expo World Congress, which is held in Barcelona every November, in

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Fig. 5 Smart City Operation and Promotion Mechanism. (Source: TPMO)

Fig. 6 Taipei City Strengthening the Linkage of International Smart Cities. (Source: TPMO)

order to showcase the achievements of Taipei smart city and lead cooperate companies to promote Taipei City.

Exchange Visits The development of smart cities is not the same in every country; Taipei City visits their relevant applications of smart cities to achieve the goal of interacting with the world. In recent years, when the Smart City Summit & Expo is held in Taipei, Taipei City will invite foreign guests to visit the city’s attractions to truly experience how a

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smart city works. Furthermore, Taipei City also plans to have international visits occasionally and exchange practical experiences or projects of city planning and development with other cities. The information then will become an essential reference resource for Taipei City Government.

Cooperation Workshops While linking with other cities, having workshops is a way to deepen the benefit of exchange and brainstorming. For instance, Taipei City Government had cooperated with City Exchange Lab from Amsterdam and discussed the problems that Taipei might face and the possible smart city solutions in a workshop.

Taipei Smart City Achievements The fields of the smart city are diversified. Following on the principle of “from public to private, from internal to external,” Taipei City begins building a smart city from building a smart government. Therefore, Taipei City continues to invest in intellectual infrastructures and information constructions, and under the premise of information security, smart transportation, smart social housing, smart education, smart health and care, and smart payment are the five major promotion categories. Together with the start-up ecosystem, the 5+N layout of Taipei Smart City is formed (Fig. 7).

Fig. 7 5+N layout of Taipei Smart City. (Source: TPMO)

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Smart Government In terms of the intellectual infrastructure construction, Taipei City has implemented intelligent road management, intelligent street lamp, pumping station automatic monitored control system, Feitsui Reservoir smart security monitoring network, etc. Through the introduction of intelligent technology, the infrastructure and management efficiency are enhanced.

Intelligent Road and Pipeline Management The underground pipelines in Taipei City are intricate. When city renewal and basic maintenance projects are carried out, it is often difficult to confirm the pipeline location which causes construction obstacles. Taipei City therefore sets up Road & Pipeline Information Center (RPIC) to provide and monitor real-time road construction information. RPIC combines with GIS system and cloud technology and builds 3D pipeline maps, so that Taipei City can quickly and accurately grasp the information of underground pipelines when dealing with disasters or planning city renewal. Smart Streetlight In addition to continuously updating LED streetlight and improving energy-saving effects, Taipei City also introduces functions such as automatic failure report, automatic adjustment of light, automatic measurement of power data, remote control, etc., so that the energy consumption and maintenance situation of streetlight in Taipei City can be effectively grasped through the control center (Fig. 8). Pumping Station Automatic Monitored Control System Taipei City has built an automatic monitoring system for 87 pumping stations in the city, a system that automates and computerizes the operation of the pumping station

Fig. 8 Smart City Operation and Promotion Mechanism. (Source: TPMO)

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and can remotely control the pumping machine and flood control facilities to instantly send back real-time running images and information to ensure the safety of Taipei City.

Feitsui Reservoir Smart Security Monitoring Control System Feitsui Reservoir is located in a mountainous area with pool signal and insufficient power infrastructure. The long-distance and low-power LoRa (Long Range) which adopts new Internet of Things technology is used to construct a wide-area network environment. It is applied to reservoir monitoring equipment, including weather stations, hydrological stations, dam monitoring instruments, and other equipment to achieve the purpose of real-time monitoring and backup of data. As for security monitoring, the vehicle and ship movements are instantly grasped through the location tracker which combines with access control system such as physical and virtual electronic fence to ensure the security of Feitsui Reservoir (Fig. 9). As for the digital infrastructure, the Department of Information Technology (DoIT) of the Taipei City Government is responsible for the overall information infrastructure, assisting departments of Taipei City Government to improve their information capabilities and convenience of municipal services. At present, DoIT has provided smart government-related services to the public from the aspect of Internet infrastructure, open data, and municipal services. Regarding Internet infrastructure, DoIT has launched Taipei Free Wi-Fi service to meet the need of the public; in the open data aspect, the Data.Taipei open data platform and Taipei geographic integration platform are constructed to integrate government data and provide for the public use.

Fig. 9 Feitsui Reservoir Smart Security Network. (Source: TPMO)

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Taipei Free: Free Wi-Fi in Taipei Public Area In 2004, Taipei City implements “Taipei Wireless Broadband Construction Plan.” Since 2006, it has provided wireless broadband with the Public-Private Partnership method. In 2011, Taipei City’s wireless broadband infrastructure is further utilized, and Taipei Free, free Wi-Fi in public area, is launched in July. In 2015, DoIT promoted the “Taipei Wireless Network Alliance.” With the concept of “connective,” it cooperates with local stores to expand the number of free Wi-Fi hotspots without additional budget. Data.Taipei Open Data Platform Taipei City adheres to the principles of openness, transparency, participation, and innovation. Taipei City continues to promote information services that facilitate the public and support the industry. The establishment of “Data.Taipei” integrates the open data of the departments of Taipei City to a single portal, providing online service, file download, and API interface, and continuously updates the database. At present, the public has been able to query the visualized environmental information such as soil liquefaction potential area and rainfall flooding simulation on the Internet, burglary and theft of bicycles and cars, etc. In addition to the open of information, Taipei City has launched the plan of establishing a big data analysis platform that integrates the data shared by departments of Taipei City, so that the information is transformed into meaningful figures for public officials to make decisions. Taipei Geographic Integration Platform In 2015, DoIT introduces the GIS ArcGIS electronic map core engine to optimize the effectiveness of the electronic map service and provides instant municipal and life information. Taipei Geographic Integration Platform also continuously integrates information of various geospatial data in Taipei City and shares with the public. In the municipal service, DoIT promotes “App.Taipei,” “Hello.Taipei – Taipei City Simple Petition System,” and other services and systems, to provide the public with integrated municipal information and services. App.Taipei In 2012, Taipei City launches “App.Taipei” application service portal to integrate and promote its own mobile application software so as to facilitate citizens to quickly obtain various apps of Taipei City. The portal site contains information such as function introduction, operating screen pictures, download links, and other information to strengthen the Taipei City Government app promotion and usage. Hello.Taipei – Taipei City Simple Petition System In 2016, Taipei City establishes “Hello.Taipei” Simple Petition System which integrated the Taipei Public Hotline 1999, the municipal mailbox, and the petition counters of various departments, together with functions of GIS positioning, instant video uploading, quick response, etc. “Hello Taipei” provides a number of personalized services to make it easier for the public to grasp the progress of the case and

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the city’s response. It also introduces an automatic case assignment system, which effectively saves manpower of Taipei City. In addition, the big data platform integrates information of “Hello.Taipei” and applies big data analysis to create value and provide municipal governance.

Smart Social Housing The purpose of building social housing is not only to realize living justice and fulfill city aesthetics but also to build a high-quality social housing that is smart, energysaving, shock-resistant, and accessible and create a new residential operation model to make social housing an industrial experiment field of smart city. “The Taipei Public Housing Smart Community Implementation Plan” has built 32 social housing units by the end of 2018, with a total number of 12,000 units. The plan is expected to fully promote the intelligentization of social housing. Through the application of smart home technologies, residents can have more timely and comprehensive care in terms of safety, health, and comfort. In accordance with the characteristics of social housing, Taipei City has set different smart themes and provided multi-intelligent services, in addition to setting up Advanced Metering Infrastructure “Smart Watt-hour Meter,” “Smart Water Meter,” and “Smart Gas Meter,” and used additional 3% to 5% of construction funding to implement smart social housing equipment and service systems, which include energy-saving smart grid, community security, intelligent parking management, smart management cloud, and related smart services (Fig. 10). In addition to the intelligentization of social housing, Taipei City has also proposed “Smart Eco-Community” project to create smart ecological demonstration community in public facilities and public buildings in the overall development

Fig. 10 Smart Social Housing. (Source: TPMO)

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area with five dimensions including transportation, tourism, ecological environment, green energy, safety and disaster prevention, and healthy living. The smart ecocommunity forms community operating methods which are innovative, energysaving, low-carbon, environmental-friendly, sustainable, age-friendly, and peopleoriented through the establishment of green building, renewable energy systems, ecological recycling agriculture, etc.

Smart Transportation The transportation development of Taipei City takes sustainability, mobility, accessibility, responsiveness, and trustworthiness as core values with a vision of green energy, sharing, security, and electronic. At present, the development of Taipei City mainly focuses on the integration of both various application services and service systems such as multi-electronic ticket payment and smart parking management system. Considering expandability and flexibility, the ultimate goal is to develop a fully intelligent road management system in the medium and long term so as to drive the development of Internet of Vehicle (IoV), Driverless Vehicle, and Autonomous Vehicle. On the other hand, it is expected to minimize traffic accidents, road congestion, and carbon emission (Fig. 11). In addition, Mobility as a Service (MaaS) is looking to create a seamless, door-todoor multi-transport integration system that increases usage of green transportation and lift-sharing services and reduces the use of private vehicles. Taipei City has promoted relevant value-added services in recent years such as the public transport monthly pass and “Taipei Easy Go” app, which are the first plans for the introduction of MaaS. In the future, after the integration of transportation and finance sector, the completion of multi-transportation integration platform, and the

Fig. 11 4U Green & Share Transportation. (Source: TPMO)

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establishment of business model, there will be enough driving force. According to the traffic application field, the smart transportation-related plans at this stage can be divided into smart station, smart bus, smart parking, smart shared transport, friendly transportation service, and smart transportation system planning (Figs. 12 and 13). Taipei City also launched 4U green & share transportation plan: (i) Shared Bicycle – YouBike: the construction of 400 rental stations in Taipei City has been completed, allowing the public to walk to the rental station in only 5–10 min.

Fig. 12 4U Green & Share Transportation. (Source: TPMO)

Fig. 13 Smart Parking Billing System. (Source: TPMO)

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(ii) Shared Electric Motorcycle – U-Motor: built by the private sector which has provided 2,000 electric motors in Taipei City; Taipei City also grants parking fee discounts. (iii) Shared Electric Car – U-Car: the private sector has provided 100 shared electric cars in Taipei City and aims to promote the shared electric car to whole Taipei City. (iv) Smart Parking – U-parking: enhances the immediacy of parking information through the Internet of Things technology to reduce the time for people to seek car detours and improves the efficiency of parking management for Taipei City and operating units. Through intelligent facilities and apps, the goal of automating billing and payment process is achievable, and the convenience of parking service is further improved, and operating costs are reduced.

Smart Health and Care The population structure of Taipei City is continually aging, which increases the demand for health and medical care. Based on the mission of providing citizens with a healthy and complete living environment, Taipei City integrates healthy cloud services and health information management to promote public health, improve physical age and quality of life, and create a smart city where “Healthy Taipei, Care with No Distance.” Taipei City hopes to establish a smart care and medical system by introducing intelligent technology and services. The Department of Health of Taipei City develops and integrates different management platform functions of community health management, medical care, and life care, and to solve the problem that the information generated by various service teams is incomplete and inconsistent. In addition, in order to improve the physical age of the elder and build an environment that promotes the health and integrates the medical care so as to lower the social health-care cost, the Department of Health establishes a “people-oriented” smart health care integrate management platform in 2018, connecting and integrating various information on health and welfare services, to develop a friendly and intelligent health and care management services, so that Taipei City can collaborate and integrate intelligent information on various health and welfare services and develop a friendly and intelligent health and care management services, then achieves the vision of Taipei City smart care and medical care. In the past, information of long-term care was scattered everywhere, and there was no one-stop service. In 2018, the related long-term care information is integrated into a long-term care integration management system. Taipei City develops a longterm care platform for the public and provides a one-stop service which allows online applications, online information enquiries, bulletin boards system, health information, etc. Taipei City also integrates service units and hospitals and establishes a long-term data analysis platform which uses big data analysis for future policy reference.

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At the same time, in order to enhance Taipei City’s public service and city competiveness in the field of smart health and medical care, Taipei City Hospital establishes an information platform that intelligently manages all medical conferences from patient admission to discharge and plans the home medical services after discharge. It saves the medical team’s manual work time, provides people-oriented care from hospital to home, and displays smart medical treatment. Through the intelligent conference management system, users not only can sort out patients’ basic information, select meeting members, and take meeting minutes online but also can conclude the meeting online and transfer the conclusion to the next process. It is the first intelligent management system that digitizes the medical communication process in the country. Taipei City also proposes “Taipei City Hospital Smart Ward Pilot Project” to identify the existing internal service process needed to be improved in branches of different medical specialties, and according to these medical specialties, Taipei City Hospital allows companies to use information technology to assist in the intelligentization of medical services, so that they not only provide intelligent services but also provide more mobilized, intelligent clinical care, community care, and administrative work environment for medical service teams (Fig. 14).

Smart Education The main axis of Taipei City’s education development is based on reverse thinking, innovative experiments, and early deployment of future competitiveness. With a prospective vision, Taipei City develops students’ literacy in all aspects such as knowledge, ability, attitude, etc. The promotion of education in Taipei City focuses on the breakthrough of innovation through technology, creating a high-quality and

Fig. 14 Taipei City Hospital Smart Ward Pilot Project. (Source: TPMO)

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smart education through forward-looking and innovative teaching mode, and integrating diverse and rich digital education resources to create an open education platform, to practice the philosophy of “Education Equity.” Therefore, Taipei City has launched various smart education software and hardware integration service procurement, to improve the teaching environment of smart education and to coordinate with the curriculum guidelines of 12-year basic education general guidelines, infrastructure construction, teacher cultivation, teaching resources sharing, and many other forward-looking aspects. Through various projects to integrate cross-domain teaching and professional resources, and to support online and offline multi-situation teaching, and extend to the field of lifelong learning and develop smart campus (iCampus), in order to achieve a simplified and upgrade ministration (Fig. 15).

Smart Campus In building smart campus, Taipei City starts from network infrastructure, hardware equipment procurement, software content development, and teaching resource integration and gradually builds the smart campus (iCampus); iCampus integrates the concept of School Social Network (SSN) and cloud and Internet technology. iCampus provides comprehensive smart education services for teachers and students and develops six areas of smart learning, smart administration, smart management, smart health care, smart green energy, and smart community. It builds a “Personal Teacher and Student Platform” which is constructed by virtualized technology as the core of the system and integrates the clod architecture as the basic of the platform. Innovative Education With the core concept of “Mobile Learning and Smart Teaching,” “Computer Programming Education and Computational Thinking,” and “Maker Education,”

Fig. 15 Smart Education Promotion Mechanism. (Source: TPMO)

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Taipei City’s Smart Education applies information and communication technology such as interactive digital learning equipment, personal teacher and student platform, mobile device, 3R technology, and AI robots to various learning courses to enhance students’ STEM+ cross-domain literacy. Besides, with the COOC Cloud, students can arrange learning progress through digital textbook and online videos compiled by teachers, so as to achieve the goal of “Flipped Classroom.”

Lifelong Education In order to provide a quality lifelong learning environment, Taipei City provides high-quality digital learning services through the “Using Taipei e-Campus for Digital Learning” project. Taipei City promotes the learning needs of citizens in a smart, systematic, and digital way. The Taipei e-Campus platform provides a rich and high-quality digital curriculum for the public to promote mobile learning and implement the concept of learning anytime and anywhere.

Smart Payment In order to create a convenient mobile payment environment so as to move toward a cashless city, the Taipei City Government launches the smart payment platform “Pay.Taipei” in 2017, which is convenient for people to inquire and pay various fees anytime and anywhere and improves the convenience of online account verification and inquiry while reducing the city’s expenditure on collection fees. “Pay. Taipei” integrates various fees of the Taipei City Government; services are provided by existing payment methods (such as APP). In the future, “Pay.Taipei” will continue to include the Taipei City’s expenditure items and collaborate with external services, gradually moving from open payment to open identity verification and open services. Through the Taipei Card 3.0 service promoted by the Taipei City Government, people who apply online and complete certification can enjoy various online and offline integration services launched by the Taipei City Government and use ID to combine innovative payment applications (Fig. 16).

Smart Start-Up In the path of promoting smart city, innovation is the key gene for city development and a key factor for keeping the city open in the promoting process. The Taipei City Government encourages private sectors to propose innovative solution, and the Taipei Smart City Project Management Office (TPMO) serves as a platform for matching innovation and technology and citizen. TPMO discusses the application scenarios of products or services with the private sector and to consider situation design from the perspective of stakeholders. TPMO then discusses the suitable experimental field with the public sector for the verification of products or services and coordinates the Taipei City Government to open the experiment field. After 3 years of hard work, the Bottom-Up project has more than 170 cases. There are a

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Fig. 16 Smart Payment Promotion Mechanism. (Source: TPMO)

total of 168 private companies cooperating, among which 61 companies are startups. Through the three aspects of start-up resources support, product/service verification, and business model testing, the Taipei City Government provides interested companies with innovative solutions to verify in Taipei City, fully demonstrating the concept of promotion that Taipei City becomes a Living Lab.

The Future of Taipei Smart City Taipei takes the lead in setting up the Taipei Smart City Project Management Office, TPMO. TPMO is independent of the Taipei City Government structure; it assists the interdepartmental communication and coordination, introduces industrial innovation through the Bottom-Up mechanism, prompts the PoC cases to gradually form policy directions, simultaneously reverses the public sector culture, and assists the Top-Down policy which allows the public sector to have more opportunities to communicate with the industry. Adhering to the philosophy of “from internal to external, from public to private,” the Taipei City has promoted the Top-Down project in the fields of smart social housing, smart transportation, smart medical care, smart education, and smart payment for more than 2 years. Taipei City engages in more than 400 industry, education, and research units, assists in more than 170 Bottom-Up PoC projects, and cooperates with 33 departments of Taipei City Government to establish the Taipei Smart City ecosystem. Therefore, the smart city promotion mechanism in Taipei City has also been internationally recognized. In 2017, Taipei City claimed gold in the Cooperative City category of the WeGO Smart Sustainable City Awards from “World e-Governments Organization of Cities and Local Governments (WEGO). In 2018, in the list of the world’s top 50 smart city governments published by the Eden Strategy Institute in

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Singapore, Taipei City ranks 16th among the 140 smart cities in the world, ranking 5th in Asia. In 2019, IMD World Competitiveness Center in partnership with Singapore University of Technology and Design presents the IMD Smart City Index 2019, and Taipei City ranks 7th among the 102 smart cities in the world. Taipei Smart City has been well-known internationally. Looking into the future, how can Taipei City strengthen the overall efficiency through the continuous rolling adjustment of the mechanism and ensure the sound development of various mechanisms and how to deepen the connection, the share of resources, and the exchange of experiences between domestic and international communities to promote smart cities will be the focus of the next stage of Taipei City.

New Promotion Framework for Taipei Smart City with 1 Core+ 7 Key Directions The Taipei Smart City promotion framework has been implemented for more than 3 years. In response to the evolution of IoT technology and changing needs of city development, DoIT and TPMO review the implementation of various fields in 2019 and refer to the promotion of advanced cities in smart cities to re-examine and revise Taipei Smart City promotion framework. Based on the current smart city promotion framework, Taipei City will take “Smart Government” as core and “Cyber Security” and “Information Infrastructure” as the two main domains to promote “Smart Building,” “Smart Transportation,” “Smart Education,” “Smart Health,” “Smart Environment,” “Smart Safety,” and “Smart Economy,” which form a “1 Core, 2 Domains, 7 Directions” promotion framework. The framework combines “Open Government,” “Citizen Participation,” “Open Data,” and “International Linkage” to promote the strategy of “from public to private, from internal to external” (Fig. 17).

Fig. 17 1 Core+ 7 Key Directions of Smart Taipei. (Source: TPMO)

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Continue to Promote Innovation Culture to Public Sector Promoting smart city requires not only investing in high technology but, more importantly, whether the public sector can face innovation and change with an open mind. Therefore, how to establish a culture of “not afraid of failure, be brave to innovate” will enable government officials to accept new technologies and new practices, so that more innovation can be implemented in the public sector, and it will become the goal that Taipei strive to achieve during the process of promoting smart city. In the future, Taipei City will continue to expand and promote the change of the culture of the public sector. In addition to deepening the acceptance of the smart city innovation mechanism through education training and case study, Taipei City will also evaluate the arrangement of full-time responsible staff of each department to speed up the establishment of a culture of innovation as well as to enable the bureaus to have a more comprehensive understanding of Bottom-Up innovation cases.

Establish Sustainable Smart City Implementation Mechanism and Specification The sustainable development of smart cities has attracted the attention of all countries in recent years, and all governments have set sustainable development as the main axis of development. In the process of promoting smart cities, Taipei City also listed sharing vehicle, renewable energy, and smart grid as key targets. In the future, Taipei City can establish the evaluation mechanism for service procurement and investment promotion, so as to define the product or bid type suitable for adopting this model, and evaluate the adjustment of accounting standards or improve the flexibility of budget project modification so that the related department can quickly respond. The adjustment of the specifications provides sufficient incentives to increase the willingness of private enterprises to cooperate. At present, the central government’s subsidy program has also gradually shifted from the model of subsidizing the purchase of hardware to service procurement. It is very helpful for Taipei City to promote service procurement. In the future, we will continue to discuss with the central government the subsidies that meet local needs, so as to meet overall development trends and promote development of sustainable smart city.

Improve Public-Private Partnership At this stage, the public-private partnership includes two parts: match the needs of departments and open experiment field of the department. The needs match is based on the estimate projects and seeks innovative solution from the industry. Because of the clear requirements, it is usually to quickly move from PoC stage to the procurement stage. However, there is no institutionalized matching model at the current stage. In the future Taipei City will evaluate the promotion projects of the department

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in the next 2 or 3 years through systematic arrangement. The department defines the importance and urgency of these projects and guides the private enterprises to propose innovative solutions, so that the department has sufficient time for the trial of PoC verification and finding a feasible innovation model. By coordinating the policy promotion projects, the PoC cases can successfully obtain the support of the department and even the funding and also greatly raise the possibility of forming bids for PoC cases. By doing so, Taipei City can meet the two benefits of solving municipal problems and introducing innovative solutions, as well as achieving the goal of solving city issues with smart city promotions.

Strengthen PoC Effectiveness The Bottom-Up mechanism promoted by Taipei City has led many opportunities for innovative ideas to be implemented, and Taipei City has stood out from many international organizations’ ranking and has been internationally recognized. During the promotion process of more than 2 years, Taipei City finds out that some PoC cases proved successful may be difficult to continue or spread due to insufficient procurement fund in the current year, making it difficult for some innovation ideas to be verified and implemented. In the future, in the aspect of strengthening the subsequent spread effect of PoC, Taipei City will evaluate to include the bureau’s participation to jointly assist in assessing whether to allocate corresponding budgets for service procurement for some cases such as meeting the urgent needs of Taipei City or relevant areas of key policies and provide flexible funds after the successful verification of PoC cases so that these innovative solutions can be implemented and spread in the current year to achieve immediate results. Taipei City will also strengthen the internal horizontal linkage mechanism of the Taipei City, so that the results of the verification case can be effectively spread to various departments so as to provide more information on the smart service as a reference for service promotion.

Broaden Collaboration and Construction Scale In the process of various constructions of Taipei City, Taipei City Government adopts a step-by-step gradual expansion method. In addition to the joint discussion and construction planning by departments of Taipei City, the private enterprise is invited through the PoC mechanism to small-scale verification of feasible solutions. In the future, the Top-Down mechanism will be strengthened to tie in with the strategic themes and goals of the Taipei City Government Strategy Map and will organize policies which support the promotion and expansion of smart city construction projects. It is expected that these projects which have completed smallscale implementation verifications and ruled out regulatory doubts will gradually expand implementation in a planned manner. It is also expected that Taipei City will be scaled up by the development of proven projects, providing not only a wider

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range of smart services for citizens but also an opportunity to enhance the international visibility of Taipei City. As for strengthening participation of all sectors, Taipei City introduces suitable smart innovation application which is designed for the needs of the public through the promotion of experiment fields planning. In order to realize the needs of the public, Taipei City first integrates policies of the departments which are relevant to the experiment fields, then surveys community opinion leaders and related organizations by in-depth interviews, and design field-related questionnaires, hold civic participation activities, and further focus on the needs of experiment field and the public (Fig. 18). With such a citizen participation mechanism, Taipei City can clearly understand the needs of the public. In addition to online and offline activities, Taipei City also gathers the needs of the public through existing online platform. For example, the 1999 and i-Voting are excellent channels for citizen to participate. In the future, Taipei City will continue to strengthen the communication with the public, promote the concept and application of smart city, and link the Smart City Council with this citizen participation mechanism to create an effective, bilateral communication model. For example, the Taipei City Government establishes a citizen-participating ad hoc group to collaborate with NGOs and NPOs to design regular communication activities in various administrative districts and to conduct collected and inductive opinions in an open and professional discussion process. These opinions will be sorted out and voted through the citizen participation mechanism such as i-Voting and thus become the basis of policy promotion. In this way, citizen’s opinions and needs can be incorporated into the key direction of smart city promotion, and therefore seek innovation proposals to create public services that fit the needs of the public.

Fig. 18 Citizen Participation Mechanism. (Source: TPMO)

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The Establishment of GO SMART Global Organization of Smart Cities, GO SMART, is officially established during 2019 Smart City Summit & Expo; it aims to bring together smart city energies, build a platform for exchange and collaboration, and solve global city problems. GO SMART uses innovative models to drive communication between local government and industry and develops substantive cooperation relationships with international cities to jointly promote Inter-City PoC. In addition to the regular events, including membership meetings, forums, workshops, exhibitions, etc., GO SMART also assists members to participate in international exposure and marketing; the most important thing is to conduct Inter-City PoC cooperation program through GO SMART by exchanging smart innovative solutions to solve city issues and improve the quality of life (Fig. 19). At present GO SMART has been supported by other 5 cities and 17 smart cityrelated companies in Taiwan, and more than 21 cities coming from 4 continents have joined to discuss the problem faced by each city, the current status of smart city promotion, and the future operation mode and vision of GO SMART. In the future, we will continue to strengthen linkage with international cities and combine the strength of domestic cities with international cities to build a global communication network and promote international cooperation. The smart city innovation solutions will be experimented in Taipei City, implemented in Taiwan, and exported to the international cities, to promote “Taipei City = Smart City” brand (Fig. 20).

Fig. 19 Global Organization of Smart Cities. (Source: TPMO)

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Fig. 20 GO SMART Members. (Source: TPMO)

Conclusion Promoting smart city cannot rely solely on the Taipei City Government’s own efforts. It must gather energy, creativity, and resources from all aspects to jointly achieve the goal of solving city problems and building a livable city. In the future, Taipei City will continue to adhere to the philosophy of “Open Government,” “Public Engagement,” and “Public-Private Partnership.” Through participation, encouragement, empowerment, etc., the government, the industry, and citizen will form partnership to participate in the promotion of smart city and improve the “smart city ecosystem.” Taipei City will also continue to improve the smart city promotion mechanism, expand the scale of construction and collaboration, and strengthen the link with other international cities. In addition, Taipei City will strengthen its contacts with international smart city assessment agencies and provide relevant information of Taipei City to enhance the performance of international competitions and also enhance the positive image of Taipei Cities and make Taipei City as an international model of smart city.

Smart City Transformation for Mid-Sized Cities: Case of Canakkale, Turkey

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Berrin Benli, Melih Gezer, and Ezgi Karakas

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Mid-sized City: Canakkale, Turkey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Smart City Transformation Initiative: “Canakkale on My Mind” CASE . . . . . . . . . . . . . . . . . . . Visionary Leadership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Collaboration and the Role of the Private Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Road Map to Smart City Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Critical Success Factors and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Governance Models for Mid-sized Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Successful Cases of Smart City Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Model for Turkish Mid-sized Cities: Case of Canakkale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

The urban areas where half of the world’s population live is expected double in just 15 years from now. According to the 2017 World Population Prospects report, the world’s population is expected to increase to 8.6 billion in 2030, 9.8 billion in 2050, and 11.2 billion in 2100. The World Bank’s data reveals that around 72% of Turkey’s population, reaching 80 million, live in urban areas, and predictions show that this rate will rise to 80% in 2030 with a population surging to approximately 88 million. This staggering surge in urban populations facing the world and especially Turkey makes it imperative to use limited resources efficiently starting immediately. The Çanakkale on My Mind project aims to define the steps needed to transform Çanakkale, situated in the heart of the region dubbed the “golden circle” along the Istanbul-Izmir axis, into a smart city and design a road map together with the participation of relevant stakeholders. Born in Çanakkale’s B. Benli (*) · M. Gezer · E. Karakas Novusens Smart City Institute, Kale Group, Turkish Informatics Foundation, Canakkale, Turkey e-mail: [email protected]; [email protected]; [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_23

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district Çan, Kale Group joined forces with the Turkish Informatics Foundation (TBV) and Novusens Smart City Institute and launched the Çanakkale On My Mind project on February 1, 2017.

Introduction The world population, rapidly increasing over the last few centuries, has reached 7.7 billion. This number is expected to reach 10 billion by 2050 and 11.2 billion by 2100. Also, the urban population has a higher rate of increase compared to the world population. The urban population is growing around 76 million per year due to overmigration from rural areas to cities as well as enhanced employment opportunities and the urban quality of life. Today, the urban population has reached 4.2 billion. In other words, 55% of the world population live in cities around the world. By 2050, the urban population is expected to reach 6 billion (70%). In Turkey, the urban population rate is much higher than worldwide. 92.3% of the population of Turkey live in cities (Karakas et al. 2019). Due to the rapid increase in the world population, 1.7 times our Earth’s resources are now needed to support the demand on natural resources. This is called as ecological footprint, and it means that humanity’s current demands are 1.7 times faster than the amount of the planet’s available natural resources. In other words, it is emphasized that the natural resources will be inadequate after a while. In addition to that, the increased level of carbon emission, global warming, and environmental pollution occur. It is possible to say that the main source of all these problems are urban areas because of the population density in cities. Therefore, creating more liveable and sustainable word will be possible only if more livable and sustainable cities are created. Moreover, the urban population density causes many other problems such as housing, transportation, security, and underemployment. As a result of all these factors, new and innovative approaches are needed to minimize both the ecological dimension and the negative quality of life impacts of the population growth. Today, the latest information and communication technologies are utilized as part of the solutions. Thus, the concept of smart city has emerged (Karakas et al. 2019; Boes et al. 2016; Lazaroiu and Roscia 2012). Canakkale on My Mind Project is a smart city transformation initiative to improve the urban quality of life and to ensure sustainable environment while providing a competitive advantage to Canakkale on a global scale. It is aimed to determine the necessary steps Canakkale should take during smart city transformation in collaboration with all local stakeholders to create a smart city transformation road map. The project was launched on 1 February 2017 under the leadership of Kale Group (a pioneer in the Turkish industry specialized in ceramic tiles, born in Çan district of Canakkale and celebrating its 62nd anniversary today), in collaboration with Turkish Informatics Foundation (working for the transformation of Turkey to the information society) and Novusens Smart City Institute as the implementing partner (Benli and Gezer 2017).

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Fig. 1 The road map of Canakkale on My Mind Project. (Source: Benli and Gezer 2017)

The main point of the project is to ensure the participation of both local authorities and local residents in all phases of the smart city transformation of Canakkale. Therefore, it is crucial to ensure sustainable cooperation among local government, public and private sectors, university, and NGOs for the successful implementation of the project. Moreover, within the scope of the project, technology is adopted as a facilitator for the smart city transformation of Canakkale. In other words, technology is accepted as a tool instead of a goal during the smart city transformation (Benli and Gezer 2017). The project consists of five phases as depicted below (Benli and Gezer 2017; Fig. 1). The first phase of the project was completed on 31 May 2017 and the second phase was completed on 31 December 2018. The third phase of the project has been completed by the end of December 2019.

A Mid-sized City: Canakkale, Turkey Two great wars in history, Battle of Gallipoli and Trojan War, which have been the story of the international movies, took place in the territory of Canakkale. In other words, Canakkale has two important destinations of historical value: Gallipoli Peninsula and The Ancient City of Troy. Moreover, Canakkale have many natural and cultural heritage values in the world as Mount Ida, Dardanelles, Tenedos, Imbros, and Assos Ancient City (Karakas et al. 2019). Canakkale is a strategically important city located in north-western Anatolia in Turkey since it is a bridge between Asia and Europe continents and is located in the center of Istanbul–Izmir axis corridor which is now called as the golden circle (Benli and Gezer 2017). Today, Çanakkale is also a trade and peace route which is open to international sea traffic. Moreover, Canakkale will become a significant gateway providing connection to Europe and Asia roads after the completion of the bridge on Dardanelles. These external factors increase the current importance of Canakkale on a national and international level. With an area of 1,016 km2 and a population of around 545,000 residents (Canakkale Governor’s Office 2019), Canakkale has three main characteristics: being a university town, being an agro-industry town, and being a tourist city (GMKA 2016). There is Canakkale Onsekiz Mart University in the city and the current number of the university students (52,915) is approximately 30% of the population of Canakkale Center (COMU 2019). Therefore, the university population is important for the sustainable development of Canakkale. On the other hand, it is

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also a quite important to make Canakkale attractive by becoming a social and technology entrepreneurship hub for the young people that graduate from university and to ensure that they stay here (Cobanoglu 2019). Moreover, Canakkale has irrigable land above the country percentage and wide range of agricultural products as well as high potential for aquaculture (GMKA 2016). Lastly, the number of foreign tourists visiting Canakkale in June 2019 is 5319 which is an important number (TURSAB 2019). As a result, maximizing the life quality for all individuals and city attractiveness as well as improving the city image and sustainability for natural and cultural resources have crucial importance for Canakkale. These steps will be made possible through smart city transformation process with Canakkale on My Mind Project.

A Smart City Transformation Initiative: “Canakkale on My Mind” CASE Visionary Leadership Visionary leadership in smart cities is one of the key factors in the success of smart city transformation. Visionary leaders understand the potential benefits of smart city transformation. By a smart city, we mean a city that invests in information and communication technologies to use its limited resources more effectively and efficiently, generates savings as a result of those investments, improves its services and the quality of life thanks to those savings, reduces its carbon footprint, respects the environment and natural resources, and does all these in an innovative and sustainable manner (as defined by Faruk Eczacibasi, Chairman of Turkey Informatics Foundation). Countries no longer compete; now cities compete in the world! Zeynep Bodur Okyay, President and CEO of Kale Group

Such transformation enables cities not only to save costs but also improves the quality of life of their citizens and improves their city’s competitiveness to attract both new citizens and new businesses. As Zeynep Bodur Okyay, President and CEO of Kale Group, said “It is no longer countries that are in competition, but cities. Every city will have to gain a competitive edge to differentiate itself from the rest. Flexible and agile cities that can diversify their resources and offer economic, social and cultural opportunities to their citizens will survive. The cities that are best equipped to produce innovative, inclusive and ethical solutions in the face of multiplying risks and threats will emerge as leaders. Cities will compete and collaborate globally as interdependent entities and will drive the future.” (Bodur Okyay 2018). This was part of the reason why Kale Group, a conglomerate of 62 years old decided to take up smart city transformation of Çanakkale, the city it was born in Turkey. This constitutes an exemplary case for organizations such as Kale Group that

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has its roots and lands in a particular place such as Çanakkale and should make it part of their DNA to give back. We strongly believe that to make such efforts more valuable and sustainable, one needs to take a holistic and systematic approach and be open to collaboration. The notion of cities competing globally rather than nations has led to the “Çanakkale on My Mind” initiative in 2016 which aims to step up Canakkale’s transformation into a smart city and contribute to improving the quality of life (Benli and Gezer 2018). The visionary approach that sets the tone in “Canakkale on My Mind” Project is outlined explicitly in the words of Zeynep Bodur Okyay. “As Kale Group we have always strived to be part of every project that does justice to Canakkale’s potential and is carried out in keeping with the city’s spirit. The economic and social investments we have made in Çanakkale, even when the smart city concept had not yet been coined, is no secret. Kale Group is a group of companies, that is designoriented, grounded in innovation, and invests in cutting-edge technologies. However, they are not a technology company. And that is why the project is named Çanakkale on My Mind and not Smart City Çanakkale. This was an intentional choice. Because it’s people that are at the very heart of the project. And that’s what’s going to make all the difference. Projects, where the ultimate aim is the happiness and welfare of its people and technology is a means to an end, are bound to succeed and will last for many years.”

Collaboration and the Role of the Private Sector Research indicates that “co-operation between organizations” is among the top critical success factors for smart city transformation, and the situation is no different for Çanakkale where it has been cited as the top critical success factor (Karakas et al. 2019). This is precisely why the city of Çanakkale has embarked on this transformation journey together with all of the relevant organizations and all local stakeholders in an inclusive manner building on collective wisdom and constructive partnership. By employing a strategic plan, a systematic approach, a common vision, good governance, financial productivity, and sustainability throughout the initiative, the chances of success for such large transformation projects are increased considerably (Benli and Gezer 2017). As collaboration is one of the cornerstones of such a holistic initiative and smart city transformation, Kale Group teamed up with the Turkish Informatics Foundation (TBV) led by Faruk Eczacibasi who continues to deliver invaluable work for Turkey’s transformation into an information society, and with Novusens Smart City Institute as the expert organization with extensive experience in smart cities in Turkey. Kale Group joined forces with a think tank organization that is supported by one of Kale Group’s competitors in the Turkish market and TBV did not hesitate either. As Faruk Eczacibasi puts it, “we need to use our minds and ideas so that Çanakkale makes a leap. No city will become smart without the minds and ideas of its citizens.”

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Moreover, with the help of project partner Novusens, a bottom-up and participative approach has been adapted to make innovation and sustainable development possible. Such exemplary partnerships are required in order to make a difference in the world. To succeed, international organizations, national governments, municipalities, and local partners from civil society, academia, and the private sector need to join forces. As stated in Zeynep Bodur Okyay’s words: “I believe in dreaming big and setting aspirational, yet realistic, goals. For me, a city has to inspire. We aspire to make Canakkale a role model, setting a precedent for other cities of similar scale in Turkey and around the world. We stand ready to act as a facilitator, catalyzer and systems integrator, bringing together every stakeholder that shares the same vision and helping them work effectively. We are happy to pave the way for national and international partnerships” (Bodur Okyay 2018). It is well known that change starts locally. Initiatives that are locally driven and that include solutions taking into account the city’s dynamics create a lasting impact. Therefore, in addition to nationwide collaboration mentioned above, engagement of local government institutions, universities, private companies, and NGOs have been sought in the “Çanakkale on Their Mind” initiative. Including citizens in this process and keeping the lines of communication and dialogue open are critical elements to this end leading to collective wisdom and good governance practices (Benli and Gezer 2017). ‘Getting together is the beginning; being together is the development; working together is the success’ – Henry Ford

In today’s world, as cities are competing with each other instead of countries, cooperation becomes a precondition to gain a competitive advantage. As Henry Ford indicated, working together is the key to success. Cooperation among local government institutions, public sector, private sector, NGOs, and academy is the backbone of smart cities (Benli and Gezer 2018). By employing a strategic plan through this project, Kale Group wants to support the prioritization and implementation of services and practices that touch people and improve life for citizens. Furthermore, a common vision, good governance, financial productivity, and sustainability are key to making sure they succeed on this journey. Even though Kale Group has initiated and steered the Çanakkale on my Mind project, public private partnerships (PPP) play a quite significant role to ensure the continuity of smart city transformation. As a result of organized seminars and trainings, stakeholders can understand the gains of a smart city and may want to invest in the smart city transformation process. In this context, entrepreneurs can also be encouraged to open ICT-based workplaces. Thus, the city becomes attractive for young and talented individuals as well as new employment opportunities are created in the city. Moreover, the city also benefits from technology provider companies during the smart city transformation. It is worthwhile to try new approaches to known problems, and Çanakkale can set an example for global mid-sized cities to transform into smart cities and people into information society for sustainable development and inclusive welfare.

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A Road Map to Smart City Transformation Canakkale on My Mind Project consists of five phases as indicated earlier. The project is currently in its third phase which was planned to be completed by the end of December 2019 (Benli and Gezer 2017).

Phase 1: Understanding In the first phase of the project, it is aimed to analyze the current situation of Canakkale and to create a smart city vision as well as to determine a road map for the project. The first phase consists of the five steps (Benli and Gezer 2017). Literature review: The related reports (the strategy and operating reports prepared by local government, public and private sectors; the investment plans; the demographic reports prepared by Turkish Statistical Institute, etc.) are examined. Also, meetings are arranged with the related individuals. Lastly, literature is reviewed to examine the successful smart cities which have similar characteristics with Canakkale. Field visit: Firstly, the local stakeholders are identified. Then, the meetings are arranged with them. Canakkale smart city survey: A smart city survey has been developed for the participant organizations and then applied to 40 people from 17 institutes. Smart city seminars: Comprehensive seminars related to both the smart city concept and Canakkale on My Mind Project are organized with the participation of all local stakeholders to raise awareness about the smart city vision. Collective intelligence workshops: Two collective intelligence workshops are organized. First for public and private sectors, universities, and NGOs, and second workshop organized for Canakkale Youth Association members. Based on this process, the existing smart city applications of Canakkale are firstly identified (Table 1). Secondly, the priority areas which Canakkale should focus during the smart city transformation and their priority levels are determined. According to the findings, smart environment (22%) and smart transportation (21%) are the priority areas for Canakkale. Please see Fig. 2 for details. At the same time, critical success factors and challenges have been evaluated. Moreover, based on the feedbacks of the collective intelligence workshops’ participants, the solution suggestions (Tables 2 and 3) and the vision suggestions (Table 4) are identified. As most of the solutions may require significant financial sources and time, a list of quick win project suggestions was also prepared (Table 5). Lastly, the project road map is prepared in this phase (Table 6). Phase 2: Vision In the second phase of the project, the seminars and workshops have continued to improve the citizens’ smart city awareness and their innovative approach. While the second phase of Canakkale on My Mind was in progress, 2018 was declared “Year of Troy” in culture and tourism to celebrate the 20th anniversary of Troy’s addition to the UNESCO World Heritage List. In this regard, Canakkale hosted a series of events including a UNESCO conference, International Council of Museums

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Table 1 The existing smart city applications of Canakkale Smart stop Smart junction

Canakkale Kentkart CABIS

E-Municipality Smart city information system Municipality call center

Municipality of Canakkale green local government and cultural center building Municipality of Canakkale biological wastewater treatment facility

UEDAS SCADA system

Showing passenger’s arrival time of buses 30 smart stop boards are available Managing traffic lights depending on traffic density via sensors and cameras Available at Canakkale Cuma Street market junction The automated fare collection system for buses Smart bike rental system 14 stations, 92 bicycles, 120 parks, and 4 mobile stations Online municipal services Online integration between city map, population, zoning status, etc. Enabling citizens to reach local government via call center, mobile applications, social media, WhatsApp Under construction. Produces its own energy via solar power, wind power, etc. Physical purification, advanced biological treatment, sludge dewatering, ultraviolet disinfection The required energy for biological wastewater treatment facility will be provided by solar energy plant newly founded. Remote access for the measurement, the monitoring, and the control of energy distribution systems

Source: Benli and Gezer (2017).

meetings, and a World Tourism Forums which are quite important for the smart city and smart tourism transformation of Canakkale. Moreover, Troy and Canakkale have been promoted worldwide through international visits such as the participation in Korea’s largest tourism meeting, Korea World Travel Fair (KOFTA 2018) (www. troya2018.com, 2019a). Within the scope of 2018 the Year of Troy, one of the main objectives was to improve tourism and cultural infrastructure of Troy National Park and its surroundings. A proposal has been made to establish Troy as a sustainable tourism destination and part of a cultural route. One of the most important achievements can be shown as the opening of the Troy Museum, being one of the most important cultural projects of Turkey and having international awards. Moreover, the infrastructural improvements of villages (Tevfikiye Archeo-Village) in the region; new sightseeing and tour routes; attractions; the culture routes such as Troy Culture Route, St. Paul Route, Aeneas route; cycling and culture paths for the alternative tourism activities (www. troya2018.com, 2019b) will all make a big contribution to Canakkale’s smart city transformation.

17% 15% 12% 10% 8% 8% 7% 6% Safety Healthy life Art and culture Housing

Smart Living 29% 28% 23% 20%

29% 25% 25% 20%

21% 20% 17% 17% 16% 10%

Smart People Online training opportunities for citizens E-participation applications ICT supported work places Self-internet access centre

Smart Government Online services Integrated services managed with real time data Transparent open state Internet infrastructure Use of sensors Electronic payment systems

31% 30% 25% 14%

Fig. 2 Canakkale smart city applications and importance levels. Source: Adapted from Karakas et al. (2019) and Benli and Gezer (2017)

Smart Mobility Traffic monitoring systems Smart junctions Advanced passenger information system Smart bus stops Charging stations Smart bike rental system Smart payment systems Smart parameters Fleet tracking, maintenance, and geolocation 5%

Smart Environment Smart grids 16% Smart buildings 16% Early warning systems for natural disasters 14% Automated trouble shooting & preventive maintenance 13% Automated environmental pollution control 12% Smart electricity / water / gas meter 11% Smart payment systems 7%

Smart Economy The technology initiatives using new business models The works increasing productivity in manufacturing industry E-commerce applications Electronic payment systems

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Table 2 Solution suggestions of corporate participants Public transport using renewable energy Increasing electric vehicles Increase bicycle stations and rental bikes as well as reducing rental price Build rail systems and encourage public transportation Build cable car line for university students Separate lane on roads for student buses Pedestrianize bazaar area in downtown Open Sarıcay (the river in city center) to traffic Increase parking lots Underground parking and solid waste management should be required for new buildings Raise the awareness of public about transportation Establish traffic control center and share real-time data via mobile applications Establish disaster recovery center Gathering noise data via sensors Increase smart water management systems and smart irrigation systems Introduce treatment of wastewater, the use of wastewater in agriculture Raise the awareness and increase the sensitivity of public about environment Establish institutions providing training for software and improve human resource strategies to meet the need for semiskilled workers Increase the use of technology Increase the citizens’ participation to City Council, Neighborhood Council, etc. Move public institutions out of the city center and green these areas as well as give priority to pedestrians Use solar power for streetlights, establish solar power system above Cuma street market and encourage the proper buildings to utilize solar power Source: Benli and Gezer (2017). Table 3 Solution suggestions of Canakkale Youth Association Build charging units working with solar power Increase and generalize the use of solar power by the municipality and public institutes Increase use of smart traffic lamps Bury electrical wires underground Build safer bicycle roads Build Esenler–Kepez tramway line Establish Gondola line for Sarıcay Free wireless and fiber internet infrastructure throughout the city Abolish fair usage quota policy for internet Establish new buildings producing self-energy and having parking lots Mobile application for CABIS Using sensors for waste management and waste bins An application to convey the wastes at home to the Municipality Renovate the old buildings and to use them as cultural center, art, and science buildings Have city library open 24 h a day and build joint study areas in the library Source: Benli and Gezer (2017).

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Table 4 Vision suggestions Corporate participants SMART CITY VISION 1 A ÇANAKKALE that is focused on education, culture, tourism, and ecology, is technologyenabled, offers a high quality of life, is integrated with the international community, uses energy efficiently, is responsive to natural disasters, and embraces a participatory approach and tolerance. Canakkale Youth Association SMART CITY VISION 1 A ÇANAKKALE – capital of happiness and peace – that embraces diversity, offers high quality of life through its natural and historic beauties and entrepreneurial and innovative approaches.

Corporate participants SMART CITY VISION 2 A ÇANAKKALE where the city contributes to its citizens and citizens contribute to their city, raises environmentally friendly younger generations, and upholds peace above all.

Canakkale Youth Association SMART CITY VISION 2 A ÇANAKKALE that upholds production over consumption, can generate its own energy, and makes its voice heard across the globe through an entrepreneurial and innovative society.

Source: Benli and Gezer (2017).

Table 5 Quick win project suggestions Develop high speed wireless access in the priority areas (parks, streets, museums, buses, etc.) of city center Mobile application to show Wi-Fi hotspots in both Turkish and English languages Build solar powered or city network charging units in the priority areas (parks, streets, etc.) of the city center Smart Garbage Collection Management and sensor applications for garbage containers Public internet access center to increase the internet usage capacity of individuals Build living labs providing an environment to young people to find solutions or to produce ideas to the problems of the city Technology supported healthy and independent wellness center: monitoring the health status of the citizens through wearable technology Smart parking lot guidance system to show empty parking spaces Smart street lighting systems with LED lighting, Wi-Fi hotspots, and air and noise pollution sensors Automated air, water, and noise pollution control and monitor with sensors Traffic monitoring application to show real-time traffic data, road maps, and important traffic notifications GESTAS passenger information system to show occupancy rates of ships and real-time locations of ships, etc. E-participation applications to involve citizens in the decisions about the city Create Canakkale open data portal City tourism mobile applications or smart kiosks to provide local and foreign tourists with the upto-date data about the city Source: Benli and Gezer (2017).

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Table 6 Canakkale on My Mind road map 1

Understanding

2

Vision

3

Strategy

4

Action plan

5

Implementation and monitoring

Identifying priority needs of the city Determination of local stakeholders: Municipality of Canakkale, Governor’s office, Provincial Directorate of Culture and Tourism, Canakkale Onsekiz Mart University, CTSO, CASIAD, CATOD, GMKA, public and private institutions, Special Provincial Administration, Environment and Urban Planning Provincial Directorate, Commodity Exchange, City Council, Consulate of Australia, volunteers, and youth associations. Forming a smart city collective intelligence platform among stakeholders as international examples The smart city collective intelligence platform should be managed by a nonprofit mindset and transparent team who treats equally all stakeholders Creating a platform management office Identifying necessary smart city applications (on the axis of economic growth, economic benefit, and social benefit) Determining the selection criteria by which smart projects will be implemented Identification of necessary technology facilitators and innovation accelerators Determination of short- and medium-term objectives for Canakkale, creation of the marketing plan for the city Development of quick win project suggestions Determination of necessary resources for the large projects to be implemented Creation of financial resources: inclusion in annual plans of relevant institutions; creating a city fund; opening smart city support funds and applying European Union funds or related projects Determining potential town twinning or university twinning options; developing international cooperation; joining Horizon 2020 projects; becoming a member of important smart city platforms and organizations like Covenant of Mayors; creating a smart city master plan; organizing events to raise smart city awareness of the city Following the process of the project and making necessary corrections by smart city collective intelligence platform Identifying key performance indicators and creating a continuous assessment process

Source: Adapted from Karakas et al. (2019) and Benli and Gezer (2017)

Phase 3: Strategy In the strategy phase of the project, an international event named “Sustainable and Smart: Çanakkale on My Mind” Conference has been organized on 19 September 2019. In the conference, all stakeholders and the participants of the project took an active role and the steps for ensuring a successful smart city transformation for Çanakkale were discussed. A total of 7 sessions hosted 33 speakers who are the experts on sustainability and smart city took the stage. Furthermore, with the

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leadership of Kale Group, Çanakkale Municipality partnered with TBV from Turkey and from Spain the City Council of Tarragona and Tarragona Smart Mediterranean Region Foundation within the scope of the Town Twinning Action between Turkey and EU Grant Scheme. The duration of the town twinning project between Canakkale and Tarragona is 12 months with a total project budget of 144,188.92 Euros where 90% of the budget is granted. The project has three main objectives. The first one is identified as to develop cooperation between the two municipalities within the framework of the smart city partnership and to launch Canakkale smart city platform based on the examination of the example of Tarragona. Secondly, long-term cooperation is aimed to further the advancement of smart city transformations of both cities. Last but not least, it is purposed to design joint projects within the scope of smart city transformation (www.yereldeab.org.tr, 2019). Within the scope of the project, an opening and strategy development meeting is held with the participation of the project stakeholders from both Spain and Canakkale such as Deputy Mayor of Tarragona, General Manager of Tarragona Smart City Platform, Mayor of Canakkale, TBV, Novusens in Canakkale on 12 March 2019. Thus, possible joint strategies as well as preparations for a memorandum of understanding between the two municipalities and a road map for cooperation have been discussed. A visit was made to Tarragona the week of 8 July 2019 by a delegation from Çanakkale Municipality. The visit aimed to observe smart city applications that contribute to the production of economic services and formation of sustainable cities with the support of technology. A project development workshop was held with the participation of 20 experts from Canakkale and Tarragona municipalities, Tarragona Smart Mediterranean Region Foundation, TBV, and Novusens to focus on the proposals and plans for the potential smart city projects in which both municipalities can invest in the future. Currently, the project website is prepared (Please check http://canakkaleonmymind.org/). As a follow-up activity, a study visit to Brussels have been done together with the participation of the project partners to allow mutual knowledge and experience sharing and joint project development opportunities. Based on the findings of the visit, a joint strategy paper and a project website have been prepared (http://smartroas.com/).

Critical Success Factors and Challenges The participants of smart city surveys and collective intelligence workshops emphasize cooperation among organizations as the most critical success factor of the smart city transformation. Also, innovative approach is the identified as another important success factor. Moreover, other important success factors are identified as financial competence, expertise in information and communication technologies, and citizen’s involvement. Turkey Smart Cities Evaluation Report in 2016 ranks cooperation among organizations as fourth in terms of critical success factors whereas it is ranked first in a similar study done for Canakkale, showing the city’s awareness of the importance of

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cooperation. Financial competence and citizen’s involvement and adaptation are shown as the other significant potential challenges. Both cooperation of organizations and citizen inclusion and adaptation are more strongly emphasized in Canakkale compared to Turkey Smart Cities Evaluation Report in 2016 (Benli and Gezer 2017; Tables 7 and 8).

Table 7 Critical success factors for Smart City Applications in Canakkale

Source: Benli and Gezer (2017).

Table 8 Key challenges for Smart City Applications in Canakkale

Source: Benli and Gezer (2017).

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Governance Models for Mid-sized Smart Cities In the literature, the components of the smart city are clearly stated. According to Boyd Cohen, one of the well accepted smart city experts worldwide and Assistant Professor of Entrepreneurship, Sustainability and Smart Cities at Universidad del Desarrollo University, there are six smart city components namely smart economy, smart environment, smart mobility, smart people, smart living, and smart government (please see Fig. 3 for the details). A critical point during the implementation of a smart city transformation is the integration of all different components of a smart city, removing the silo-structures in the organizations.

Successful Cases of Smart City Transformations A smart city initiative should be able to create a framework for the description of how a city is designed and how the potential challenges can be managed. For a successful smart city initiative, organizational, technology, and policy factors followed by natural environment, infrastructure, economy, society, and government

Fig. 3 Smart Cities Wheel. Source: Cohen (2012)

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related factors need to be managed well. In this sense, visionary leadership is one of the most important elements impacting all the factors and affecting success of a smart city initiative (Karakas et al. 2019; Chourabi et al. 2012; Buhalis and Amaranggana 2014; Letaifa 2015; Nabben et al. 2016; Kumar et al. 2020). Expertise of the project team is quite significant while implementing the smart city transformation. Also, technically and socially skilled ICT leadership is essential. These conditions are fulfilled on the Canakkale on My Mind initiative in different ways. This project has been led and managed by Kale Holding, in cooperation with Turkish Informatics Foundation and Novusens Smart City Institute, who also has extensive expertise in smart cities. Moreover, good communication with citizens and all stakeholders; identifying clear and realistic objectives; planning and improving a road map; developing strategies for the needs of all stakeholders without focusing on a single area; and regulating political obstacles are the other important steps for a successful smart city initiative (Karakas et al. 2019; Chourabi et al. 2012; Buhalis and Amaranggana 2014; Letaifa 2015; Nabben et al. 2016; Kumar et al. 2020). In the case of Canakkale on My Mind Project, all these points are handled through different mechanisms whose details are described in the following sections. Above all, a smart city initiative should have a smart city vision. In other words, a smart city initiative should create an enthusiasm and have ambitions, like smart city’s contribution to environmental, economic, and social sustainability, as well as the quality of life. Therefore, it would be possible to convince citizens and stakeholders to support and contribute to smart city transformation. Thus, the citizens could have awareness of the smart city initiative as well as environmental and sustainable approach it is envisioning. That would also be a possible common ground for stakeholders. Since smart city transformation is a continuous process, the citizens should adopt this as a lifestyle observe and support the efforts through behavioral changes. All these factors would require a smart city initiative with a visionary leadership which is also addressed by the Canakkale on My Mind Project. The last point to focus on is the role of technology in the smart city transformation journey. Technology needs to have an enabler role for a smart city which means technology is certainly not a smart city goal, but rather it is a tool for the smart city. Internet of things, big data, cloud computing, virtual reality, augmented reality, robotic technologies, blockchain, mobile technologies, 5G and fiber optic infrastructure, and SCADA are the technological advances that are frequently utilized in smart city applications (Benli and Gezer 2018).To sum up, the critical success factors and the potential difficulties for a smart city initiative are summarized based on the literature review in Table 9. Based on the smart city model of Boyd Cohen, a smart city has had three evolutions. The Smart City 1.0 model is a technology-centered model dominated by large/multinational technology leading companies. Since first smart city examples in the world are based on the Smart City 1.0 model, the smart city concept has always been paired with the concept of technology. However, technology should be adopted only as a tool for improving the urban quality of life. According to the Smart City 2.0 model, smart cities should be run by the local authorities of the city instead

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Table 9 The success factors for a smart city initiative Organization

Potential difficulties Project volume; the attitude and behaviors of managers, differences among stakeholders, lack of cooperation, incompatible objectives, resistance to change, conflicts

Policy

Potential political pressures affecting ICT initiatives (the decisions of city council, municipality, etc.), political instability

Technology

Lack of employees having integration skills, lack of cooperation among the ICT firms or their departments, unclear ICT vision

Economy

Lack of innovation, productivity, employment level and experts Inadequate resources and high cost, lack of integration in the urban systems, the existing software or applications which are not suitable for smart city infrastructure, threats to security and privacy Not having a clear vision for the city, focusing on only technology during the smart city transformation process

Infrastructure

Management

Nature

Increasing the importance of social, economic, and technological resources of the city without considering the nature, deconstruction of natural environment

Necessary strategies Expertise of the project team, socially and technically skilled ICT leader, clear and realistic objectives, measurable project outputs, identifying relevant stakeholders, ensuring citizen participation, planning and creating road map, good communication, trainings for smart city, finding sufficient and innovative funds, examination of best practices, creating living labs or workplaces for citizens to create solutions together The cooperation among city council, municipality, governance, etc., preparation to remove the relevant legal barriers, providing appropriate political condition allowing the minimization of urban problems Organizing ICT trainings; considering the city’s technological capacity, resource availability, institutional willingness and changing cultural habits before the ICT infrastructure changes, determination of necessary technologies Creating opportunities for industrial development Ensuring integration among urban systems Finding the necessary economic resources

Transparent management approach; understanding the needs of each stakeholders; focusing on a clear vision and making long-term plans; determining the characteristic of the city, especially the strengths in order to prioritize smart city applications based on that and then gain a more powerful competitive advantage Protection of natural resources and the relevant infrastructure

(continued)

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Table 9 (continued) Society

Potential difficulties The education level of individuals, the creativity levels of individuals, participatory structures, conflicts among citizens or societies

Necessary strategies Trainings about the smart city, equal treatment while considering and meeting the needs of different communities, adopting multidisciplinary approach and including different actors, utilizing collective intelligence

Source: Karakas et al. (2019), Chourabi et al. (2012), Buhalis and Amaranggana (2014), Letaifa (2015), Nabben et al. (2016), Kumar et al. (2020).

of technology providers. Based on the Smart City 2.0, municipalities mostly determine what the future of their city should be together with the visionary mayors and city managers. In this model, city managers focus on technology support to improve the urban quality of life. Barcelona, Singapore, and Rio are the successful examples implementing this model in the world. Lastly, Smart City 3.0 model has been improved with a citizen participation approach. Thus, successful smart cities such as Amsterdam and Seoul have begun to implement the technology-supported and citizen-centered smart city model in order to manage next generation smart cities. Boyd Cohen predicts that the integrated version of Smart City 2.0 and Smart City 3.0 models will be the best model of the future. In other words, the best smart city transformation should be managed by the local authorities with citizen participation as well as the support of the technology. Canakkale on My Mind initiative is based on the combination of the Smart City 2.0 and the Smart City 3.0 (Benli and Gezer 2018).

A Model for Turkish Mid-sized Cities: Case of Canakkale Canakkale on My Mind initiative is the first smart city project in Turkey which started with the visionary leadership of a conglomerate born in that city, Canakkale, which collaborated with a prominent NGO aiming to contribute to Turkey becoming an information society that was matched with the enthusiasm of local stakeholders. The president of TBV (Turkish Informatics Foundation 2019), Faruk Eczacibasi summarizes this situation as follows: “We start out to make this exceptional city a real smart city by creating a collective mind together with local government and citizens, public and private sectors, academics and NGOs. The solution proposals provided here about smart urbanization will be example for all other provinces of Turkey.” Moreover, Zeynep Bodur, President and CEO of Kale Group, also supports Eczacibasi with these words: “We know very well that change starts locally. Projects that are locally driven and that include solutions taking into account the city’s dynamics create a lasting impact. Our goal is to make Çanakkale an example to other cities in Turkey and, in fact, make Çanakkale one of the success stories in the

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world. There is no reason that Çanakkale should not be cited in international literature as an innovation model in the near future?” To sum up; Canakkale on My Mind initiative is a first in its formation and approach in the sense that it can be considered as a give back initiative that uses technology as an enabler for the welfare of the city’s citizens, while taking a systematic and holistic approach through participation of all its stakeholders and using a smart city transformation methodology.

Conclusion Based on the researches of the United Nations, about half of the world’s population exceeding 7.5 billion lives in cities now. Moreover, this ratio is estimated to increase 70% in 2050 and Turkey has already passed this foresight. Therefore, there is a growing need for cities that invest in information and communication technologies to use limited natural resources more effectively and more efficiently; saves as a result of these investments; improve the quality of life with these savings; reduce to carbon footprint left in nature; respect the environment and natural resources; and implement all these with innovative and sustainable methods, or shortly smart cities. In this context, Canakkale on My Mind initiative aimed to identify the necessary steps for the smart city transformation journey with all the local stakeholders and to create a transformation road map for Canakkale, which is located in the heart of Istanbul–Izmir axis called the golden circle. Canakkale on My Mind Project is based on the integration of Boyd Cohen’s Smart City 2.0 and the Smart City 3.0. In other words, the visionary leadership of Kale Group and Zeynep Bodur Okyay, combined with citizen participation, engagement of local authorities, and the utilization of technology as a tool are the main characteristics of Canakkale’s smart city transformation. Moreover, on a tactical level, field visits, smart city surveys, smart city trainings and seminars, collective intelligence workshops were used to support the essential ingredients of smart city projects, visionary leadership, citizen participation, and cooperation between organizations. The importance of visionary leadership, citizen participation, and cooperation are evident in successful smart city examples in the world such as Amsterdam, Barcelona, Copenhagen, Vienna, and Montreal. In almost all smart city applications, the local government organizations lead the transformation process while there is effective cooperation among the public sector, private sector, NGOs, and academia. Moreover, the smart city platforms are managed in such a way that all stakeholders are treated equally; objectively; in a transparent manner; with the aim of creating social benefits. Continuing the smart city transformation process initiative will contribute to the livability and sustainability of Canakkale in a world where cities compete with each other rather than countries. Moreover, the city’s natural and cultural resources supported by the physical infrastructure and smart technologies will help Canakkale become a social, human, and creative hub of the region (Benli and Gezer 2017).

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References 2018 Troya Yili Canakkale. (2019a). 2018 Troya Yili Kapsaminda Dünya’da Troya Etkinlikleri Devam Ediyor. http://www.troya2018.com/2018-troya-yili-kapsaminda-dunyada-troyaetkinlikleri-devam-ediyor/. 25 Nov 2019. 2018 Troya Yili Canakkale. (2019b). Troya ve Civari. http://www.troya2018.com/category/genel/. 25 Nov 2019. Benli, B., & Gezer, M. (2017). Canakkale’s Roadmap to becoming a smart city. TBV-Novusens Smart City Institute, commissioned by Kale Group. http://canakkaleonmymind.org/wp-content/ uploads/2019/09/Kale-Group_TBV_Canakkale-On-My-Mind_Report.pdf. 1 Nov 2019. Benli, B., & Gezer, M. (2018). Smart City transformation-smart municipalities conference. Novusens Smart City Institute Bodur Okyay, Z. (2018). This is what a smart city should do for its people. https://www.weforum. org/agenda/2018/10/smart-city-people-canakkale-connected-iot-urban/. 8 Nov 2019. Boes, K., Buhalis, D., & Inversini, A. (2016). Smart tourism destinations: Ecosystems for tourism destination competitiveness. International Journal of Tourism Cities, 2(2), 108–124. Buhalis, D., & Amaranggana, A. (2014). Smart tourism destinations enhancing tourism experience through personalisation of services. In Z. Xiang & I. Tussyadiah (Eds.), Information and communication technologies in tourism (pp. 553–564). Cham: Springer. Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J. R., Mellouli, S., Nahon, K., Pardo, T. A., & Scholl, H. J. (2012). Understanding smart cities: An integrative framework. In 45th Hawaii international conference on system sciences. Cohen, B. (2012). “What Exactly is a Smart City”. Fastcoexist.com. Retrieved 2012. COMU. (2019). Annual number of students. http://ogrenciisleri.comu.edu.tr/istatistikler/yillaragore-ogrenci-sayilari.html. 2 Nov 2019. GMKA. (2016). Çanakkale investment guide for agriculture and livestock. https://www.gmka.gov. tr/dokumanlar/yayinlar/Canakkale-Tarim-Hayvancilik-Rehberi.pdf. 2 Nov 2019. Karakas, E., Atay, L., & Cobanoglu, C. (2019, April 18–19). Digitalization of cities and smart city project: Çanakkale on my mind implementation. In International congress on digital transformation (pp. 43–64). Kumar, H., Singh, M. K., Gupta, M. P., & Madaan, J. (2020). Moving towards smart cities: Solutions that lead to the Smart City Transformation Framework (vol. 153(C)). Technological Forecasting and Social Change, Elsevier. Lazaroiu, G. C., & Roscia, M. (2012). Definition methodology for the smart cities model. Energy, 47(1), 326–332. Letaifa, S. B. (2015). How to strategize smart cities: Revealing the SMART model. Journal of Business Research, 68, 1414–1419. Nabben, A., Wetzel, E., Oldani, E., Huyeng, J., Boel, M., & Fan, Z. (2016). Smart technologies in tourism: Case study on the influence of iBeacons on customer experience during the 2015 SAIL Amsterdam event. Holland: NHTV Breda University of Applied Sciences. Turkish Informatics Foundation. (2019). Aklım Fikrim Canakkale. http://www.tbv.org.tr/aklimfikrim-canakkale,DP-1126.html. 25 Nov 2019. TURSAB. (2019). Tourist numbers and tourism income. https://www.tursab.org.tr/istatistikler. 2 Nov 2019. Yerelde AB. (2019). Gelecegin Akilli Sehirleri için Ortaklik Projesi. https://www.yereldeab.org.tr/ sehireslestirme/Haberler/TabId/450/ArtMID/3640/ArticleID/4890/Geleceğin-Akıllı-Şehirleriİ231in-Ortaklık-Projesi.aspx. 25 Nov 2019.

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Contents What Do We Consider a Smart City? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Plan for a Smart and Connected City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Developed in Cooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Brochure: Smart & Connected (https://international.stockholm.se/globalassets/ovrigabilder-och-filer/smart-city/brochure-smart-and-connected.pdf) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What Makes Stockholm a Super Smart City? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Extensive Fiber Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Examples of E-Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Preschool Portal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Residents’ Parking Permits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Report Problems in Traffic and Outdoor Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Radon Reading Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Heat Pump License Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Care Diary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Apply for a School . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Apply for a Building Permit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Komet: Web-Based Parent Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Online Applications to Art School . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Open Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data per Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Stockholm Open Award . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Innovative Solutions and International Smart City Cooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hammarby Sjöstad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hammarby Sjöstad: A Neighborhood with Integrated Environmental Solutions . . . . . . . . . . Stockholm Royal Seaport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The GrowSmarter Project, Smart Refurbishment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312 Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312 Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312

Abstract

For Stockholm a smart city is quite simply a city that utilizes digitalization and new technology to simplify and improve the life of its residents, visitors, and businesses and to offer the highest quality of life and the best environment for business. The way forward to make Stockholm a smart and connected city is to, via innovation, openness, and connectivity, make the city more economically, ecologically, democratically, and socially sustainable. A smart city is also a sustainable city reaching the city’s goals to be sustainable from different aspects such as being fossil fuel-free by 2040 and reducing greenhouse gas emissions. In 2019, the city received the Smart City Award for its GrowSmarter project at the Smart City Expo World Congress in Barcelona. The Swedish capital was commended for its “innovation, openness, and connectivity” and efforts to improve living conditions for residents.

What Do We Consider a Smart City? For Stockholm a smart city is quite simply a city that utilizes digitalization and new technology to simplify and improve the life of its residents, visitors, and businesses and also to offer the highest quality of life and the best environment for business. The way forward to make Stockholm a smart and connected city is to, via innovation, openness, and connectivity, make the city more economically, ecologically, democratically, and socially sustainable. A smart city is also a sustainable city. One of the citty’s goals is to be fossil fuelfree by 2040 as a way of reducing greenhouse gas emissions. In 2019 the city received the Smart City Award for its GrowSmarter project at the “Smart City Expo World Congress” in Barcelona. The Swedish capital was commended for its “innovation, openness, and connectivity” and efforts to improve living conditions for residents. Stockholm has a long history of being a leader in information and communications technology with many prominent companies and startups as well as established multinationals. Swedes are known for innovation: companies like Ericsson, Electrolux, Volvo, IKEA, and H&M set the standard, building world-leading international corporations. For 20 years ago, the city decided to invest heavily in an open fiber network. This turned out to be a brilliant move that today has generated billions in returns and fiber access to 100% of businesses and 95% of homes. The company is owned by the City of Stockholm itself, and private corporations are able to lease fiber on equal terms with service providers (Fig. 1).

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Fig. 1 The City of Stockholm is a globally renowned smart city

Plan for a Smart and Connected City On April 3, 2017, the City Council adopted a strategy to further develop the smart city through coordination of the City’s work on digitalization. In order to reach its vision of becoming a smart city, Stockholm decided to stimulate, guide, and coordinate different digitalization projects. The strategy for Stockholm as a smart and connected city, together with the City’s upcoming digitalization program, describes how this should be done. All new investments should be based on the needs of the people who live or work in the city – and also those just visiting.

Developed in Cooperation The strategy to become a smart and connected city was developed together with residents, academia, business, and analysis of global developments. • The City invited inhabitants of all ages to a direct dialogue at the Stockholm City Hall. • Dialogues through social media gathered more than 3350 people who provided feedback. Here, they expressed their views on the vision to become a smart and connected city and evaluated the city’s current digital interfaces as well as made suggestions for solutions that could be part of the smart city.

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• Meetings were held with employees of the City of Stockholm, as well as with representatives from startups, academia, and business. • The City of Stockholm, in cooperation with the Royal Institute of Technology, Ericsson, Vattenfall, ABB, Skanska, and Scania, established the innovation arena Digital Demo Stockholm. This arena runs projects to develop sustainable, innovative, digital solutions that contribute to improving quality of life for the people of Stockholm. Another partnership is Urban ICT Arena in Kista Science City, where the City together with the industry and universities test new technologies and services. • The city took part of experiences of other countries and cities. Selected initiatives in other smart cities have been used as inspiration. • An active exchange of best practice was also done with other cities that have made progress in their efforts to become smart cities.

Brochure: Smart & Connected (https://international.stockholm.se/ globalassets/ovriga-bilder-och-filer/smart-city/brochure-smart-andconnected.pdf) Summary of the strategy for Stockholm as a smart and connected city (Fig. 2) (https:// international.stockholm.se/globalassets/ovriga-bilder-och-filer/smart-city/summaryof-the-strategy-for-stockholm-as-a-smart-and-connected-city.pdf)

Fig. 2 The City of Stockholm’s strategy for a smart and connected city builds on all aspects of sustainability to support the highest quality of life for its citizens and the best environment for business

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What Makes Stockholm a Super Smart City? Extensive Fiber Network One important part of Stockholm’s modern ICT history is the company Stokab (http://www.stokab.se/In-english/), founded by the City of Stockholm in 1994. The deregulation of the telecom market, which had taken place the year before, was the reason for establishing the company. Despite proposals by a number of national parliamentary parties to divide up what was then the Telecommunications Administration into an independent, neutral infrastructure organization and a service organization, it remained intact and instead became the company Telia. Stockholm’s politicians believed that a neutral stakeholder was needed who could provide basic IT infrastructure to all on equal terms in order to generate competition, diversity, and a range of choice within telecommunications and data. To achieve this, the IT infrastructure company Stokab (http://www.stokab.se/Documents/Nyheter% 20bilagor/Stokab_eng.pdf) was formed through a political consensus. The company’s mission is to build, lease, and maintain a passive fiber-optic network to help foster favorable conditions for IT development and the positive development of the Stockholm region. Because Sweden was among the first EU countries to open its telecom market to competition, it was difficult to simply copy others’ solutions. Other countries, however, have since copied a number of creative institutional solutions generated in Sweden during these years. One of these is known as the Stokab Model, that is, the way in which the fiber-optic-based municipal network in Stockholm is organized. This model’s organization of the new network industry departed radically from the traditional approach to organizing such industries. The Stokab Model was based on two important insights: • The first was that a dynamic development of the new markets opened up by the Internet and broadband required competition between operators with a free right of establishment. • The second was that the high fixed costs of building networks in the city. It was neither desirable nor possible to justify the cost of digging up streets and running cable or pipes to properties multiple times for multiple suppliers (http://www. stokab.se/Documents/Nyheter%20bilagor/Stokab_eng.pdf). Since Stokab started in 1994, the goal of the municipally owned company has been to build a competition-neutral infrastructure capable of meeting future communication needs and spur economic activity, diversity, and freedom of choice, as well as minimize disruption to the city’s streets. Stokab leases fiber-optic networks that telecom operators, businesses, local authorities, and organizations use for digital communications. Leasing agreements are structured on favorable terms to encourage IT development and strong growth in the Stockholm region.

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In addition to fiber optics, Stokab (http://video.stockholm.se/video/9432050/ fibre-network-in-stockholm) provides space in nodes/hubs where customers can install communication equipment needed to connect their own networks to others’ networks. Over the years, Stockholm’s network has grown. In 2015, more than 90% of households and nearly 100% of businesses in the City of Stockholm are able to connect to the network. The fiber-optic network was also extended to cover Stockholm’s archipelago, so that all its larger, inhabited islands are connected. The network has also been extended via Mälarringen, which connects separate municipal networks around the Mälardalen region. The network is used by more than 100 telecom operators and 500 companies in Stockholm. Since the company is selffunded, it does so at no expense for public finances and benefits to end users include low commercial offers and flexibility of services supporting the city’s competitiveness and innovation capacity (Fig. 3). According to research institute (Forzati & Mattson 2013), Stokab’s network has generated a national economic profit of at least SEK 16 billion. This profit takes the form of more jobs, increased property values, and lower broadband prices. The network has also allowed for the extension of the 4G mobile networks, with four operators. It also creates conditions conducive to developing services, including

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Fig. 3 The Stokab fiber-network now incorporates most separate municipal networks in the Mälardalen region

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cloud services, smart e-services, and other innovations. Thanks to its well-developed open fiber network, Stockholm is well equipped to meet today’s challenges and that tomorrow’s. In 2015 the City of Stockholm was awarded the European Broadband Award from the European Commission with the jury saying that “Stokab has been a European pioneering model for municipal broadband development.”

E-Services The City of Stockholm’s e-services play an important part in the mission to offer fast, easy, and top-class service to Stockholmers, based on individual choice and preference. To achieve this objective, the City of Stockholm prepared an e-strategy (https:// international.stockholm.se/globalassets/ovriga-bilder-och-filer/e-strategy-city-of-sto ckholm.pdf) describing the road ahead. The strategy points out that efficient public services are key factors in a thriving city, and they are characterized by a common desire to prioritize citizens’ different needs and desires. The city provides support and facilitates everyday life. Examples are: to easily apply for permits; finding your way around the town; being able to run errands around-the-clock. As part of this goal, the City of Stockholm offers e-services that make it more convenient than ever to be a Stockholm resident.

Examples of E-Services Below are some of the numerous e-services (https://international.stockholm.se/gov ernance/e-governance/, https://international.stockholm.se/globalassets/ovriga-bild er-och-filer/e-tjanster_broschyr-16-sid_4.pdf) offered by the City of Stockholm. The list of services totals several hundreds (https://www.stockholm.se/-/Omwebbplatsen/Alla-e-tjanster/#index_A).

Preschool Portal One of the first services in the e-service program was the ability to apply for preschool. The service is available both as an open application and as one requiring login with digital identification. Applicants log in using their digital ID to accept or reject an offer. With the new version of this, e-service improves services for parents while also making administration and information easier for preschools. Both staff and parents can find out about day-to-day information, such as activities and what food is served for lunch. They can also make bookings and report absence. Preschool staff can use the portal to find and manage important information quickly and easily. The service makes administration easier when it comes to pupil withdrawals and charges. Parents will also be able to monitor their children’s creative output securely online.

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Residents’ Parking Permits Another service relates to residents’ parking permits. To get a permit, you need to own a car and live at an address where residents’ parking applies. It is also possible to record a payment for a period of time. With their digital ID, users can log in and register a permanent or temporary change of vehicle or service suspension. The service is available to around 60,000 citizens. In its benefit estimate, the Traffic and Waste Management Administration believes that around 75% of these will be using the service within 3 years of its launch, which corresponds to four full-time jobs.

Report Problems in Traffic and Outdoor Environment File a complaint or leave your point of view on Stockholm’s traffic and outdoor environment. You can choose between praise, error report, question, idea, and complaint. This service is available both as an app for smart phones and as an eservice. When used as an app, the service is very simple. If a person finds, for example, graffiti or broken park benches, the person can simply with the app report the location by GPS and send a photo and file under a heading. The maintenance teams then easily can prioritize repair. The app received more than on hundred thousand reports already in 2017 (Fig. 4).

Fig. 4 The application “Tyck till” or “give your opinion” has made it possible for the citizens to easily report faults to the maintenance teams, facillitating and speeding up repair

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Radon Reading Search One more basic e-service is the radon reading search, which was developed by the Environment and Health Administration. Radon readings in Stockholm’s residential areas are collected in a database, which can be searched by a building name and/or street address. The Environment and Health Administration previously used an external phone service for enquiries about radon, for instance. Now that the radon e-service is property-based, administrators can focus on informing property owners what they can do to reduce their radon levels, for example.

Heat Pump License Applications The e-service application for a heat pump license makes life far easier for property owners while at the same time ensuring that the Environment and Health Administration receives correct applications directly in its operations system. To make it possible to provide an e-service, all 14,000 borehole licenses were digitalized to create a map view. Around 60% of all the applications are received via the eservice, and every day building owners and heat pump suppliers alike use the site to find out how to apply and how the situation stands in the vicinity of their properties (Fig. 5).

Fig. 5 In the heat pump application, citizens that want to apply for a permit to drill a hole in the bedrock for a heat pump can easily site it and get quicker feedback about the permission

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Care Diary Elderly people themselves can use the Care Diary as an easy way of keeping track of decisions and documentation, for example, personal details, implementation plans, and day-to-day records of measures that have been taken. With their consent, close friends and relatives can also access this documentation. Records are taken from the City of Stockholm operations system for elderly care. A digital ID is required to log in and view the information.

Apply for a School This service deals with applications for school and makes it easier for parents to choose a school for their children. Parents can also track where their application is in the school selection process. The service covers both local authority and independent schools. It also means that head teachers have more time to plan ahead of the new school year, which makes staff planning far easier.

Apply for a Building Permit The City Planning Administration deals with around 9,000 planning issues a year. The e-service is divided into several parts and informs people how to apply for a building permit, where to find current plans, and how to interpret them. You can also order maps ahead of an application and track your application through the process. It is also possible to register when construction work begins.

Komet: Web-Based Parent Training Stockholm’s district councils train parents in using the Komet method to help improve communication with their children. School and preschool teachers are also trained in using the Komet program, which helps ensure calmer, more secure pupils and children’s groups. A version of the targeted parents’ program has been developed, and an e-service project enables parents to come into contact with the Komet program.

Online Applications to Art School The Stockholm School of Arts educates 15,000 children and young people aged 6–22 in art and design, dance, music, and theater skills. Its activities cover the whole city, in 20 or so of its own premises and something like 80 schools. The 14,000 course applications are received as forms, which are then registered. Residents are able to apply, supplement, and amend their applications and see where they are in the queue. This e-service will streamline planning and administration while also improving the level of service.

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One of the City’s goals is to make services and information more accessible and to help citizens, businesses, and staff communicate more easily by digital means: information and information management are therefore strategically important, something which is emphasized by the City setting aside major resources for this purpose. Part of this venture is the Stockholm City e-archive, which files all digital document transactions automatically. Two things are required to meet the City’s objectives: • The people of Stockholm must be able to find information via a single search portal, no matter where it is stored. • The information must be stored in a common e-archive as soon as transactions are made.

Open Data Since 2011, Stockholm City has published open data in several areas. The goal is to promote innovation and openness; the City is working actively to provide open data through the portal Open Stockholm (https://international.stockholm.se/governance/ smart-and-connected-city/open-data/). Public information is to be used not only by authorities but also by businesses and the public to create new services. The data is open to anyone (free) and can be accessed digitally via APIs/Web services. The City of Stockholm provides one third of all open data from the Swedish public sector with information from more than 100 open data sources.

Data per Area Culture and Archive Data Among the city’s cultural and archival data, there are links to the City’s building registers. Here is also available 35,000 pictures and documents from Stockholm’s history and associated metadata. Population Data The City of Stockholm collects population statistics as a basis for planning the service that is under the responsibility of the municipality, such as childcare, school, planning of social services activities, and forecasts for tax revenues. Traffic and Parking Data The traffic data contains road- and traffic-related data that Stockholm City collects for traffic planning, maintenance, and project planning. Environmental Data The environmental data contains maps and measurement data that the City of Stockholm has produced in order to describe the environmental situation within the municipality. There are also data from regional and national environmental monitoring.

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Activities and Satisfaction Surveys The unit database contains information about all of Stockholm’s business locations and is also the basis for Compare Service on stockholm.se. Geodata Stockholm City is responsible for maintaining basic geographical data across the city. Among our geographic data are different types of maps and aerial photos.

The Stockholm Open Award The City of Stockholm also organizes the Open Stockholm Award competition to stimulate new ideas and increase accessibility to the city’s service through the development of mobile services and apps based on the city’s open data. The ambition of the competition is that a winning project can be completed when the competition is completed.

Innovative Solutions and International Smart City Cooperation The City of Stockholm is aware of the great potential that smart city solutions can have on reducing the city’s impact on the environment. Since 1995 the city has reduced its greenhouse gas emissions per capita by 60%. Much of this has been accomplished by the transition from single oil furnaces in buildings to more district heating with cogeneration of heat using renewable fuels. Now the city needs new smart solutions to go further in reducing its emissions. These solutions need to better engage the inhabitants themselves who stand for most of the emission by the way they use energy for transport and heat/hot water.

Hammarby Sjöstad Hammarby Sjöstad: A Neighborhood with Integrated Environmental Solutions Stockholm City has put tough environmental demands on buildings, technical installations, and traffic environments in Hammarby Sjöstad. The urban area received its own environmental program with the aim of reducing the total environmental impact by half compared to an area built in the early 1990s. Hammarby Sjöstad also received its own cycle, the Hammarby model, which describes the environmental solutions for energy, water and sewage, and waste. Sweden’s export council has developed a model for the sustainable city – SymbioCity – which is based on experiences from Hammarby Sjöstad (Fig. 6).

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Fig. 6 The Hammarby model illustrates the circular model on how water, waste, and energy are produced and used in Stockholm

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Stockholm Royal Seaport In 2009, the Stockholm City Council decided that Stockholm Royal Seaport would be designated an area with an environmental profile with the mandate to determine what is possible in the current situation and push the boundaries where possible, to become a model of sustainable urban development. The built environment is robust over time which requires that buildings and facilities are designed with high quality. Materials, water, and energy are resources that must be used efficiently by, for example, creating eco-cycles. Using nonhazardous materials reduces impacts on human health and the environment. The generation and use of renewable energy are encouraged to make the area fossil fuel-free. Key Figures 2017 • The energy consumption is 40% lower than the n requirements. • One hundred percent of the properties are connected to a vacuum waste collection system, and 100% of kitchens have a waste disposal unit. • The amount of residual waste in 2017, 215 kg/apart./year compared with 242 kg/apart./year in 2015. • Twenty-two percent of the soil has been remediated so far, equating to 40 football fields. • Twenty-eight percent fill material has been reused in 2017 (Fig. 7).

Fig. 7 The Royal Seaport is the follower of Hammarby Sjöstad. This area with more than 15,000 new dwellings is Stockholm’s largest new development area, testing smart solutions and using farreaching environmental technologies

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The GrowSmarter Project, Smart Refurbishment GrowSmarter (http://www.grow-smarter.eu/solutions/) was 5-year project (2015–2019) supported by the European Commission led by the City of Stockholm. It focused on implementing smart solutions in refurbishment areas. This is extra important as refurbishment is often neglected compared to the flashy newly built areas in cities. In Europe, more than 200 million inhabitants live in areas buildt from the 1960s to 1970s. These residential areas are now in need for renovation. There is an enormous potential for building energy-efficient and smart solutions both in these buildings and in the neighborhoods. The GrowSmarter project included three Lighthouse cities, Stockholm, Cologne, and Barcelona, as well as five Follower cities, Valletta, Suceava, Porto, Cork, and Graz. GrowSmarter received funding from the European Commission’s Smart Cities and Communities Horizon 2020 research and innovation program. The scope of the project was to: • Demonstrated and validated 12 economically and environmentally sustainable integrated smart solutions in the three Lighthouse cities • Fostered collaboration between cities, businesses, and academia to transform the smart solutions into business models to be rolled out across Europe • Improved the quality of life for European citizens, reduce environmental impact, and create sustainable economic development GrowSmarter took a holistic approach to sustainable growth. The demonstrations in the Lighthouse cities were not the primary aim, but a means to contribute to solving city challenges and create validated business cases to initiate a market roll out of the smart solutions to Follower cities and the rest of the European market, thus helping Europe Grow Smarter. The 12 solutions were designed to meet the three pillars of sustainability. The main goal of the project is to demonstrate and market 12 smart solutions for: • Low-energy districts – More than 120,000 m2 of building space were renovated with an improved energy efficiency by 60%. – Smart energy-saving by providing information on real-time energy usage and waste levels to tenants is a key tool to help them see and reduce their own environmental footprint. – Smart local electricity management reduced grid fluctuations and saved electricity. • Integrated infrastructures – Smart streetlights – New business models for district heating and cooling – Smart waste collection, turning waste into energy – Big open data platform

Fig. 8 The GrowSmarter project demonstrated 12 smart solutions for smarter cities in Europe. The practical demonstrations were replicated in many other cities and helped create a market for solutions that both reduce greenhouse gas emissions and creates new jobs, thus helping Europe GrowSmarter

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• Sustainable urban mobility by improved the logistics for goods and also reduced the need for private cars in cities – Smart building logistic and alternative-fuelled vehicles – Sustainable delivery – Smart traffic management – Alternative fuel-driven vehicles for decarbonizing and better air quality – Smart mobility solutions (Fig. 8) GrowSmarter evaluated these solutions against targets related to climate change, energy usage, transport emissions, and jobs. The demonstrations both helped induce replication both in the participating cities and in other international cities as well. Introducing innovations is often regarded with skepticism. Why introduce smart streetlights with sensors when the existing system with one switch turning them all on at dusk and off at dawn has worked for over 100 years? One demonstrated and evaluated it was shown that these solutions worked and saved half the energy needed for convention streetlights. After that the city has decided to exchange 24,000 of them.

Fig. 9 In Stockholm the GrowSmarter project has renovated several buildings from the 1960s into energyefficient and smart buildings

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This is just one out of numerous examples of how the project has helped overcome the anxiety for innovations and change. The GrowSmarter project is an example of how Stockholm shares its experiences outside of its borders and together with other cities puts effort to help cities Grow Smarter stimulating economic growth and simultaneously reducing greenhouse gas emission (Fig. 9).

Conclusions The City of Stockholm is in many ways a smart city. The city utilizes digitalization and new technologies to simplify and improve the life of its residents, visitors, and businesses. As for all large organizations, testing new ideas through pilot projects has also been a successful method to get past the reluctance for change. Testing new technologies and methodologies before introducing them in a larger scale has helped avoid backlashes and help replicate the smart measures, as good examples are easily taken up by others once shown. Several reports and city comparisons have pointed out Stockholm as a leading smart city. For the City of Stockholm the challenging work continues and is developed further to meet the city’s goals to both be fossil fuel-free and to be the world’s smartest city by the year 2040.

Cross-References ▶ Smart Energy Frameworks for Smart Cities: The Need for Polycentrism

Reference Forzati and Mattson (2013). Acreo Rapport acr055698. https://www.ssnf.org/globalassets/sverigesstadsnat/fakta-och-statistik/rapporter-av-andra/acreo-stokab—en-samhallsekonomisk-analysacr055698sv.pdf

Smart City Wien: A Sustainable Future Starts Now

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Thomas Madreiter, Angela Djuric, Nikolaus Summer, and Florian Woller

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vienna Is on Its Way . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart City Wien Framework Strategy 2014 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart City Wien Monitoring Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart City Governance Is the Key to Success . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart City Wien Framework Strategy 2019–2050 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thematic Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Energy Supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mobility and Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Digitalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Economy and Employment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Water and Waste Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Social Inclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Science and Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Participation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E_OS: Renewable Energy from Sewage Sludge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Neighborhood Oasis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smarter Together . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WAALTeR: Active, Healthy Ageing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sag’s Wien App . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Citizens’ Power Plants: Community-Funded Solar Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Auto Bus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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T. Madreiter (*) Executive Group for Construction and Technology, City of Vienna, Vienna, Austria e-mail: [email protected] A. Djuric · N. Summer · F. Woller Smart City Agency, UIV Urban Innovation Vienna GmbH, Wien, Austria © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_9

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Smart Traffic Lights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vienna Provides Space: Digital Twin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . BRISE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Werkstadt Junges Wien: Co-Creating a Child and Youth Strategy for Vienna . . . . . . . . . . . . . Conclusion and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Vienna is among the most successful cities worldwide where quality of life, infrastructure, and innovation are concerned. Numerous studies and rankings place Vienna in top positions in terms of competitiveness, with the promise of further dynamic development. However, the City of Vienna is facing challenging times: the population in the federal capital is growing and will reach the two million mark over the course of the next decade. This development goes hand in hand with a rising demand for energy, demand for affordable and functional housing, and a need for strong mobility concepts. At the same time, climate change and a severe shortage of natural resources, especially fossil fuels, represent the big global challenges of the coming decades. This is where Vienna’s Smart City strategy comes into play. Smart City Wien is a long-term initiative by the City of Vienna to improve the design, development, and perception of the federal capital. The initiative looks at a cross-section of the city, covering all areas of life and includes everything from infrastructure, energy, and mobility to all aspects of urban development. It has set itself the task of modernizing the city in order to reduce energy consumption and emissions significantly without having to forego any aspects of consumption or mobility. To achieve this, the city government has adopted a framework strategy to attain its key objective for 2050: High quality of life for everyone in Vienna through social and technical innovation in all areas, while maximizing conservation of resources. This chapter first gives an overview of the history of framework strategies with an emphasis, among other things, on monitoring, which is essential to highlight successful areas as well as those which require more work. The second chapter describes the thematic fields of the Smart City Wien Framework Strategy 2019– 2050. The last chapter deals with various projects that have already been implemented or are in the process of being implemented and provides practical examples.

Introduction Making the transformation into Smart City Wien a reality is an enormous endeavor that will last for decades to come. The tasks associated with this transformation have to be borne by many shoulders. Policy-makers and administrators as well as local companies, the scientific community, culture and the arts, and – last but not least – every single citizen are all required to rise to the challenge. The City of Vienna can already look back on a decade of experience regarding this transformation and draw

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conclusions about what worked well and what did not. By applying this governance knowledge, Vienna hopes to set standards and meet the goals of Smart City Wien.

Vienna Is on Its Way From the beginning, Smart City Wien has shown commitment to Europe’s development goals up to 2050. Guided by the EU Strategic Energy Technology Plan (SET Plan), former Mayor Dr. Michael Häupl initiated Vienna’s transformation into a more sustainable urban living space in 2011. A project funded by the Austrian Climate and Energy Fund gave stakeholders inside and outside the city administration the opportunity to team up for collaboration. From 2011 to 2013, expert policymakers and administrators as well as representatives of the scientific and business communities and civil society worked out a strategic basis for the Smart City Wien Framework Strategy. In 2014, Vienna City Council adopted an initial draft document incorporating visionary concepts and action plans. On June 25, 2014, legislators at the local level made the so-called Smart City Wien Framework Strategy the legally binding foundation for Smart City Wien.

Smart City Wien Framework Strategy 2014 The first Smart City Wien Framework Strategy envisaged a smart transformation as the development of a city that assigns priority to and interlinks the issues of energy, mobility, buildings and infrastructure. In this, the following premises applied: radical conservation of resources, the development and productive use of innovation and new technologies, as well as a high and socially balanced quality of life (Vienna Municipal Administration 2014). The underlying goal was to safeguard the city’s ability to tackle future challenges in an integrated manner. The signature characteristic of the Smart City Wien Framework Strategy that set it apart from approaches taken by many other cities was its holistic approach and focus on social components. Social inclusion was integrated as an essential element of the strategy, with improving the everyday life of Vienna’s citizens being assigned the same importance as climate-related and ecological objectives. The City of Vienna consciously chose this approach, asserting that a city is only smart if all of its people have equal opportunities, enjoy a high quality of life and have access to the same degree of participation. The first Framework Strategy was built on three pillars - resource conservation, quality of life and innovation. It formulated objectives in numerous thematic fields ranging from education, economy and healthcare to energy, mobility and infrastructure. Objectives were long-term and inextricably linked to existing initiatives, plans and specialized sectoral strategies of the City of Vienna. As a superordinate framework, Vienna’s Smart City strategy is intended to provide orientation and ensure the coherence of all activities. Far-sighted, intelligent decisions in the past have made Vienna the city with the highest quality of life worldwide. However, to maintain these high standards, it

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proved necessary to strive for constant assessment and new and innovative solutions as climate change and increasingly scarce resources call for a new global approach and continuous innovation. Thus, a truly smart city must keep pace with the pulse of the times, cleverly adapting to changes.

Smart City Wien Monitoring Process Smart City Wien is a complex endeavor that touches upon every sphere of urban life. As one can only manage what one can measure, serious monitoring of transformational progress quickly turned out to be indispensable. An evaluation mechanism was therefore developed in 2016. Smart City Wien regularly reviews the extent to which the city is on track to achieve the goals of its framework strategy. This monitoring methodology is not only a basis for controlling the process, but also creates a platform to improve dialogue and collaboration between all stakeholders involved. It acts as a catalyst for city-wide governance and serves as a tool for communication and mobilization. The first monitoring cycle was carried out in 2017. Special attention was paid to extensive cooperation and building upon existing data, evaluations and reports. All steps in the monitoring process involved municipal departments as well as associated organizations and enterprises. Focus groups, interviews and workshops were used for detailed discussions about content and procedural questions. The rationale was that a well-coordinated approach would elicit maximum support (ownership) from all actors. Vienna’s first monitoring report in 2017 painted a positive overall picture: for 23 out of 51 individual objectives Vienna was fully on track, while the attainment of another eleven objectives was rated as being largely on track. However, the monitoring process also drew attention to shortcomings that will require increased efforts in the coming years. Assessments also showed that it is crucial to identify interrelated topics and potentially conflicting objectives in order to ensure a coherent implementation of the Smart City concept in Vienna. As many conflicting objectives became apparent, the need for proper management of complex highly interrelated urban systems (by measuring, addressing, discussing and prioritizing) will challenge traditional methods of government: growth versus conservation of resources, housing versus green spaces, affordable housing versus high ecological building standards, greening of roof surfaces versus solar collectors, etc. to name just a few (City of Vienna 2018). However, a particular effect of the Framework Strategy with its integrated approach is that the individual thematic fields und objectives should and will become more closely interlinked so that synergies emerge. It thus becomes clearly evident how activities in one area also produce a positive impact elsewhere, for example, when eco-friendly forms of mobility simultaneously improve traffic safety, reduce noise pollution and encourage healthy physical exercise. At the same time, seeing the whole picture also highlights conflicting objectives and allows an open debate on what should take priority.

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But the evaluation also flagged up thematic fields which were insufficiently covered by the Smart City Wien Framework Strategy in 2014 and/or which have not yet been linked to concrete objectives. This includes e.g., digitalization, adjustment to climate change, social innovation and the necessity of a joint approach throughout the entire metropolitan region: a professional dialogue and exchange of up-to-date data across municipal institutions and administrative bodies were seen to be particularly important. Due to the lack of a central data management system integrating all reports and data, coordination and cooperation across organizational units, tiers and sectors is in great need of improvement.

Smart City Governance Is the Key to Success Open innovation – aka sourcing innovation from distant fields and markets – frequently involving citizens as early as possible via participatory processes and ensuring a coherent implementation of the strategy beyond organizational and departmental boundaries are key challenges of Smart City governance. Across the world, cities are increasingly becoming the focus of policy-making on innovation and around climate and energy issues. By forming alliances cities can join forces to advocate their common agendas, for instance with regard to safeguarding the principles of general public interest and provision of public services, or ensuring that their thematic priorities are incorporated in EU funding programs. Unsurprisingly, two mindsets, above all, are important for Vienna’s future as a Smart City: enabling steady and constant evolution and creating space for the new. The new – be it services, corporate plans and business models, forms of mobility, modes of social interaction or cultural expression – hardly ever fits into established structures and remits. A few innovations are easy to integrate into tried-and-tested mechanisms and quickly produce positive results. Others start out as a challenge for the existing set-up. Making a commitment to the Smart City also means that the management structures of the city, in particular, will be repeatedly put to the test and so must be ready to be very adaptable. The innovation focus of Smart City Wien calls for new tools and approaches in the design and delivery of municipal services. In this respect the municipal administration is setting the bar very high: the quality of services is to be maintained at the same high level while taking even greater account of the various needs of everyone living in Vienna, thus demonstrating how modernization can be used to uphold and enhance quality of life. The full potential of the Smart City approach can only be realized if tasks and challenges are viewed from a more interdisciplinary, inter-agency perspective, cutting across the boundaries between remits and working together on joint solutions. It is often local action – supported or facilitated by appropriate measures – that succeeds in overcoming these boundaries. Non-cooperation, on the other hand, incurs high costs due to inconsistencies, duplications or gaps that then require readjustments later on.

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Overall management and coordination of the strategy is affected via a governance structure that reflects the complexity and breadth of content of the undertaking. In particular, the Smart City governance structure is designed to perform the following functions: (1) orientation for sectoral strategies and packages of measures, (2) initiation of key cross-departmental projects, (3) addressing stakeholders outside the public sector and (4) strategic and quality management through monitoring. In order to perform these functions, Smart City Wien utilizes personnel and financial resources on multiple levels. The responsibility of the policy-making level is to define a clear policy line for Smart City Wien. It issues policy instructions, approves planned measures and makes available the required resources. The level of the Chief Executive Office of the City of Vienna ensures the strategic coordination of Vienna’s Smart City initiative. Among other things, this also includes ensuring that sectoral strategies are aligned with the Smart City goals, initiating cross-cutting projects and measures, evaluating the monitoring results, discussing strategic courses of action and resolving conflicting objectives. It also guarantees the regular exchange of knowledge within the municipal administration at the operational level and promotes the development of suitable measures and projects on priority issues. Moreover, civil society, and particularly representatives of the scientific and business communities, is to be assigned an even greater role. All activities of the City of Vienna are supported by a Smart City Agency. The main task of this multidisciplinary team is to be a neutral innovation broker initiating and coordinating projects, advising and supporting municipal actors, managing stakeholder enquiries as a point of liaison for new partnerships, communication and networking activities and providing support to the Smart City governance structure. It is important to stress that Vienna’s evolution into Smart City Wien can only succeed if the goals and targets receive widespread support far beyond the city’s policy-making and administrative capacities, with a broad spectrum of stakeholders participating via interdisciplinary beacon projects, public-private partnerships, pilots, living labs and research partnerships/joint ventures, as well as participatory formats and alliances of all kinds. For instance, close coordination and collaboration with Vienna’s neighboring federal provinces and the local authorities within the Smart Region is essential. The Platform for Energy and Climate Action (Smart Region) under the auspices of Planungsgemeinschaft Ost, the joint planning organization of the three federal provinces of Vienna, Lower Austria and Burgenland, is one springboard for cooperative strategies and measures across the administrative boundaries. Federal-local collaboration and city partnerships will be equally important.

Smart City Wien Framework Strategy 2019–2050 Even though another three decades lie ahead until 2050, the coming years are of decisive importance. Vienna’s population continues to grow (Fig. 1) and the consequences of the climate crisis are becoming more and more evident – in the form of

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Fig. 1 Population trends 2009–2019; comparison of European cities. (City of Vienna, Statistics Vienna 2020)

extreme weather events such as torrential rainfall, droughts and heatwaves. The climate crisis is one of the major challenges of the twenty-first century, and one that will have a far-reaching impact on everyone’s life – in cities as well as in the countryside. For that reason, it is even more important that major cities take their future into their own hands rather than letting events take their course. By implementing its Smart City Wien Framework Strategy Vienna intends to do precisely that. However, as monitoring has shown, a long-term-strategy of this kind needs to be subject to adaptation and adjustment. This is particularly the case if a city is eager to tackle the consequences of the climate crisis and disruptive technological innovation holistically. The revised and updated version builds on the strategic guidelines, goals and objectives of the 2014 Smart City Wien Framework Strategy and develops them further. Close cooperation between representatives of the municipal administration and its associated enterprises and organizations, plus involvement of external experts from the fields of academia, business and representative bodies, was of decisive importance in the revision and update process. During the process, all of the thematic fields were subjected to critical examination. Consequently, existing objectives were tightened up and new ones defined (Vienna Municipal Administration 2019). For instance, “Digitalization” and “Participation” were incorporated into the strategy as new thematic fields. Moreover, a stronger focus was placed on current developments and challenges - especially on interplays between thematic fields. In addition, a materiality analysis was performed on all 17 Sustainable Development Goals of the UN 2030 Agenda and their 169 targets, and the results taken as a basis for the revision process.

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Thematic fields are no longer assigned to only one of the three dimensions Quality of Life, Resources and Innovation. Instead, each thematic field now refers to all of these headline goals, i.e., radical conservation of resources, contributing to quality of life and social inclusion, and the focus on innovation and digitalization as the key instruments for viable sustainable development. New issues such as adapting to the consequences of climate change, establishing a circular economy and drastic reductions in raw material consumption are addressed by the revised strategy. Target values for CO2 emissions and underlying calculation methods have also been adjusted: until now, the greenhouse gas reduction targets of the Climate Protection Programme of the City of Vienna (KliP) and the Smart City Wien Framework Strategy were always defined in relation to 1990 as the baseline year. From now on, 2005 will be taken as the baseline year. 2005 is the EU-wide standard baseline year for all CO2 emissions targets (comprising emissions inside and outside the ETS - Emissions Trading System). In parallel, monitoring indicators had to be re-examined and partially re-defined as well as adjusted to updated strategic objectives. The findings of the first monitoring cycle in 2017 were of vital importance to refine strategic adjustments. Collaboration has been key for strategy formulation and methodological set-ups. Likewise, partnership between municipal entities, associated enterprises in the public sector and external stakeholders from the private sector and civil society will play an essential role in bringing the strategy to life.

Thematic Fields The Smart City Wien Framework Strategy provides guidelines for the city’s transformation into a more livable and sustainable habitat up to the year 2050, touching upon almost every sphere of urban life. In 2017, a monitoring report flagged up those areas in which approaches required significant adjustments in order to mitigate conflicting objectives, incorporate new goals and respective requirements and keep Vienna’s focus on sustainable urban development as the main agenda. With digitalization and participation being integrated as new priorities, the Smart City Wien Framework Strategy now addresses 12 thematic fields which will be outlined hereafter (Vienna Municipal Administration 2019).

Energy Supply A secure, affordable, environmentally sound, needs-based energy supply is and remains one of the most important prerequisites for Vienna’s high quality of life and economic development. Therefore, one of the central objectives in the field of energy supply is to maintain Vienna’s high level of energy security. In order to concurrently meet the CO2 emission reduction targets, the city’s energy system requires radical transformation. This radical transformation will, among other things,

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be achieved by investing in the development of smart grids. The Smart City Wien Framework Strategy furthermore envisions a clear reduction in energy consumption combined with a step-by-step conversion to renewables. This requires an interplay of different factors: in future, per capita energy consumption for heating, hot water, and air-conditioning in buildings is to be drastically reduced. At the same time, the share of renewables is to be increased through a gradual conversion from gas and oil-powered heating systems to district heating, solar energy, or heat pumps. In the transport sector, a shift to eco-friendly modes of transport and an electrification of motorized private and goods transport is needed. The new demand for electricity in the transport sector should of course be satisfied from renewable sources. Other energy consumption, such as electricity for lighting or electronic devices and the energy consumed by trade and industry, is expected to decline only slightly. The shift to renewable sources in these sectors is therefore indispensable. In order to be more independent on the energy market, Vienna intends to extensively increase renewable energy production within the municipal boundaries. Apart from these technical measures, comprehensive awareness-raising, information and education campaigns are required in order to achieve appropriate changes in the consumption behavior of the Viennese population.

Mobility and Transport Mobility and transport are of pivotal importance to almost every city. They have a decisive impact on the daily routine of its citizens and are a major driver of a city’s success as a business location. At the same time, in many cities transport is the sector responsible for the greatest share of total greenhouse gas emissions. In Vienna, close to a third of final energy consumption is attributable to transport. In order to become a truly smart city, Vienna envisions convenient, safe, barrier-free, affordable mobility for all, whether or not they have their own car. Radical conservation of resources and preventing traffic-related carbon emissions means reducing the need to travel wherever possible, shifting journeys to efficient modes of transport, and making the transition from fossil fuels to carbon-free propulsion systems for all vehicles. Thanks to digitalization, virtual mobility can already replace physical mobility to some extent. The design of urban neighborhoods is another important lever to reduce traffic-related carbon emissions. By ensuring an attractive local mix of functions – housing, education, employment, shopping and leisure – within a short distance, Vienna enables its citizens to bike and walk as they go about their daily lives (Fig. 2). Shifting the transport sector towards cycling, walking, and the already wellestablished public transport system frees up public space, which to date has been primarily geared to the needs of car traffic. This allows for a more equitable urban layout that meets the needs of the citizens. New mobility options such as automated vehicles, perhaps in combination with sharing schemes, have the potential to help

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Fig. 2 Modal split 1993–2019. (City of Vienna, Statistics Vienna 2020)

further reduce the number of private motor vehicles in future. The City of Vienna will work with operators to ensure that such options are designed sustainably and safely for all road users. For all these considerations a close cooperation with Vienna’s neighboring local and regional authorities is essential in order to manage the traffic volumes crossing the city boundaries.

Buildings The combination of historic built fabric and numerous new buildings both shapes Vienna’s cityscape and underpins its unique atmosphere. Some 90% of the 170,000 or so buildings are used for housing. In view of the forecast population growth, at least 75,000 additional dwellings will be needed by 2030. A sufficient supply of affordable, high-quality housing thus has to be provided while simultaneously substantially reducing consumption of energy and resources and carbon emissions. This requires new buildings to be constructed to near-zero-energy standard, existing buildings to be fully reinsulated, and heating and energy systems to be gradually converted to non-fossil fuels. In terms of design, the future planning of buildings has to promote the use of eco-friendly building materials which are used as efficiently as possible and can be largely reused or recycled at the end of the building’s useful life. In line with the concept of a city of short distances, a good functional mix is striven for within neighborhoods and, where possible, also within buildings. The use of eco-friendly modes of transport can be supported by provision of attractive, easily accessible parking facilities for bikes, scooters, etc., either inside the building or close by. Further potential to make buildings more sustainable can be tapped by using building information modelling tools, which allow all parties involved in planning, operating and refurbishing a building to access comprehensive information across the entire life cycle of a building. Last but not least, a greater emphasis will be placed on protecting residents – particularly vulnerable groups such as socially isolated elderly people – from the heatwaves caused by the climate crisis. To this end, suitable installations to contain the threat of heatwaves are required, such as external sun blinds, shading, external water cooling, façade greening or rainwater circulation systems.

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Digitalization Digitalization and automation are major forces that drive the transformation of cities and societies. They permeate economic life, working life and community life and are transforming urban infrastructures. The City of Vienna strives to actively manage digitalization in all these spheres, providing up-to-date infrastructure, supporting stakeholders and keeping the municipal administration and its associated enterprises fit for the future. In doing so, the City of Vienna focuses on the rights and needs of everyone living in Vienna: Vienna does not view digitalization efforts as an end in themselves, but leverages the new technological possibilities to create equality of opportunity and an inclusive urban society. The municipal administration provides low-threshold access to digital information, public services and barrier-free participation and engagement for all social groups and puts special emphasis on social innovation processes. The basis for this is digital education and targeted training to equip everyone with the necessary digital skills, accompanied by special efforts to close the digital divide with regard to sex, age, ethnicity and people with special needs. At the same time, in the interests of equal opportunities and resilience the municipal administration will continue to provide services and information via non-digital channels. Over the course of the next decades, the City of Vienna will thus use digital data, tools and artificial intelligence in applications that help to conserve resources and maintain the city’s high quality of life, which will result in a modern, needs-based digital infrastructure benefitting Vienna’s citizens and its municipal administration. The data being mined will moreover be used to support decision-making, for realtime management of urban systems, and will be made available as open government data, especially for scientific, academic and educational use.

Economy and Employment A diverse economic structure, a well-trained workforce, a pronounced capacity for innovation, social calm and the maintenance and expansion of modern, fit-forpurpose infrastructure are prerequisites and conducive factors for a competitive and resilient metropolis. In Vienna, knowledge-based and technology-led services account for by far the largest share of regional added value. At the same time, the city boasts a highly productive industrial base. Conducive conditions and a supply of attractive sites and premises are to ensure the continued presence of both sectors. Unusually for a major city, Vienna also has a thriving agricultural sector within the municipal boundaries. This agricultural production is also to be safeguarded for the future and geared towards maximum resource efficiency and environmental sustainability. The opportunity for productive participation in the labor market is a decisive factor in terms of high quality of life. The growing city must provide low-threshold, non-discriminatory access to the labor market for all. As a location

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for smart businesses, Vienna itself becomes a driver of jobs the more the public and private sectors invest in eco-friendly technologies and services and the circular economy. Another factor essential to a city’s quality of life and economic attractiveness is the provision of public infrastructure and services which are affordable, of high quality, and available to all citizens and companies. Digitalization will help make these services still more easily and widely accessible. Vienna’s evolution into an environmentally and socially sustainable location will entail profound structural changes. The central guiding principle here is the transformation from the linear economic model to a circular economy, in which all stakeholders have to play their part. Public and private companies are required to develop new resource-efficient, circular processes, products, and business models. The municipal policy-makers and administration have to ensure transparent rules to create a reliable framework for business as well as providing incentives through subsidy schemes, collaborative partnerships and as a consumer. Last but not least, the forward-looking consumer behavior of Vienna’s citizens ultimately makes the transformation possible.

Water and Waste Management Ensuring the city has a secure supply of high-quality drinking water and reliable, environmentally sound disposal of all waste, waste water and sewage are key urban services and major factors in Vienna’s high quality of life, which is why the City of Vienna relies on self-sufficiency in these areas. With its spring water mains from protected mountain springs, Vienna has sufficient water supply capacity for the long term, even with ongoing population growth and the growing incidence of heatwaves due to climate change. Nevertheless, rainwater management measures are being intensified to benefit from sustainable urban water cycles. The extensive modern sewer network and the main sewage treatment plant ensure the eco-friendly disposal of municipal waste water and sewage. The high-quality standards in water management are guaranteed for the long term through consistent maintenance, refurbishment and needs-based expansion of infrastructure, coupled with optimum operational management. Waste management plays a key role in the transformation to a circular economy; therefore Vienna promotes waste prevention measures and aims to enable an increase in the proportion of waste recycled or reused as secondary raw material through its waste collection systems.

Environment Even today, climate change is already giving rise to an increasing number of very hot days (when the maximum temperature exceeds 30 °C) and very warm nights (when the temperature does not fall below 20 °C), which impair people’s health and quality

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Fig. 3 Temperature in °C; average temperatures were above the long-term average in eleven out of twelve months 2019. (City of Vienna, Statistics Vienna 2020)

Fig. 4 Land use in Vienna 2018. (City of Vienna, Statistics Vienna 2020)

of life and have a high cost to the economy (Fig. 3). Far-sighted planning and timely prevention and protective measures are therefore urgently required. Minimal environmental pollution and intact ecosystems are essential for healthy living conditions and a high quality of life in cities. Prevention and reduction of air, water and soil pollution and of heat and noise are thus central pillars of Smart City Wien, alongside the preservation and expansion of green spaces, soil functions and biodiversity and a healthy, sustainable diet and food production (Fig. 4). This can be supported by environmentally aware mobility habits and responsible consumption patterns. Close collaboration with the federal government and the EU is required here, especially with a view to meeting the environmental goals of the Smart City Wien Framework Strategy.

Healthcare Good health is seen by many as the most important commodity and is thus essential to individual well-being. The city’s health policy activities therefore focus on

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maintaining, promoting, improving and, where necessary, restoring the health of the Viennese population. The basis for this are Vienna’s Healthcare Goals, which is one of many sectoral programs that concretizes the Smart City Wien Framework Strategy and thus supports its implementation. Vienna’s Healthcare Goals cover living and working conditions as well as the urban environment. Key principles include equality of opportunity for all sectors of the population – including socially disadvantaged groups – and taking due account of the specific needs and health risks of women. A particular challenge in terms of health are the changed environmental conditions brought on by climate change, most notably the growing incidence of extreme weather events such as unusually long periods of hot weather, drought and torrential rainfall. The related changes in vegetation zones mean that allergenic plants and disease-carrying insects which were previously absent or rare in Vienna will become more widespread in the city and surrounding region. Furthermore, the impact of airborne pollutants is growing. A comprehensive package of measures for climate action and adaptation to climate change in all thematic fields of the Smart City Framework Strategy is therefore of prime importance if Vienna is to achieve its healthcare objectives, which include the prolongation of healthy life expectancy, the continued provision of high-quality medical care for everyone, as well as the promotion of active ageing and health literacy.

Social Inclusion Technological developments, digitalization and automation, and above all advancing climate change, affect everybody – but not necessarily equally. Today more than ever, the focus of Smart City Wien therefore remains on social cohesion and equal opportunities. In Smart City Wien, social inclusion also means digital inclusion. Digital transformation should not be allowed to open up new social divisions, but must also benefit those groups who do not (or cannot) yet engage with new technologies in their everyday lives. Therefore, an explicit objective of Smart City Wien is to utilize new technologies and developments in the field of digitalization to help promote social inclusion. Beyond that Vienna will continue to promote diversity, gender equality, and opportunities for participation for everyone who lives in Vienna. Through investments in public infrastructure, municipal services and subsidized housing Vienna intends to guarantee its high quality of life and a decent standard of living for everyone in the long run (Fig. 5). Last but not least, social inclusion in Vienna also means strengthening community cohesion, fostering urban competences and taking a stance for fair working conditions, adequate wages for gainful employment, and social welfare schemes.

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Fig. 5 Types of housing 2011; as a share of total housing. (City of Vienna, Statistics Vienna 2020)

Education Smart City Wien sees itself as a responsible learning space for a sustainable, resource-aware future; as a forum for discourse that provides room for a substantial debate about the kind of future development we aspire to in Vienna. This space not only generates understanding and support for the headline goals of resource conservation, sustainability, and a community-based urban society, but also provides people with the capacity for independent action. That’s why Vienna strives to provide everyone – starting from the earliest possible age right through to elderly citizens – with low-threshold access to high-quality, inclusive educational facilities. The City of Vienna as well as a large number of organizations – from nurseries to adult education and from youth centers to cultural societies and sports clubs – share institutional responsibility in this learning space. Other social systems such as families or neighborhood groups also influence the quality of this space. Emphasis is also placed on overcoming educational disadvantage among individual social groups, challenging stereotypes through gender-sensitive teaching, and establishing an environment that promotes equality. This helps to create a Smart City where stereotypes no longer play a role in educational and career choices. Apart from the traditional curriculum, digital education, education for sustainable and resourceefficient development and public engagement programs to open up access to Vienna’s multifaceted arts and cultural scene will be further expanded in future.

Science and Research Research and new technologies are key drivers of added value, creating a competitive economy and good-quality jobs. They are also tools that allow us to address issues that Smart City Wien will face in the future. Vienna must therefore be in a position to take up research findings from all over the world and use them productively. At the same

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time, it is essential that the city builds on its own capacities as a high-ranking innovation, research and higher education hub. Hosting some 200,000 students in 2019 and around half of Austria’s basic research facilities, Vienna has excellent institutional prerequisites and is the largest university city in Central Europe and the German-speaking region. Vienna is also the seat of numerous innovation-led companies and corporate headquarters which carry out R&D activities in the city. Through Smart City Wien this position is to be firmly established and strengthened. Digitalization is a decisive factor in accelerating developments in business and research. In the academic field digitalization will drive innovation across disciplines, thus generating new benefits for science, research and society. In Vienna, targeted support will be given to digital humanities (scholarly activity at the intersection of digital technologies and the humanities) and digital humanism (using findings from the humanities and social sciences in the development of digital solutions). Other essential factors are research capabilities in the social sciences, looking at issues such as diversity, gender and the distribution of power and assets, as well as the creation of a framework for the development of social innovations. By facilitating and initiating innovation challenges related to Smart City Wien, the City of Vienna will take part in the innovation process and encourage cooperation between the municipal administration, higher education and research institutions, companies and end users.

Participation Truly sustainable development can only be realized if everyone living in a city is able to play a part and indeed actively does so – in discussion processes, in the elaboration and implementation of projects, by contributing their knowledge and experience, and through responsible consumer behavior and mobility choices. A service-led approach which provides infrastructure and a broad spectrum of services is important for the city’s general quality of life, but should not limit or substitute for the individual initiative of its citizens. In fact, quite the opposite: precisely a sweeping initiative like Smart City Wien, which treads completely new ground in many areas, not only requires allies who back decisions, but indeed the creativity and know-how of as many people as possible. Smart City Wien is therefore to be evolved and implemented on a participatory basis by providing numerous different opportunities for engagement and space for discussion, co-creation and involvement in decision-making, e.g., through participatory budgeting. These processes to negotiate the future of the city are likewise important learning processes, as communicated interests and needs can be taken into consideration as a basis for useful solutions.

Projects Having an elaborate strategy is only the first step. Implementation is what makes the Smart City Wien Framework Strategy become a reality. Numerous projects are being implemented by the City of Vienna, its public utility companies and city-owned

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enterprises. Private companies, non-profit organizations, and, needless to say, citizens have also been getting involved through public-private partnerships and multi-stakeholder collaborations. The following section will present some of the projects mirroring the broad spectrum of Smart City Wien.

E_OS: Renewable Energy from Sewage Sludge Vienna’s main wastewater treatment plant is gearing up for energy self-sufficiency. The energy-optimizing sludge treatment (E_OS) project is due for completion in late 2020. This means that in future Vienna’s main wastewater treatment plant will be able to selfgenerate over 100% of the energy it requires for wastewater treatment from biogas, thus saving 40,000 t of CO2 emissions per year. In the new facility the sludge, a residual product of wastewater treatment, is condensed and heated to 38 °C before being pumped into six 35-m-high anaerobic digestion tanks. Inside these hermetically sealed digesters, biogas is produced (during the anaerobic stabilization phase). Biogas consists of two-thirds energy-rich methane, a recognized renewable energy source, which is subsequently converted into electricity and thermal power in cogeneration plants. To implement E_OS, the main wastewater treatment plant has completely reconstructed the preliminary sedimentation and first biological stages of the plant. The new process technology for the first stage minimizes the energy required for ventilation while maximizing the energy content of the sludge, so the gas yield and resulting energy production are higher than in conventional sewage treatment plants. The project also included the installation of new facilities for sludge digestion and biogas extraction and utilization. Mechanical pre-thickening of the sludge ensures a high dry matter content of around 8%, which means that less energy is needed to heat the sludge in the digester to the required temperature of approx. 38 °C. It also allows the volumetric capacity of the digestion tanks to be reduced to around 75,000m3 each. Reject water from sludge dewatering containing high levels of nitrogen is treated separately, thus ensuring that most of the nitrogen continues to be removed. Vienna’s main wastewater treatment plant thus complies with all the water purification standards required by law. The operator of the plant made careful preparations for E_OS, building a 1:600scale pilot plant which confirmed the energy-yield projections of the 2010 feasibility study. Vienna City Council approved the project in 2012, and construction work began in 2015 following the positive outcome of the environmental impact assessment. Partial operation has been implemented step by step, and the new facility will become fully operational at year-end 2020. Vienna’s main wastewater treatment plant will then generate 15 nW-h more electricity and 42 nW-h more thermal energy per annum than it consumes itself, meaning that its entire energy requirement – almost 1% of the city’s total energy consumption – will be covered by renewables. Any surplus will be fed into the public grid. In addition, the heat generated during the process will be used on site for sludge digestion or fed into Vienna’s district heating network. Vienna’s main wastewater treatment plant is thus setting new standards for sustainable wastewater treatment within its size category and making a substantial contribution to achieving the objectives of the Smart City Wien Framework Strategy.

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Neighborhood Oasis Vienna citizens have a wealth of ideas for making the city more livable. A parklet in a parking bay? A fitness session or community dinner party in the street? The Neighborhood Oasis project creates opportunities for translating these ideas into reality. The public space belongs to everyone, and people should be able to use it to co-shape, discover, and enjoy their neighborhood. The NGO Local Agenda 21 Vienna advises interested parties and supports them in implementing their projects. Projects funded by the association and implemented by local people to date include bread-baking on a grass verge, community parklets and flower beds, dinner parties in the street and a summer festival with minigolf. Even in 2020, despite Covid-19, the NGO has implemented 87 local projects, including 78 parklets, 13 street parties, and other activities in the public space. The projects are scattered across 19 municipal districts, i.e., almost the entire city. Vienna is a growing city, and it is becoming increasingly important to find multiple uses for public outdoor spaces. The aim, as set forth in urban programs such as the STEP 2025 urban development plan and the Smart City Wien Framework Strategy, is a livable, inclusive city with socially diverse neighborhoods and engaged citizens. Local Agenda 21 Vienna supports this aim via its Neighborhood Oasis initiative.

Smarter Together Since 2016 Vienna has been working with its partner cities Munich and Lyon on the beacon project “Smarter Together,” in which over 30 project partners are piloting technical and social innovations in established urban neighborhoods. The overarching objective is to promote sustainable renewal within the existing urban fabric. Local residents in Vienna’s Simmering district are involved in some 40 individual projects within the project area, including, e.g., building refurbishment works and an e-car sharing scheme at a housing complex in the project area. Many local residents visit the project’s mobile public outreach unit, or reserve an e-cargo bike direct and free of charge via the dedicated website. Conventional e-bikes are also available to hire in close proximity to the project area. Education likewise plays an important role in climate action. Smarter Together projects in the education sector include an extension to a school with four brand-new zero-energy gymnasia, two solar benches, and an array of educational workshops. Smarter Together is also spearheading the development of the Simmering Education Quarter. In line with the Smart City Wien ethos, a dedicated FIWARE open-source data platform permits all kinds of data gathered in the context of the project to be analyzed for research purposes. Commercial project partners such as Siemens and Post AG (Austrian postal service) are using the data to develop smart logistics solutions, and a whole range of other partners from business and industry, R&D, civil society organizations and the municipal administration are collaborating closely

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under the initiative. Smarter Together stands out for its integrated approach to climate action and measures to enhance urban quality of life. All the implemented measures have entered the monitoring and evaluation phase, which will last until 2021. The primary focus is on assessing the extent to which the findings and experience can be rolled out to other projects, with a major emphasis on participatory processes and knowledge management (peer-to-peer learning). In addition, new business models are to be developed to allow these solutions, all of which have been tested by real people in their everyday lives, to be replicated elsewhere. Smarter Together is the largest EU-subsidized Smart City urban renewal initiative in Vienna. In 2017, it won the VCO Mobility Award in the category “Active Mobility and Public Space.”

WAALTeR: Active, Healthy Ageing The WAALTeR project addressed the question of how digital technologies can enhance quality of life for senior citizens in Vienna. 91 Viennese senior citizens tested a series of tools and services in their everyday lives over an 18-month period, with the focus on activities, mobility, social integration and communication, safety, and health. The digital tools tested included a tablet installed with a dedicated WAALTeR app and a Bluetooth link to a blood pressure gauge and telemedicine functions, smart watches with mobile and home emergency call buttons, and an indoor fall detector. 76 further volunteers without digital support tools for everyday life acted as a control group. WAALTeR combined all the tools and services in a single all-in-one solution. The aim was to usefully integrate digital assistance systems into the everyday lives of elderly people, so all the tools and services were designed for easy use by this target group. The participants’ subjective feeling of safety in the home was enhanced by the alarm button, presence detector and fall detector functions, while the fall prevention program and a pedometer encouraged regular home exercise. Telemedicine applications allowed blood pressure and blood sugar measurements to be transmitted to the user’s general practitioner. The senior citizens also had access to a digital neighborhood support network, a calendar for events and appointments, video telephony, e-mail and a route planner customized for their individual mobility preferences to support social interaction and participation in community life. WAALTeR supports the objectives of the Smart City Wien Framework Strategy in multiple ways. The project demonstrated how a Smart City can develop the digital skills of its senior citizens, providing a key to increase their social integration and combat social isolation in old age. At the same time, it also helped to promote healthy ageing and active mobility. The collected data show how important digital tools and services can be in the lives of senior citizens, provided they are involved at the development stage. After all, senior citizens are not a homogeneous group and technology is not a universal remedy that is automatically useful regardless of external social factors.

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Sag’s Wien App Be it a malfunctioning traffic light, soiled park benches or damaged road signs – the Sag’s Wien app provides people living in Vienna with a fast and easy way of notifying the municipal administration. All they have to do is take a smartphone photo of the problem or send a written notification. Whatever the concern, it will be communicated to the municipal administration in less than a minute. A dedicated team is on call to come out and put things right. Sag’s Wien makes Vienna more mobile, more personal, more networked and more attractive – and it facilitates dialogue between the public and the local authority. Sag’s Wien is a digital participation tool for the people of Vienna and a milestone on the way to becoming a smart and digital city. The app is one of the first projects implementing Vienna’s “Digital Agenda” and was developed in a process involving engaged members of the public. The Sag’s Wien app thus supports the implementation of the Smart City Wien Framework Strategy in several different ways, with digitalization of municipal services and increasing opportunities for public involvement and participation both being key components of Smart City Wien. Among all the many incoming reports there are one or two that are truly bizarre. The team on duty couldn’t believe their eyes when they received a message headed “Please remove this corpse!” Luckily the “corpse” turned out to be a damaged and discarded old bicycle, which was removed by the Municipal Department for Waste Management and Street Cleaning just a few hours later. This is just one of many examples that show how the Sag’s Wien app helps the City of Vienna to address the concerns of local people and make the city a more attractive place to be. In the first year-and-a-half following the app’s release, the City of Vienna received nearly 30,000 notifications from the public, 97% of which could be resolved.

Citizens’ Power Plants: Community-Funded Solar Energy The citizens’ power plants project launched by Vienna’s municipal energy provider Wien Energie allows everyone to participate in the development of eco-friendly electricity generated by solar photovoltaic panels. Especially in cities, where the majority of the population live in rented accommodation, people do not usually have the option of installing solar panels on their own roof. With its citizens’ power plants, Wien Energie gives private individuals the opportunity to make a collective investment in clean energy. Wien Energie is in charge of operating the photovoltaic plants. In return for their investment the co-owners of the plants receive an annual remuneration in the form of vouchers from Wien Energie over a period of 5 years. This community funding model benefits the environment, Wien Energie and investors alike. Vienna’s first citizens’ power plant opened on 4 May 2012 on the premises of Donaustadt power station, and Wien Energie has been expediting the expansion of the model ever since. Over 30 solar and wind plants are already supplying the city with carbon-free energy. The thousands of local investors and the continued high

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levels of interest in the model demonstrate the Viennese public’s strong commitment to climate action. A secure, affordable, environmentally sound, needs-based energy supply is and remains one of the most important prerequisites for the city’s high quality of life and economic development. The Smart City Wien Framework Strategy envisages that by 2030 30%, and by 2050 70% of Vienna’s final energy consumption will originate from renewable sources. This requires both investment in power plants within the urban area as well as imports of renewables from the surrounding region and/or via long-distance cables. The citizens’ power plants are making a major contribution to renewable power generation within the municipal boundaries and in the wider region and are thus helping to meet the energy targets of the Smart City Wien Framework Strategy.

Auto Bus Wiener Linien, Vienna’s public transport provider, is testing two self-driving buses in the area of Vienna’s largest urban development project. Since June 2019 the two buses have been transporting real passengers along a 2 km-long circular route through Seestadt. Since it is a test service, passengers can travel on the buses for free. During this pilot phase there is always an operator on board, who can take manual control of the vehicle in case of emergency. The project team has been preparing for the operational phase since April 2018. The emphasis in the preparatory phase was on all aspects of safety. Before launching the pilot service at Seestadt the buses were extensively tested on a Wiener Linien test site. Now that the buses are able to safely transport passengers on the road, they follow the planned route with less than a centimeter of deviation. In compliance with the legal provisions, the buses will not exceed a speed of 20 km/h during the pilot phase. The two electric buses are running in Seestadt as part of a research project which is being undertaken, among other reasons, to further develop sensors that are important for autonomous driving, to test IT security systems and to see how passengers respond to the buses. The goal is to permanently increase the efficiency and operational safety of automated vehicles. The electrically powered minibuses are equipped with 2D and 3D sensors which constantly scan the surroundings for potential obstacles. With the aid of other localization systems, the positions of the buses can be determined to within a centimeter at all times. Seestadt was chosen as a test site as it serves as an urban lab for innovative Smart City solutions in Vienna. Numerous innovative projects at Seestadt are helping to make Smart City Wien a reality.

Smart Traffic Lights Vienna is installing a smart traffic light system to cut waiting times for pedestrians and improve traffic flow, thus simultaneously reducing stop-and-go-related

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emissions. The innovative devices are equipped with an in-built camera capable of detecting people within an 8/5-meter radius of the traffic lights. Once they verify that the pedestrian wants to cross the street, the signal automatically changes to green without having to press any buttons. The green phase can be extended in the case of large groups of people who need more time to cross. If pedestrians leave the waiting area before the lights have turned green, the traffic lights do not switch to green so there are no unnecessary waiting times for motorized traffic. To maintain the privacy of pedestrians, the images are only analyzed locally and do not leave the camera. The system functions exclusively with geometric information, from which it deduces a possible intention to cross. The smart traffic lights will replace Vienna’s 200 push-button pedestrian crossings, which have certain limitations: some people are not prepared to wait and cross the street while the traffic lights are on red, with all the associated risks. Others press the buttons just for fun, causing unnecessary tailbacks and frustration among drivers who have to stop even when nobody is crossing the street. The Institute of Computer Graphics and Vision at Graz University of Technology researched and developed the system within a two-year period. Not only does it require precise and efficient software, but the hardware also has to be the size of a powerful computer, yet small enough to fit into a small switch box. The traffic lights will initially be installed in selected locations to verify that they work correctly.

Vienna Provides Space: Digital Twin Several departments of the Vienna city administration are in charge of different aspects of public space management, depending on the intended purpose or type of use. As a result, prospective users of public space may be required to submit applications to different municipal departments for a single project, which complicates the process for the user. It also complicates the information exchange between different units and services of the city administration, owing to the variety of internal information interfaces and different IT solutions. At the same time, many official approval procedures require on-site inspections as a basis for drawing up expert statements. In response to this challenge, the City of Vienna launched the public space management program Vienna Provides Space. The program aims to re-organize and optimize the whole process, including the responsibilities for the management of objects and activities in the public space. Internal processes are being restructured and simplified; at the same time, users are being provided with a new customer interface in the form of a (digital and physical) one-stop shop based on a state-of-theart software solution. Moreover, the entire legal framework for official permits for activities in the public space is being evaluated and updated. The overall goal of the program is to make it easier for residents and businesses to use public space. The foundation of the project is a comprehensive survey of all existing objects and structures in public areas, which was conducted by means of a mobile mapping exercise. This generated three-dimensional, georeferenced images of the whole city, which are a prerequisite for visualizing data (i.e., inventory data

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and permits) in the new jointly used IT system. The result is a digital twin of Vienna which will replace many of the on-site inspections usually required for permit applications, thus saving both time and expense. Vienna Provides Space gives a reliable, comprehensive overview of all objects located in the city’s public space, which makes it easier to manage public space as a whole. This also creates the basis for decluttering urban public space and supporting fair use of public space for everyone.

BRISE The City of Vienna is developing its BRISE project -–Building Regulations Information for Submission Envolvement – with numerous partners. Together with the Vienna University of Technology and in collaboration with the construction industry the city is eager to digitalize building permit procedures (submission and approval of construction projects). This partial automation will save time: in the near future, building permit procedures could be executed up to 50% faster. Property developers will not only receive building permits quicker and more easily; immediate feedback from automated preliminary checks will also increase planning security and enable timely corrections. Citizens will benefit from 3D models that visualize buildings and make construction projects transparent even before construction works begin. All of the information contained in such a 3D model remains available over the entire life cycle of a project: all relevant steps and data can be viewed, from planning through to the completion of a building. Accordingly, BRISE will increase the productivity of planners in terms of costs, deadlines and quality from 2020 onwards. With a lot of construction activity going on in Vienna, large amounts of test data are available, allowing research results to be subjected to a reality check in everyday life. In the medium term – in line with Vienna’ s Smart City Framework Strategy – a fully digital submission and approval procedure will also contribute to greener and more resource-efficient buildings as it holistically integrates and automates sound inspection of definable quality criteria and standards. Building Information Modelling (BIM) utilizes software to improve planning and construction of buildings, based on a digital 3D model. BRISE adds AI and AR technologies to the mix, which enables everyone involved in the project – from architects and property developers via construction engineers through to facility management - to exchange ideas efficiently and collaborate from start to finish via a technical interface (IFC - Industrial Foundation Classes). The EU is funding BRISE with 4.8 million euro via its Urban Innovative Actions (UIA) initiative.

Werkstadt Junges Wien: Co-Creating a Child and Youth Strategy for Vienna Smart City Vienna builds upon technological and social innovation to achieve its long-term goals. Werkstadt Junges Wien is a great example of the latter

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complementing technological innovations of all kinds. The largest participation project with children and young people in Vienna’s history, Werkstadt Junges Wien let over 22,000 young residents of the Austrian capital have their say. The main objective of Werkstadt Junges Wien was to create a cross-sectoral Child and Youth Strategy for Vienna based on the expertise of children and young people regarding their own needs and realities. Every resident of Vienna between 4 and 19 years of age was invited to participate, and the youngsters were truly free to set the agenda and decide on priorities: only after the children and teenagers had made their choice were experts invited to analyze and add on to the results. In June 2020, the first cross-sectoral legally binding Child and Youth Strategy with 193 specific measures was adopted by the local legislator, Vienna City Council. Input gathered throughout the participation process was co-interpreted by a renowned social science institute. In this manner, researchers helped to identify nine topics of highest relevance to children and young people. Drawing on that, Vienna’s Child and Youth Strategy mirrors many priorities of Vienna’s Smart City Framework Strategy. Among other ideas and needs of vital importance, Vienna’s young residents were especially keen to grow up in a clean and healthy environment and climate. They want to be able to live sustainably in their city and have parks, green spaces and water on their doorsteps. To make this possible, they want their city to plant trees, reduce waste, protect animals and save energy. 1309 workshops with 22,581 children and young people were held by educators, youth workers, teachers and volunteers with their respective groups. Relying on these existing relationships of trust made it possible to include children and young people from diverse backgrounds. Moreover, approximately 1000 relevant stakeholders participated actively as workshop facilitators. As one fifth of Vienna’s population is under 19 years of age, all participants, their families, and their communities will benefit from the outcome and the measures undertaken to implement the strategy. From now on, all municipal departments will declare which measures they already have in place that contribute to achieving those strategic goals and how to fill gaps with new ones.

Conclusion and Outlook The City of Vienna has already set the course for further Smart City implementation: In line with the Paris Climate Accord, the Vienna City Council adopted a new climate policy framework (Wiener Klimapaket). It will facilitate the interplay of the city’s three essential instruments in the field: Smart City Wien, Vienna Climate Council and Vienna Climate Budgeting. Moreover, Vienna joined the “Climate KIC” knowledge and innovation community of the European Institute of Innovation and Technology (EIT). As Vienna’s sustainability strategy the Smart City Wien Framework deploys climate protection and adaptation to climate change in order to secure the city’s high quality of life. Furthermore, Vienna’s mayor and vice mayor are advised on climate policy challenges and relevant communal measures by the Vienna Climate

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Council, which is an independent body established in 2019. Last but not least, the Vienna Climate Budgeting process will annually assess the city’s budget planning in terms of its climate impact. Both, the Paris Agreement and the Smart City Wien Framework Strategy require not only a transformation of the city’s energy system, but extensive systemic change in all areas of urban life. In order to manage this complexity, a Smart City Roadmap – drawing on the city’s strategic long-term goals – will identify major levers and measures until 2050.

References City of Vienna. (2018). Urban development and planning. Monitoring report 2017. https://www. wien.gv.at/stadtentwicklung/studien/pdf/b008520.pdf Statistics Vienna. (2020). Vienna in figures 2020. https://www.wien.gv.at/statistik/pdf/ viennainfigures-2020.pdf Vienna Municipal Administration. (2014). Smart City Wien framework strategy. https://smartcity. wien.gv.at/site/files/2019/07/Smart-City-Wien-Framework-Strategy_2014-resolution.pdf Vienna Municipal Administration. (2019). Smart City Wien framework strategy 2019–2050. https:// www.wien.gv.at/stadtentwicklung/studien/pdf/b008552.pdf

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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . NEOM Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Content Analysis (Articles and Blogs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Content Analysis (Pictures and Videos) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . NEOM Smart City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Internet of Things Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Economy (SE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Living (SL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Governance (SG) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Environment (SE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Mobility (SM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart People (SP) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Most of the nations in the world are supporting the creation of smart cities to provide better life to their urban citizens. Saudi Arabia, which has been known for its oil resources, is not an exception. The country has started the constitution of a smart city called “NEOM” from scratch. In this chapter, an attempt has been made to study the NEOM smart city. As the smart city phenomenon is contemporary and is still evolving, the case study method for exploration has been used. S. Madakam (*) Information Technology, FORE School of Management, New Delhi, India e-mail: [email protected] P. Bhawsar Strategic Management, Indian Institute of Management, Sirmaur, India © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_86

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Secondary data, i.e., blogs, articles, pictures, and videos, were collected for review and analysis. Content analysis is carried out, and for thematic narration, ATLAS.ti software has been used. The chapter exposed that the NEOM project is located in Tabuk Province, in the peninsula of Saudi Arabia in the northwest of the Saudi Kingdom. The NEOM project includes land within the Egyptian and Jordanian borders with an area of around 26,500 km2. The word NEOM was coined from two words “NEO” ¼ “New” and “M” ¼ “Mostaqbal” ¼ “Future,” “NEOM” ¼ “New Future.” This project was brainstormed and exhibited first time at the Future Investment Initiative conference in Riyadh (Saudi Arabia), on October 24, 2017, by the Saudi Crown Prince Mohammed bin Salman. The former Alcoa chairman cum CEO and Siemens AG former president and CEO Klaus Kleinfeld is the principal director. NEOM smart city demonstrates high profile of new urbanism, design, architecture, and technology principles. It will have own tax and labor laws and unique judicial system in order to provide a healthy environment to its region-specific residents, investors, and employees.

Introduction The cities are under latent threats, which can appear abruptly. The recent COVID-19 pandemic is one such example that stagnated human life all around the world including the metropolitan areas. In bird’s-eye view, a few moments of the human habitation is only seen in cities in the last 2 months, except the concrete forest of towers, buildings, houses, roads, poles, and forest greenery, blue lakes, and black mountains. It were only the security personnel from the administration who were visible on the roads to prevent the public gatherings. These personnel also ensured the protocol of social distance among public. Municipality workers were in the streets for sanitizing public living environments and medical staff to give treatment for the COVID-19 patients. Moreover, the ruling government around the globe faced severe dilemma in crafting policies for saving human lives. Corporates had to stop their businesses even when it meant huge losses, the manufacturing units shut down, and the laborers rushed to their native places; educational units were forced to give vacations or conduct their academic programs through online modes. Shopping malls, theatres, gardens, and public places remained close to maintain the social distance. Only a few groceries, medical shops, and hospitals remain functional in cities 24/7 to serve the citizens. A citizen could not relish his/her freedom to movement due to the corona pandemic. Like this, the cities are always concerned about their sustainability being affected by various factors and invaders. The cities always need to ensure protection from misfortunes including fire accidents, terrorism, and natural calamities, including earthquakes, floods, famines, and pandemics like COVID-19. Moreover, these cities are overburdened with the problems of population (natural birth as well as sudden rural migration); pollution (water, air); insecurity (women and children); and insufficiency (houses, hospitals, toilets, education, theme parks). Besides, transportation problems, power thefts while distribution, water leakages during transmission, unauthorized slums built up, lack of civic

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sense, and inefficiency in governance will handicap cities in providing the citizen services on time and round the clock in any metropolitan area. Therefore, the cities are unable to provide the quality of life to urban citizens. At present, one more factor is added to the woes of the cities that the labors (workers) in unorganized sectors are in reverse migration due to coronavirus. This has led the huge impact on the factory workforce in blue-collar jobs and significantly has affected the economy of the cities as well as the nation. To be candid, while the cities have reached a position in the name of development and comfort, the cities are not sure if they will live in the coming years and if they will exist in the future. In this backdrop, there is an urgent need all over the world to rethink about cities, better policymaking, collaborative work, building new urban solutions, taking advantage and deploying the technologies, citizen engagement, social inclusion and environmental concerns for the economy, and sustainability of the cities in future. For this, we must convert the existing cities into smart ones or build the new smart cities in the rural areas, remote places, not in the neighboring cities. In this light, the new urban spaces should come up in the non-living spaces or region or remote areas to resolve urban glitches in the existing cities across the globe. Masdar (UAE), PlanIT Valley (Portugal), GIFT (India), Meixi (China), and Putrajaya (Malaysia) are some of the best example of upcoming smart cities sprouting from the scratch. That means all the countries set out plantation of new smart cities in their countries to provide quality of life to their urban citizens. In this light, the “NEOM” smart city is one of its best examples for the new sustainable urban space, which is coming in a non-living regional area in Saudi desert like an urban oasis “The City of Future.” This smart city will embrace the technology in every aspect of human life, boost the Saudi economy in a big way, and provide the quality of life or happy life and reason for sustainable future city by carbon footprint reductions along with reverse rural migration from cities. This book chapter will give details on smart cities, six dimensions, and in depth about NEOM smart city (Saudi Arabia). The next section will discuss in detail the research methodology used for scribbling this book chapter right from data collection, data analysis and interpretations.

Research Methodology The research manuscript (book chapter) is based on the secondary (online) data collected mainly from the Google, Google Scholar, and other databases between the periods from March 1, 2020 to August 20, 2020. Moreover, the data have been composed of the research articles, news articles, the NEOM smart city website, blogs, forum discussions, corporate white papers, and YouTube videos on NEOM projects. The data was in an unstructured format including text, ppts, pdfs, pictures, and videos. Therefore, this type of methodology comes under qualitative research; having said that this concept is contemporary hence falls under an exploratory study. We know that qualitative researchers are interested in understanding the meaning people have constructed, that is, how people make sense of their world and the experiences they have in the world (Merriam 2009). Others emphasize an

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epistemological stance: qualitative research is a research that uses methods such as participant observation or case studies, which result in a narrative, descriptive account of a setting or practice. Qualitative data analysis is a combination of both coding down (deduction) and coding up (induction) (Berg 2001). The latest qualitative analysis software “Atlas.ti 7.0,” latest version has been used to analyse the data; upload the documents, selecting quotations, generating the codes on par with literature variables and constructs – relating to smart city dimensions, and then grouped them into families and superfamily. ATLAS.ti 5.0 and NVivo 2.0 are among the best available and potentially most useful qualitative data analysis (QDA) tools (Lewis 2004). Network diagrams are drawn for different city dimensions. Moreover, the thematic narrations done based on the frequency of codes from documents in order to understand NEOM Smart City in a 360-degree view. Content analysis and network diagrams are the outputs for elaborating the NEOM smart city depth and breadth of phenomenon. Moreover, word clouds are graphical representations of word frequency that give greater prominence to words that appear more frequently in a source text. The larger the word in the visual, the more common the word was in the documents. This type of visualization can assist evaluators with exploratory textual analysis by identifying words that frequently appear in a set of interviews, documents, pictures, audios, videos, and/or other online data. The “word cloud” evaluation has mainly been qualitative, taking place through interviews and as an assessment of clouds by individuals (Hearst and Rosner 2008; Viégas et al. 2009).

Literature Review Cities are contemporary metropolises that concentrate human and social activity, engineered to support and develop the physical environment and the people within it (Cavada et al. 2014). These cities are preferred dwelling places for human beings. Besides, cities play an important role in boosting the national economy. However, these urban places are now suffering from several urban glitches including overcrowding, slums, traffic, insufficient infrastructure, citizen security, environmental damage, and inefficient governance, to name a few. According to the United Nations Project, by the year 2100, the world will be having more than 10 billion population. The future will be challenging, as the natural resources are not growing in proportion to the population growth. The present and future generation’s dependency on fossil fuel is going to be a major challenge, specifically when the generation is rushing towards cities for better opportunities. It is noteworthy that cities consume 70% of the world’s energy and account for 75% of greenhouse emissions. The statistics are alarming towards critical issues like climate change and global warming, etc., which are leading to unsustainable future. There was a time in the ancient world when people used to live near river/water bodies, but now is the time when humans want to be surrounded by cities fostering digital assets along with civic amenities. Technology has brought disruption all around the globe. Digital devices have become an integral part of the urban

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ecosystem. With the concept “smart” gaining impetus, the concept of smart cities is also developed to resolve urban issues. Smart city is a contemporary topic that is gaining widespread popularity, as it is perceived as a winning strategy to cope with some of severe urban problems such as traffic, pollution, energy consumption, and waste treatment (Benevolo et al. 2016). The concept of a smart city is becoming relevant for both academics and policymakers (Albino et al. 2013). Smart city has been recognized as interdisciplinary and is characterized by the efforts of governments to increase the quality of life in the cities (Pereira et al. 2018). A smart city operates as a seamlessly integrated platform where information links the various infrastructures, systems, organizations, and citizens’ goals and values. For this, the IoT technology integrations are essential. Smart cities are one among the engines for growth of any national economy. The concept of a smart city was born in 1992 under the name digital cities. It is in 2008 for the first time the name “smart city” appeared. Since then, different nomenclatures like wired cities, Internet cities, cyber cities, connected cities, etc. have been used interchangeably. The definition is not elusive however, and it is interdisciplinary. Several Standards Developing Organizations (SDOs) including the International Telecommunication Union (ITU), International Electrotechnical Commission (IEC), and the International Organization for Standardization (ISO) have all started efforts in earnest to develop a globally accepted definition of a “smart city” (Kondepudi and Kondepudi 2015). There is no one-size-fits-all definition for smart cities. It is a term lacking consensus. “A smart city is a city well performing in a forward-looking way in six characteristics, namely, economy, people, governance, mobility, environment, and living.” Moreover, some authors defined it as “the use of Smart Computing technologies to make the critical infrastructure components and services of a city – which include city administration, education, healthcare, public safety, real estate, transportation, and utilities – more intelligent, interconnected, and efficient” (Washburn and Sindhu 2010). A city “combining ICT and Web 2.0 technology with other organizational, design and planning efforts to dematerialize and speed up bureaucratic processes and help to identify new, innovative solutions to city management complexity, in order to improve sustainability and livability” (Toppeta 2010). Giffinger et al. (2007) have identified six dimensions of smart city, namely, (1) smart governance, (2) smart environment, (3) smart mobility, (4) smart economy, (5) smart people, and (6) smart living (Fig. 1). Smart city is an integration of technology in supporting civilization to support a sustainable future. The large deployment of the Internet of Things is enabling smart city projects and initiatives all over the world (Hammi et al. 2017). This urban complex system has involved the application of advanced Internet of Things technologies and devices in different city axioms including transportation, education, waste management, governance, security, and utility services. Smart city is an integration of IoT technologies and devices that are visible in multiple urban areas. The large-scale deployment of IoT technologies within a city promises to make city operations efficient while improving the quality of life for city inhabitants (Chakrabarty and Engels 2016). IoT technologies for the smart city include cards, RFIDs, QR codes, sensors, actuators, Wi-Fi, Bluetooth, GIS, GPS, social media,

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Fig. 1 Smart cities – six dimensions (Giffinger et al. 2007)

big data, etc. IoT is at the heart of smart cities. The intrinsic trait of IoT lies in its assimilation with the physical objects. Kevin Ashton at MIT, USA, first coined the term IoT in the year 1999 and the release of Prof. Neil’s book on When Things Start to Think. IoT has given new definition to automation as it connects the physical world with the virtual world of the Internet for identification, monitoring, and control. There is hardly any sphere of human life that has not been touched by IoT, be it environment, biology, medicine, engineering pharma, manufacturing, and last but not the least tourism. A smart city is a product of the application of IoT technology. Smart city is the integration of IoT technologies for ecological balance and sustainable future generation to provide quality of life. Smart city infrastructure is forming a large-scale Internet of Things system with widely deployed IoT devices, such as sensors and actuators that generate a huge volume of data (Cheng et al. 2017). A study by Su et al. (2011) witnessed that smart city is widely used in daily livelihood, environmental protection, public security, city services, and other fields. Smart cities aim for real-time monitoring of cities and hassle-free utility services to civilians. Smart city developers like IBM, Hewlett-Packard, Wipro, Bosch, and Cisco are the technologic behemoths that are engines in enabling smart city development. The aim of governing bodies and urban planners across the globe is to differentiate their cities by various means. In the twenty-first century, cities also compete to attract both citizens and businesses. Competitiveness of cities lies in creating and sustaining the differentiation (IBM Smart Cities 2020). A smart city is a new means to create scientific and data-driven differentiation. The foundation of smart cities will be built by technology, driven by a few key multinationals. It is going to be a complicated network of interconnected systems. Moreover, General Electric, which is a

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renowned name in renewable energy sources, has taken a lead in this development by supporting the world’s carbon-neutral city. Similarly, Microsoft, Aruba, IBM, Cisco, Hitachi, and HP, to name a few, are in line to support smart cities construction across the globe. Smart cities are meant to serve the 3 M principle, wherein monitor, measure, and manage are the three aspects to enhance quality of life in an urban setting. Smart cities attempt to manage cities in a scientific manner. It is a new mechanism of urban governance along with a vision to build a sustainable future. The main objective of any “smart city” constitution is to provide “quality of life” along with economic and sustainable development. Singapore smart city is a good example of the world’s most celebrated smart city project. According to Saunders and Baeck (2015), a smart economy is made up of three pillars, namely, collaborative economy which is about smart way of using city resources; crowdsourcing, i.e., smart way of collecting data; and collective intelligence, that is, a smarter way of making decisions by the virtue of data generated. Smart cities are backed up by a massive amount of digital data collected about society to manage and plan future cities. Data is the foundation of any smart city constitution. The underlying idea is to use data in getting everything done, right from monitoring cities to delivering services. Moreover, now Urban Analytics is eye catchword across the world smart cities. Urban Analytics, is nothing but the usage of the advanced AI, machine-learning techniques, algorithms to build models, simulations with the Internet of Things technologies, by generated urban data for city operational efficiency. Besides, in this complex and interconnected world of the IoT, the “smart cities” are increasingly important. The world as a whole is bolstering the agenda and need of new smart cities development or conversion process of brownfield sites into smart cities by individual budget allocations already started. Governments, corporates, non-governmental organizations, citizens, and planners/ developers are finding trajectories for sustainable urban spaces. Moreover, the “smart cities” concept is multidimensional including transportation, security, governance, water, power, waste, public utility services, infrastructure management, and vitally urban dwellers. These dimensions should be deployed by various Internet of Things technologies to roll out urban glitches. As the IoT devices are used in all city dimensions, a massive amount of data is going to be generated for every second. Hence, it is the need of the hour to understand the urban data and their associations to take better decisions by using machine learning techniques and algorithms under the umbrella of Urban Analytics.

NEOM Case Study Content Analysis (Articles and Blogs) The word “NEOM” was coined from the first three letters of the ancient Greek word “NEO” meaning the “new,” and the fourth letter “M” is taken from the Arabic word “Mostaqbal” meaning the “future.” In whole, the word “NEOM” means the “New Future.” The brainchild of Crown Prince Mohammed bin Salman’s NEOM smart

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city demonstrates new paradigm of urbanism, design, architecture, and technology principles. The city planning, designing, and construction will be initiated with an initial investment of $500 billion and will be completed by 2025. Designed as a huge human enterprise, in the Gulf of Aqaba, with solar power, wind power, and geological power parks, NEOM will have robots taking care of all manual work (Brusell 2018). The NEOM project is located in Tabuk Province, in the peninsula of Saudi Arabia in the northwest of the Saudi Kingdom. The smart city project includes land within the Egyptian and Jordanian borders, extended along with Aqaba Gulf and 468 km of coastline with beaches and coral reefs, as well as mountains up to 2500 m high, with a total area of around 26,500 sq. km. The NEOM smart city was born from the ambition of Saudi Arabia’s Vision 2030 to see the country develop into a pioneering and thriving model of excellence in various and important areas of life. NEOM smart city is envisioned to become the world’s finest smart city. It is a part of “Saudi Vision 2030.” Since long, the economy of Saudi Arabia has relied on oil exploration and its exports. Saudi Arabia has been the founding member of the Organization of the Petroleum Exporting Countries (OPEC). It is the second largest OPEC member country of the world. In order to reduce the nation’s dependence on oil and to provide an alternate to the future economic prosperity, the conceptualization of NEOM smart city took place. World’s largest consultancy firms, namely, McKinsey & Company, Boston Consulting Group, and Oliver Wyman, were instrumental in advising the creation of NEOM smart city. Finally, announced on October 24, 2017, it is a lofty and ambitious project, where the Saudi Arabia aims to innovate beyond innovation. The NEOM smart city will be a located in deep desert. The dream city will cater to infrastructure worth $ 500 billion. Spread over 25,000 km2, it will be 33 times the size of New York City. The future smart city is conceptualized keeping in view Sustainable Development Goals. It being a megaproject, the reliance is kept on renewable source of energy. The entire project will function on renewable sources of energy for which vast area of land will be utilized to support solar panels and wind turbines. Water from the Red Sea will be used and will be transformed into drinkable water by means of desalination technology. NEOM is a project that has never happened on the planet before. Supported by “cloud sensing technology,” there will be provision of artificial rain. An artificial fake moon will light up the shore of red city. Not only the virtual moon but also provisions will be made so that the entire coastline of Red Sea will glow in the night. Robots will serve as maid, flying cars, robotic dinosaurs, and holographic teachers to impart learning and will be the key attraction triggers in the city. The construction of NEOM smart city will take place in phases. The regulatory body of Saudi Arabia aims to attract foreign investments in this megaproject. The idea of “white beaches and mild climate” is a facilitation for investors. The city will demonstrate a new model for sustainability by showcasing new standards of health, environment protection, and productive use of technology. The entire project is expected to be completed by 2025. In the first phase, an airport has been constructed, which is the first airport to operate using 5G wireless network. The NEOM airport is officially registered as an international airport. Saudi Airlines operated its first flight SV2030 to NEOM Bay Airport in

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mid of 2019. With an operational airport, NEOM smart city is not more than 6 hours reach for almost 40% of world’s population. International travelling from this city will lead in reduction in time and distance to world’s major cities like London, Singapore, Tokyo, Hong Kong, Dubai, Cairo, New York, etc. NEOM smart city is strategically placed to fulfil its vision to be the next tourist destination of the world. The city aspires to be an attraction trigger for tourists from all around the world. Inspired by the Singapore’s “Gardens by the Bay,” NEOM smart city aims to bypass many such wonders by means of technology. Backed up by technology, wonders at NEOM city will add value to it to the extent that it would be no surprise to call NEOM city as the “Eighth Wonder of the World.” NEOM smart city is envisioned with a different perspective. It is going to foster a new sort of tourism what can be called as technological wonder tourism. Moreover, with NEOM, it is going to be a technology tourism by means of a smart city principle. Here in NEOM, the main purpose of the smart city will not be restricted to an alltime objective of providing quality of life to citizens, but here the focus is on the prosperity of economy by attracting tourism and foreign capital and inflow. Smart city supporting technological tourism can present a new model for the economic growth of a country. Tourism can be an integral part of cities through cities’ economies, employment, and business opportunities. It is going to enhance technology by means of IoT technologies. NEOM smart city will be a new wonder; where it will attract and engage tourists to make their experience unique by means of a city’s technology tourism. NEOM smart city will be a shift from the traditional purpose of a smart city of providing 24/7 urban operational efficiency. NEOM is one of the Saudi’s mega and futuristic city. NEOM smart city is a real need of the hour in Saudi where quality of life will be provided to city dwellers with all city amenities with the embedded IoT technologies in all city dimensions. The city planning, designing, and plantation are in line with sustainable practices. NEOM smart city will function independently from the existing Saudi government framework. It will have own tax and labor laws and unique judicial system in order to provide a healthy environment to its region specifically residents, investors, and employees who will be stakeholders in this city. The public of Saudi Arabia along with international investors will be the major investors. NEOM megaproject will focus on smart living conditions that will drive the future of human civilization. Besides, the project is attracting the nine specialized investment sectors including food, energy and water, mobility, advanced manufacturing, biotech, technological and digital sciences, media, and entertainment with livability as its foundation. The NEOM is built on six main pillars: (1) prioritizing humans with the help of creating an “idyllic society” with comfortable living conditions; (2) healthy living and transportation facilities by encouraging walking, cycling, and advanced technologies in transportation infrastructure; (3) automated public services through “e-government” or smart governance practices; (4) “digital air” with high-speed Internet access and online education free of charge to all citizens through this initiative program; (5) sustainability practices by using only renewable energy and its buildings with a motto of “have a net zero carbon

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footprint”; and (6) innovative construction in which it will encourage new techniques and materials to ensure it can meet its future requirements. Its strategic location is designed to take benefit of trade routes on the Red Sea, Gulf of Aqaba, and Suez Canal, while King Salman Bridge will create a direct link between Saudi Arabia and Egypt. In Saudi Arabia’s Vision 2030 National Development Plan, the government sets out its ambition to have three Saudi cities recognized in the topranked 100 world cities. Many of the newest and best-publicized smart city projects are notable for their large-scale resources, locations in or near large urban areas, and efforts to build smart cities from scratch, including that of Abu Dhabi’s Masdar and Saudi Arabia’s NEOM (Lam and Givens 2018). Though there has been much literature on smart cities, however, because of the NEOM smart city project was set out since 2017, there is not much information on this urban oasis. It is known that the “Tower of Babel” was a clue to the reactional structuring of the primeval history (Sasson 1980). The construction of NEOM Smart City as significant as to building the construction of the great cemetery work at the Tower of Babel (Bible; Genesis 11:1-9). This means that it will be a prodigious construction work, which will be a unique, mega, greatest, and smart infrastructure work. In Saudi Arabia’s Vision 2030 National Development Plan, the government sets out its ambition to have three Saudi cities recognized in the top-ranked 100 cities in the world (Vision 2030). The project’s promoters aspire to brand a new land where a plethora of unique development opportunities could contribute to make NEOM a global hub for trade, innovation, and knowledge. Future technology in transportation, growing and processing food, healthcare, and digital air all should contribute to providing NEOM’s residents a unique lifestyle (Farag 2019). The NEOM project is spacious and vast with conditions suited to generate energy from solar and wind (Mehmood et al. 2018). Designed as a huge human enterprise, in the Gulf of Aqaba, with solar power, wind power, and geological power parks, this futuristic city aims to build the transformation of the Kingdom into a leading hub via the introduction of value chains of industry and technology. NEOM is an attempt to do something that’s never been done before, and it comes at a time when the world needs fresh thinking and new solutions (NEOM 2018). NEOM smart city corporation announced its first step to creating the world’s leading cognitive cities that rely on leading technology for digital services (Where Is Neom? 2017). The next section in this chapter will explore the NEOM smart city content analysis based on the pictures and videos, which are associated to this project.

Content Analysis (Pictures and Videos) The videos and pictures [secondary data] were collected online (Internet data) by surfing the Google database and NEOM smart city web portal. The qualitative software ATLAS.ti 7.0 is used for data analysis. Hermeneutic Unit (HU) – HU is the first part that comes to our mind in ATLAS.ti qualitative data analysis software; the creation of project and naming is as usual like in other software. Generally, Hermeneutic Unit (HU) is the project file name, in

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which it holds all the primary documents, quotations, codes, memos, network diagrams, and other objects. Here in this project, HU was named NEOM smart city. Primary Documents (PDs) – In this NEOM smart city project, there are 36 primary documents (PDs) uploaded including 26 pictures and 8 videos. Quotations – The quotations in the case study are a selection of sentences or portion of pictures/videos which are used to select different types of codes relating to academic literature variables or items. Codes – Codes are the literature variables (items) of constructs. The codes were generated from the pictures and videos by selecting the quotations. Network Diagrams – The best way of representation of the data analysis of the documents is network diagram notation. These are visualizations of data, codes, and other objects in Hermeneutic Unit (HU); networks are one type of report/output that can be generated in qualitative analysis. Word Clouds – Word clouds are also one of the outputs in qualitative analysis, which exhibits the most frequently used words; it represents visibility of the research/observational phenomenon. Word clouds are recent qualitative output. Hence, all the six dimensions in this smart city project are denoted in the network diagram format along with the items. The NEOM smart city network analysis diagrams drawn so for are the (1) smart economy, (2) smart mobility, (3) smart environment, (4) smart living, (5) smart governance, and (6) smart people. Besides network diagrams for smart city and Internet of Things (IoT) technologies in NEOM smart city are drawn as shown in next sections (Fig. 2).

NEOM Smart City NEOM smart city is Saudi Arabia’s ambitious project, and it is a megaproject. The word “NEOM” is derived from two words: “NEO” is a Latin word which means “New”, and “M” is derived from the Arabic word “Mostaqbal,” which means “Future.” Therefore, in toto, NEOM means “New Future.” In a sense, it is a futuristic city, the best place to live. King Salman (Saudi Arabia) brainstormed this smart city project first time on October 24, 2017 at Future Investment Summit in Riyadh. This city will be a hi-tech city with advanced urban technological deployments by 24/7 citizen services in all city dimensions to bring the quality of life. The living and working conditions are healthy and thus attractive to tourism also; hence, NEOM is becoming a future destination city across the globe. The next section will discuss the IoT technologies network diagram along with its internal associated codes.

Internet of Things Technologies Figure 3 depicts that NEOM smart city will be constituted by full of advanced technologies, i.e., Internet of Things (IoT). The technologies include 5G for highspeed telecommunication and video streaming and robotic process automation (RPA) in replacing routine works to reduce the labor as well as cost. Besides the

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Fig. 2 NEOM smart city

virtual reality (VR), augmented reality (AR) will be part of every business and daily life. Because of technological advancements and high-speed Internet, mobiles and the software will build NEOM citizens’ life completely digital. The futuristic technologies and smart devices will lead to digital services for every NEOM citizen. Moreover, smartphones, drones, self-driving cars, AI, and robots are some of the technologies that make city life more comfortable and fully automated. Of course, the legacy technologies are also part of systems for business systems integration performance; the oil technologies and space explorations are some prime and inevitable working technologies in the city. The city is completely planned for full automation; cybersecurity mechanisms will be inevitable in all city dimensional deployment and communications.

Smart Economy (SE) The above Fig. 4 depicts the smart economy phenomenon in NEOM. Construction of NEOM is a completely new urban business model with an initial investment of $500 billion. This is a mega and ambitious project and going to boost Saudi Arabia’s economy, thus leading to an increase in Saudi’s gross domestic product (GDP). There will be a lot of foreign direct investment (FDI) inflow, public investment fund,

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Fig. 3 IoT technologies

and local companies’ participation, and huge investments will become part of the city constitution. This smart city project is majorly becoming an international business hub with friendly business manner, primarily concentrating innovative way in water and energy, media and entrainment, food processing, advanced manufacturing, future Internet technologies, healthcare, marine business, and oil production sectors. The best part of this city is its location advantage, growth of marine business, international trade, and international collaborations. This city is majorly setting up with a special economic zone (SEZ), thus causing new business opportunities and huge employment creation by setting up industrial complexes and new urban infrastructure development, which will have the potential to transform NEOM city’s economy as well as the entire Saudi’s economy.

Smart Living (SL) Lifestyle in NEOM smart city is going to be unique. The city will be constructed with full of smart buildings, smart offices, manufacturing hubs, healthcare facilities, shopping malls, stadiums, entertainment parks, etc. Uber driving, smart lighting

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Fig. 4 Smart economy

systems, swimming pools, online education system, media, and communications are some of the best services that will facilitate quality of life in NEOM. Residing here is a completely new way of living, men and women mingling in the streets, more participation in public life, open-mindedness, and social cohesion leading to more enjoyable citizen life. The futuristic NEOM is going to have beach sports facilities including rugby, diving, trekking, and wakeboarding, to name a few. Food technologies and healthy food specifically seafood will cause a healthy life. Briefly, the city is going to provide quality of services 24/7 to all citizens that will lead to a better life. The codes generated in Fig. 5 are exhibiting the smart life.

Smart Governance (SG) The state of the art smart governance practices in NEOM can be viewed in Fig. 6. Smart governance practices will be leading the feasibility of NEOM smart city. The political initiative by King Salman during Future Investment Summit at Riyadh in 2017 is the stepping stone for this project. Since the project is sharing the three

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Fig. 5 Smart living

international continent borders including Asia, Europe, and Africa, many political strategies are brainstormed, and finally international cooperation is initiated among Saudi Arabia, Egypt, Jordan, and Israel hormoniously. Besides, many international collaborations are in progress among the governments and business companies for investment in the project. Policy formulations in terms of taxes, regulations, leasing of land, and business ethics are underway by political interventions. There will be more public-private participation business model to make the project more feasible. Right from the project planning, designing, constitution, and governance will be completely digitized. For this, many bureaucrats, senior officials, business tycoons, employees, and common public are involved in NEOM construction.

Smart Environment (SE) The best part of NEOM project is it being sustainable oriented, ensuring no harm to the natural environment – more elaborately, pollution-free environment. Hence, the city

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Fig. 6 Smart governance

project officials are concentrating on renewable energy sources like power generation by solar panels, tidal energy, and wind energy and are setting up smart energy systems for energy harvesting. Moreover, natural gas and nuclear power are some of the additional energy sources in NEOM. Besides, low extraction of water from the ground, desalination of seawater, optimum water usage through smart water management systems, and water conservation systems are some of the water management practices, along with water sector management by water expertise. Greenfields, smart agriculture management, and tech forming are some of the practices leading to a sustainable city. Smart tourism and attracting more tourists is one of the best practices. As a part of smart climate phenomena, smart environment practices are taking place in the entire city; it can be viewed in Fig. 7 in the form of smart environment codes.

Smart Mobility (SM) The smart mobility facilities are very essential services in NEOM smart city project as shown in Fig. 8. As usual to every city having all sorts of public travelling modes, this

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Fig. 7 Smart environment

city is also going to have advanced road transportation modes including buses, fourwheelers, Uber taxi sharing, bicycles, and metro trains to access the entire city in a fraction of minutes. Self-driving cars and autonomous vehicles will be also part of public transportations. The beauty of this city is marine travelling; because of being situated on the bank of the seashore. Besides, as it is located in the center of the world, most of the population can easily reach to this city within 8 hours of the journey for tourism or business. The international airport setup will reflect more on international travelling ease. Besides, Tabuk Airport is another domestic destination to reach NEOM city. King Salman Bridge, multi-flyovers, good road connectivity and embedded transportation technologies, and on-time journeys will lead healthy journeys in the city. However, the city is encouraging the women in the driving field, for the first time in Saudi Arabia’s history. Moreover, the NEOM city project will have digital logistics by drones.

Smart People (SP) As coded in Fig. 9, this smart city project NEOM will be feasible only with the help of smart people. There are many great minds and political leaders’ engagement in formulating and building this smart city project specifically King Salman (Saudi Arabia). There are many noble minds behind this project. There are many great

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Fig. 8 Smart mobility

experts working day and night in the constitution of NEOM city for future generations. There will be more knowledge workers, skilled people, good athletes, and better humans that can be found in this city. As per the data, there will be more women empowerment because of more political initiatives and interventions. Because of good educational facilities and online education, citizens of NEOM will become smarter. Smart farmers will do tech farming. However, this city attracts the most talented people across the globe. There will be an equal number of humanoid robots wandering in the streets along with people in the city. Due to cosmopolitan life and good housing facilities, here the future generations will have a better life. People of other cities can also enjoy their vacations in this beautiful aesthetically designed NEOM smart city. There will be complete social transformation (social change) in Saudi Arabian’s citizens’ life, like never before.

Discussions Giffinger et al.’s (2007) six dimension definitions on the smart city including smart economy, smart environment, smart governance, smart mobility, smart people, and smart living, later, there were many conceptual dimensional definitions in the smart

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Fig. 9 Smart people

city phenomenon. For example, according to Frost & Sullivan’s report by Vadgama et al. (2015), the smart city components consist of smart governance and smart education; smart healthcare; smart building; smart mobility; smart infrastructure; smart technology; smart energy; and smart citizen dimensions. Like that, many authors come up with their own smart city dimensions. Moreover, most of the studies on smart cities supported/tested the Giffinger’s six dimensions due to proper segregation of the city dimensions. Hence, in this case study, from the above qualitative analysis by using ATLAS.ti software with the help of the articles, blogs, videos, and pictorial data analysis, the six thematic narrations were drawn with respect to the Giffinger’s six dimensions. Moreover, in case of NEOM smart city, the city dimensions are looked into nine sectors such as (1) food, (2) energy, (3) water, (4) mobility, (5) advanced manufacturing, (6) biotech, (7) technological and digital sciences, (8) media, and (9) entertainment in the reports. However, the six dimensions of smart economy, smart mobility, smart environment, smart living, and smart people in NEOM smart city are going to flourish in the near future. Before NEOM city plantation, the place in which NEOM build-environment was like a desert without any oasis; which is completely unsustainable, and uneconomical to human survival (Fig. 10).

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Fig. 10 Before NEOM smart city https://arabiangazette.com/wp-content/uploads/2017/10/ NEOM-Saudi-Arabia.jpg

After the Saudi Prince brainstorming in Future Investment Summit 2017 and other Saudi political interventions and foreign collaborations, now the city is becoming in reality. Now the lifeless desert is transforming into a liveable urban oasis in Tabuk Province, in the peninsula of Saudi Arabia. The development is not only in those nine sectors but also in other aspects like tourism, sports, design and construction, services, health and well-being, education, and liveability. Moreover, all these sectors generally come in one of the Giffinger’s six dimensions. Therefore, after all the developments with the help of Internet of Things technologies with huge labored work, the NEOM smart city will be like an urban oasis in Saudi desert in the future as shown in Fig. 11.

Conclusion In the case study, both content analysis of blogs and articles and content analysis of pictures and videos were understood exactly in synchronization of the upcoming NEOM smart city variables. The same kind of codes are discovered in both the content analysis. That means the second analysis is evidencing the first analysis. This research article explores the NEOM smart city in a crystal clear manner. The city will provide quality of life to city dwellers and boost the national economy in a big way, by setting up a special economic zone. Of course, sustainability practices by installing energy efficiency systems and the use of wind and solar power will be predominantly found. Besides this, the new business models with smart mobility, entertainment, theme parks, tourism, electronic manufacturing industries, and smart

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Fig. 11 NEOM smart city: the urban oasis in Saudi desert (http://www.neomzone.com/images/ neom_sea2.jpg)

buildings will be the basic components. Thus, the NEOM smart city will become the urban oasis in the desert of Saudi to lit the lights of Saudi people with the modern urban facilities to provide happiness. The case looks forward to seeing the NEOM smart city as the greatest smart city in the near future in Saudi desert, like the renowned historical Babel Tower in the Bible.

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Tehran in the Path of Transition to a Smart City: Initiatives, Implementation, and Governance

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Kiarash Fartash, Amirhadi Azizi, and Mohammadsadegh Khayatian Yazdi

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Background of Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Experiences and Measures for Smartening Tehran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Governance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Transportation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart City Application in Fighting the Covid-19 Pandemic in Tehran . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Governance Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Citizenship Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Technological Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Economic Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

The increasing population and urbanism in Tehran as the capital and politicaleconomic center of Iran has brought about several challenges such as environmental pollution, traffic, and reduced quality of life (QoL) in recent years. Deficiencies in the integrated urban monitoring and management system are also aggravating the situation. To deal with such problems, Tehran municipality has made extensive efforts for smartening Tehran with the vision of increasing the citizens’ QoL by providing a healthy living environment, happiness, efficient mobility, integrated urban infrastructure and management, and a vibrant economy. The ultimate goal of smartening Tehran is not merely utilizing the ICT K. Fartash (*) · A. Azizi · M. Khayatian Yazdi Institute for Science and Technology Studies, Shahid Beheshti University, Tehran, Iran e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_76

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infrastructure, rather the adoption and implementation of six dimensions of “smart economy,” “smart mobility,” “smart environment,” “smart infrastructure,” “smart governance,” and “smart life” at the same time. Furthermore, this chapter describes and analyzes the policies, programs, and measures taken to realize the six dimensions of smartening Tehran. It also presents some key applications of smartening efforts, including combating the prevalence of Covid-19. Additionally, challenges in implementing each aforementioned dimension are explained. This chapter concludes by classifying the challenges of Tehran in the transition to a smart city, and solutions to overcome these challenges are also presented under four categories: governance, citizenship, technological, and economic challenges. Lastly it should be noted despite the substantial efforts and tangible achievements of implementing Smart Tehran, there are relatively critical challenges and shortcomings to this end that call for the cooperation and interaction of all public and private stakeholders to beat them off.

Introduction In today’s world, cities, more than anything else, have turned into super systems that incorporate various subsystems such as citizen communities, transportation networks, energy suppliers, communication networks, and businesses that are constantly interacting with one another. With reference to the United Nations report in 2019, more than 55% of the world’s population now lives in cities, while this is expected to increase to approximately 70% by 2050 (Bohloul 2020). In other words, urbanization is recognized as an important trend in the twenty-first century and has brought about various challenges and opportunities that affect the social, economic, and environmental aspects of nations (United Nations 2018). Urbanization has caused various challenges that are even more impactful in developing countries. Reduction of natural resources, the need to establish proper infrastructure, the increase in environmental pollution, carbon dioxide emissions, heavier traffic jams, and so forth are among the greatest challenges that most major cities around the world now are also dealing with their negative effects (Raj and Roman 2015). In this regard, a number of solutions have been put forward to deal with and reduce the risks of urban development, one of the most prominent of which is the utilization of modern technologies and especially digital technologies and innovations in mechanisms, operations, and service delivery processes to citizens. To this end, the widespread term “smart city” is widely used these days. With the development of megacities and urbanization, urban communities are mainly faced with several challenges. Some of the most important challenges might be summarized as follows: • Population growth: With more than 55% of the world’s population now living in cities, a growing pressure would be on urban infrastructures such as

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transportation, accommodation and housing, water, electricity, and utilities. Many of these infrastructures need to be redesigned, and this would incur considerable expenditure. • Polarized economic growth: According to the McKinsey Institute Report (2012), 600 megacities alone throughout the world will generate more than 65% of world GDP by the end of 2025. • Increased greenhouse gas emissions: The increasing trend of greenhouse gas emissions is forcing city officials to formulate sustainable strategies for energy development and distribution, transportation, water management, urban planning, and construction of eco-friendly (green) buildings. • Economy: Considering the economic issues and problems of cities, their ability to respond to the pressures caused by an increased urban population will be severely limited. These issues alongside some others seem to be appropriate responses to increase efficiency, reduce costs, and improve quality of life by adopting scalable solutions that make use of information and communication technology. Cities that use this approach and its solutions on a large scale are called Smart Cities or Smart + Connected Communities (S + CC) (Falconer and Mitchell 2012). This should also be noted that the development of smart city strategies depends more on the acceptance and welcoming of smart cities by citizens rather than being reliant on technology. “Smartening” is not only limited to launching digital intermediaries in the old infrastructure or facilitating the implementation of urban management programs but also includes the use of technology and data with the aim of making more efficient decisions and increasing the citizens’ quality of life (MGI 2018). As the capital and largest megacity of the Islamic Republic of Iran, Tehran is estimated to be 1 of the 30 most populous megacities in the world by 2025. The city provides 48% of the country’s budget and has a share of over 25% in the economy (with an amount of $450 billion in 2017 – according to the estimation of the World Bank for 2017) in Iran despite the fact that the growing trend of urban development leads to challenges such as climate change caused by maximized energy consumption, air pollution, and increased traffic and has continuously reduced the quality of life (QoL) in it. Moreover, being home to state and governmental bodies and organizations has increased the expenses of the city. On the other hand, the largely unsustainable financial and revenue sources of urban management, the poor participation of citizens in paying taxes, and the limited share of the government in financing the city are a number of other challenges facing Tehran (TMICTO 2018a). Henceforth, urban smartening may possibly be a solution to the problems of Tehran and, in fact, warns us of the need to push Tehran toward becoming a smart city. The smartening approach in Tehran does not merely mean deploying urban ICT infrastructure; rather, it is comprised of six dimensions that are to be taken into account by decision-makers all together: “smart economy,” “smart transportation,”

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“smart environment,” “smart infrastructure,” “smart governance,” and “smart life” (Farjood 2019).

Background of Smart Cities During the past couple of years, we have witnessed a dramatic rise in utilizing information and communication technologies (ICT) in software and hardware projects, and the use of these technologies in the development of cities has led to increased efficiency and effectiveness of urban and social operations in various ways. Jargons such as cyberville, digital city, electronic city, flexicity, information city, and telicity have been used to describe such cities to date. Nevertheless, the term “smart city” is an abstract concept that comprises all previous labels and has been used more frequently in recent years. Still, there are nonconformities among experts and practitioners in different fields of science and technology when it comes to describing the meanings and examining the functions and dimensions of smart cities, but simply put, a smart city is a place that former services and networks are made more efficient, flexible, and sustainable with the use of telecommunication technologies and digital information, to improve the operations for the benefit of its citizens. In smart cities, digital technologies are becoming better public services for citizens and better use of resources while also having less impact on the environment (Mohanty et al. 2016). Table 1 represents some definitions of a smart city from the perspective of experts in this field. According to numerous experts, the strength of smart cities is in equipping urban places and services with advanced technologies, but above all, the most considerable concern in the development of such cities has been the lack of citizens’ connection with these dimensions of development. “Technology without community” is a term that became trendy after failure of the Cisco project in encouraging citizens to use brand-new urban applications and in making the city of Songdo in South Korea smart. Based on statistics, approximately two-thirds of the Songdo population did not show interest in using new urban services in 2019 (▶ Chap. 1, “Smart Cities: Fundamental Concepts” by James et al.). As a result, two new components were added to smartening up the cities: government and community engagement. Additionally, according to Mosco (2019), three key actors play a role in smartening cities: citizens, governments, and technology development companies. Smart cities are formed by establishing a network connection among these three actors, and eliminating each of them would bring about major challenges to the formation of smart cities. Many smart city theorists still emphasize one of these actors more, i.e., some focus on the critical role of governments, some on the need for citizens to engage with urban development, and many on the technological aspect of development. This has been due to differences in definition and interpretation about the concept of smart city among them. Table 2 illustrates the top smart cities based on three distinct indicators, each of which related to one, two, or all three of the actors.

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Table 1 Main definitions of smart city (Bohloul 2020; Danilina and Majorzadehzahiri 2019) Sources Korolev (2015)

Bakici et al. (2013)

Peris-Ortiz et al. (2017) Harrison et al. (2010) Barrionuevo et al. (2012) Komninos (2011)

Kourtit and Nijkamp (2012)

Lazaroiu and Roscia (2012)

Definitions A smart city means the effective integration of physical, human, and digital systems in the urban environment to achieve a sustainable and prosperous future for citizens A smart city is a high-tech city that connects people, information, and urban elements through new technologies to create a sustainable and green environment for innovative and competitive business as well as a high-quality living The concept of smart city integrates ICT and various physical devices connected to the Internet of Things (IoT) to optimize the efficiency of urban operations and services and connect to citizens A smart city is a city that brings together social, physical, technological (ICT-based), and commercial infrastructure to increase insights and collective benefits Smart city pays attention to the smart and coordinated use of all available resources and technologies for the development of sustainable, habitable, and integrated urban centers Smart cities are those built on the creativity of citizens, their knowledgebased institutions, and their digital infrastructure for communication and knowledge management, making them cities with a high capacity for learning and innovation It is a city build upon innovative and knowledge-based strategies that aim to increase the economic, social, competitive, and logistical performance of cities. These cities are based on a combination of human capital, social capital, high-tech communication tools, and partnership-based entrepreneurial capital A smart city is a city with a medium or high level of technology, continuous interaction, sustainability, attractiveness, and appropriate safety

Table 2 Indices ranking smart cities, with top three cities (Roland Berger 2019; IESE 2019; Statista 2019) Source Smart City Strategy Index (SCSI) IESE Cities in Motion Index

Statista

Dimensions Strategic planning; policy and infrastructure action fields

First Vienna, Austria

Second London, UK

Third St. Albert, Canada

Technology; governance; mobility and transportation; human capital; social cohesion; economy; social cohesion; environment; urban planning; international outreach; technology Governance; sustainability; transport and mobility; digitalization; innovation economy; expert perception; living standard

London, UK

New York City, USA

Amsterdam, Netherlands

Zurich, Switzerland

Oslo, Norway

Bergen, Norway

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In a 2018 report, McKinsey Global Institute (MGI) also categorized the dimensions of city smartening considering the three actors in smart city development in which economy, mobility, security, healthcare, environment (water, energy, and waste), economic development, and community engagement have been taken into account as the most important dimensions of smart cities (MGI 2018). Smart Economy – Information and communication technology (ICT) has drastically changed the economic form of the world, one by creating services and products that did not exist before and the other by recreating services that already existed. Today, banks, insurance companies, and stock exchanges have undergone a dramatic transformation with the advent of information and communication technologies and have moved towards the digital economy, which has had a significant impact on the domestic production of countries. Singapore and South Korea are among the countries that have made great leaps in the field of the digital economy. These countries have set the stage for economic growth in their countries by providing digital infrastructure. The mindset of innovation, entrepreneurship, economic reputation of their cities, activity and productivity, labor market flexibility, and international position are considered as the criteria of a smart city (Bina 2018). Smart Mobility – Many references to smart mobility are closely related to improved traffic, but while acknowledging its importance, aspects of mobility in a city are not just limited to traffic problems. Basically, special attention to traffic is the result of the cities’ expansion and the increase in traffic due to population growth (Mataix Gonzalez 2010; Monzón and dela Hoz 2009). Cities, where smart transportation apps have been developed, are likely to see an average of 15–20% reduction in their citizens’ daily travel time by 2025. Indeed, the effectiveness of each of these applications depends on several variables such as population density, transportation infrastructure, and citizen behavioral patterns. For example, in a city like New York, smart technologies can reduce urban commuting time by 15 min. Meanwhile, in developing cities with heavier traffic, it can lead to a 20- to 30-min reduction in daily travel time (MGI 2018). • Public transportation: In general, in cities where there are expansive and developed public transportation systems, citizens use applications that create a better travel experience for them. Mobile applications in this field, using timely information from digital traffic signs, make it possible for daily travelers to choose the best route to their destination. By collecting and analyzing data related to traffic and public transportation system, city officials can have a more complete overview to make better decisions to improve or change bus routes; set up cycling routes; install smart traffic signs; and allocate budget for infrastructure development. Many urban transportation systems, such as Houston and London, have discontinued ticket use, and payment is only made through digital payment systems. • Traffic reduction: Utilization of navigation and smart parking applications have played an effective role in reducing traffic load. According to an MGI report, a 13% increase in the speed of urban traffic in Moscow, bearing in mind that the

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number of cars has increased by one million, highlights the importance of using these applications. Smart Security – There are steps that city authorities can take to reduce cyber security risks. The most common vulnerability test is penetration testing, in which a third-party security company is used to simulate a cyberattack. This process includes analyzing the status quo, then presenting the findings, and finally recommending measures to prevent any potential attack. Another way is to protect connected devices based on IoT technology. Implementing a security solution based on this technology can prevent attackers from infiltrating or making disruption. The security of a smart city depends on various features and factors including (Koren 2019): • Data encryption: The use of authentication systems and encryption of all urban data is mandatory to prevent cybercriminals from infiltrating. • Security analysis and monitoring: A security platform that enforces security measures such as the isolation of identified damaged devices by searching for potential indicators of compromise (IOCs). • Multi-environment support: Smart city data security platforms need to be implementable and applicable in all urban systems and support (hybrid) cloud environments to ensure that no device or server is left unattended or disconnected. Smart Healthcare – High population density and heavy traffic in cities make health services a challenge. Technology plays a critical role in medical care services, and changes occur in this field at a rapid pace every day. In a study done by MGI, the potential impacts of healthcare applications on the disability-adjusted life years known as DALYs index (years of life lost due to premature death or disability) were examined, which is the main indicator used by the World Health Organization to assess the global impact of disease spread. This index presents the effects of mortality and disease spread in the form of a number and not only indicates the years lost due to premature death but also the length of time spent in disabilities. The development of these digital applications in cities has the potential to reduce the DALYs index by 8–15% (MGI 2018): • Advanced treatment of chronic diseases: The ultimate application that helps prevent, treat, and control disease conditions can lead to significant changes in developed countries. Remote patient control systems based on the prevention approach have the potential to reduce health-related challenges by 4% in highincome cities. In these systems, digital equipment is used to receive information and send it safely to doctors, medical centers, and experts for evaluation and diagnosis. This data can be used to send alert messages to both patients and doctors. • Use of data to combat preventable diseases: Cities can use data and the results obtained from its analysis to identify high-risk populations and implement prevention plans with greater accuracy. These plans can be in the form of sending health messages about vaccination, observing hygiene principles, or the

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implementation of antiviral treatment diets. In low-income cities with high infant mortality rates, data-driven health promotion programs often focus on maintaining the health of mothers and infants, which can eventually lead to a 5% reduction in DALYs index. Developing cities can also achieve this 5% reduction through developing infectious disease monitoring systems to be ahead of epidemics and pandemics and be able to manage them accordingly. This was the plan that was implemented in cities such as Rio de Janeiro and Miami. • New ways to connect with patients: Technology can help empower people to stay healthy and use prevention instead of treating illnesses after they occur. In the city of Louisville, Kentucky, for example, sensors have been installed in asthma and respiratory equipment of patients to collect data. This information is then integrated into a digital platform, along with personal instructions on the precautions and care that should be taken into consideration for each individual. Remote treatment, which includes providing clinical counseling through videoconferencing, removes barriers to accessing healthcare. This way can help save the lives of many people in low-income and developing cities that are in dire need of doctors. Smart Environment – Parallel with the development of urbanization, industrialization, and consumerism in cities, environmental conditions are also deteriorating in cities, and technology is one of the best and most effective options to meet this challenge. Generally, the results of the studies indicate the possibility of a 10–15% reduction in greenhouse gas emissions, a 20–30% reduction in water consumption, and a 10–20% reduction in per capita waste production as a result of the development of applications in the field of environment (MGI 2018): • Production and emission of greenhouse gases: In a city where constructions are an important source of greenhouse gas emission, the development of building automation systems can reduce emissions by about 3% in most homes. Another important application of this method is the dynamic and flexible electricity pricing, which allows electricity distribution companies to increase electricity tariffs during peak hours. Reducing electricity consumption and encouraging less use of the electricity during peak hours will reduce the use of backup power plants or so-called peaking power plants that produce more greenhouse gases. Methods such as smart traffic signs, dynamic pricing during peak hours, and other transportation-related applications can also reduce greenhouse gas emissions. • Air quality: Using some applications related to transportation and energy saving can also lead to improved air quality. In order to address this issue directly, it is necessary to install sensors to control and measure air quality. Although these sensors cannot eliminate air pollution, they can identify the sources of its emission and provide the necessary information for further actions. Beijing, for example, was able to reduce deadly emissions from aircraft by using pollution detection sensors by about 20% a year and enact regulations in that regard. On the other hand, sharing air quality information with the people through smartphone apps allows people to take appropriate protective measures and reduce the negative impact on health by about 3–5%. • Water source protection: New advanced water meters are equipped with the ability to control consumption and send digital messages to the consumer and

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thus can encourage people to protect valuable water sources. The development of such features can lead to a 15% reduction in water consumption in high-income cities where water consumption in the household domestic sector is high. Of course, this plan will not be effective unless it is presented along with a pricing strategy. In many developing cities, the biggest cause of water loss is water leaks from pipes. Developing sensors and analyzing the data derived from them can reduce water loss by 25%. • Reducing dry waste production: Sometimes, some waste recycling programs that are not completely technology-based face limitations, and undoubtedly the use of technology can help reduce unrecycled dry waste. Developing a digital tracking and payment application for waste collection and disposal and monitoring the type and load of waste that is disposed will incur costs. But these types of applications must be implemented together with other policy initiatives that try to build trust. Especially in developing cities where household budgets are limited and also unsupervised waste recycling is widely spread. Accordingly, the results obtained from the smartening of cities in the mentioned dimensions are as follows: 30–300 lives saved each year in a city of 5 million, 30–40% fewer crime incidents, 8–15% lower disease burden, 15–30 min shaved off the daily commute, 25–80 l of water saved per person per day, and 20–35% faster emergency response times. These results might act as a proper justification for the development of smart city trends during past years (MGI 2018). Community Engagement – The widespread use of smartphones and social networking platforms have changed the way billions of people interact and connect with each other. These technologies, which enable communication with anyone anywhere in the world, are used as a means for fast and direct communication. In this regard, MGI has surveyed citizens to assess the positive impact of digital applications on people’s feelings about communicating with the community and city officials. Before using these apps, only 13% of citizens said they were in touch with the government and city officials, and 24% claimed it was important to have such a connection with the local government. Research results show that the development of digital platforms and applications can double the sense of community connectedness and triple the sense of citizens being connected to the government (MGI 2018). The most successful cities in developing smart applications in various aspects of mobility, security, utilities, healthcare, and economic development in the five regions of North America, Latin America, Asia-Pacific, Europe, and the Middle East-Africa are illustrated in Fig. 1. Reviewing keywords like “smart cities,” “Internet of Things,” “5G connectivity,” and “information and communication technology” in scientific publications of the Scopus database also reflects the development trend of smart cities in the field of science (Fig. 2). Besides, the development of technologies pertinent to smartening cities, including Internet of Things, 5G connectivity, cloud computing, and artificial intelligence, from the patents perspective, confirms the accelerating trend of technology development in this field (Fig. 3).

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Fig. 1 Deployment of smart city Applications in all domains (MGI 2018)

Trend of scientific publications based on keywords related to the subject of smart city (Scopus 2020) 60000 50000 40000 30000 20000 10000 0 Smart Cities

Internet of Things

5G Connectivity

Information and Communication Technology 12621

2012

472

2572

1404

2013

1029

4191

1377

14764

2014

1656

6000

2272

17935 16775

2015

3078

8366

4322

2016

5164

14537

7518

18967

2017

8930

21693

11411

22410

2018

13599

33626

17101

25556

2019

19832

48566

23136

30929

Fig. 2 Trend of scientific publications on smart city (Scopus 2020)

Experiences and Measures for Smartening Tehran As the capital and largest city of Iran, Tehran is estimated to be listed among the 30 most populous metropolises in the world by 2025 with a share of over 25% in the economy of Iran. However, the growing trend of urban development has brought about challenges such as climate change due to increased energy consumption, air pollution, and heavier traffic and has dropped its rating in terms of QoL. Moreover, being home to state and governmental bodies and organizations has increased the

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Trend of Patent applications based on Technologies related to the subject of smart city (Digitalstat 2020) 25000 20000 15000 10000 5000 0

Internet of Things (IoT)

5G Connectivity

Cloud Computing

Artificial Intelligence (AI)

2012

1289

1017

0

1880

2013

1947

1235

0

2068

2014

2112

1843

0

2441

2015

3479

2013

16602

3529

2016

4831

2056

18887

5028

2017

8059

2962

20776

9846

2018

13775

4398

21843

19799

2019

10982

4629

21101

Fig. 3 Trend of patent applications on technologies related to smart city (Digitalstat 2020)

expenses of the city. On the other hand, the largely unsustainable financial and revenue sources of urban management, the poor participation of citizens in paying taxes, and the limited share of the government in financing the city are a number of other challenges facing Tehran (TMICTO 2019a). Tehran is currently faced with many setbacks, and it can be considered as a meeting point of multidimensional and intertwined issues and challenges. When it comes to statistics, Tehran is ranked 199th in terms of quality of life worldwide with a population of about 9 million by the end of 2019. More than 50% of Tehran’s residential areas are unsafe and prone to destruction for the time being. More than 90% of citizens are dissatisfied with environmental pollution and traffic. Concerning urban transportation, 40% of trips are made by cars, and the metro has a share of less than 20%. From an environmental point of view, the number of unhealthy days in terms of air quality index in Tehran has reached 105 days, and about 8000 tons of waste is produced daily in this city. Meanwhile, at the socioeconomic perspective, we are witnessing an increase in social inequality and class division reaching a Gini coefficient of 0.38 (Smart Tehran 2020). However, according to the Global Innovation Index in the ranking of the top 100 clusters of science and technology, Tehran is ranked 43rd above cities such as Berlin, Madrid, Zurich, and Ankara. It is important to note that the Islamic Republic of Iran (represented by Tehran), along with Brazil, China, Turkey, India, and Russia, are the only countries with developing and emerging economies that are represented among the top clusters of science and technology (GII 2020) (Table 3). This position indicates the appropriate sources of science and technology (according to the spatial clustering of inventors and scientific publications’ authors, the considerable rate of patent requests under PCT, as well as basic science and engineering publications) in Tehran regarding the transition to a smart city.

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Table 3 Tehran status in top cluster rankings (Bergquist and Fink 2020) Tehran Status in Top Cluster Rankings (Bergquist and Fink 2020) Rank

Cluster name

Economy

PCT applications

Scientific publications

Share of total PCT filings, %

Share of total pubs, %

Total

1 2 3 4 5 10 20 30 40 41 42 43 44 45

Tokyo-Yokohama Shenzhen-Hong Kong-Guangzhou Seoul Beijing San Jose-San Francisco, CA Paris Chicago, IL Minneapolis, MN Xian Brussels Portland, OR Tehran Berlin Madrid

JP CN/HK KR CN US FR US US CN BE US IR DE ES

113,244 72,259 40,817 25,080 39,748 13,561 6,167 6,444 775 3,171 6,270 149 3,333 1,521

143,822 118,600 140,806 241,637 89,974 93,003 57,976 25,157 60,017 39,066 12,349 62,530 35,640 50,547

10.81 6.90 3.90 2.40 3.8 1.30 0.59 0.62 0.07 0.30 0.60 0.01 0.32 0.15

1.66 1.37 1.63 2.79 1.04 1.07 0.67 0.29 0.69 0.45 0.14 0.72 0.41 0.58

12.47 8.27 5.52 5.18 4.83 2.37 1.26 0.91 0.77 0.75 0.74 0.74 0.73 0.73

Upon establishment of Tehran’s Smart City Center in 2017, Tehran Municipality ICT Organization as the main public institution in Tehran smart city governance recognizes smart Tehran in its vision statement as a city with increasing quality of life, based on developed private, public, and citizens’ participation and a place to live a healthy and happy life with smooth traffic and integrated infrastructure that has efficient urban management and dynamic economy (Farjood 2019). The smart city approach does not merely mean deploying urban ICT infrastructure; rather, it is comprised of six dimensions all together: “smart economy,” “smart transportation,” “smart environment,” “smart infrastructure,” “smart governance,” and “smart life.” Although the concept of smart cities is often technology-oriented, what is referred to as smart Tehran is not solely technology-focused and includes two other theoretical foundations: first, the concept of “openness” as in open-source software, open innovation, and governance that emphasizes on the sharing of ideas and data and, second, the concept of “participation” at all levels of society, including participating citizens and participatory governors (Nazemi 2019). In the following paragraphs, while examining the dimensions of Tehran city smartening, the challenges of implementing each of them are examined, and based on the classification indicators of smart cities in the report of Smart City Index (2020), Tehran will be compared with selected examples (cities with the population close to Tehran from developed and developing countries reviewed in the Smart City Index).

Smart Governance In implementing the Smart Tehran program, two influential institutions in the Tehran smart city ecosystem play a critical role. From a broad perspective, the Smart Tehran Strategic Council, chaired by the Mayor of Tehran, is considered as the main policymaker of the Smart City at the municipal level. In this regard, the Secretariat and Tehran’s Smart City Center located in the Information and Communication Technology Organization of Tehran

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Municipality are regarded as the decision-making, coordinating, and executive arm of the Smart Tehran Strategic Council in planning, leading, and monitoring projects. They are also in charge of setting indicators for the Smart Tehran project; creating juridical, regulatory, legal, and executive capacities and guarantees required for investment and development of innovative services in the smart city; establishing cohesion, integration, and organization of all activities related to technology, innovation, and communication at the level of Tehran Municipality; and attracting the maximum participation of all stakeholders in the smart city ecosystem, including citizens, businesses, innovative groups, policy-makers, legislators, regulators, operators, decision-makers, and other related investors (Farjood 2019).

Smart Governance Challenges in Tehran Unlike many cities in the world, urban management in Tehran is not under the supervision of a single institution or organization and is not done in an integrated manner among organizations engaged in urban management. In the field of implementation, Tehran city management has caused parallel work, executive interference, and in some cases non-implementation and non-responsibility of the organizations due to the multiplicity of organizations involved in urban management and service providers to citizens and the lack of an integrated system. In the field of supervision, the lack of an integrated monitoring system among all organizations has led to this issue that Tehran city management hasn’t shown any improvement in the proper implementation of procedures and the correct and purposeful performance of its duties (Khansari et al. 2015). In general, the most important challenge of the governance system in Iran is the mismatch between authority and responsibility, and policy-making is usually done by an institution that is not at the core of the field. In Iran, decisions are often made by councils and organizations in which the results of the votes are not transparent, and as a result, the responsibility for decisions will not be on any individual, institution, or organization (Nazemi 2020). In the Table 4, Tehran is compared with selected cities (extracted from the report of the Smart City Index 2020) regarding the indicators of smart urban governance. Table 4 Tehran status in comparison with selected cities in terms of governance indicators in a smart city (Smart City Index 2020; Danilina and Majorzadehzahiri 2019)

Tehran Seoul London Jakarta Shenzhen

9,135,000 9,774,000 10,313,000 10,323,000 10,749,000

Not-ranked 47 15 94 67

Suitable Suitable Suitable Suitable Suitable

Fair Suitable Fair Suitable Fair

Inappropriate Fair Fair Suitable Suitable

Inappropriate Suitable Fair Suitable Suitable

Identificatio n Documents Processing

Online Voting

Residental feedback on gov.projects

Corruption of city officials

Information on local government access

Smart city Rank (out of 109 cities)

City

Population (in 2020)

Tehran status in comparison with selected cities in terms of governance indicators in a smart city (Smart City Index 2020;Danilina and Majorzadehzahiri 2019)

Fair Suitable Suitable Suitable Suitable

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Table 5 Cases related to smart waste management in the third Tehran Urban Development Plan (Tehran Council 2019) Article (58) of Tehran’s third development plan (waste management)

Upgrading the necessary standards in measuring and monitoring services such as collection and transportation, source separation, and managing the services price for delegation of services to the private sector Establishment of an integrated online waste management system from the generating stage to final disposal and dumping Online monitoring and organization of controlled and uncontrolled waste sorting and recycling activities

Smart Environment The most major concern of urban managers of Tehran in terms of improving the urban environment, public spaces, and quality of life is the smart management of wastewater and waste. To this end, Tehran City Council has upheld a legal article specifically to waste management and has required the municipality to implement it in compiling the third 5-year urban development plan (Table 5). Henceforth in 2019 and with the cooperation of Iran National Innovation Fund, Tehran’s Smart City Center held the first event in a series of events to provide innovative and technological needs of Tehran Municipality called Inno Tehran in order to examine the innovative and technological requirements for the development of smart urban waste management. By the end of 2019, Tehran metropolis waste management in terms of statistics is as follows (Fig. 4). In the Smart Tehran program, smart waste management is the outcome of communication among four systems: public supervision and control system (TOZIN), workers’ organizing system, vehicle tracking system, and status report system. Table 6 presents the activities covered by each of the systems. Ministries of Energy and Petroleum are held responsible for decisions on “water” and “energy” management throughout the whole country of Iran and consequently Tehran, and due to the multiplicity of decision-makers and the lack of unity in management, there has been no practical smartening at the urban level so far. However, the mentioned ministries and their officials have repeatedly highlighted the need to smarten generation, distribution, and consumption of energy at a macro and especially urban level. They have also considered the implementation of this approach as a solution to deal with high energy consumption in Iran to save energy resources.

Tehran Challenges in Implementing a Smart Environment Lack of criteria for the accurate evaluation of the performance or effectiveness of the smart city project and also the lack of appropriate monitoring systems are the most important environmental challenge in smartening the city of Tehran. There are several indicators that need to be measured, but those indicators need to be aligned

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Fig. 4 Waste management of Tehran from the statistical perspective (TMICTO 2019b)

with the project objectives. For example, if the main objective of a smart city implementation project is to acieve a green city, the evaluation and measurement of environmental indicators will be of higher priority. For the city of Tehran, this issue requires identification of the various parameters in Tehran and similar cities to extract appropriate environmental criteria. Another considerable point is focusing too much on technology while paying attention to all aspects of city life, and integration in making decisions about the smart city is very critical (Haghighi et al. 2018). In Table 7, Tehran is compared with selected cities (extracted from the report of the Smart City Index 2020) regarding the indicators of a smart environment.

Smart Infrastructure The infrastructure development of Tehran urban smartening has been the responsibility of the Digital Economy Development and Smartening Office of the Science and Technology Vice President of the Islamic Republic of Iran, the Ministry of ICT, as well as Tehran Municipality ICT Organization (TMICTO) and Fanavaran Shahr Institute as Urban Electronic Services Office (TMICTO 2018b). “My Tehran” application project is the most noticeable infrastructure achievement of Tehran Municipality ICT Organization that has bridged the gap between

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Table 6 Smart waste management systems of Tehran municipality (TMICTO 2019b) TOZIN comprehensive system Recording the stages of waste management process from the collection to dumping and disposal, recycling, and sale in all executive and supervisory units Calculation of costs and revenues from the urban waste collection Continuous control and monitoring of executive operations from source to the final stage Workers’ organizing system Continuous, ongoing, and real-time monitoring of workers’ performance and their information Implementing unified policies at the level of worker-based departments Vehicle tracking system Continuous and online monitoring and tracking of waste collection vehicles in urban areas from source to waste disposal Accelerating and facilitating the collection of performance information of contractors’ vehicles Preventing intentional or unintentional violations such as exceeding the speed limit and going out of range Status report system Integrated management of urban services in accordance with the items of transportation and waste collection and mechanized cleaning services Monitoring and evaluating the performance of contractors in urban areas and intermediate stations Optimal resource allocation and operations analysis in macro executive planning

Preventing and reducing violations of employees and contractors in the implementation and provision of information in a timely manner Identifying discrepancies and the reasons for their occurrence in reports and other controllable components of the system Providing various reports at all levels in order to make management decisions Mechanized transfer of workers’ performance information to the electronic status report syste Assessing the status of workers’ housing through the corresponding checklists and applying the results in performance appraisal of related contractors Mechanized transfer of machinery performance information to the electronic status system Checking the faultless operation of the equipment and machinery of each vehicle Integrated connection of the database to the map of Tehran and establishing communication between GIS and groups

Carrying out calculations related to the amount of performance and deductions and penalties of contractors in the regions based on the provisions of the contracts Reducing the cost of services by eliminating unnecessary steps and saving time and repetition of operations Issuance of the final monthly status statement resulting from the collection of the approved daily status statements

citizens and Tehran municipality. Using smart ICT technologies and via connecting to more than eight national systems and main databases, this system was launched in 2019 to provide integrated online services to citizens. The services provided in “My Tehran” smart system are presented in the Table 8 below.

Infrastructure Challenges in Tehran Smartening A major challenge in the field of infrastructure shortage is the development of information and communication technology infrastructures independently and

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Table 7 Tehran status in comparison with selected cities in terms of smart environment indicators (Smart City Index 2020; Haghighi et al. 2018)

9,135,000 9,774,000 10,313,000 10,323,000 10,749,000

Not-ranked 47 15 94 67

6.6 16.9 25.5 13.6 12.3

Fair Suitable Suitable Suitable Suitable

Fair Suitable Fair Suitable Suitable

Air pollution status

Monitoring air pollution

Green spaces

Recycling rate, %

Population (in 2020)

City

Tehran Seoul London Jakarta Shenzhen

Smart city Rank (out of 109 cities)

Tehran status in comparison with selected cities in terms of smart environment indicators (Smart City Index 2020; Haghighi et al. 2018)

Inappropriate Inappropriate Inappropriate Inappropriate Suitable

Table 8 “My Tehran” system services (TMICTO 2019b) Citizen account

New traffic plan system

Transparency system

Citizens’ participation and innovation portal

Smart atlas of Tehran neighborhoods Urban automation system Raya navigation application

Car parking system (on the street) Comprehensive urban transaction system

Smart management of the citizens’ request process Gathering citizens’ information to increase the quality of services Access to records of citizens’ requests Managing and monitoring the citizens’ vehicles Reducing pollution by restricting the passage of cars to the city center Car rental system Uploading annual budget information of municipalitie Uploading the performance report of municipalities and urban contractors Creating an atmosphere for citizens’ exchange of opinions, suggestions, and objections Creating an atmosphere of idea sharing to reduce the city problems Registration and submission of various business plans and startups General specifications of urban neighborhoods Investigating physical activity and environmental indicators Reviewing of economic indicators Online provision of services that previously required physical presence Routing and navigation based on urban online traffic Providing information on urban places Possibility of adding information by citizens Registration of car information in “My Tehran” system Paying car parking fee virtually and online Discovering car parking spots online Collecting transactions information Virtual conducting of auctions and tenders Transparency of the trading process

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Table 9 Tehran status in comparison with selected cities in terms of smart urban infrastructure (Smart City Index 2020; Danilina and Majorzadehzahiri 2019)

9,135,000 9,774,000 10,313,000 10,323,000 10,749,000

Not-ranked 47 15 94 67

Inappropriate Suitable Suitable Suitable Suitable

Fair Suitable Suitable Suitable Suitable

Identificatio n Documents Processing

Fair Suitable Suitable Suitable Suitable

Internet speed

Fair Suitable Suitable Suitable Suitable

IT skills training

availability of smart apps

Tehran Seoul London Jakarta Shenzhen

availability of online information

City

Smart city Rank (out of 109 cities)

Population (in 2020)

Tehran status in comparison with selected cities in terms of smart urban infrastructure (Smart City Index 2020; Danilina and Majorzadehzahiri 2019)

Fair Suitable Suitable Suitable Suitable

regardless of use in other fields. For instance, the integration of infrastructure in the various fields of energy, transportation, and information technology must be taken into account. Another challenge in this area is adequate infrastructure security. Given the integration of infrastructure and, consequently, services in the smart city, citizens’ data and other sensitive information would be accessible in an integrated and at the same time extensive platform. This highlights the importance of security. Security must be considered in all components of the infrastructure, including cards, terminals, assembly centers, data centers, communications, and so forth. Internet access limitations are another serious challenge. Due to the low bandwidth of the Internet in Iran and the need for highspeed Internet for many services that can be provided in the smart city, this issue is of utmost importance. Besides, the complexity of designing and creating models that support IoT should be considered, models that can interpret and apply sensor information, such as spatial coordinate information, on this network (Haghighi et al. 2018). In Table 9, Tehran is compared with selected cities (extracted from the report of the Smart City Index 2020) regarding the indicators of smart urban infrastructure.

Smart Life “Technology without community” has been the biggest concern when it comes to the development of smart cities. According to statistics, the level of Tehran citizens’ acceptance of the service systems of Tehran Municipality ICT Organization from 2007 to 2018 is as follows. In this chart, the decrease in the number of active users in recent years has been due to the unification of services in integrated smart systems, while the citizens’ engagement has increased (Fig. 5). Besides, another component that affects citizens’ quality of life is citizen safety that is defined and being pursued under the “Smart Safety and Fire Services” development program. Nowadays, new technologies regarding firefighting and

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Trend of users’ number in urban service systems (TMICTO 2019b) 90000 80000 70000 60000 50000 40000 30000 20000 10000 0 2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

Fig. 5 Trend of urban service systems users in Tehran (TMICTO 2019b) Table 10 Cases related to firefighting management in Tehran’s third urban development plan (Tehran Council 2019) Article (82) of Tehran’s third development plan (municipality tasks regarding firefighting)

Development of a comprehensive plan for smart firefighting with emphasis on Internet of Things (IoT) development strategies and sustainable communication Development of regulations for the establishment of a smart fire alarm system (monitoring) with the priority of sensitive urban centers and important, sensitive, and high-risk buildings

safety services not only help maintain the safety of firefighters but also increase the service quality and speed. To smarten this field more, it is critical to pay attention to four important components: the use of various sensors at the incident scene to help early detection, better identification of the location, and spotting of forces; collecting and using data for optimal use of resources and forces; facilitating communication and interaction among data systems; and development of smart systems to improve decision-making. Indeed, in all these cases, “data,” including its collection, processing, and optimal use, plays a pivotal role. In this regard, Tehran City Council has required the municipality to manage and control the smartening of fire stations and safety services observant to the legal article in Tehran’s third development plan (Table 10). In addition to public institutions and organizations, startups in the field of smart retailing and smart transportation have emerged in Tehran and were warmly

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welcomed by users. We will dig deeper into these startups in “Smart Economy” and “Smart Transportation” sections.

Tehran Challenges in Implementing Smart Life Citizens are the most important element in defining smart life in smart cities. Therefore, the biggest challenge in this field is to change the lifestyle and urban culture, as well as the citizens’ engagement with the smart elements of cities. Sociocultural challenges and constraints in Tehran urban smartening can be classified into several categories (Haghighi et al. 2018): • Insufficient Citizen Engagement – Citizens may resist changes rooting from the Tehran Smart City Plan for a variety of reasons. For example, one of the reasons is the need to change consumption patterns and lifestyles. This is noteworthy that the development of a smart city requires a change in the behavior of each of the project stakeholders, renovation of consumption pattern and lifestyles, and consideration of future issues such as resource use, climate change, and so on. The structure of a smart city must first be in the body of society and the daily lives of citizens. In other words, the smart city is not achieved by an enforced policy or as the result of a “top-down” process. Rather, it must be placed in the heart of society as a “bottom-up” process and as self-organization. In fact, the responsible organizations should guide society in such a way that the desired behavior and lifestyle appear among the citizens. • Challenges Related to Consumption Culture – People’s consumption patterns are changing and moving toward consumerism. At the same time, energy and its supply is a major problem for the world today. There must be enough energy in the right place and at everyone’s disposal. Creating a culture of proper use of energy and preventing its loss is a great challenge. • The Need for Education, Promotion, and Culture – One of the issues that is widely gaining ground in today’s world is the confrontation between culture and technology. The issue of culture building for using technology is an important issue for sociologists and experts in the field of culture and society. As a result, the smart city also needs to build culture. This culture building is generally classified into two categories. First, actions based on ICT can be like the public culture of citizens in terms of familiarity and use of electronic services through the content provision, educational programs, broadcasting of radio and television programs, urban environmental advertising, and other similar measures in this field. The second category would be actions in the field of cultural and behavioral education and promotion, traffic knowledge, or culture building regarding environmental protection, for example, through the preparation of an executive educational plan or improving citizens’ culture about the segregation of waste (dry and wet) from the source. In Table 11, Tehran is compared with selected cities (extracted from the report of the Smart City Index 2020) regarding the indicators of smart urban life.

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Table 11 Tehran status in comparison with selected cities in terms of smart life (Smart City Index 2020; Haghighi et al. 2018)

Tehran

9,135,000

Seoul London Jakarta Shenzhen

9,774,000 10,313,000 10,323,000 10,749,000

Notranked 47 15 94 67

Mean years of schooling

Lifelong learning opportunities

Minorities rights

Cultural activities satisfiction

Life expectancy at Birth (Years)

Smart city Rank (out of 109 cities)

City

Population (in 2020)

Tehran status in comparison with selected cities in terms of smart life (Smart City Index 2020; Haghighi et al. 2018)

76.2

Fair

Suitable

Suitable

10.0

82.8 81.2 71.5 76.7

Suitable Suitable Fair Suitable

Inappropriate Suitable Suitable Suitable

Suitable Suitable Suitable Suitable

12.2 13.0 12.9 13.9

Table 12 Development trend of infrastructures in Tehran Municipality Traffic Control Company – cumulative frequency (TMICTO 2018b)

Tehran Traffic Control Company

Studied cases Video surveillance camera installation Installation of speed cameras – number of cameras Length of optical fiber infrastructure – km Implementation of smart management system on highways – km Smart system for recording running red lights – km Air pollution control devices

Development trend 2015 2018 134 386 57 109 1340 1590 53 76 192

192

100

176

Smart Transportation Following the development of “My Tehran” system and also the new traffic plan of Tehran (quota of cars to enter the city center for 20 days in each season called air pollution reduction plan) to control air quality, Tehran has been equipped with traffic control cameras, and it is now possible to follow it in “My Tehran” system (Table 12). The smart car parking system in Tehran is also being updated. In this system, car owners will benefit from features such as access to information about the nearest empty parking space in any area and also online fee payment for parking fees based on tariffs while registering the specifications of their cars (Fig. 6). Additionally, alongside public institutions, two successful startups named “Snapp” and “Tap30” offer services to Tehran citizens in the field of smart transportation of urban passengers by benchmarking the Uber system in the USA (Fig. 7).

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Fig. 6 Mechanism of a smart car parking system (TMICTO 2018b)

Fig. 7 Successful smart transportation statups in Tehran (Rasta 2020a, 2020b)

Tehran Challenges in Implementing Smart Transportation The most significant challenge in the development of transportation in Tehran is to finance it. According to the law, the central government is to pay part of the development costs, including 50% of the cost of metro development, 33% of the citizens’ ticket share as a citizen subsidy, etc., which according to reports has been paid only up to 3% so far (Farahani 2020).

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Table 13 Tehran status in comparison with selected cities in terms of smart transportation (Smart City Index 2020; Aghaei 2017)

Tehran

9,135,000

Seoul London Jakarta Shenzhen

9,774,000 10,313,000 10,323,000 10,749,000

Notranked 47 15 94 67

Online traffic information

Car-sharing Apps

Bicycle hiring

Public transport satisfaction

Traffic congestion

Public transport, %

Smart city Rank (out of 109 cities)

City

Population (in 2020)

Tehran status in comparison with selected cities in terms of smart transportation (Smart City Index 2020; aghaei 2017)

26.0

Inappropriate

Fair

Inappropriate

Suitable

Fair

19.0 28.5 28.0 29.1

Inappropriate Inappropriate Inappropriate Fair

Suitable Suitable Fair Suitable

Fair Fair Suitable Suitable

Fair Fair Fair Suitable

Suitable Fair Suitable Suitable

In addition to the lack of financial resources for development, Tehran faces other challenges such as low share of public transport (45%), lack of even distribution of public transport in different areas, and also the poor organization of parking spots on the sides of the streets (Aghaei 2017). In Table 13, Tehran is compared with selected cities (extracted from the report of the Smart City Index 2020) regarding the indicators of smart transportation.

Smart Economy According to the report of Digitalstat (Iran Digital Economy Database), 37 products in the field of smart city development have been registered in Tehran by the end of 2019. The most popular technologies among these products are illustrated in the chart below (Fig. 8). The most commonly used citizen software among the inhabitants of Tehran is Digikala retailing software. Possessing 94% of Tehran’s online retail market share, the startup now has 26 million unique visitors monthly across Iran and has directly created 4000 jobs (Rasta 2020c) (Fig. 9).

Tehran Challenges in Implementing Smart Economy With the implementation of a smart city and entrusting a large burden of providing services to private businesses, defining new business models for them will be crucial. On the other hand, it should be noted that one of the indicators of development in any society is the amount of production and offered services. A city, as a large community, must also offer products and services. Providing services and products in the smart city should be purposeful and according to a program set with the needs of citizens. Business prosperity (mainly e-business) is one of the goals of a smart city. But defining, developing, and adapting new business models in order to achieve goals has its own complexity (Haghighi et al. 2018). In a sense, one of the challenges in the technical field is the lack of a complete and comprehensive list of physical

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Relative frequency of technologies in the development of products for smartening Tehran (Digitalstat 2020)

6%

6% Digital Platform 35%

9%

Integrated Information System Big Data Intenet of Things

19%

Robotics Industrial Automation

25%

Fig. 8 Share of key technologies in products for smartening Tehran (Digitalstat 2020)

Active Customers of “Digikala” Startup (Rasta 2020c) 5000000 4500000 4000000 3500000 3000000 2500000 2000000 1500000 1000000 500000 0 2013

2014

2015

2016

2017

2018

2019

Fig. 9 Users of “Digikala” retailing Startup (Rasta 2020c)

services provided in the municipality of Tehran. Many activities are also done in an ordered and unplanned (and, consequently, undocumented) form. Therefore, identifying services that can be provided is difficult in some cases (Penco Group 2010). Moreover, in smart city projects, individuals and managers focus too much on information and communication technology, while it is very important to take into

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Table 14 Tehran status in comparison with selected cities in terms of smart economy (Smart City Index 2020; Danilina and Majorzadehzahiri 2019)

Tehran Seoul London Jakarta Shenzhen

9,135,000 9,774,000 10,313,000 10,323,000 10,749,000

Not-ranked 47 15 94 67

16.3 50.0 33.5 48.1 23.8

Fair Fair Suitable Suitable Suitable

Fair Inappropriate Suitable Fair Suitable

Online access to job listings

Creating new jobs

Employment finding services availability

Unemploym ent, %

City

Smart city Rank (out of 109 cities)

Population (in 2020)

Tehran status in comparison with selected cities in terms of smart economy (Smart City Index 2020; Danilina and Majorzadehzahiri 2019)

Inappropriate Suitable Suitable Suitable Suitable

consideration all aspects of city life and integrity in making decisions about the smart city. In Table 14, Tehran is compared with selected cities (extracted from the report of the Smart City Index 2020) regarding the indicators of the smart economy.

Smart City Application in Fighting the Covid-19 Pandemic in Tehran One of the issues that has come to the world’s attention following the widespread outbreak of Covid-19 in early 2020 is the use of innovative smart city technologies to slow down or break the chain of the virus outbreak. Urban management and decisions in the smart cities are based on data analysis, and various technologies and tools are used to improve the citizens’ quality of life. Besides, efforts are being made in these cities to confront urban challenges actively, rapidly, and preventively using the collected information and data and new technologies such as 5G connectivity, Internet of Things (IoT), block chain, big data, and artificial intelligence (AI). Meanwhile, with the outbreak of coronavirus and the importance of implementing preventive measures, the role of the smart city has become more prominent. Therefore, Tehran Municipality ICT Organization, in cooperation with the World Metropolis Association, has published a categorization of technological approaches for fighting the coronavirus pandemic (TMICTO 2020) (Fig. 10).

Conclusion From a general perspective, the challenges that Tehran is facing on its way to move to a smart city are divided into four categories: governance challenges, citizenship challenges, technological challenges, and finally economic challenges.

Promote online services and shopping

ü ü

Teleworking

the needs of citizens

ü Electronic payment

Minimal intervention

Smart Detection

Transformation

and receiving permission ü Observe social distance

Travel management

monitor compliance

violating social distance laws

ü Determining the fines for

ü Use smart urban facilitics to

(and restrictive measures)

Smart monitoring

Maximum intervention

Confrontation / continuation

Healthcare Services

Citizens waste classification based on the disease and determining how to collect the patients waste

control and monitoring (traffic control cameras) ü Using digital twins

ü

ü Using urban equipment for

Monitorning and tracking

ü Using the capacity of startups in diagnosing the disease

ü Launching a traffic ban ü Possibility to pass only by requesting

Categorize jobs Present teleworking programs

Technological approaches to fighting the Covid-19 outbreak

ü Digitization of services ü Integrate services and use mega apps ü Launching a digital platform to meet

Prevention / preparation

Information and awareness

Urban and citizenship data collection (Self-declaration) Hospital data collection Use apps and maps for notifications

Fig. 10 Technological approaches to fighting the Covid-19 outbreak (TMICTO 2020)

ü ü

ü

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Governance Challenges The most important challenge for Tehran in this regard is the lack of integrated urban management, in a way that the organizations participating in urban management are out of a single and independent whole. Another issue that affects the transition to the smart city is the multiplicity of decision-making and executive institutions in urban affairs, which leads to deficiency in coordination, lack of responsibility, and sometimes overdoing and interference among different institutions. Moreover, the shortfall in integrated monitoring system among all organizations has led to this issue that Tehran city management hasn’t shown any improvement in the proper implementation of procedures and the correct and purposeful performance of its duties. Considering that one of the main goals of smart city development is to transfer the burden of service provision from urban management to private businesses, three measures are proposed to improve the governance challenges of Tehran. In the first stage, it is necessary for an independent institution to take on the task of policy-making and decision-making in all aspects of urban management. In the next stage, an organization independent of the policy-making body should be held responsible for managing the integrated supervisory system, and, finally, the executive affairs will be left to the private sector.

Citizenship Challenges The smart city is not achieved through a mandatory policy or a top-down process alone, and citizens may resist the changes resulting from the Tehran Smart City Plan for a variety of reasons. Citizens of Tehran, as the end customers of urban management services, can play an effective role in supervising and putting forth strategic proposals. So far, such supervision has not been implemented properly for reasons such as the lack of a comprehensive plan for citizens’ supervisory engagement at the level of involved organizations, lack of an incentive system to attract such engagement, and lack of an informative system on the importance of using citizens’ opinions and providing feedback on the use of opinions. Also, as the smart city is developed for the people and to improve services to citizens, people must be able to use the facilities and services of the smart city, and this raises the need for public education and extensive culture building.

Technological Challenges The main challenge in the field of infrastructure shortages is the development of information and communication technology infrastructures independently and regardless of use in other fields. Also, due to the integration of infrastructure and, consequently, services in the smart city, citizens’ data and other sensitive information would be placed in an integrated and at the same time wide platform, and this issue doubles the importance of observing security guidelines. Restrictions on Internet

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access is another major challenge. Due to the low bandwidth of the Internet in Iran and the need for high-speed Internet for providing many services in the smart city, this issue becomes increasingly significant. Besides all these, the complexity of designing and creating models that support IoT must be considered.

Economic Challenges The development of a smart city requires large investments from different perspectives. However, in developing countries, given the low incomes and economic hardship, developing and equipping smart cities are not a budget priority. Therefore, the need to promote a new model of service delivery is felt more than ever. In this model, it should be noted that the provision of services and products in the smart city should be purposeful and in accordance with the needs of citizens, and the task should be left to the private sector. The prosperity of business, especially e-business, is one of the goals of a smart city. Nevertheless, it is necessary to reduce the complexity of the contractors’ agreements with the city administration and make the outsourcing process more transparent in order to attract the attention of the private sector to cooperate with the municipality more than before. Maximizing the engagement of all stakeholders in the smart city ecosystem, including citizens, businesses, the innovation system, as well as legislatures and investment groups, is a key principle to the success of the smart city concept. After all, in addition to the above and considering the structure of urban governance in Iran, inter-sectoral cooperation and synergy of all stakeholders in urban governance, including various urban management, governmental, and governance institutions, are also of particular importance and impact. These would undoubtedly have a massive impact on the success rate in advancing the goals of the Smart Tehran Plan.

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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conceptualizing Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Developmental Perspective of Post-apartheid South Africa . . . . . . . . . . . . . . . . . . . . . . . . . . . Tools for Post-1994 Spatial Restructuring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Current Realities of the Post-apartheid City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Background to eThekwini Municipality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Planning Perspective of Umhlanga . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Insight into Umhlanga . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Umhlanga: Responding to the Tenets of New Urbanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transport Sustainability of Umhlanga . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Umhlanga as a Communication Node . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Umhlanga and the Non-place Urban Realm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Safety and Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Housing and Quality of Life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PPPs: A Winning Card for Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

The notion of an “intelligent city” has gained popularity over the last two decades in the urban realm. This paper aims to establish the extent to which Umhlanga (in Durban – South Africa) embodies the principles of an intelligent city. Using the Place, Urban realm theories, aligned with Hollands’ three framing principles of the intelligent city, the paper argues that transforming cities to the status of smart cities is a gradual process that is driven by both international pressure and local demand for better intelligent services. Framed in business entrepreneurship and planning systems, Umhlanga has evolved to be well infrastructure equipped meeting and activity C. Erwee (*) · L. Chipungu · H. Magidimisha-Chipungu University of KwaZulu-Natal, SOBEDS, Durban, South Africa e-mail: [email protected]; [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_77

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space. In the study area, the physical space configuration allows for cross-boundary transactions within the intelligent city principles which occur via cyberspace and ITCs. However, despite this success, the city still exhibits elements of spatial exclusivity in the use of urban space – a factor which the government needs to address if spatial equality in emerging intelligent cities is to be attained.

Introduction Despite the challenges presented by cities, it is indisputable that cities are drivers of economies or engines of economic growth and social development (Daniels 2004, p. 501). Cities foster innovation and produce a large portion of global output – reinstating their role as economic wombs for countries’ economies. Daniels (2004) states that cities “serve a primary economic function as the locations where new forms of economic activity and economic organization evolve and gain higher value.” Daniels (2004, p. 501) goes on to describe cities’ functions as “key nodes of capital accumulation, reinvestment in new sectors and focal points of the development of specialised services,” underpinning the importance of proactive, effective, and efficient planning of the urban fabric in order to promote economic growth and enhance the quality of life for all. Judith Rodin, president of the Rockefeller Foundation, weighs into this discourse by arguing that “with the right strategies, cities can use Information Communication Technologies (ICT) to ‘advance resiliency’ to a wide range of climate and social changes while fostering economic growth (Green 2011, p. 1). This chapter aims to establish the extent to which Umhlanga (in Durban – South Africa) embodies the principles of an intelligent city. Using the Place Urban realm theories, aligned with Hollands’ three framing principles of the intelligent city, the paper argues that transforming cities to the status of smart cities is a gradual process that is driven by both international pressure and local demand for better intelligent services. The process is guided by both business entrepreneurship and the municipal planning systems that guide the development process. The interface of these two factors has seen Umhlanga Ridge Town Centre emerge as a physical meeting place well-equipped for face-to-face encounters while at the same time providing a nonphysical realm which avails itself through the availability of appropriate infrastructure. This physical space therefore allows for cross-boundary transactions which occur via cyberspace and ITCs. As discussed below, this book chapter is divided into five sections with each section dealing with a specific theme, namely, Introduction, Conceptual Framework, Background to eThekwini Municipality, Methodology, Insight into Umhlanga, and the Concluding Remarks.

Conceptualizing Smart Cities The inconsistency in the interpretations of the smart city concept points out that the different uses of the concept create a hazy blur around what truly underpins the “real” smart city, as opposed to cities which adopt the word “smart” because of its

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buzzword appeal. Hollands (2008) warns against cities adopting the label “intelligent (smart) city” for the sake of its attractiveness – or as a sort of marketing tool – without being truly representative of what a smart city is or should be. Hollands’ (2008) three framing or core elements of a smart city are closely related to Nam and Pardo’s infrastructure-based services (mirroring “technology”), business-led urban development (indirectly relative to “institutions”), and social inclusion, learning, and development (people-centered approach). Infrastructure is the “backbone” to a city’s economy and functioning. Carter (2013) makes an important distinction between the elements of infrastructure, distinguishing them as physical and non-physical infrastructure. Physical infrastructure refers to underlying structures that support city systems, drainage systems, sewerage systems, transport networks, bridges, energy channels, and so on. Nonphysical infrastructure has more direct relevance to Hollands’ (2008; Backhouse 2015) first of three framing topics on which he bases a smart city: infrastructurebased services. This constitutes the invisible components of infrastructure which are holistic to cities of the information age. This infrastructural component “occurs in cyberspace and creates functional relationships between the city and humans” (Carter 2013, p. 505), through systems including mobile technology and social media. Hollands’ (2008; Backhouse 2015) relates his first discourse to the topic of ICTs and their involvement in enhancing the way in which information and knowledge is disseminated among people and institutions to enhance city functioning and promote a higher quality of life for all citizens. This relates to the services and infrastructure components of a city which, through “smart computing technologies,” can promote efficiency and intelligence through digital interconnectivity (Backhouse 2015). Healthcare, transportation, local government administration, education, and public safety are all made more efficient and effective through such measures. Business-led urban development refers to the creation of attractive business environments by facilitating infrastructure investment as a drawcard to entice skills and investment into the city (Backhouse 2015). Smart cities become an investment magnet for businesses due to the quality environments they promote. Moreover, this topic emphasizes the competitive goal among cities to achieve economic growth, which is enhanced by capital investment into these cities as well as skills accumulation. Here, innovation is harnessed to benefit the needs and desires of the elite, the so-called skilled – skilled enough to fit the mold of human intelligence which the intelligent city invites. Backhouse (2015, p. 3) describes these knowledge elitists as “knowledge workers”, and those who fall short of the mold fall short of the benefits. Here, the primary goal which reigns supreme above all is the maintenance of social harmony so as to avoid the disruption of achieving business and economic-oriented goals. The last of the three elements is social inclusion, learning, and development, which speaks to the element of community participation through the needs identified by the people, in a bid to create the quality of life they strive to have, through collaborations with government (Hollands 2008 in Backhouse 2015). The services and needs which people have will differ from city to city and place to place; it is therefore crucial for this collaboration between civil society and government to be adopted in order for context-specific needs to be met. Without people, there would be

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Table 1 Principles of the intelligent (smart) city Author Kumar (2015)

Nam and Pardo (2011)

Technology et al. (in Mozannenzadeh and Vettorato 2014, p. 689)

IBM (2009; cited in Mozannenzadeh and Vettorato 2014, p. 689)

Principle Innovation economy Urban infrastructure Governance People Institutions Technology Economy People Environment Governance Mobility Building People Business Transport Communication Water Energy

Source: Authors’ (2017)

no city, smart or not, and in order for growth to occur – economically, intelligently, sustainably – the advantages of intelligent living need to be fostered. With all the varying – but closely related principles of an intelligent city – Nam and Pardo (2011) suggest that these principles (see Table 1) are context-specific to the city’s individual, differentiated environment to that of another and therefore can differ slightly. These three components are directly related to the many principles of the smart cities which various authors allude to in their various platforms as shown in Table 1.

The Developmental Perspective of Post-apartheid South Africa South Africa’s political transition phase in the early 1990s went through a rigorous restructuring and policy transformation phase with much of its focus on reconciling the fragmented South African Society and restructuring the landscape to accommodate and promote previously disadvantaged areas. During this transition phase, integrated spatial development planning was paused from being implemented due to local governments being reorganized (Donaldson 2001). What transpired was the formulation of fragmented policies – policies which were formulated in isolation from each other with little to no cross-departmental collaboration resulting in disintegration across policy documents and a clear picture of conflict and fragmentation across government departments – launching the democratic government off shaky ground with a “confused, cumbersome planning system” (Donaldson 2001, p. 2). The focus of post-apartheid planning polices moved away from the rational, modernist planning which had a focus on general urban planning, to policies which were more centered on promoting integration through development planning (Donaldson 2001). In conjunction with the realization for the need to undo apartheid

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legislation, the Constitution (Act 108 of 1996) puts emphasis on local authorities’ at municipal level to facilitate the constitutional goals which include improving the quality of life for all citizens, to promote a democratic and open society which will equally protect all citizens through law and to create society which is based on social justice, human rights, and democratic values. The Integrated Development Plans (IDPs) became a key tool in guiding local governments to achieve the visions of the Constitution (Odendaal 2016; Sutherland et al., 2013) and reintegrate people and spaces. Moreover, the democratic planning paradigm has adopted the “Central Place Theory” (CPT) – which includes the hierarchical system of development nodes – as the trumping spatial planning perspective adopted by all levels of government. Formally, the “growth pole theory” was the overarching theoretical paradigm upon which the hierarchy of South African settlements were spatially developed, with specific relevance to KwaZulu-Natal (Nodal Development report, 2015). The urban ills – rooted in Apartheid – which had contaminated and fragmented the South African society and the way in which it was spatially ordered was never going to be corrected by a straightforward “quick-fix” (Donaldson 2001); and, as Donaldson (2001, p. 1) states, “restructuring, transforming, restructuring and integrating separate and divided cities pose pertinent spatial planning challenges.” The Reconstruction and Development Plan (RDP) of 1994 was the ANC’s baseline policy document through which it aimed to redress the apartheid ills. The Urban Development Framework of 1997 (UDF) was also a key policy and, together with the Development and Facilitation Act (DFA), went hand-in-hand in terms of spatially restructuring the urban environment. The new planning approach put much emphasis on the compact city design which was to be encouraged in order to minimize urban sprawl and support sustainable city building. However, as stated in the Local Agenda (2000, p. 2; in Donaldson 2001, p. 3), “there is no universal answer and no model for a sustainable African city.” The current status of South African city development is not necessarily defined by a development model, such as the “Apartheid city model,” but what Donaldson (2001) proposes is to analyze “post-apartheid” development outcomes against the apartheid city model in order to understand the current status of contemporary South African cities, “in an attempt to create a way of interpreting place and space within the context of a twenty-first century South African city identity” (Donaldson 2001, p. 3). The twenty-first century South African cities are rooted in “emerging new spaces” (Donaldson 2001, p. 3) such as the conversion of previously fragmented suburbs to developments in the form of buffer zones. Thus, what Donaldson (2001) refers to as “new urban spatial outcomes” are what replace areas formally referred to by the race groups they were allocated to.

Tools for Post-1994 Spatial Restructuring As mentioned above, the UDF and the DFA went hand-in-hand as key policies adopted by the democratic government of South Africa to address the spatial fragmentation and integrate the urban environment. The UDF is driven by four

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key implementation programmes: (1) integrating the city, (2) improving housing and infrastructure, (3) promoting urban economic development, and (4) creating institutions for delivery (Donaldson 2001). Integrating the city through Integrated Development Plans (IDP) prepared by cities as their 5-year development plans was core to overall city integration and development, post-1994, as were the principles of environmental management, in situ upgrading of informal settlements and townships, encouraging higher density development and integrating land-uses, and reforming the planning system. Development corridors integrated with urban transport routes are believed to be key to achieving integration. Additionally, policies which encourage compact city development outline numerous principles such as, the disparagement of urban sprawl, higher residential densities, inner city regeneration, urban infill, housing and service provision, mixed land uses, encouraging public transport nodes, better employment accessibility, and the development of corridors are “fundamental elements of contextualization” (Donaldson 2001, p. 3). A much elaborated point in this paper is that of development nodes in decentralized areas. They are a common feature of current cities and date back to the 1970s in the form of suburban shopping centers – a parallel characteristic associated with Umhlanga in terms of it having being developed as a key investment node around the biggest shopping center in the Southern Hemisphere. What is common in these nodal areas are mixed land uses and more compact development which welcomes a higher urban density, often resulting in these nodes growing so much so that they are regarded as “cities-within-cities” (Donaldson 2001, p. 4). Nodal developments have also become a key tool in the restructuring of space, as a way of inducing investment into decentralized areas, often spanning large enough development to be considered “cities-within-cities” (Donaldson 2001, p. 4). Donaldson (2001) attributes the Central Business Districts’ (CBD) inner city decline associated with traditional city challenges. Corridor development became a leading element of metropolitan planning policy in the early 1990s. It does, however, have roots in the apartheid government’s “industrial de-concentration” philosophy which dates back to the beginning of the 1980s – making it relevant to South Africa’s inter-urban development scale. Secondly, the concept of urban corridors also had relevance to the intra-metropolitan scale and was adopted as a tool to counteract the panning practices of the 1970s and 1980s (Donaldson 2001; Hindson 1996). The second of both approaches is most relevant to the current contemporary planning thought upon which urban development is based on (Donaldson 2001; Hindson 1996). The apartheid city model’s low-density urban sprawl presented a situation of vast travelling distances between places of residence and work. Thus, it became pertinent for post-1994 policies to pay acknowledge the potential of transport routes as a form of urban integration between sparsely separated areas and would offer an opportunity for economic advancement to previously disadvantaged areas characterized by low-income earnings. The industrial decentralization strategies adopted in the 1970s brought about contestation due to the inefficiencies therewith associated, and so in 1982, the South African government reassessed the RIDP (Donaldson 2001; Hindson 1996).

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What the information above illustrates are the vast differences which sprawled, segregated settlements present as compared to densified cities – which are usually the ones which are able to fast track themselves to smart city status. Dispersion hampers the ability to maximize infrastructural benefits, which otherwise densified areas leverage much of their success off – in terms of developmental potential, in terms of business attractiveness, and in terms of densified foot-traffic to-and-within these areas because of the infrastructures they offer, which foster economic growth through business enticement. Infrastructure investments require capital mass, and the cost of investing in such is unfavorable as compared to investing in denser areas. Through The Housing Act of 1997, government strived to create communities which offered a better quality of life through their improved habitability and safety, by focusing on housing and infrastructure provision, explained Donaldson (2001). RDP housing developments were carried out by the democratic government in mass rollouts. Going against the UDFs mandates to create densification through compact city models; “the UDF (1997: 31) propagates urban densification projects ‘aimed at moving away from the ‘one household one plot’ scenario” Donaldson (2001, n.p), the RDP housing roll outs were lacking in infrastructure and amenities, were characteristic by ‘matchbox’ houses, (Donaldson 2001) encouraged sprawl, denied the ease of access to economic opportunities, and overall failed to. The housing was located on cheap land far from the city center and ignored aspects of sustainability such as economic, social, environmental, and cultural sustainability.

The Current Realities of the Post-apartheid City The realities of the post-apartheid city and its associated spatial changes are attributed to three overarching concerns, as presented by Schensul and Heller (2010): 1. Both social and spatial fragmentation of the post-apartheid city have worsened. Cities in South Africa are continuing to sprawl outwards, despite efforts by governments to induce spatial integration. De-industrialization, green-field developments, and suburbanization are all characteristics of the current South Africa cities. According to Schensul and Heller (2010), the inequalities of the past have only been deepened, illustrated by the high-end, privately developed gated communities and the ever-expanding informal settlements. 2. The spatial changes which have occurred in the city are largely to do with the neoliberal market forces. Schensul and Heller (2010, p. 3) explain that “the postFordist economy has increased income inequality between skilled and unskilled workers and further segmented the housing market. Concentrated manufacturing industry has been displaced by smaller, more flexible production units and services fuelling the sub-urbanisation of the economy and multimodal patterns of growth.” Moreover, spatial inequality has witnessed a shift in segregation – from race, to class; in light of the dismantling of racial barriers which controlled movement patterns. This is evident in the price of land (Schensul and Heller 2010).

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3. Racial desegregation along with economic desegregation has been ill-enforced by the state, regardless of their committed efforts to reintegrate cities in response to apartheid segregation. What is more intriguing is that the government’s attempts to assist the poor through housing provision, housing under the Reconstruction and Development Program (RDP), have tended to be developed on cheap, unfavorable land in the peripheral areas, with no consideration for social services. This has been dammed as a reinforcing apartheid-akin spatial structures and forms of exclusion. Areas characterized by crime, degeneration, and social dilapidation are attributed to longstanding histories of poverty, unemployment, and homelessness which have subsequently become part and parcel of the post-modern urban formation. What is becoming more and more evident in the post-apartheid city formation is the increase of privately developed and owned gated communities which are controlled security enclaves. However, Jacobs (1962, p. 42 in Donaldson 2001, n.p) proposes that “thinning out a city does not ensure safety from crime and fear of crime. This is one of the conclusions that can be drawn within individual cities too, where pseudosuburbs or superannuated suburbs are ideally situated to rape, muggings, beatings, holdups and the like” presents effects which trickle into the urban surrounds of the controlled security enclaves. Control of “non-white group areas” represents areas with little or no control. Donaldson (2001) attributes this to the absence of land-use regulations being implemented in terms of informal trading – which is a dominant characteristic of the African streets. It is the very absence of control measures that have allowed the platform of informalities and the mixture of land uses.

Background to eThekwini Municipality Before KwaZulu and Natal were consolidated in 2000, during the process of municipal restructuring when eThekwini was introduced as a metropolitan region, KwaZulu was an adjacently located rural homeland to Durban Metropolitan Area (DMA), providing a labor pool close to the urban core of the city (Sutherland et al. 2014). At the inception of the unicity’s consolidation in the early 2000s, the city’s land area increased by a major 68%, proceeding the Municipal Demarcation Act of 1998s restructuring process, which saw the rural land areas – previously excluded from the city’s boundary, such as the outer west and the north local council – being incorporated into the new Metro of eThekwini’s jurisdiction (Respondent 1; Sutherland et al. 2014). As shown in Fig. 1, Sutherland et al. (2014) explain that about 45% of the Municipal area is rural; peri-urban constitutes 30% of the land, while the remaining 25% is urban area. What separates the character of rural land in the eThekwini metro compared to rural land elsewhere in South Africa is the fact that 90% of eThekwini’s rural land is geospatially defined by features from hills, uneven landscapes, dispersed settlements representing the traditional dwelling type, and communal land

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Fig. 1 eThekwini Metropolitan Region. (Source: Sutherland et al. (2013, p. 11))

holdings, unique to KZN’s Ingonyama Trust – a traditional land holding Trust. This has direct implications for municipal management systems to provide services to these areas, which in turn impacts on the sustainability of municipal finances due to the costs involved with regard to pulling services to these dispersed settlements, at great distance from existing infrastructure, with complicated access. Although there is significant urban migration being experienced in the cities, eThekwini’s rural areas have also, in recent years, experienced a great amount of growth in terms of a residential aspect (eThekwini IDP 2015/2016). EThekwini’s population as of January 2014 stood at an approximated 3.6 million people and is projected to reach an estimation of about 4 million in the next 4 years, by 2020 (Sutherland et al. 2013, p. 3). The greater city region is subject to major socioeconomic challenges, with close to 42% of the population living in poor living

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conditions associated with poverty. Sutherland et al. (2013) elaborate on the spatial challenges of the city which have resulted in majority of the population travelling vast distances between places of work and economic opportunities, stating that “the spatial form of Durban is economically challenging as there is a clear separation of residential uses from economic activity which means that in most areas people do not live where they work” (Sutherland et al. 2013, p. 3). Sutherland et al. (2013) explain that the neoliberal policy agenda is a dominant policy agenda adopted by eThekwini municipality – as is the case with most of the country’s development areas – presenting a challenge for the local metro to find a balance between the socioeconomic divide which suffices from the pro-growth agenda of neoliberalism and the needs of the poor, who are often without the most basic of services. Urban sprawl is a long-standing characteristic of the city, presenting current trends of fragmentation which are not very far removed from those experienced by the blatant segregatory patterns of the apartheid era. According to Sutherland et al. (2013), urban sprawl and fragmentation present a further challenge for achieving efficient development which correlates with sustainability and equitability, stating that “The eThekwini Municipality, as with all development spaces in South Africa, has to balance the more dominant neo-liberal pro-growth agenda with the poor-poor agenda. The city’s sprawled, fragmented spatial structure is a major obstacle to achieving “sustainable, efficient and equitable development” (Sutherland et al. 2013, pp. 3–4). This is one of the city’s biggest reigning challenges, despite efforts to counter-address this mammoth issue through its post-1994 restructuring policies (Sutherland et al. 2013). Gillham (2002; in Larice and Macdonald 2004, p. 290) puts forward some of the most widely cited characteristics of sprawl (and which are applicable to the evolution of Umhlanga), as leapfrog or scattered developments and commercial strip development. Leapfrog development is stated by Gillham (2002; in Larice and Macdonald 2004, p. 290) as being “Subdivisions, shopping centres, and office parks that have ‘leapfrogged’ over intervening tracts of farmland or forest or both.” What is evident in these types of developments is the open patches of land left between them and other existing developments, which eventually get developed on as time progresses. This pattern of sprawl is indicative of contemporary suburbia and “exurban fringe areas” (Gillham 2002; in Larice and Macdonald 2004, p. 290). Given the locality of Umhlanga and its history of being nothing but vast area of sugarcane land before the new millennium, it can be considered a leapfrogged development; an urban development which went from nothing to the Umhlanga one sees today, in less than a 20 year timeframe. Moreover, commercial strip development rings true to the relevance of Umhlanga’s development when taking Gillham’s (2002; in Larice and Macdonald 2004, p. 290) description of it into account, stating that “commercial strip development is characterised by huge arterial roads lined with shopping centres, gas stations, fastfood restaurants, drive-thru banks, office complexes, parking lots, and many large signs.” The security-conscious, convenient office complexes which are characteristic of Umhlanga; the off-street parking lots which make doing business in Umhlanga safe, convenient, and efficient; the variety of shopping centers: Gateway and the Crescent; the widespread choice of restaurants and fast-food outlets within the Umhlanga Ridge

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New Town Centre (URNTC); all point to Umhlanga’s relevance as a form of commercial strip development sprawl. Although all the above is true to Umhlanga, one must bear in mind that eThekwini Municipality is evidently conscious of the long-standing issues of South Africa’s spatial fragmentation and sprawling patterns of development with highly uneven levels of development and have consequentially made an asserted effort throughout its “Package of Plans” to respond to this issue by earmarking Strategic Economic Intervention nodes (eThekwini Municipality 2010) – such as Umhlanga, in the Northern Municipal Planning Region (NMPR) in locations which have been cherry picked for investment and the creation of employment opportunities (eThekwini Municipality 2013, 2014). Thus, the applicability of the nature of Umhlanga as a product of “sprawl” should not be seen in the negative light in which “sprawl” is often associated with. As shown in Fig. 2, the appointment of Strategic Economic Investment Nodes forms part of the planning approach adopted in the eThekwini Metro to induce

Fig. 2 eThekwini Strategic Economic Intervention Areas. (Source: eThekwini Municipality (2010, p. 58))

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investment into areas of more convenient accessibility to those who are at a financial disadvantage of relying on employment in Durban’s CBD due to its distance from many outward lying areas which are characterized by poor development, high levels of unemployment, and poverty (eThekwini Municipality 2010, 2013, 2014).

Methodology In order to establish the extent to which Umhlanga is a smart city, information provided in this chapter was obtained through both secondary and primary data sources. A mixed methodological approach was employed with specific tools and strategies being used to collect the required information. Secondary data was collected from various publications in the form of books, journal articles, and online publications which shed light on an opportunity to further investigate the area of study in a localized context and government frameworks, such as eThekwini Municipality’s Integrated Development Plans (IDP), Spatial Development Plans (SDPs), and the Umhlanga Ridge new Town Centre (URNTC) design framework. On the other hand, primary data collection involved collecting data from key informants through GIS mapping, straight observations, and a combination of nonprobability snowball and purposive sampling. For in-depth interviews, using unstructured open-ended questionnaires were used to gather information from key informants. This was combined with a snowball approach meant to obtain rich information from other equally influential people involved in the development of the city. The sampling method which was adopted for the quantitative data collection application was carried out in the form of purposive, numerical questionnaires. A cluster sample approach was adopted, with two main clusters providing the target population: (1) the mixed use developments with the specific target being the business component and (2) the high-density residential communities. A cluster sample size of 100 people was applied in order to balance information from key informants. Observation also came hand as a data collection tool. These observations were recorded by means of photographic recording – a more qualitative approach to data collection, to validate as close to the truest reflection of what was observed, for the purpose of data analysis, such as observing which buildings were plausible for sampling allocations. This form of observation recording was also used to offer photographic evidence of the types of buildings, infrastructure, and use of public spaces (among others) within the URNTC.

The Planning Perspective of Umhlanga The inceptive development plans for URNTC predate the consolidation of the eThekwini Metropolitan Municipality and were passed by the North Local Council (Moreland Properties 1999). The vision for the development of Umhlanga was to

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foster an integrative approach to planning in the north, as a response to addressing the segregatory spatial and socioeconomic patterns of development which were entrenched by the Apartheid government. Moreland Properties (1999, p. 39) made its intensions clear for the development of the URNTC, stating that: The Umhlanga Gateway New Town Centre will cement the current development dynamic in the northern region and will provide the foundation and impetus for continued growth and maturation in a holistic, integrated and comprehensive manner.

This to a large extent is responsive to eThekwini’s vision which states that: By 2020 the eThekwini Municipality will enjoy the reputation of being Africa’s most liveable city, where all citizens live in harmony. This vision will be achieved by growing its economy and meeting people’s needs so that all citizens enjoy a high quality of life with equal opportunities, in a city that they are truly proud of. (Sutcliffe n.d.)

Part of the plan was to create employment opportunities. This is in line with the IDP (p. 214) states that “The New Growth Path intends to reduce unemployment from 25% to 15% through the creation of 5 million jobs by 2020, while the NDP (2011) aims to do the same between 2021 to 2030 by providing an additional 6 million jobs.” This is equally underlined in the URNTC rezoning report (Moreland Properties 1999, p. 1) which states that: By lending weight to the objectives of wider planning concerns, and by establishing an urban new town centre that has both created, and grown on, the initiatives of the Gateway Resort, an important sub-regional threshold is crossed in terms of urban consolidation, access to opportunities, balancing the metropolitan pattern of development, promoting a stronger local economy and generating economic growth and job opportunities in keeping with population growth and the development dynamic.

The second of eThekwini’s 8-point plan – outlined in the 2015/2016 IDP – is aimed specifically at developing a prosperous, diverse economy and employment creation, under which there are a number of strategic focus areas pinpointed to achieve this, namely: Economic leadership and intelligence, facilitating partnerships, maximising the benefits of infrastructure development, nodal and corridor development, investment promotion and facilitation. . . and ensuring sustainable livelihoods. (eThekwini Municipality 2015/2016, p. 215)

Further reference to the URNTC is found in the strategic focus area (SFA) which ushers in the need for “Facilitating Development in Priority Nodes and Corridors.” It specifically states therein that New Urbanism developments are adopted “to reverse the effects of the Apartheid city” (eThekwini Municipality 2015/2016, p. 218). eThekwini’s 2015/2016 Integrated Development Plan (IDP) review makes specific mention of Umhlanga’s developmental success – a critical example of what is expected in the municipality and the country at large.

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Box 1 Land-Use Budget for Umhlanga. Source: eThekwini (2015/2016, p. 55)

Umhlanga is a unique mixed-use style development which incorporates a space spanning with a total of 150,000 squared meters for a combination of mixed-use and commercial land uses, 100,000 squared meters of office space, and residentially zoned land for the total of 3000 units, the overall development is stated to “attract approximately R10-billion in investments to the area and generate 65,000 construction jobs and 16,000 permanent jobs”

The employment aspect of the planning component of Umhlanga is very clear in the study where it was found that a 79.2% overall response shows employment opportunities being one of the key attractions to Umhlanga (see Fig. 3). The remaining 20.8% of respondents did not select employment opportunities as a key attraction, as many of the respondents do not work in Umhlanga but may engage in Umhlanga for residential or recreational purposes instead. 95% of those who live and work indicated that they were attracted to Umhlanga for employment reasons; 97.4% of those who only work in Umhlanga indicated that employment opportunities attracted them to Umhlanga – with one person not indicating this element as attractive to them – although they are solely employed in Umhlanga. These findings illustrate that Umhlanga is a true representation of what the planning frameworks set out to achieve, by creating job opportunities in the area. A response of close to 80% of respondents being employed in Umhlanga – hence their attraction thereto – is telling this.

Insight into Umhlanga Umhlanga – a booming development node with an ever-attractive urban fabric – presents promising opportunity for growth in the Northern Municipal Planning Region (NMPR) of greater Durban due to its strategic location within – what is Fig. 3 Percentage of respondents attracted to URNTC by employment opportunities. (Source: Authors’ (2017))

20.83%

Yes No 79.17%

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now, and has been since the early millennium – the eThekwini Metropolitan boundary (eThekwini Municipality 2010). The URNTC rezoning report (Moreland Properties 1999, p. 40) states that “the Umhlanga Gateway New Town Centre site lies at the core of a region that has become the investment and development focus of the Durban Metropolitan Area. The focus is epitomised by the start of construction of Old Mutual’s multi-million-rand Gateway ‘Shoppertainment Centre’ and the unparalleled success of the la Lucia Ridge Office Estate.” The NMPR’s economy is driven by a range of activities, including agriculture (mainly sugarcane – much of which was rezoned for the development of the Umhlanga Ridge New Town Centre – Fig. 4), manufacturing and warehousing with relation to domestic goods and services production, tourism (with a focus on the nodes of Umdloti and Umhlanga – popular coastal holiday hubs), and entertainment, business and retail (eThekwini Municipality 2013, 2014). A study carried out by Vancometrics (2005, in eThekwini Municipality 2013, 2014) concluded that Umhlanga is one of the most affluent parts of the north region, along with Durban North and La Lucia – all of which represent high levels of development and employment, respectively, compared with less affluent, poorly developed areas in the north region which represent unemployment rates as high as 56.6%, including Inanda, Ntuzuma, and KwaMashu (INK). Despite the unfavorable levels of destitution within the NMPR, an appreciable impact is expected for the north’s economy, owing to the high levels of development commencing in and planned-for within the region (eThekwini Municipality 2013, 2014). The King Shaka International Airport and Dube Trade Port will play a significant role in achieving this, with the Trade Port planned to offer new facilities relating to airfreight and logistics “within a national multi-modal transport network” (eThekwini Municipality 2013, 2014, p. 45). eThekwini Municipality (2013, 2014) recognizes the significance of the “multimodal transport network” and the infrastructural investment therewith attached, with regard to new opportunities for business. Associated businesses will include those related to ICT, “high-value manufacturing” (eThekwini Municipality 2013, 2014, p. 45), logistics, and agri-processing, but are not restricted thereto (eThekwini Municipality 2013, 2014). Given the magnitude of infrastructural and economic development in Umhlanga and its surrounds over the past decade and a half, the “city within a city” debate has come to light with regard to this study, and research shows that Umhlanga has marked its place on the local map as a “city within a city,” confirmed by Respondents 1 and 2. Respondent 1 offers a standpoint from which he defends Umhlanga as a city within a city, stating that: A city is defined around its economy. Is it the bases of a new urban economy? If it is, then it’s a city. If it has an urban economy – it’s a city. What is the net product of the city? It is its economy and that doesn’t mean an economy making money for developers, it’s about creating jobs, creating energy, employment. So there’s nothing distasteful about creating economies... we need it so badly it’s unbelievable. And it’s not some capitalist agenda either; creating economies is terribly important in communist, in society – social societies. Whatever you doing you have got to create economy. Economies aren’t a capitalist idea, it’s how you run economies which starts to go into the nuance of whether they are socialist or capitalist.

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Fig. 4 eThekwini metro spatial regions. (Source: eThekwini Municipality (2013, 2014, p. 10))

Based on Umhlanga’s strategy, Fig. 5 shows that Umhlanga’s favorable location has allowed it to be the high-investment node that it prides itself. Respondent 3 explained that the inland corridor along the R102; the coastal corridors, the M4 and the N2; and then between that what one could identify with as “the old sort of apartheid buffer strip” all contribute to the favorability of the identification of this location for the inducement of high-investment development. Moreover, the major transport infrastructure upgrades to the M41/N2 interchange, its prime location relative to King Shaka International Airport and Dube Trade Port – a mere 20 km or so to the north; and its overall easy and efficient accessibility to and from

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Fig. 5 Locality of Umhlanga New Town Centre with regard to arterial access routes. (Source: http://www.cornubia.co.za/location/)

Umhlanga makes this node even more attractive to investment due to the hassle-free experience which companies can offer their clients. Thus, Umhlanga’s locality to these strategic movement corridors evidently sits at a place where the east and west could start to be integrated into the marginalized periphery. This integration is already being fostered and is evident in the locational relationship between the Umhlanga New Town Centre and Cornubia – which sits almost adjacent to Umhlanga on the western side of the N2 corridor – which “actually starts to stitch this unconnected spatially inefficient city form in this region,” states Respondent 3. Umhlanga has thus played a significant role in providing subregional employment and economic opportunities, with the NMPR contributing around “15–17 % of the GDP (R 20-23bn)” (eThekwini Municipality 2013, 2014, p. 45), rendering it “significant in terms of the Municipality’s GDP” (eThekwini Municipality 2013, 2014, p. 45), since much of the NMPR is dominated by grave levels of unemployment and poverty. Additionally, Umhlanga has lent itself to blurring the vivid lines of the previous patterns of spatial segregation by inducing integration through the strategic placement of investment nodes (post-eThekwini) – and in the unique case of Umhlanga, much of which is owed to its sustainable compact city design. Respondent 3 sheds light on the spatial pattern of Durban just after the dispensation of the democratic government (pre-eThekwini), explaining that at that time, majority of Durbanites resided north of the Umgeni River, but travelled south for employment; so the one objective for proposing Umhlanga as an investment node was to “try and reverse that flow to create work opportunities for people who worked north of the Umgeni” (Respondent 3). Traffic patterns about 20 years ago would have resembled an 80:20 percent ratio for traffic travelling south versus north, daily; and today it is “probably 50:50, even slightly more coming north than going south” explains Respondent 3, further. The Umhlanga New Town Centre was originally rezoned as “Umhlanga Business Centre” in the mid-1990s, during the period when the proposal for a shopping center only as big as 60,000m2 – compared to the subregional shopping complex, Gateway,

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which would soon after change the plans for Umhlanga – was put on the table (Respondent 3). The original plans for Umhlanga were very much suburban in nature, explains Respondent 3. It was out of the city and was designed around a shopping center on a very low density scale, which was to support a local community (Respondent 3). Respondent 3 further explains that although not entirely sure on the rationale behind the smaller, proposed shopping center being exchange for the superregional shopping center, today known as “Gateway” – due to it being slightly before his time with Moreland (now Tongaat Hulett Developments) – he suspects that it stems from the retail side of things when Old Mutual proposed the shift in the mall’s magnitude. Moreland took serious consideration of the idea of the proposed superregional venture and saw scope in the extended opportunities which could be leveraged off of this, resulting in an international trip to Europe and the States by some of the Moreland team members and consultants, in the hope of being able to borrow some sustainable design solutions from abroad and adopt them in the design of Umhlanga. What found considerable favor with the consultants and team was New Urbanism as a compact city design notion and soon thereafter became core to Umhlanga Ridge New Town Centre’s design (Respondent 3). Umhlanga Ridge New Town Centre’s development principles are outlined in the “Annexure C: Special Zone” section of the 1999 rezoning report (Moreland Properties 1999) are summarized in Box 2.

Box 2 Design Principles of Umhlanga Ridge Town Centre. Source: Moreland Properties (1999)

Design principles which inform Umhlanga 1. 2. 3. 4. 5. 6.

A grid-based structure Pedestrian friendly urban design Human scaled Urban quality of life Mixed-use developments Maintenance of a clean, well-managed, and safe environment.

The initial vision for the development of the URNTC was to create a high intensity environment which boasted a mixed-use town center, centered on the principles of New Urbanism – which Respondent 3 explains to have been “very novel at the time, as it had not been done in Durban.” The vision for Gateway shopping complex was to create a shopping center which would be integral to the town center, providing porosity and flow of people and activities, and essentially to create an integrated, dense, compact city where people can walk around happily and have all their amenities there and being able to live, work, and shop in the area (Respondent 3; Moreland Properties 1999). From a spatial planning perspective, the development of Umhlanga has always been based on a Package of Plans approach (Moreland Properties 1999). The North

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Local Council was a very small local authority which predated the uni-city – a time when budget allocations did not exist, explained Respondent 2. Thus, the North Local Council relied public private partnerships (PPP) between themselves as the local authority and private sector from a funding point of view (Moreland Properties 1999; Respondent 2). By doing so, local council was able to leverage rates from PPP developments, such as that of the Umhlanga Ridge New Town Centre (URNTC). Since the consolidation of the unicity in the early millennium, the URNTC has found itself incorporated into eThekwini Municipality’s planning frameworks as a strategic investment node within the metropolitan boundary, still adopting a Package of Plans approach (eThekwini Municipality 2010, 2013, 2014).

Umhlanga: Responding to the Tenets of New Urbanism The design model upon which the URNTC is based – new urbanism – is underpinned by mixed-use development in order to create a more compact city form and increase densification therein: “A mixed-use development is a real estate project with planned integration of some combination of retail, office, residential, hotel, recreation and other functions. It is pedestrian-oriented and contains elements of a live-work-play environment. It maximises space usage, has amenities and architectural expression, and tends to mitigate traffic and sprawls” (Niemira 2007, p. 54; cited in Wardner 2014, p. 4). Mixed-use developments offer a combination of benefits such as the optimization of infrastructure and the minimization for the need for motorized transport, thereby increasing walkability and incorporating various activities within closer proximity (Wardner 2014) – all which tie directly with the design principles of New Urbanism. Wardner (2014) explains that it is becoming an increasingly popular strategy by local government authorities to promote mixed-use developments to combat the rising challenges which cities of the current era are subject to, such as rapid urbanization; disasters, both man-made and natural; and the irrefutable environmental damage caused by humans, resulting in climate change. The former runs parallel to the objectives of the intelligent (smart) city, to offer itself as a solution to such arising challenges (Nam and Pardo 2011; Mosannenzadeh and Vettorato 2014). This is evident in the PPP between the private sector, to model the design of the URNTC on the principles of New Urbanism, and eThekwini Municipality, by supporting the development thereof and incorporating the URNTC into the local IDP and accompanying spatial plans which highlight Umhlanga as a key investment node, with it being referred to as a “secondary CBD” (eThekwini Municipality’s (2015/2016, p. 218). The strategic focus area (SFA) aimed at “facilitating development in priority nodes and corridors,” which forms part of the focus areas aimed at achieving Plan 2 of eThekwini Municipality’s (2015/2016, p. 218) IDP, makes explicit mention of New Urbanism being adopted to reverse the ills of the Apartheid city, “by creating all-inclusive live, work and play environments within a racially segregated municipal area.” Respondent 3 elaborated on the appointment of Umhlanga as a strategic investment node in the greater eThekwini Metro region, explaining that “generally

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cities in the world are poly-nuclear so you have got to create other opportunities; look to other markets to enable other investment to happen. It cannot all happen in the city centre so you create these other opportunities,” such as the mixed-use nodal development which Umhlanga represents. Although mixed-use developments presented themselves in urban planning around the 1960s and 1970s, it was not until the 1987 Brundtland report on Environment and Development was submitted to the World Commission that the term “mixed-use” gained prominence in the world of planning (Wardner 2014). The key emergence of this report was the then newly directed emphasis on “sustainable development,” which became the key driver for the move toward adopting mixeduse developments as a responsive tool to address such (Wardner 2014). This information coupled with the visual evidence in the design of the URNTC points to deliberate attention having been paid to the sustainability agenda and the development thereof. From the attention paid to greening of the area through landscaping, to the shaded walkways and the use of the man-made dam within the Chris Saunders park (Fig. 6) to irrigate the said vegetation, and the efforts to incorporate sustainable building measures, the URNTC presents ample evidence of “sustainable” consciousness. Typical mixed-use developments range in height up to a maximum of eight stories in the mixed-use zones, such as can be seen within the URNTC as shown in Fig. 7. These developments resemble a typical new urbanism design with the street-level floors – sometimes the second floor included – being occupied by commercial activities such as offices and shops, while the 2nd to 3rd floor up is occupied by residential apartments with the uppermost floor often designed for luxury pent housing. Zones which are mixed-use commercial would have all floors occupied for commercial such as hotels and spas or office blocks – such as the Ignition Group commercial building and the Hotel Marina (Figs. 8 and 9).

Fig. 6 A picturesque view of Chris Saunders park, the dam, and the greenery. (Source: http://www. urtc.co.za/page.aspx?ID¼7188)

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Fig. 7 Mixed-use developments with retail and residential uses. (Source: Authors’ (2017))

Fig. 8 Mixed commercial activities within URNTC. (Source: Authors’ (2017))

Fig. 9 Hotel Marina Sidewalk Café, Hotel Marina, URNTC. (Source: http://www. themarinaumhlanga.co.za/sidewalk_cafe.php)

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Wardner (2014, p. 5) states that “creating mixed-use promotes urban quality by making settlements more attractive, liveable and memorable.” By its emphasis thereto, the URNTC’s incorporation of varying activities all within walking distance from one another, the presence of alternative transport modes, its level of connectivity and accessibility, its commitment to achieving densification through its compact city modelling, its consciousness of sustainability and the need to make it a priority in modern living, and its attention to the elements of place-making – which all play a hand in enhancing the quality of life for its citizens – illustrate that the URNTC ticks many a box of the intelligent (smart) city agenda. This illustrates the commitment of the South African government to address the wider national concerns, such as enhancing integration between communities and bettering the life of the majority of its citizens through job creation and economic growth; the development of the URNTC can be deemed an “intelligent” city for all that it has successfully executed in terms of achieving the above-mentioned priority goals.

Transport Sustainability of Umhlanga An analysis of investigation into the primary modes of transport used by respondents to work (from home) was carried out to establish the extent to which people use alternative modes of transport to the private car (see Table 2). This is in line with keeping the New Urbanism design principles which strive to create a compact city which lessens the dependency on the private motor vehicle. The overall findings in Table 2 indicate that the use of the private car trumps the use of any other mode of transport, representing a high figure of 87%. It is understandable that 86.8% of people who only engage in Umhlanga for work Table 2 Primary transportation mode used from home to work and back

LiveWork

Live and work Live only Work only Neither

Total

Count % within Live-Work Count % within Live-Work Count % within Live-Work Count % within Live-Work Count % within Live-Work

Source: Authors’ (2017)

Private car 17 85.0%

Walk 2 10.0%

Public transport 0 0.0%

Private car and walk 1 5.0%

20 100.0%

5 100.0%

0 0.0%

0 0.0%

0 0.0%

5 100.0%

33 86.8%

0 0.0%

5 13.2%

0 0.0%

38 100.0%

5 83.3%

1 16.7%

0 0.0%

0 0.0%

6 100.0%

60 87.0%

3 4.3%

5 7.2%

1 1.4%

69 100.0%

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Fig. 10 Transportation mode used by respondents within URNTC. (Source: Authors’ (2017))

purposes use a private car, as well as 100% of people who only live in Umhlanga but travel outside the boundary for work, as there is no reliable public transport system – except for minibus taxis which operate along Umhlanga Rocks Drive, just outside the study boundary – which service the town center. Out of the 20 respondents who live and work in Umhlanga, 85% of them drive to work in their private cars, while only 10% (two people) walk, and 5% (one person) mixes their mode of transport between a car and walking. Figure 10 indicates the usage of the private car by the respondents. Once people have travelled to Umhlanga (excusing the use of the private car from places of vast distances), the figure which represents the use of the private car within and between other places in Umhlanga is still unfavorably high to the principles of New Urbanism and sustainability, representing an overall high of 75% (Fig. 11). Although URNTC has been designed as a compact city based on mixed-use developments, in trying to keep all activities in close proximity, only 19.4% of the respondents walk within Umhlanga. The expectation of the New Urbanism design is that automobile would be replaced by other modes of low-carbon emitting transport such as walking and/or cycling. An alarming figure of people who live and work in Umhlanga (95%) uses private cars to get around Umhlanga. eThekwini’s 2010–2015 Integrated Transport Plan (ITP) outlines the policy objectives in support of achieving a sustainable public transport system which trumps private automobile usage, by prioritizing: public transport upgrades and budget allocations at the expense of car users. (eThekwini 2015/2016, p. 262)

This indicates URNTC’s commitment to long-term, sustainable development by taking advantage of the infrastructures provided by the municipality, from which it

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Fig. 11 Percentage of respondents who choose walking over driving within URNTC. (Source: Authors’ (2017))

can leverage benefits and thereby densify. Respondent 3 acknowledged that one of the most critical shortcomings in Durban is the lack of public transport – a point which Respondent 1 desperately emphasized. Respondent 1 expressed his frustrations about the lack of public transport in Umhlanga (Town Centre included), by stating that: It is my greatest embarrassment with regard to the town centre; is its lack of public transport and there is just nothing to latch onto.

Respondent 1 explained that despite the private sector’s desperate cries to the municipality to assist in integrating a public transport network into Umhlanga, nothing is materialized because of municipality’s unwillingness to climb on board with those requests, while the private sector’s hands are tied by municipal laws. In light of the lack of a reliable, sustainable public transport network, what Respondent 3 suggested was that creating denser types of development – such as what Umhlanga strives to be – enables public transport to happen, and it is already in the development stage in the form of the new Integrated Rapid Public Transport Network (IRPTN). What the study suggests with regard to the preferred transportation modes adopted by the respondents to get to work and back home again alludes to the fact people are stuck in the South African mentality of having to drive far distances between places. People have therefore failed to make a conscious effort to minimize the use of the private car – even when they are within walking distance of their places of work from their homes – as per the New Urbanism design principles upon which Umhlanga Ridge New Town Centre is designed. Figure 12 shows that there are ample footpaths provided throughout the town center, which eradicates the excuse of having nowhere safe from vehicular traffic and nowhere convenient to walk. For an

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Fig. 12 Footpaths and greening in URNTC. (Source: Authors’ (2017))

urban development modeled on the principles of New Urbanism, alternative modes of transport to the private car are underutilized, although the infrastructure is provided. The need to blend the natural environment with the built-up areas is visually demonstrated by the greening efforts meant to create “place making” (Moreland Properties 1999) and sustain the natural environment. The automobile plays a pivotal role in the manifestation of urban sprawl; Gillham (2002, in Larice and Macdonald, p. 295) states that: History and economics tells us that without a transportation system capable of serving this pattern, sprawl simply would not exist.

With regard to South Africa’s historical past of spatial segregation, cars became the dependent mode of transport by which people were connected over vast distances across the dispersedly spread out settlement patterns. In the wake of post-1994 democratic change whereby integration has replaced segregation as a key spatial planning initiative, densification of areas has become a priority in order that the dependability on the automobile will be reduced – among all the other benefits of densification – yet one cannot ignore that it is a mere 22 years on from democracy and it is going to take a much longer while for the scattered landscapes of this country to be “stitched-up.” Adding to that, South Africa’s generally poor public transport network – in terms of safety, in terms of reliability, and in terms of authentication – leaves many no alternative but the private automobile. Therefore, the private car is still a very dependent mode of transport for a large number of the South African Population. Umhlanga, as a key node in the spatial integration plans of the municipality shows a deliberate attempt to integrate places of far wider dispersion into areas of opportunity. However, despite the design to reduce the dependency on cars, internally – automobile usage is still dependent mode for outsiders to access Umhlanga because of its strategic locality within the greater Durban region.

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Umhlanga as a Communication Node The eThekwini Municipality’s IDP outlines the importance of the Integrated Urban Development Framework as being one of government’s focal initiatives due to it being key in leveraging the potential of South Africa’s towns and cities which have utmost importance as drivers of economic growth and employment generators. In as much as urban areas present rising challenges in relation to rising urbanization levels, they offer many advantages in terms of their connection to international markets, their concentration of economic opportunities, access to new technologies, and “the reality of knowledge economies” (eThekwini, p. 11). There is almost an even break between the respondents who live in Umhlanga and have Wi-Fi at home and those who do not, with just over half (51.4%) having household Wi-Fi – see Fig. 13. The study evidence as shown in Table 3 found that only 51.39% of the respondents who live in Umhlanga are equipped with household Wi-Fi access. The highest count of people who have household Wi-Fi represent those who do not actually live in Umhlanga but work there (equating to 52.6%). This speaks volumes to the fact that there is such a high investment of fiber optic currently being rolled out in the URNTC, yet the residential estates are not equipped with the infrastructure. When probed about whether or not the residential estates within the town center benefit from the fiber optic rollouts in the town center, Respondent 4 alluded to the fact that

Fig. 13 Residents of Umhlanga having household Wi-Fi. (Source: Authors’ (2017))

Table 3 Wi-Fi accessibility at work (cross-tabulation) Live-Work

Yes Live and work Live only Work only Neither

Total Source: Authors’ (2017)

Count % within Live-Work Count % within Live-Work Count % within Live-Work Count % within Live-Work Count % within Live-Work

17 85.0% 2 33.3% 29 76.3% 3 37.5% 51 70.8%

No 3 15.0% 3 50.0% 9 23.7% 3 37.5% 18 25.0%

9999 0 0.0% 1 16.7% 0 0.0% 2 25.0% 3 4.2%

20 100.0% 6 100.0% 38 100.0% 8 100.0% 72 100.0%

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there are bureaucratic barriers brought about by “middlemen” wanting to make profit out of the installation process, and often it is up to the body corporates of these estates to decide whether or not to offer the service to their residents. Since these issues may hinder one and all from benefitting from fiber, many households might opt for Internet via cellular networks and the use of data – which is much costlier than fiber. Thus, attention needs to be paid to diffusing the bureaucratic barriers which hinder the residents from benefitting from cheaper connectivity, since the infrastructure is in place. Table 4 shows that there is a much higher representation of people with Wi-Fi access at work as compared to those who have household Wi-Fi access, representing a total of 70.8% and 85% of people who live and work in Umhlanga have Wi-Fi access at work, and 76.3% of people who only work in Umhlanga have got Wi-Fi access at work. The fact that there is a higher return of positive responses to workWi-Fi accessibility indicates that the fiber optic rollouts in the URNTC are favorable to business affairs and are therefore enticing to businesses opting for URNTC as a viable location to do business in. The fact that 76.3% of respondents have Wi-Fi accessibility at work still accounts for nearly a quarter of those who do not, and this points to a number of possible reasons, including some companies may opt for landline Internet connections for certain positions in companies as a preventative measure for employees abusing Wi-Fi for their personal use. Additionally, companies which provide certain services which do not rely on Internet, such as the call centers, would not necessarily provide free Wi-Fi usage to employees who operate switch boards, since it is of no benefit to the company’s profit generation to do so. The percentage of people who work in Umhlanga and have indicated that their companies compete in the global market – presented in Table 4 is represented by 68.4% of people who only work in Umhlanga and 70% of people who live and work in Umhlanga. This indicates quite a high concentration of businesses which participate in globalization and therefore require faster, cheaper Internet which fiber optics enables. Having faster, cheaper Internet allows for businesses to complete more transactions in shorter periods of time, adding to economic growth and speaking to Table 4 Live-Work companies that compete in global markets (cross-tabulation) Live-Work

Live and work Live only Work only Neither

Total Source: Authors’ (2017)

Count % within Live-Work Count % within Live-Work Count % within Live-Work Count % within Live-Work Count % within Live-Work

Yes 14 70.0% 2 33.3% 26 68.4% 1 12.5% 43 59.7%

No 6 30.0% 3 50.0% 11 28.9% 4 50.0% 24 33.3%

9999 0 0.0% 1 16.7% 1 2.6% 3 37.5% 5 6.9%

20 100.0% 6 100.0% 38 100.0% 8 100.0% 72 100.0%

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the “non-place urban realm” which was coined by Melvin Webber – being able to communicate and exchange business across physical boundaries, without having to physically travel or embark on face-to-face engagement. The overall count of participants who engage in Umhlanga’s leisure or recreational activities such as coffee shops, restaurants, and so on represents 80.6% as presented in Fig. 14, while 19.4% of the overall sample size indicated that they do not engage in Umhlanga’s recreational activities. A huge majority of those who live and work in Umhlanga (95%) make use of such activities. Every person who only lives in Umhlanga engages in these leisurely activities, while 76.3% who only work in Umhlanga engage in such, and only 50% of those who neither live nor work in Umhlanga travel to Umhlanga to partake in what Umhlanga offers on a recreational level. Table 5 presents the findings on the respondents’ use of Umhlanga’s public open spaces. Statistics from the Table shows that 38.9% of the respondents do not engage in the public open spaces provided for in Umhlanga – be it the Chris Saunders park or the children’s park (see Figs. 15 and 16). The balance of the respondents do engage in the public open spaces; be it at one, the other, or both – provided they have children to enter the children’s park. Just over 60% of all respondents make use of the public open spaces provided in URNTC, including Chris Saunders park and the children’s park. The children’s park as shown in Fig. 16 is particularly unique to Umhlanga, boasting strict monitoring controls and a “no children, no entry” policy (St. Clair 2016). A child minder is provided by Umhlanga Ridge Management Association to ensure the safety of the children by monitoring who comes in to the play area (Respondent 5). The child minder has got radio access to the URMA control room for quick-response purposes. St. Clair (2016) indicates that this colorful, attractive family environment is open on Tuesday to Sunday and closed on Mondays for maintenance purposes. Umhlanga sits strategically positioned, nestled between the M4 highway and the Eastern boundary of Northern Urban Development Corridor (NUDC), while

Fig. 14 Engagement with Umhlanga’s leisure activities. (Source: Authors’ (2017)) 19.44%

Yes No

80.56%

Neither

Work only

Live only

Live and work

Source: Author 2017

Total

LiveWork

Count % within LiveWork Count % within LiveWork Count % within LiveWork Count % within LiveWork Count % within LiveWork

Table 5 Usage of Umhlanga’s public open spaces

28 38.9%

3 37.5%

21 55.3%

0 0.0%

No 4 20.0%

29 40.3%

3 37.5%

14 36.8%

3 50.0%

Chris Saunders park 9 45.0%

9 12.5%

2 25.0%

2 5.3%

1 16.7%

Children’s park 4 20.0%

5 6.9%

0 0.0%

0 0.0%

2 33.3%

Chris Saunders and children’s parks 3 15.0%

1 1.4%

0 0.0%

1 2.6%

0 0.0%

9999 0 0.0%

72 100.0%

8 100.0%

38 100.0%

6 100.0%

20 100.0%

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Fig. 15 Chris Saunders park. (Source: Author (2017))

Fig. 16 The children’s park, URNTC. (Source: Authors’ (2017))

Cornubia sits almost opposite to Umhlanga, on the Western side of the NUDC (eThekwini 2015/2016). King Shaka International Airport is roughly an 18 km drive in a North-Westerly direction from Umhlanga and is easily accessible via the M4 highway and the N2 highway, respectively. Umhlanga’s position in relation to Durban’s CBD is roughly the same distance in the opposite direction (SSE) to its relation with King Shaka International Airport. Its prime positioning and all that it has to offer in terms of commercial activity, the anchorage of key businesses in the marketplace, recreation, and, most importantly, accommodation – with a choice of

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Fig. 17 Gateway Hotel. (Source: http://kznpr.co.za/?s¼umhlanga&submit.x¼0&submit.y¼0)

Fig. 18 Holiday Inn Hotel, URNTC. (Source: http://kznpr.co.za/?s¼umhlanga&submit.x¼0& submit.y¼0)

hotels within the town center and surrounding areas – all within a short distance of each other make Umhlanga a favorable, convenient node for the exchange of face-toface meeting purposes over Durban’s CBD (see Figs. 17 and 18).

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Umhlanga and the Non-place Urban Realm Hogan (2011) mentions that Melvin Webber’s early presuppositions, rooted as far back as the early 1960s, predicted that future communities would eventually become forged out of economic and socially connected networks, as opposed to the traditional creation of community – through physical space and proximity. He (Castells, cited in Graham 2004, p. 83) states that “advanced telecommunications, internet, and fast computerised transportation systems allow for simultaneous spatial concentration and decentralisation, ushering in a new technology of networks and urban nodes throughout the world, throughout countries, between and within metropolitan areas.” This shines a spotlight on the central role which ICTs play in the non-place place urban realm, creating these so-called communities without propinquity – as Melvin Webber referred to them. As Nam and Pardo (2011) suggested, ICTs enhance the function and efficiency of cities, promoting economic growth through their ability to connect people far and wide and make instant business transactions across international borders without the need to be physically present for such to occur. This is apparent in URNTC, bordering on a 60% concentration of businesses within the town center which engages in the global markets which represents a considerable portion of the research sample’s response, with exactly a third having indicated that their businesses do not compete in the global markets (see Fig. 19). This indicates two parallel findings with regard to the intelligent city: 1). close to two thirds of the responses indicate global market engagement, translating to the fact that the URNTC provides the necessary infrastructure and environment for such to take place. Many businesses in the URNTC are call centers which are reliant on ICT infrastructure such as telecommunications which enable them to carry out business in a “non-place urban realm” fashion – where business efficiency is optimized by telephonic and other tele-communicative means (Respondent 1, Respondent 4). Annexure A illustrates the existing fiber optics which have been rolled out in the URNTC (although outdated; there is continuous fiber optic provision in the URNTC, Fig. 19 Respondents whose companies compete in the global market. (Source: Authors’ (2017))

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the supply of which is controlled by the business demand, explained Respondent 4). 2) The fact that a third of the overall response indicated negative to the posed question shows that the URNTC offers a variety of opportunities. This points to its goal of promoting inclusivity by not being exclusive to one particular caliber of business, and in as much as the smart city is one which attracts knowledge through education and skilled workforce which fosters a more desired quality of life (Nam and Pardo 2011), it ultimately strives to offer itself as a “humane city that has multiple opportunities to exploit human potential” (Nam and Pardo 2011, p. 285). Additional to the non-place urban realm is the parallel importance of face-to-face communication. Boden and Molotch (in Graham 2004, pp. 101–102) term face-toface communication or “co-present interaction” as “compulsion of proximity.” Their stance on the importance and relevance of co-present interaction lies in the fact that these types of communication exchanges which occur through human interaction in places present a richer kind of trust and commitment than those which are forged over electronic communication. Castells (in Graham 2004, p. 83) reiterates the importance of the relationship between place-based communication and the nonplace urban realm by claiming that “social relationships are characterised simultaneously by individuation and communalism, both processes using, at the same time, spatial patterning and online communication,” with Odendaal (2011, p. 2377) sharing the same sentiment of the interdependent relationship between the two, stating that “ICT enables spatial transcendence yet encourages physical proximity.” This is realized in the URNTC and indicated by a 55.56% response of respondents indicating that they engage in face-to-face communicative work affairs in public places such as coffee shops and restaurants, which speaks to Umhlanga’s ability to offer itself as a secure and convenient communication node with the array of facilities it offers (see Fig. 20). Observations done on some of the restaurants and coffee shops within URNTC found that many of these public eateries offer Wi-Fi – although not necessarily unlimited – for a specific amount of time to their patrons. Fig. 20 Respondents who engage in work matters in places of leisure, i.e., restaurants/coffee shops. (Source: Authors’ (2017))

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With today’s reliance on ICTs, accessible Wi-Fi in public places becomes a drawcard for people, for work, and for social purposes. Wardner (2014, p. 1) explains that the success of mixed-use developments lies in the product of the enhancement of social networks, stating that “it enhances social networks when opportunities for chance face-to-face meetings are increased.” This is enhanced by the ease of access which people have to engage in such “present” communication. Weber’s emphasis on the importance of accessibility to and from the city is realized in the case of Umhlanga, with the N2/M41 interchange being a major access route to the study area and its surrounds. Additionally, accessibility from the coastal side via the M4 corridor means that the URNTC is positioned at an optimal location which is accessible from major traffic corridors which carry heavy volumes of traffic between the northern and southern regions of the greater Durban Metro. Being a mere 20 km north of Durban’s CBD and the same distance, south of Dube Trade Port (see Fig. 21), Umhlanga sits at optimum convenience in terms of position for those traveling in from afar via the airport and those traveling from the CBD side looking for a safer, less congested, and more convenient meeting place for face-to-face engagements. Businesses in the area boast offices deigned to welcome, warmly, people entering these spaces in person for the day-to-day, face-to-face communication which is still place-bound applicable – even with the world’s technological advancements, such as the Internet which makes the need for these engagements less frequent. Additionally, the area has a wide variety of eateries, coffee shops, and public spaces which are inviting for face-to-face

Fig. 21 Aerial view of the study area with transportation networks. (Source: http://www. skyscrapercity.com/showthread.php?t¼627033)

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communication. Umhlanga’s constant buzz of people – no matter the time of day – is testament to the relevance of face-to-face communication in a technologically orientated world. People are consumers by nature, and no matter how easy technology makes communication between people on a global scale, people rely on place-based “bases” where the usual day-to-day routines can be carried out; the non-place urban realm – although very relevant today – is second in line to the order of these two flip-sided notions. It is human nature for people to always gather where consumables and services are conveniently located, i.e., towns and cities. Thus, face-to-face or place-based communication will forever be relevant, no matter how advanced technology grows. Place-based communication offers a type of unspoken communication. The body language, presentation, and gestures of a person offer so much more than what is missed over communication through technology. On the other hand, how applicable is Umhlanga in terms of the non-place urban realm and the flip side of a person’s reliability on face-to-face communication? Can one be place-based in Umhlanga, yet be part of a global network at the same time, through the use of ICTs? With fiber optics and Internet accessibility, Umhlanga allows for this. Business deals can be closed over ICT devices and networks while sitting behind an office desk, a coffee shop table, or one of the many popular restaurants in the area. As presented in Fig. 21, this is reiterated by the types and magnitude of investment being injected into Umhlanga; the “caliber” of business which it attracts is driven by networked infrastructure such as ICTs and transport routes.

Safety and Security One of the major drawcards which attracts people to Umhlanga is the aspect of a safer environment (Respondent 1, Respondent 2, Respondent 3, and Respondent 5). Crime in South Africa is a daily threat, and people are attracted to areas which are well governed in terms of security (claimed Respondent 1). Security surveillance in Umhlanga is undertaken by enforce security, which is contracted by the Umhlanga Ridge Management Association. Among the variety of choices of most attractive factors to Umhlanga, Table 6 shows that 56.9% selected safety as one of them. Nearly 70% of those who live in Umhlanga selected safety as one of the main attractions to Umhlanga, while half of those who only work in Umhlanga selected safety. However, only half of those who neither wok nor live in Umhlanga are attracted to Umhlanga because of the safety it offers. As shown in Fig. 22, the CCTV camera technology adopted in Umhlanga operates in “virtual reality” whereby virtual realities of different scenarios can be created to solve certain situations. In essence, this surveillance technology allows the operator to create “virtual wall” with the cameras. Respondent 1 and Respondent 5 explained that the cameras are then able project a virtual place that people start to penetrate. Instead of multiple TV monitors being analyzed for more than 10 min at a

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time by one person – which soon becomes too overloading for one person – this Cathexis technology allows virtual alarms to be created which will ring and alert the control room operator that the virtual reality for which it has been set to ring for has been penetrated. Respondent 1 explained this by elaborating that “the cameras project a virtual realm of how the street should be and where there is an alarm ringing, someone has walked across and goes through a boundary that we know is an impenetrable boundary.” Once the alarm rings, the control room operator(s) are alerted to the monitor which is linked to the camera which raised the alarm, and the operator can then watch in real life what is happening. This is the point at which the control room operator can identify what is going on in the public realm of URNTC and respond with the necessary precautions such as sending out a patrol car or an on-foot security guard (Fig. 23). eThekwini Municipality (2013, 2014, p. 20) acknowledges the importance of safety and security for all its citizens and is committed to creating a city which ensures that this right is being fulfilled as expressed in the IDP which states that “The safety, health and security of citizens are critical to quality of life. The Constitution asserts the rights of all citizens to be safe, healthy and secure. The Municipality has committed itself to creating a caring city, with all citizens, businesses and visitors feeling safe and confident that their health and security needs are being met.” With regard to URNTC, Enforce Security and the Umhlanga Ridge Management Association (URMA) operate in partnership to provide a security service to the residents and visitors of Umhlanga, through their advanced CCTV surveillance technology, their on-foot security guards, and the high-visibility of their security vehicles (Respondent 4). Respondents 1, 2, 3, and 5 all confirmed that this service (which stretches beyond security) is funded by the levies paid by the building tenants of Umhlanga – both businesses and residents – to the Umhlanga Ridge Management Association. The levies paid also cover the maintenance and cleaning of the URNTC

Table 6 Cross-tabulation of respondents who are attracted by safety to URNTC Live-Work

Yes Live and work Live only Work only Neither

Total Source: Authors’ (2017)

Count % within Live-Work Count % within Live-Work Count % within Live-Work Count % within Live-Work Count % within Live-Work

14 70.0% 4 66.7% 19 50.0% 4 50.0% 41 56.9%

No 6 30.0% 2 33.3% 19 50.0% 4 50.0% 31 43.1%

20 100.0% 6 100.0% 38 100.0% 8 100.0% 72 100.0%

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Fig. 22 CCTV street surveillance in URNTC. (Source: Authors’ (2017))

Fig. 23 Response Vehicle provided by Umhlanga Ridge Management Association. (Source: Authors’ (2017))

and its assets (Respondent 3 and Respondent 5; Moreland Properties 1999).This is in line with Moreland Properties’ (1999, p. 39) rezoning report which states that: A fundamental aspect of the proposals is the commitment to an ongoing review of design quality, urban management, cleaning, maintenance and, most importantly, security within both the public and private domains.

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Fig. 24 Security huts, guards, boom-gates, and CCTV cameras in URNTC. (Source: Authors’ (2017))

Further analysis of the study area shows that those who live in Umhlanga have a higher level of satisfaction with safety issues, as opposed to those who interact with Umhlanga only for employment or other activities such as recreational ones. This could be a direct result of the residents’ safety experience in the URNTC because of the tight security measures adopted in their gated housing estates which are equipped with guards, boom gates, and CCTV surveillance which all contribute to the feeling of security (this is presented in Fig. 24).

Housing and Quality of Life Umhlanga can be classified as a middle- to high-income environment. In terms of the respondents’ income brackets, the study found equal percentages of 30.6% represent the overall percentages of those who earn between 0-R10 000 and R11 000- R20 000. Table 7 shows that there are also higher-income earners representing about 13.9% with income ranges between R21 000 and R30 000. Only 5.6% of the respondents earn between R31 000 and R45 000. This is a true representative of the income image of respondents despite the fact that 10% of the respondents felt uncomfortable to discuss their salaries. The biggest percentage of people who live and work in Umhlanga earn between R11 000 and R20 000 – representing 35%.

Neither

Work only

Live only

Live and Work

Source: Authors (2017)

Total

Livework

Count % within LiveWork Count % within LiveWork Count % within LiveWork Count % within LiveWork Count % within LiveWork 22 30.6%

6 75.0%

14 36.8%

0 0.0%

0–R10 000 2 10.0%

Table 7 Cross-tabulation of income bracket in the study area

22 30.6%

1 12.5%

14 36.8%

0 0.0%

R11 000–R20 000 7 35.0%

10 13.9%

0 0.0%

4 10.5%

1 16.7%

R21 000–R30 000 5 25.0%

4 5.6%

0 0.0%

1 2.6%

0 0.0%

R31 000–R45 000 3 15.0%

5 6.9%

0 0.0%

2 5.3%

2 33.3%

R45 000 + 1 5.0%

9 12.5%

1 12.5%

3 7.9%

3 50.0%

9999 2 10.0%

72 100.0%

8 100.0%

38 100.0%

6 100.0%

20 100.0%

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Only 10% of this category earns up to R10 000. This says a lot about the affordability factor of living in Umhlanga on a relatively low- to middle-income salary. However, this is still a small representation of the whole sample size which limits a true reflection of the whole area. On the other hand, 25% of the people who responded in this category earn between R21 000 and R30 000, while only 15% earn a more comfortable salary – in the higher earning category between R31 000 and R45 000. One person earns more than R45 000, and two people out of this category did not respond. Those who only live in Umhlanga earn above R21 000, up to R45 000 – with only 50% of this category having answered this question. Those who only engage with employment in Umhlanga earn as little as between 0 and R10 000 – 36.8%, with the same proportion earning between R11 000 and R20 000. An overall observation shows that: Although this is a very small representation of the overall participants, this – when compared to the earnings of those who live in and work in Umhlanga – suggests that the job opportunities and the lifestyle which Umhlanga has to offer is favorable for a higher quality of life.

This income level further points to the quality of life experienced in Umhlanga. One such indicator is private healthcare which features a lot in the findings of this area. Table 8 shows the overall count of respondents who have got private medical coverage up to 65% of the total, as opposed to 34.7% who do not have private medical aid. Quite interesting to note is that 75% of the respondents who live and work in Umhlanga have got private medical aid plans, while only 25% do not have private medical aid. Of the respondents who only live in Umhlanga, 83.3% of them are on private medical aids, while 16.7% do not have access to private medical aids. Those who only work in Umhlanga represent a total of 63.2% on private medical aid, and 36.8% have no private medical cover. The respondents who neither live nor work in Umhlanga represent a 37.5% to 62.5% ratio of those who have private

Table 8 Study respondent use of private medical aid

Live-Work

Live and work Live only Work only Neither

Total Source: Authors’ (2017)

Count % within Live-Work Count % within Live-Work Count % within Live-Work Count % within Live-Work Count % within Live-Work

Private medical aid Yes No 15 5 75.0% 25.0% 5 1 83.3% 16.7% 24 14 63.2% 36.8% 3 5 37.5% 62.5% 47 25 65.3% 34.7%

Total 20 100.0% 6 100.0% 38 100.0% 8 100.0% 72 100.0%

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medical aid (the former), compared to the latter who do not have private medical aid. This translates to the fact that those who live and work in Umhlanga can afford to have medical insurance, thereby translating, again, to the upliftment of the quality of life which Umhlanga offers. It is therefore not surprising that in the URNTC area, there are three different private medical centers within the study boundary: Umhlanga Netcare Hospital, Gateway Private Hospital, and Medstone Medical Centre (see Fig. 25). This finding is consistent with “Smart Health” which is one of the aspects of an intelligent (smart) City (Mozannenzadeh and Vettorato; Nam and Pardo 2011). More so, Umhlanga’s provision of new, private medical facilities is equipped with some of the latest smart healthcare technologies since these developments fall within the notion of “leapfrogged” (Gillham 2002; in Larice and Macdonald 2004) developments which are associated with infrastructural and technological issues. According to Respondent 3, housing provision within the New Town boundary caters for all income groups – a positive outcome Umhlanga New Town Centre’s design. Respondents 1, 2, and 3 explained that 10% of every development is made in the high-density, residentially zoned area – “Parkside” – to the right of the study boundary which borders Autumn Drive. This is in line with the government’s Inclusionary Housing Policy which mandates developers to incorporate 10% for inclusionary housing. As presented in Fig. 26, Respondent 3 makes an example of the new developments along Herrwood Drive, having stated that “there are prime, high-end apartments selling for probably more than 10 million; and then you have the likes of Manhattan Mews – which is an example on the other side of apartments on the ‘backside’ or the west side of Gateway – again with quite a variety of income groups. It does cater across the board.” The going rate for the inclusionary units in the Fig. 25 Gateway Private Hospital and Medstone Medical Centre. (Source: Author (2017))

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Fig. 26 Residential Estates, Herrwood Drive, Umhlanga. (Source: http://kznpr.co.za/? s¼umhlanga&submit.x¼0&submit.y¼0)

residentially zoned area is anything between R500 000 and R1million (Respondent 3) – with the higher priced units falling within the mixed-use zone as opposed to the residentially zoned area. Respondent 1 further noted peoples’ perception of Umhlanga being a capitalist society, stating that: It doesn’t matter what is designed, the system gets hold of it; and the system – of course – is capitalist. And has it been perverted? I don’t think so. Is the economy still fundamentally very agate to say there’s a helluva lot of crossover of the socio-economic profile? More-so than you will get out of the Durban city centre. I don’t think you will find a product that more naturally morphs into a very agated socio-economic profile.

What this information alludes to is the fact that neoliberal policies: 1. Encourage the private sector to lead development within governmental guidelines 2. Create a situation whereby the market determines the value of the developments, which can have adverse effects 3. Create socioeconomic upliftment for the immediate area and surround and create employment opportunities 4. Increase the gap between the rich and the poor In this regard, those who can afford to buy into mixed-use developments can enjoy the appreciation of their investments as rentals, and land values are said to increase over the long-term in mixed-use developments as compared to free-standing developments typical of a single use nature (Wardner 2014). Further to that, Wardner (2014, p. 6) explains that mixed-use developments – such as that of the URNTC –

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give the land more value, stating that “for developers, the economies of scale in developing mixed-use projects generate construction efficiencies and more rapid realisation of the site’s potential,” much of which has to do with the stretching of infrastructure between a higher concentration of users. Umhlanga’s developers have, however, made a concerted effort to provide housing opportunities which are marginally protected from true market value, by being allocated as inclusionary, such as 10% of the residential estate. However, bearing in mind that it is a private development, Respondent 1 and 2 ascertained that it is not the private sector’s objective to provide housing for the low-income bracket. However, there is an effort on the private developers’ side to try and minimize the gap of exclusivity. Additionally, Cornubia mixed-use developments are strictly a low-income orientated development since it lies adjacent to Umhlanga on the Western side of the N2 highway. The C8 BRT line is planned to connect Bridge City and Umhlanga via the interception of Cornubia, which will create enhanced integration between these areas.

PPPs: A Winning Card for Smart Cities The Umhlanga Ridge New Town Centre’s inceptive designs predate the eThekwini Municipality and were approved by the North Local Council – which was integrated into the eThekwini Metropolitan Municipality following the marriage of all local councils in the greater Durban area into the uni-city, in the early millennium (Moreland Properties 1999; Respondent 1; Respondent 2). Respondent 3 acknowledged that without government’s support, the implementation of the vision for Umhlanga could not have materialized, stating that “we can’t do anything without government; so we own the land – we’ve got a big responsibility in making sure that what we do on the land is the right thing, and that it adds value for the greater society – but we operate within municipality’s [then- North Local Council; now – eThekwini] plans.” Respondent 2 shares the same sentiment from the public-sector side stating that “if they didn’t have us, they would never have had that (Umhlanga).” Respondent 2 extends on the North Local Council’s partnership with the private sector, stating that “the Borough (the North Local Council) had no money to fund it so without them we would have had nothing. All we did was facilitated the applications and got the rezoning approved and they (THD) brought in the money to make this thing go live,” so the partnership was very much based on the financials coming from the private sector (THD) and development expertise from a statutory legal point of view – in terms of application administration – coming from council, explained Respondent 2. Respondent 3 explained that everything carried out in town planning is statutory driven and relies on legislation; the town planning ordinance stipulates the laws and legal processes which are applicable at that particular time, for a particular development. The Town Planning Ordinance which applied to the development of Umhlanga was 27 of 49 and, in particular, 47, claimed Respondent 2.

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Most of the investment which is injected into the development of Umhlanga is put in place by the private sector, guided by the eThekwini Municipality’s package of development plans – or “Package of Plans” (Moreland Properties 1999; eThekwini Municipality 2013, 2014) – such as the Integrated Development Plans (IDP), Spatial Development Plans (SDP), Spatial Development Frameworks (SDF), Precinct Plans, and the like. This approach to development is underpinned by neoliberalism and forms the basis of development planning in the Durban metropolitan region (Sutherland et al. 2013). Public-private partnerships drive the development of the intelligent (smart) city and are an essential aspect to their success, as most governments have an obligation to direct their funding allocations to socioeconomic and basic needs programmes for the poorer populous and therefore rely heavily on the private sector to fund large-scale developments for the facilitation of economic growth. The N2/M41 interchange at Mount Edgecombe is a prime example of the PPP between eThekwini Municipality and the private developers; it illustrates the current activity of the public sector through its infrastructural investment which private sector is able to tap into and leverage benefits from. Much of the developmental success of Umhlanga is owed to the public-private partnership forged between local authorities – at inception, the North Local Council, and, since the introduction of the unicity, the eThekwini Metro – and the private sector, with Tongaat Hulett Developments playing a vital developmental role. The 1999 rezoning report for URNTC (Moreland Properties 1999, p. 29) acknowledged the need for an ongoing, sturdy relationship between private sector (then – Moreland Properties) and public sector, stating that: “It is vital that a project of this size receives dedicated, ongoing attention and this requires that Moreland maintains close liaison with the North Local Council.” As a result of the above, a steering committee was introduced to represent all parties involved in the venture of developing the URNTC, Moreland, the Council, and the Lot Owner’s Association, which would effectively guide the management of the project (Moreland Properties 1999). The relationship between the private and public sectors, respectively, was based on a balance between the North Local Council providing the planning frameworks, while Moreland properties (now THD) were responsible for the implementation thereof (Respondent 2; Moreland Properties 1999). Respondent 1 describes the relationship between THD and the North Local Council as having been a “close working relationship,” owing everything which needed to be done to see Umhlanga’s plans come to life, to the collaboration between them. Respondent 3 commends the North Local Council’s ability to “embrace with open arms” the new planning approach which Umhlanga represented – given that planning within a special zone with a “basket of rights” was all essentially a new kettle of fish, which the North Local Council were extremely supportive of, and is what effectively enabled everything to happen, claims Respondent 3. Respondent 1 reiterated the ease with which the relationship between THD and the North Local Council was forged; for the North Local Council, Umhlanga promised to generate a good rate base which was appealing to them. The PPP between all those involved in Umhlanga is further illustrated by the allocation of infrastructural responsibilities between public and private sector. As part of Moreland’s (THD) development approval, and their position as the Primary

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Developer in Umhlanga, they were expected to deliver on the construction of all the bulk services in Umhlanga, from storm water and sewers to the roads, while it was the municipality’s responsibility to provide the basic services to the serviced plots once they had been signed off after developmental completion by the developers (St. Clair 2016; Moreland Properties 1999; Respondent 1). Moreover, the Metro strives to promote “customer care” by building ICT solutions and e-governance. This is evident in their public-private collaboration with a number of data providers to invest in infrastructure, both physical and nonphysical. Teraco Data Environments launched a R35 million data infrastructure center in Umhlanga in 2011, in partnership with eThekwini municipality (Teraco Data Environments 2011). Additionally, eThekwini municipality has partnered up with the Durban Institute of Technology, ISETT, Siemens, Business Connection, and the Department of Trade and Industry to establish “SmartXchange,” an ICT Hub. Teraco Data Environments states that “the city’s progressive broadband strategy and significant infrastructure investment is fulfilling its goal of being Africa’s first ‘Smart City’” (Teraco Data Environments 2011). Following the municipal restructuring in the early millennium which resulted in the unicity being consolidated and operational from 2002, Umhlanga being pulled back into the eThekwini Metro stirred a very strong feeling of “denial,” suggests Respondent 1. Respondent 1 explains the hesitation and reluctance of the new Metro to shift their focus away from the city center, as they felt the city should be fundamentally designed around its core center, and so they did not subscribe to a “poly-nuclear” city or metropolitan format. Respondent 1 further expands on this by suggesting that “they still saw themselves as fundamentally a centralist city centre mentality; many of them had to find road maps to come and see it (Umhlanga).” However, Umhlanga has fast grown into a renowned location which attracts people with varying interests through all it has to offer, and its current developmental success suggests that the relationship between eThekwini Municipality and the private investors has maintained a cooperative partnership throughout the near-two decades.

Concluding Remarks The evidence which the success of the URNTC presents in its new urbanism design is steadfast in its contrast to the function of Durban’s degenerated CBD. Sutherland et al. (2013, p. 3) state that “the spatial form of Durban is economically challenging as there is a clear separation of residential uses from economic activity which means that in most areas people do not live where they work.” It is therefore clear that the mixed-use nature of activities within the URNTC – which present a notion of “liveworkplay” (Wardner 2014: 1) – is the very response which speaks to the intelligent city in a localized context, a context which is specific to the issues of segregation and sprawl and a context of offering solutions such as densification to very dispersed Natalian spatial formation. Moreover, the parallel function that the URNTC has, offering itself as a geographically bound place for face-to-face communicative interactions with all the necessary and inviting amenities for such, which people –

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no matter the level of technological advancement they find themselves accustomed to – are still highly dependent on and its provision of the necessary technological infrastructures which lend themselves to the non-place urban realm where people and communities can interact in a non-physical presence put the URNTC on the pedestal of a localized intelligent city. Ultimately, what an intelligent city is about is how it is interpreted in the “smartest” possible way to the challenges at hand in contextual circumstances, offering the best possible version of itself to what the city itself needs for the upliftment of itself and its people. Moreover, the URNTC offers mounds of evidence which fall in line with Hollands’ three framing elements of the intelligent city, rendering it an “intelligent city” based on such findings.

References Backhouse, J. (2015). Smart city agendas of African cities. Johannesburg: University of the Witwatersrand. Carter, T. (2013). Smart cities: The future of urban infrastructure, 22 November 2013. BBC. [Online] Available at: http://www.bbc.com/future/story/20131122-smarter-cities-smarterfuture. 29 Mar 2016. Daniels, P. W. (2004). Urban challenges: The formal and informal economies in mega-cities. Cities, 21(6), 501–511. Donaldson, R. (2001). A model for South African urban development in the 21st century?. SATC 2001. Department of Geographical Sciences, Vista University, Mamelodi Campus. Available at: https://repository.up.ac.za/bitstream/handle/2263/8192/5b5.pdf?sequence=1. eThekwini Municipality. (2010). Integrated Development Plan 2010/2011, Durban: eThekwini Municipality. eThekwini Municipality. (2013). Spatial development framework report 2013/14. Durban: www. durban.gov.za. eThekwini Municipality. (2014). eThekwini Municipality industrial land study and land strategy development. Durban: Economic Development Department. eThekwini Municipality. (2015/2016). Integrated development plan: 2012/2013–2016/2017 5 Year Plan. South Africa. Gillham, O. (2002). What is sprawl? In M. Larice & E. Macdonald (Eds.) (2004), The urban design reader. London: Routledge. Graham, S. (2004). The cybercities reader. London: Routledge. Green, J. (2011). What is an intelligent city? 6 August 2011. American Society of Landscape Architects. [Online]. Available at: https://dirt.asla.org/2011/06/08/what-is-an-intelligent-city/. 27 Mar 2016. Hindson, D. (1996). The apartheid city: construction, decline and reconstruction. In Villes du sud, sur la route d'Istanbul (Istanbul, juin 1996) (pp. 75–106). Hogan, R. (2011). Community without propinquity: Closing event. This is tomorrow, Contemporary Art Magazine. Milton Keynes. [Online]. Available at: http://thisistomorrow.info/articles/ community-withoutpropinquity-closing-event. 27 Aug 2016. Hollands. (2008). Will the real smart city please stand up? Intelligent, progressive or entrepreneurial? City, 12(3), 303. Taylor & Francis. Jacobs, J. (1962). Death and Life of Great American Cities. Johnathan Press: London. Kumar, P. (2015). What’s the real meaning of ‘smart city’? 1 July 2015. [Online]. Available at: http://www.smartcityprojects.com. 22 Aug 2016. Moreland Properties. (1999). Umhlanga gateway new town centre: Rezoning report June 1999. South Africa.

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Mosannenzadeh, F., & Vettorato, D. (2014). Defining smart city: A conceptual framework based on keyword analysis. Journal of Land Use, Mobility & Environment. 4–6 June 2014. Naples: TeMA. Nam, T., & Pardo, T. (2011). Conceptualizing smart city with dimensions of technology, people, and institutions. Centre for Technology in Government, University at Albany, State University of New York, U.S. Odendaal, N. (2011). Splintering urbanism or Split agendas? Examining the spatial distribution of technology access in relation to ICT policy in Durban, South Africa. Urban Studies, 48(11), 2375–2397. Odendaal, N. (2016). Smart city: Neoliberal discourse or urban development tool?. In The Palgrave handbook of international development (pp. 615–633). Palgrave Macmillan, London. Schensul, D., & Heller, P. (2010). Legacies, change and transformation in the post-apartheid city: towards an urban sociological cartography. International Journal of Urban and Regional Research, 35(1), 78–109. St. Clair, M. (2016). The Ridge Chronical. Issue One. Umhlanga Ridge Management Associations. La Lucia Ridge, South Africa. Sutherland, C., Robbins, G., Scott, D., & Sim, V. (2013). Durban city report: Chance2Sustain. Durban: University of Kwa-Zulu Natal. Sutherland, C., Hordijk, M., Lewis, M., Meyer, C. & Buthelezi, S. (2014). Water and sanitation provision in eThekwini Municipality: A spatially differentiated approach. Environment and Urbanisation 26(2), 105–128. https://doi.org/10.1177/0956247814544871. Teraco Data Environments. (2011). Available at: https://companies.mybroadband.co.za/teraco/ 2011/06/ Wardner, P. (2014). Explaining mixed-use developments: A critical realist’s perspective. Christchurch, New Zealand: University of the Sunshine Coast.

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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Songdo, South Korea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sejong, South Korea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Masdar, UAE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amsterdam, The Netherlands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . San Francisco, USA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Brisbane, Australia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Concepts of Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Development of Smart Cities in Indonesia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Concept of Bandung Smart City (BSC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Features of Bandung Smart City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bandung Command Center . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LAPOR! (Layanan Aspirasi dan Pengaduan Online Rakyat/Community Online Complaint and Aspiration Service) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Single Number Emergency Call 112 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bandung Panic Button . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bandung Planning Gallery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion: Challenges and Opportunities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Urbanization, which is mostly unplanned and sprawling in growth, is a global phenomenon. The digital revolution based on information and communication technology is leading to smarter urban solutions to achieve the long-term goal of sustainable urban areas. Both new and existing cities are implementing the concept of a smart city to become more sustainable, livable, resilient, and smarter. Many local governments want to enhance the quality of experience as well as the D. Arfiansyah (*) · H. Han School of Built Environment, University of New South Wales, Sydney, NSW, Australia e-mail: d.arfi[email protected]; [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_92

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quality of life for their residents through the use of smart urban technologies. However, the concept of a smart city is still unclear with limited conceptual frameworks to assist cities to understand this emerging urban development paradigm. This chapter aims to improve understanding of making a smart city in the existing Bandung City in Indonesia. It focuses on the concept, features, challenges, and opportunities. The city is following the six smart city dimensions from Citiasia Center for Smart Nation of smart governance, smart economy, smart living, smart society, smart environment, and smart branding with three main aims: to control, to connect, and to observe. The chapter also highlights features of Bandung Smart City such as Bandung Command Center, Community Online Complaint and Aspiration Service, Single Number Emergency Call 112, and Bandung Panic Button and Bandung Planning Gallery, followed by a discussion on the challenges and opportunities of the smart city.

Introduction The digital revolution is changing the way of life and even the future of societies with the growth of urbanization around the world. The revolution has also expanded the meaning of urbanization itself. The term urbanization was developed during the Industrial Revolution 1.0 with the change from an agricultural economy to an industrial economy. Villagers migrated to cities to work in factories as new opportunities emerged from the discovery of concepts such as steam power, mechanization, and electrification. At that time, the boundaries of the physical dimension were visible, in stark contrast to today’s digital world where the boundaries of the physical dimensions are vague or may not even exist. Urbanization can no longer be manifested as mere physical migration, but is also the migration of lifestyle and sociocultural interactions of communities from rural to urban patterns (Ministry of Communication and Information Technology Republic of Indonesia 2017). Urban populations have been growing at a rate of unlikely 60 million inhabitants each year over 30–40 years (Goonetilleke et al. 2014). The world’s population is becoming increasingly urbanized, growing from 2.3 billion (43%) in 1990 to 4 billion (54%) in 2015. The emergence of many large cities of 5–10 million inhabitants and megacities of 10 million or more inhabitants, particularly in lowand middle-income regions of the world, is also a major theme. In 1995, there were 22 large cities and 14 megacities, and by 2015, there were 44 large cities and 29 megacities. Most megacities are located in developing countries, and this trend will continue as several large cities in Asia are projected to become megacities by 2030 (United Nations Human Settlements Programme 2016). Unfortunately, this growth is mostly unplanned or informal and sprawling in nature. There is also an ongoing worldwide paradigm shift from cargo-oriented development (COD) to transit-oriented development (TOD) and digital-oriented development (DOD). DOD is being encouraged by the global COVID-19 pandemic which has forced the world’s population to do (almost) everything from home, such as work from home (WFH) and study from home (SFH). Travel restrictions,

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specifically at the local restriction level, have reinforced the importance of selfsufficient neighborhoods. New city planning should ensure urban utilities and amenities in the neighborhoods where citizens live as well as zoning flexibility and repurposing of the space, as many people will continue to work and study from home (Baswedan 2020). All cities need to be smarter, as 68% of the world population is expected to be living in urban areas in 2050 (United Nations Department of Economic and Social Affairs 2018). Various solutions have been implemented to address the unsustainable and emerging urbanization including adopting new paradigms to make cities more sustainable, resilient, and smarter. The paradigm shift aims to generate wealth, livability, and health for society and create eco-friendly cities (Yigitcanlar 2009; Albino et al. 2014). However, these solutions are challenging to implement in many parts of the world. Rapid urbanization and dependence on fossil fuels have caused challenges in providing crucial services to urban dwellers such as safety and security, accessibility, social equality, clean energy, amenities, affordable shelter, and healthy built and natural environments (Gilbert et al. 2013; Konys 2018). City governments are seeking smarter urban solutions through innovative services, efficient mechanisms, as well as smart and sustainable infrastructure (Yigitcanlar 2015). In the early 2000s, Lara et al. (2016) introduced the idea of a smart city. It was initially conceptualized as technology assisted through sensors, surveillance cameras, control centers, autonomous driving, and connected infrastructure and communities and was assumed to increase productivity, efficiency, innovation, and safety (Trindade et al. 2017; Zawieska and Pieriegud 2018; Faisal et al. 2019). The key objective of smart cities is to enhance the quality of life through the use of smart urban technologies (Yigitcanlar and Kamruzzaman 2018, 2019). Other objectives are to increase urban innovation and economic output through sustainable industrial ecosystem growth (Ioppolo et al. 2016; Arbolino et al. 2018; Aldieri et al. 2019). However, the concept of sustainability has not been sufficiently integrated into smart city practice. It has generally been used as a supplementary goal (Han and Hawken 2018; Martin et al. 2018). A smart city should generate high-quality, sustainable, and livable places for all rather than offer state-of-the-art digital technology services only for the urban elite (Leem et al. 2019 in Yigitcanlar et al. 2019). During the last decade, the smart city global movement has emerged as a solution to urbanization problems, despite the practice constraints, due to aggressive technology promotion by global technology, construction, and consultancy companies (Chang et al. 2018). Leem et al. (2019) and Yigitcanlar et al. (2019) noted the practice of smart citybased urban and regional development has emerged in various parts of the world, for example:

Songdo, South Korea Songdo International Business District is a Korean exemplar of new smart city concept development (Shwayri 2013), initially inspired by development in Dubai in the United Arab Emirates. Songdo was built from the ground-up next to Incheon

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Fig. 1 Songdo International Business District. (Source: https://www.kpf.com/projects/newsongdo-city)

International Airport and supported with advanced technology from Cisco Systems (Fig. 1). It is a master-planned international business hub developed on seareclaimed land. The smart city development is planned to be fully completed in 2020 and will have about 65,000 inhabitants and 300,000 employees (Yigitcanlar et al. 2019).

Sejong, South Korea Sejong is located within 2 h drive of Seoul on an area of 72.91 km2 (Fig. 2). The target population is 300,000 dwellers consisting of government officials as well as their families and workers of related companies and institutes moved from Seoul and its adjacent cities. The Korean central government led this massive-scale greenfield development project in a top-down approach. Various ideas were collected and adapted during the planning process through international design competitions with consulting committees and public participation on a range of issues from urban structure to community design as a public participation (Leem et al. 2019).

Masdar, UAE Masdar is a planned desert smart city project located near the United Arab Emirates’ capital city of Abu Dhabi (Fig. 3). In line with Abu Dhabi’s Vision 2030, the Masdar smart city development project was initiated in 2006. The smart city is designed as a living laboratory for sustainable urban technologies and was one of the first projects in the Middle East aiming for a master-planned, zero-carbon, sustainable, and smart

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Fig. 2 Sejong smart city. (Source: https://smartcity.go.kr/wp-content/uploads/2019/08/에코델타 시티-국가시범도시-조감도-1.jpg)

Fig. 3 Masdar city masterplan. (Source: https://masdarcity.ae/en)

settlement (Cugurullo 2013). The city is widely viewed as a role model for Middle Eastern smart cities (De Jong et al. 2019). When the development is completed in 2025, it will have 50,000 dwellers; 1,500 clean-tech companies; start-ups staffed by 10,000 new workers; a research university; and 60,000 daily commuting employees (Sgouridis and Kennedy 2010).

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Amsterdam, The Netherlands The Amsterdam smart city project was jointly initiated by the City of Amsterdam, Amsterdam Economic Board, and Internet operators in 2009 (Fig. 4). The initiative aims to turn Amsterdam into a more sustainable city with two principles: to enable stakeholders to apply innovative technologies and to stimulate behavioral change with end-users (Sauer 2012). “The project’s starting point was not only providing technical solutions, but the collaboration, co-creation and partnership between stakeholders in the city to achieve sustainable and smart solutions” (Yigitcanlar et al. 2019). The project was developed in a quadruple-helix partnership model between public, private, academia, and community. The operational aim of the smart city project was to help achieve ambitious sustainability targets set in Europe (Manville et al. 2014).

San Francisco, USA Smart city strategies are seen as an important method for San Francisco to build a sustainable urban future (Fig. 5). Many Silicon Valley-based companies have their headquarters in San Francisco, due to the high quality of life offerings to talented staff, affordability, and tax benefits. With a large number of Internet-based companies, the city has free Wi-Fi hotspots in public locations. For instance, the main road downtown has a 5-kilometer-long free Wi-Fi zone (Hudson 2010; Zhu et al. 2017).

Fig. 4 Amsterdam smart city. (Source: https://i0.wp.com/www.smartcitieslibrary.com/wp-content/ uploads/2017/11/headercampagne_v2.jpg?fit¼1400%2C803&ssl¼1)

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Brisbane, Australia Brisbane, the state capital city of Queensland, is one of the early adopters of the smart city concept (Fig. 6). Queensland’s 1998 Smart State Strategy supported Brisbane’s transformation into a smart city. Initiated in 2007, the smart city policy was an applied economic development and land use macro plan for Brisbane as the nucleus for smart city development (Yigitcanlar et al. 2012). Various strategies from smart city policy are recommended to transform Brisbane into a wealthy smart city, including creating a legible structure plan, uniting disparate precincts, creating definitive pedestrian spines, linking the city center by mass transit, defining a knowledge corridor, investing in sustainability, developing effective planning processes, and developing a smart city model (Hortz 2016). These strategies resulted in the development of Brisbane’s knowledge corridor, a milestone project that physically connects all key innovative institutes of the city. This corridor is very active and is gaining international recognition for Brisbane as a prosperous smart city (Pancholi et al. 2015; Esmaeilpoorarabi et al. 2018).

Fig. 5 San Francisco. (Source: https://www.ucsfhealth.org/-/media/project/ucsf/ucsf-health/ article/hero/accommodations-2x.jpg)

Fig. 6 Brisbane City. (Source: https://www.brisbane.qld.gov.au/planning-and-building/planningguidelines-and-tools/brisbane-city-plan-2014)

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These six cities have distinctive smart city characteristics and visions and are widely referred to as the best practice in the literature (Albino et al. 2014; Ching and Ferreira 2015; Russo et al. 2016). Three of the six are new cities, completely built from the ground-up. However, much development is in existing cities. Many new and existing smart cities will emerge globally as 68% of the world population is expected to be living in urban areas in 2050 (United Nations Department of Economic and Social Affairs 2018). Development plans will increasingly focus on the modification and modernization of infrastructure, services, and economic systems. The demands of physical space for development plans will also mean an increase in digital space utilization. All city activities including the public and private sector and the most important component, its citizens, need to be integrated and connected to achieve equity, inclusivity, resilience, and responsiveness. Spending on smart city products and development is projected to grow by 20% annually from over 300 billions of dollars in 2015 to over 750 billions of dollars in 2020. During this period, Europe is expected to grow at a rate of 13% per year, Asia Pacific 37%, Latin America 27%, the Middle East and Africa 23%, and North America 14% (ARUP 2017). This chapter investigates the making of a smart city in the existing Bandung City in the Republic of Indonesia, including its concept, features, challenges, and opportunities, by reviewing the literature.

The Concepts of Smart Cities Advances in science, engineering, and technology lead to better health and more prosperous lives for billions of people. However, this growth disturbs the earth’s natural systems, visible in ecological and climate emergencies. Climate change is the predominant risk as the world warms 2 °C above preindustrial levels. Consequently, disturbances have started to dramatically affect not only the quality of life and wellbeing of people but also other species on the planet (Albouy et al. 2016). Globally, the large technology, construction, start-up, and consultancy companies promote the idea of technology as the rescuer (Paroutis et al. 2014), with formulation of a new ideology to address poor urbanization practices and energy resource choices with technological solutions (van den Buuse and Kolk 2019). First, the ideology initiated the intelligent city and then the concept of a smart city. Now, smart cities are widely seen as urban settlements that adopt space-age technologies to address various urbanization challenges. In a broad definition, smart cities are an urban environment where technology allows for an efficient relationship between data and its applications to deliver a responsive, resilient, and healthy environment. Certain aspects are becoming key consensus factors. Firstly, smart cities are primarily built on information and communication technology, related to collecting and analyzing data. Secondly, they are characterized by different actors that use this technology in a distributed and only loosely controlled fashion. Lastly, smart cities share a goal of making better decisions, made possible by unprecedented access to data information and aggregation into useful insights,

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leading to a better quality of life for their citizens. Data storage and network communication developments have made it possible to collect necessary data. The progress in artificial intelligence is another critical factor that makes it possible to analyze the data available (▶ Chap. 30, “Towards Autonomous Knowledge Creation from Big Data in Smart Cities” by Nowaczyk et al). The concept of a smart city, particularly the sustainable development of smart cities, has become popular during the last two decades, not only for scholars in technology, science, urban and environmental planning, development, and management but also for urban policymakers and professional practitioners. Digital technologies are a powerful enabler in stimulating paradigmatic shifts in urban development-related visions, strategies, implementation, and learning (Yigitcanlar et al. 2019a). Although the idea is widespread, smart cities are still in the early stages. Smart city optimists claim that, through time, the concept and its practice will eventually evolve and mature (Yadav et al. 2019). However, Yigitcanlar et al. (2018, p. 156) emphasize that “the delay in the conceptualization will highly likely result in inefficient policies, poor investment decisions, and not being able to address the urbanization challenges properly in a timely and adequate manner.” Hence, the entire planning process of smart cities needs to be reevaluated. “Particularly, a crosscheck is required that smart city projects will be creating the desired outcomes targeted at the beginning of the planning stage since most of the smart city initiatives are not integrated with the urban planning mechanisms of that city” (Yigitcanlar et al. 2019). Caragliu et al. (2011, p. 67) had useful views on what makes a city smart: “(a) The utilization of networked infrastructure to improve economic and political efficiency and enable social, cultural, and urban development; (b) An underlying emphasis on business-led urban development; (c) A strong focus on the aim of achieving the social inclusion of various urban residents in public services; (d) A stress on the crucial role of high-tech and creative industries in long-run urban growth; (e) Profound attention to the role of social and relational capital in urban development, and; (f) Social and environmental sustainability as a major strategic component for smart cities.” The significant limits of the currently available smart city framework have led to the development of new conceptual frameworks. Figure 7, by Yigitcanlar (2018), is a framework which aims to establish the missing link between smart city development frameworks and sustainable urban planning and development processes. The conceptual framework (Fig. 7) is based on an input–process–output–impact model (also containing a “system of systems” view) that is a widely used model in urban and regional planning (Fincher 1972; Chadwick 1978 cited in Yigitcanlar et al. 2019). Assets of a city, which are put into use through various processes, are the main inputs of a city’s smart urbanism endeavors. These processes include the key drivers of technology, community, and policy. When assets and drivers are successfully operationalized, various desired outputs are expected to be realized. The procedure is to generate sustainable and knowledge-based development output (for instance, in the economic, societal, environmental, and institutional development domains) to achieve the desired outcomes. The resulting impacts from the desired

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Fig. 7 Smart city conceptual framework. (Source: derived from Yigitcanlar 2018, in Yigitcanlar et al. 2019)

outcomes of productivity, innovation, livability, well-being, sustainability, accessibility, governance, and planning transform the city into a smarter one (Yigitcanlar et al. 2019). Mosco (2019) identified three types of drivers that can catalyze the development of a smart city: state-driven, private- or corporate-driven, and citizen-driven. Depending on the primary driver, the operational logic of a smart city may have implications not only for its general functionality but also for the impact of the smart city network on citizens and the impact of citizens on the city. Due to the diversity of definitions and applications used to identify cities as being smart, many cities remain focused on one of these key aspects: technology, governance model, citizen wellbeing and engagement, or sustainability (▶ Chap. 1, “Smart Cities: Fundamental Concepts”).

The Development of Smart Cities in Indonesia Indonesia is one of the nation’s developing sustainable smart cities. Some Indonesian academics and practitioners interpret the smart city as highly connected to the cybercity where information and communication technology is the backbone of the

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concept. On the other hand, Indonesian planners consider that a smart city is a matter of willingness to improve the capacity by considering local wisdom incorporated with knowledge development where technology is part of the solution. For Indonesia, global experiences can be used to recognize the advantages and anticipate the disadvantages for the Indonesian context by taking into account the national development planning system as well as the diversity of Indonesian culture (Sutriadi 2018). In the transformation process toward smart cities, information and communication technology has a vital role. Investment in the sector in the ASEAN in 2014 had reached US$100 million and will continue to increase by 15% per year to reach US$150 million in the next few years (Chua and Dobberstein 2016). Indonesia’s technology penetration is far behind other ASEAN countries, and much groundwork is required to implement and develop smart cities in Indonesia. However, the direction of implementation is also becoming clearer with the commitment and encouragement from the Indonesian government as an administrator. Indonesia’s program titled Gerakan Menuju 100 Smart City or the Movement Towards 100 Smart Cities started in 2017 and targets 100 cities and regencies in Indonesia for smart city development and to become role models for others. There were two major achievements in the Movement Towards 100 Smart Cities: 24 cities and regencies were given smart city technical guidance in 2017 and 50 cities and regencies in 2018 (Rizkinaswara 2018). As an act of government support for the development of smart cities in Indonesia, the Ministry of Communication and Information Technology is working with the Ministry of Home Affairs, the Ministry of State Apparatus Empowerment and Bureaucratic Reform, the Ministry of National Development Planning, the Ministry of Public Works and Housing, and the Office of Presidential Staff on the program (Ministry of Communication and Information Technology Republic of Indonesia 2017). Smart city development in Indonesia has also increased with the emergence of national smart city performance measurement and rating agencies. The smart city index by the Citiasia Center for Smart Nation (CCSN) measures and ranks 98 cities, 412 regencies, and 34 provinces in Indonesia. It evaluates regional readiness in smart city performance against six elements: governance, economy, branding, living, society, and environment. Cities and regencies with the best results are then recognized annually by Kompas newspaper in collaboration with the Institut Teknologi Bandung (Bandung Institute of Technology) through the Indonesian Smart City Index. Cities/regencies with the best results in the six elements receive a Smart City Awards (Ministry of Communication and Information Technology Republic of Indonesia 2017). The Movement Towards 100 Smart Cities launched by the Ministry of Communication and Information Technology aims to encourage cities and regencies in Indonesia to accelerate development using the concept of smart city, starting from the preparation of a smart city masterplan, planning, and implementing a smart city “Quick Win” program and implementing a smart city development roadmap in 5–10 years (Ministry of Communication and Information Technology Republic of Indonesia 2017).

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Bandung City was included in the first batch of cities and regencies to receive smart city technical guidance. Bandung City on Java Island has over 2.4 million inhabitants on over 16,700 hectares. Its urban agglomeration called Bandung Raya (the Greater Bandung), which consists of Bandung Regency, West Bandung Regency, Bandung City, and Cimahi City, has over 8.6 million inhabitants or 18% of the total population of West Java Province.

The Concept of Bandung Smart City (BSC) Bandung City is the capital of West Java Province and the third largest city in Indonesia. The city is located within 3 h drive of Jakarta (see Fig. 8). In the national spatial planning, Bandung City is part of a national strategic area based on considerations of defense and security, economic, social, and cultural growth; use of natural resources and/or high technology; and/or the function and carrying capacity of the environment. Bandung City is designated as the priority area of the Bandung Basin, an area that has national strategic value. The national strategic value includes the area’s ability to spur economic growth in the region and its surrounding areas and promote equitable regional development. In the West Java Province spatial planning, Bandung City is directed as the core city of the National Activity Center with the main activities of trade and services, creative and high-technology industries, and

Fig. 8 Bandung City map. (Source: http://perpustakaan.bappenas.go.id/lontar/file?file¼digital/ 127002-[_Konten_]-Konten%20C8751a.pdf)

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tourism. To support the national and provincial strategic planning, Bandung City Government has committed to increasing its public services by developing the Bandung Smart City. The main goal of Bandung Smart City is to achieve a city that functions optimally in managing various city resources effectively and efficiently and to solve the challenges and problems of the city using innovative, integrated, and sustainable solutions where technology is the driving force for creating solutions supported by strong infrastructure and human resources to provide city services that improve the quality of life of its citizens for a livable and lovable Bandung City (Bandung Smart City 2020a). There are six clusters in Bandung Smart City (2020a) (Fig. 9): (a) Smart governance: Smart governance is a smart city dimension meaning governance that is implemented smartly. Governance can change traditional patterns in the bureaucracy to produce a business process that is faster, more effective, efficient, and communicative and always makes improvements. Important

Fig. 9 Smart city dimensions. (Source: https://www.docdroid.net/A60JbZ5/citiasia-smart-nationbooklet-pdf)

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success factors for smart governance are institutional, organizational, and leadership (▶ Chap. 5, “Smart Cities Data: Framework, Applications, and Challenges” by Bilal et al). The goal of smart governance is to achieve effective, efficient, communicative governance and civil service of local government and to continue to improve bureaucratic performance through innovation and integrated technology adoption. Traditional patterns of governance can be changed in various ways, but technology adoption will accelerate these changes. (b) Smart economy: A smart economy in a smart city is intended to create an economic ecosystem in the region that can meet the challenges in a disruptive information age and demands a rapid level of adaptation. The goal of the smart economy dimension is to create an ecosystem that supports the community’s economic activities that are in line with the regional leading economic sectors which are adaptive to the changes occurring in the current information era, as well as improving the community’s financial literacy through various programs including creating a cashless society. This goal is realized by developing three elements in the smart economy: industry, welfare, and transaction. (c) Smart living: Smart living is a smart city dimension to ensure the feasibility of the standard of living of its residents. The feasibility of this standard of living can be assessed from three elements: life pattern feasibility (harmony), feasibility of health quality (health), and feasibility of mode of transport (mobility). Those elements support the mobility of people and goods in a smart city. (d) Smart society: A smart society is a smart city dimension that focuses on humans as the main element of a city. In a smart city, human interaction has moved toward a socio-technical ecosystem where the physical and virtual dimensions of the lives of the city residents are increasingly intertwined. The interaction between residents is increasingly strong and not separated by technological mediation. The goal of a smart society in a smart city is to create a socio-technical ecosystem of a humanist and dynamic society, both physical and virtual, to create a productive, communicative, and interactive society with high digital literacy. The goal of smart society is realized by developing three elements in smart society: community, learning, and security. (e) Smart environment: A smart environment means the standard of living, lifestyle, and things around the smart city. It is not the actual environment of a smart city. A smart environment aims to provide the basic necessities of life and better interaction between citizens and their surroundings (▶ Chap. 5, “Smart Cities Data: Framework, Applications, and Challenges” by Bilal et al.). Smart environment is as concerned for the environment in urban development as the physical infrastructure for residents. A smart environment in a smart city aims to realize sustainable development which makes technology elements the driving element, including protection, waste, and energy. (f) Smart branding: Smart branding is innovation in marketing the region to increase regional competitiveness by developing three elements: tourism, business, and the face of the city.

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Fig. 10 Three main elements of Bandung Smart City concept. (Source: https://commandcenter. bandung.go.id/konsep-smart-city-kota-bandung/)

The concept of Bandung Smart City has three main elements (Bandung Command Center 2020a) (Fig. 10): (a) Control: Controlling the course of city development, management, and data analysis as well as monitoring the performance of all regional apparatus organizations and civil servants and supported by applications such as e-government, e-budgeting, e-performance remuneration, and personnel information systems. (b) Connect: Building a government system that is interconnected and integrated to support smart government and smart society and building more effective public communication patterns (complaint services, open data, and open communication/social media). (c) Observe: Monitoring and observing all forms of city events and situations, such as traffic, weather, floods, city security, and public order disturbances, supported

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by more than 400 CCTVs from various agencies in Bandung City which are integrated into the Bandung Command Center.

Features of Bandung Smart City Bandung Command Center Cities become smart when they act on their sensed, gathered, and analyzed data. Actuation can include humans in the loop (decision support), which requires the visualization of the data and features derived either on a screen or other interfaces, or can be fully autonomous (▶ Chap. 4, “Urban Computing: The Technological Framework for Smart Cities” by Bouroche and Dusparic). Data visualization is the process of transforming data and information into interactive visual representation such as pictures, graphs, and charts. Enabling humans to make sense of the big data present in smart cities is highly important because big data visualization is challenging. It is complex and challenging because the data comes from several sources and also has a significant impact on decisionmaking. However, the practical efficiency has changed with the development of virtual reality, augmented reality, mixed reality, and Google Maps (Hashem et al. 2016). Data in smart cities is collected automatically with the help of different sensors and can be used for long-term analysis. Several tools can be used to make long-term decisions depending on the data. Data in smart cities is available to city dwellers via the Internet using different visualization methods such as dashboards (Fig. 11) which present data in the form of charts and statistics (▶ Chap. 5, “Smart Cities Data: Framework, Applications, and Challenges” by Bilal et al.). “City dashboards are performance management tools, allowing quick access to key performance

Fig. 11 Bandung City dashboard. (Source: http://data.bandung.go.id/dashboard/)

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indicators via data visualizations and simple metrics” (▶ Chap. 4, “Urban Computing: The Technological Framework for Smart Cities” by Bouroche and Dusparic). Bandung Command Center is one of the smart city icons in Bandung City. Equipped with sophisticated technology, it aims to improve public services from the Bandung City Government to the community. Officially operating from January 2015, the Bandung Command Center is a Bandung City Government innovation in public services and important in the government efforts to realize Bandung Smart City (Bandung Command Center 2020e) (Fig. 12). Bandung Command Center has two main functions: to improve external public services and to facilitate internal services in the decision support system. For public services, Bandung Command Center has three excellent facilities: LAPOR! (Layanan Aspirasi dan Pengaduan Online Rakyat/Community Online Complaint and Aspiration Service), NTPD (Nomor Tunggal Panggilan Darurat/Single Number Emergency Call) 112, and Bandung Panic Button, an android-based application. For the decision support system, the Bandung Command Center provides various kinds of information collected from various applications owned by Bandung City. For example, to support decision-making on city revenue, information is available in the form of a dashboard for the Regional Revenue Management Agency for Bandung City. For decision-making on infrastructure, the Manpro (Project Management) application developed by Bandung City Public Works Office provides the required information. For decision-making on public services, data sources include LAPOR!, Infographics, CCTV footage, and analysis of public complaints through social

Fig. 12 Bandung Command Center. (Source: https://commandcenter.bandung.go.id/)

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media. As a decision support system, the Bandung Command Center provides reports on city problems as a basis for leadership instructions within the Bandung City Government. Reports are weekly, based on data on violations, irregularities, and public complaints compiled from CCTV, LAPOR!, social media, and OPD (Organisasi Perangkat Daerah/Regional Apparatus Organization) application data (Bandung Command Center 2020e).

LAPOR! (Layanan Aspirasi dan Pengaduan Online Rakyat/Community Online Complaint and Aspiration Service) LAPOR! is a social media application built and managed by the Office of Presidential Staff to engage public participation and increase two-way interaction between the community and the Bandung City Government in the supervision of development programs. Public participation and interaction are gained through acceptance and follow-up aspirations and complaints, all of which are well documented in the LAPOR! application with state-of-the-art technology features and are easily accessible to the public. LAPOR! not only serves aspirations and complaints about development programs, but it can also be used in the supervision of public services in cooperation with the Ombudsman of Indonesia. LAPOR! can be used internally by government agencies including local governments as an integrated system of aspiration management and complaints. LAPOR! features can be used as disposition reports received by the relevant regional apparatus organization to be further supervised electronically (Bandung Command Center 2020b).

Single Number Emergency Call 112 Single Number Emergency Call 112 can guide the community in state of emergency conditions. It is designated as a single number for emergency calls to make it easier for people to remember and contact emergency services. Through 112 the emergency services receive and follow up community reports on fire, crime, accidents, ambulance needs, and emergency health care (Bandung Command Center 2020d).

Bandung Panic Button Another public service provided by the Bandung Command Center to the community is the Bandung Panic Button, an android-based application. This application is a centralized and integrated security solution for the community in a state of emergency. The Bandung Panic Button application is the official application used by the Bandung City Government and operated by the Bandung Command Center. The Panic Button application makes it easier for residents to send emergency messages to the Bandung Command Center, which then immediately responds to the emergency (Bandung Command Center 2020c).

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Features of Bandung Panic Button: 1. SOS feature Through this feature, users can send emergency messages to the Bandung Command Center faster. The center can then find the user’s current location, profile data, and family data. This family data can help the center inform the family about the condition that is being experienced by the sender of the emergency message. 2. Specific emergency This feature helps users to report incidents such as fire, accident, crime, and health that occur nearby. All these reports immediately receive responses from the center. 3. News Users can get the latest information on events in Bandung City. 4. Public service Users can access information on public facilities around them such as the location of a police station, hospital, or house of worship. 5. Emergency number and report This feature makes it easy for users to contact the call center for an emergency condition and connects to LAPOR! application. 6. Transport information Through this feature, users can get information on the route of active DAMRI (the state-owned company in the transport sector) and angkot (city local transport) in Bandung City. Geographic Information System (GIS)-based visualization is widely used for analyzing and decision-making for spatial data. Urban planning, traffic data monitoring, environmental decision, and modern modes of transport use GIS-based visualization. Visualization in a smart city context provides an interactive and easy way for users to use environmental tools (Hashem et al. 2016; Pan et al. 2016). Such an environment can integrate 3D touch screens with smart city applications which enable policymakers to translate data into knowledge or information for fast decision-making (Hashem et al. 2016). “The information extracted from different platforms and environments will be used to represent information based on the requirement of the user. GIS-based visualization will create efficient and flexible devices for the smart city toward realizing the vision of a smart environment” (▶ Chap. 5, “Smart Cities Data: Framework, Applications, and Challenges” by Bilal et al.).

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Command or operation centers are forms of centralization in smart cities that provide data integration for practical results. However, there are strong constraints on what is possible. Those centers can create bottlenecks in terms of performance as well as innovation capabilities. In most cases, centers focus on “quality of service” delivered to their citizens. The next step is to focus on “quality of experience” which requires more distributed and decentralized models, where public and private sectors can contribute and collaborate in building smart cities. Citizens also need to be included more throughout the process as the ultimate goal of smart cities is a better quality of life for citizens (▶ Chap. 30, “Towards Autonomous Knowledge Creation from Big Data in Smart Cities” by Nowaczyk et al.).

Bandung Planning Gallery Bandung Planning Gallery was officially opened by the Mayor of Bandung at Bandung City Hall in August 2017. The gallery, on the south side of Bandung City Hall, is the first urban planning gallery in Indonesia. It presents a kaleidoscope of urban development in the past and present and urban planning in the future. Various development models are displayed, ranging from a model of the city center of Bandung to a three-dimensional map of the Bandung Basin (Fig. 13). The gallery has three main functions: it distributes city planning information to the public, it provides transparency of community participation in development, and

Fig. 13 Bandung Planning Gallery. (Source: https://twitter.com/ridwankamil/status/ 892312471905513472/photo/2)

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it supports the media to become a communication bridge between the government and the public. Visitors are expected to be able to understand the past that shapes Bandung’s identity now, understand the challenges and problems in the present, and be part of the solution with the government toward a Dignified Bandung 2025. Through the Bandung Planning Gallery, the visitors are not only able to see the future Bandung but also a big picture of an ideal city. The gallery is also a point of connection between past wisdom and future hopes. The gallery features history and urban planning in various creative products. Visitors can view the history of Bandung City in the form of interactive animated videos and/or video mapping, from how the Bandung Basin was formed from ancient lakes to current geographical conditions and the development of Bandung City into a smart city. The gallery also has augmented reality technology to enable visitors to see the planning of the Bandung transport system and the history of “Bandung Baheula” or Old Bandung and feel the sensation of being in a smart city. The gallery contains the past, present, and future of Bandung particularly in urban mobility, a smart city, and virtual reality of Bandung Technopolis. There is also a consultation corner on spatial planning and development design. The “Post It Room” has special dome structures to facilitate citizen participation in the form of Post-It notes. Visitors can write their dreams for the future of Bandung City and the role that they play to support Bandung City. Visitors can also share their experience through social media platforms (Bandung Command Center 2017). The Bandung Planning Gallery is also an innovative way to present an open government where the government opens the widest possible access to the public to access development information. Urban planning in Bandung City has changed. The community can actively participate in building the city. Development in Bandung City is not only using a top-down approach but also a bottom-up approach and very transparent in the form of educational and technological space (Bandung Planning Gallery 2020).

Discussion: Challenges and Opportunities The development of smart cities can reform society and the quality of life through features such as digital connectivity, a digital transport system, smart health management, and increased inefficiency and accessibility in cities. Likewise, the interest in smart cities has been improved up to a certain threshold with the use of information and communication technology. Long-term objectives of smart cities aim to enhance the quality of services provided to citizens, and that will ultimately improve the quality of life (Khatoun and Zeadally 2017). However, it also opens security and privacy challenges for people living in smart cities (Menouar et al. 2017; Braun et al. 2018; ▶ Chap. 5, “Smart Cities Data: Framework, Applications, and Challenges” by Bilal et al.). As well as challenges in information and communication technology infrastructure, Bandung Smart City (2020b) has identified several future challenges and opportunities based on the six clusters of Bandung Smart City in Table 1.

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Table 1 Six clusters of challenges and opportunities for Bandung Smart City No. 1

Cluster Smart governance

Challenges 1. Promoting open government 2. Improving public services 3. Improving civil servant performance

2

Smart economy

3

Smart living

1. Creating 1 million entrepreneurs 2. Promoting e-commerce 3. Promoting small and mediumsized enterprises’ (SMEs) product to the international market 4. Promoting innovation to improve city competitiveness 1. Promoting intelligent, efficient, and integrated transport systems 2. Promoting a healthy environment and good quality of life

4

Smart society Smart environment

5

1. Promoting participation and engagement 1. Promoting sustainability, energy efficiency, and building climate resilience 2. Improving the safety and cleanliness of streets and open spaces 3. Better predicting and responding to disaster 4. Promoting a safe city

Opportunities 1. Providing an integrated dashboard to gather and analyze information from social media 2. Providing integrated public services 3. Providing an integrated call center 4. Enhancing government business process through business process automation 1. Seeking investors for SMEs 2. Providing soft loan for SMEs 3. Cooperating with other countries to promote SMEs’ product overseas 4. Making an incubation center 1. Providing better pedestrian, cycle, and vehicular flows and reducing congestion 2. Encouraging more people to walk or use their bikes 3. Seeking investors to build an integrated transport system 4. Seeking collaborative technologies to help bring communities together 5. Helping older people live independently longer 6. Seeking a solution to improve citizen well-being 7. Enhancing Bandung Smart Card function as identity card and payment 1. Using technology to engage better with Bandung society 1. Seeking low-cost, innovative solutions that can monitor and predict blocked gullies in high-risk disaster areas (e.g., IoT technology) 2. Seeking low-cost, innovative, technological solutions in the form of products, services, or applications to tackle illegal dumping 3. Seeking efficient public infrastructure 4. Building Bandung Technopolis 5. Building integrated CCTVs with Intelligent Operation Center (continued)

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Table 1 (continued) No. 6

Cluster Smart branding

Challenges 1. Stunning Bandung 2. Bandung Technopolis 3. Business lounge and investment gate

Opportunities 1. Encouraging creative industries 2. Developing as a tourism destination.

Conclusion This chapter provided an overview of the concept of smart cities. First, it showed how the new ideology to address urbanization challenges and energy resource choices with technological solutions led to the formulation of the smart city concept. Then, it presented the objectives of smart cities and best practice smart cities widely referred to in the literature. Smart cities vary around the world. Some cities are new cities; however the great majority of development is in existing cities. This chapter also discussed Indonesia’s Gerakan Menuju 100 Smart City program (the Movement Towards 100 Smart Cities) and the development of a smart city in the existing Bandung City. Several programs, hundreds of applications, and national smart city performance measurement and rating agencies have been developed to support Indonesia’s smart cities. Bandung Smart City has several features that provide data integration for practical results: Bandung Command Center, LAPOR!, Single Number Emergency Call 112, Bandung Panic Button, and Bandung Planning Gallery. Regardless of its achievements and various applications, Bandung Smart City still faces many challenges and opportunities. Information and communication technology infrastructure must support citizens’ participation as a sustainable smart city is based on humans. More public and private sector participation and engagement is needed as well as an integrated system to provide smarter services to achieve the long-term objectives of smart cities to enhance the quality of services to citizens and ultimately improve quality of life.

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Part III Human Dimension

Social Inclusion in Smart Cities

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Víctor Manuel Padro´n Na´poles, Diego Gachet Pa´ez, Jose´ Luis Esteban Penelas, Olalla García Pe´rez, Fernando Martín de Pablos, and Rafael Mun˜oz Gil

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Social Inclusion and Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ICT Standards as Tools for Social Inclusion in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Mobility and Social Inclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interconnected Public Spaces and Social Inclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Related Projects About Inclusion in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inclusive Accessibility in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5G Connectivity and Social Inclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Apps and Inclusive Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Cities and Women . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Unmanned Kiosks: The Best Way to Join Citizens with Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interconnected Public Spaces (IP-Spaces) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Elders’ Demographic Facts and Their Connection to Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . Elders’ Oriented IP-Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Elder Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Elder Activities in the Context of IP-Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Technological Interfaces for IP-Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Related Legislation with the Use of Technology in Interconnected Public Spaces . . . . . . . .

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V. M. Padrón Nápoles · O. García Pérez Universidad Europea de Madrid (Ingeniería Industrial y Aeroespacial), Madrid, Spain e-mail: [email protected] D. Gachet Páez (*) · F. Martín de Pablos · R. Muñoz Gil Universidad Europea de Madrid (Ciencias y Tecnología de la Información y las Comunicaciones), Madrid, Spain e-mail: [email protected]; [email protected]; [email protected] J. L. Esteban Penelas Universidad Europea de Madrid (Diseño, Arquitectura y Construcciones Civiles), Madrid, Spain e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_42

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MUSA: An Inclusive Smart Bus Stop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MUSA Smart Bus Stop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MUSA Smart Stop and IP-Spaces Current Developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

As the Smart City concept evolves, it necessarily incorporates more sustainability and inclusiveness features. New demands of citizens (such as participation in the decision-making processes and activities, and the need for services designed for minorities and excluded social groups) produce a paradigm shift in the sense of “Human Cities.” Smart Cities and digital inclusion efforts are moving rapidly. Multiple initiatives are taking place all around the world using different technologies to address accessibility, safety (especially for women), and social inclusion of vulnerable groups. However, these efforts remain widely dispersed. Without better collaboration between local governments, citizens, and other authorities, there is a notorious risk of leaving behind people with disabilities and the elderly. In the Smart City’s context, the mobility of people plays a crucial role in mitigating the social exclusion of vulnerable groups. It ensures their access to basic services and their social and employment relationships. One of the most vulnerable groups of citizens is the elderly. They demand special requirements in the design of smart mobility. At the same time, Smart City’s technologies can be used to maintain the elderly’s quality of life. This leads to the concept of Interconnected Public Spaces. A mixture of physical and virtual environments, generating interconnections at a planetary scale, that can be used to attract elderly people for collectively sharing experiences outdoors in public spaces (parks, squares or bus stops), increasing their physical form and stimulating them mentally, socially, and emotionally. Currently, MUSA project is implementing the abovementioned concepts.

Introduction Throughout the last century, concerns about sustainable development and the scalability of cities have become increasingly relevant issues. Since the end of the eighteenth century, these topics were already present among the concerns raised by the main players of urban architecture. Such is the case of Ebenezer Howard, who in his manifesto Garden Cities of Tomorrow (Howard 1898) treated urban planning as the way of transforming slums into neighborhoods that provided new opportunities. One of the first architecture movements to influence the beginning of modernization of the main European cities came from French architect Eugène Hénard. In his speech at the Royal Institute of British Architects, Hénard said: “My purpose is to investigate the influence that the progress of modern science and industry can have

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on the planning of Cities the Future. The Cities of Tomorrow will be more susceptible to transformation and adornment than the Cities of Yesterday” (Hénard 1910). The characterizing ideas of the “cities of the future” have been transforming over the years, adapting to architectural trends, technological advances, historical events, as well as to each city’s specific development. The “Green Cities” idea emerged in Europe in the interwar period, in an attempt by architects to replace the purely industrial conception of the cities with more green areas. However, the idea of “the return to Nature” – to the original Paradise – maintained in the Landscaped City, has always been present in the theories and thoughts of contemporary urbanism (Rykwert 1975). Two notorious examples of this movement would be Le Corbusier, with his work “The City of Tomorrow and its planning” (Le Corbusier 1929), and Eliel Saarinen, who authored “The City: Its Growth, Its Decay, Its Future” (Saarinen 1943). The influence they had on the cities of Europe and North America still lasts. The transformations of the cities have gradually been shifting its focus onto the idea of the comfort for the inhabitant, as denoted by the French urban planner Raymond Lopez. His work “L’Avenir des Villes” (Lopez 1964) viewed urbanization as an “indispensable instrument for life and the vitality of men.” In Spain, Miguel Fisac proposed a future of “convivial cities” to replace the existing model, as seen in his publication “La Molécula Urbana” (Fisac 1969). His book raised concerns about the development of cities and the need to apply technologies that would not quickly become obsolete, aware of their rapid advancement. On the other hand, the proposal of Arturo Soria’s “Linear City” in 1885 represents one of the fundamental references that relate infrastructure to nature, as a part of the new Garden City concept. It is framed within the idea of the urban and social context that organizes the urbanism of the future (Benévolo 1980). The lexicon that describes the characteristics of a Smart City has been changing, largely due to the different stakeholders that have taken part in the concept of Smart City, adapting it to their priorities and interests. The term “Sustainable Cities” gained momentum in the 1950s, being widely used in English-speaking countries, as well as those facing climate problems and looking for solutions to mitigate them. In the 1990s, the term “Digital Cities” took center stage, as a consequence of the exponential growth that technology had been experiencing since the late 1980s. The European Commission created the program called “European Digital Cities” (1996– 1999), with the intention to rely on the digitization of cities to help their complex growth and sustainability, making citizens increasing their participation for decisionmaking. Toward the end of first decade of the 2000s, the term “Smart City” emerged strongly, with connotations of sustainability and social inclusion, but without forgetting its bases supported by new technologies emerging from the Internet age. This last term has a branch called “Inclusive Smart Cities.” Cities and urban areas are often guilty of their own barriers to accessibility: old and historical cities contend with strict heritage laws, while others simply feel they cannot begin to change an entire area of established buildings and spaces. Today, people with different disabilities still face common obstacles, most of which are easily solved. Examples of such barriers include the lack of wheelchair ramps, lifts,

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accessible toilets, and shops without step-free access, for those with disabilities related to physical mobility. For citizens with audio-visual disabilities, a lack of facilities in areas such as train stations or bus stops generates obstacles. People on the autistic spectrum can suffer from the clutter and noise from some city areas. Therefore, the contemporary city contains physical, technological, and smart infrastructures, which generate different barriers for different citizens. Fortunately, some cities are starting to take the lead on accessibility and inclusivity. As technology reshapes the lives of all of us, many are starting to envision how it could also reshape the lives of those with disabilities.

Social Inclusion and Smart Cities In the context of Socially Sustainable Cities (Su et al. 2011), we have the opportunity to make all the city’s services accessible for everyone. Every day, the cities in the world have to deal with problems and solutions to provide an acceptable quality of life for their citizens. Taking into account that by 2050 more than 70% of the world population will live in cities (United Nations 2008), the quality of life of citizens will be affected by challenges such as demographic change, sustainability, mobility, energy consumption, and more aspects that need to be considered by the local governments and authorities. Most of those challenges can be mitigated with the use of new technologies such as the Internet of Things (IoT), Big Data Analytics, or High Speed Networks, but the social cohesion and the global participation in city life for minorities (such as immigrants, refugees, and the elderly) do not seem to be improving. In fact, the relations between individuals and groups are probably more precarious in a solely technology-based Smart City concept. New demands from citizens, such as participation in decision-making processes and activities, transparency of information and the need for specifically designed services, taking into account true expectations for minorities and excluded social groups, form a new concept in the sense of Human Cities (Oliveira and Campolargo 2015). The definition of social exclusion is not clear and is treated in different ways according to the social sciences and the political vision. In our case, we refer to this concept in terms of the relationships between members of certain groups, such as the elderly or minorities, and its special relationship with two basic services provided by Smart Cities, mobility and accessibility. It is necessary not to mix the concepts of exclusion and poverty. Social exclusion is not necessarily related to poverty. The term “social exclusion” should be extended to the lack of opportunities to access the services provided by Smart Cities, such as mobility services, health services, or even, the use of social networks or technological tools that improve the lives of citizens. Although the concept of poverty implies the distribution of material goods, exclusion refers to the lack of access to services or decision-making mechanisms. In fact, exclusion is a concept that describes the relationships between individuals, groups, and authorities.

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In the context of Smart Cities, social exclusion is strongly related to mobility. This relation can be described as: “The process by which people are prevented from participating in the economic, political and social life of the community because of less accessibility to opportunities, services and social networks, due in part to insufficient mobility both in society and an environment built on the assumption that all citizens have the same conditions of mobility” (Kenyon et al. 2003). This situation is especially true in cities, where public transport services are poor and not well adapted to the needs of special groups such as the elderly and impaired. Some efforts are underway to mitigate these situations by offering smart transport services adapted to a different user groups and promoting social cohesion and relationships (Padrón-Nápoles et al. 2020).

ICT Standards as Tools for Social Inclusion in Smart Cities Smart cities and digital inclusion efforts are moving fast around the world, but these efforts remain constantly dispersed and unintegrated. Without better collaboration between local governments, citizens, and other authorities, there is a notorious risk that Smart Cities will leave behind people with disabilities and the elderly. In developed countries, Smart Cities programs are making large investments in technology-based services, but accessibility and inclusion of people with disabilities and minorities, to those services are poor. In this context, accessibility standards for applications provided by information and communication technologies (ICT) are important factors in the design of more inclusive Smart Cities, especially when there is an increasing availability of information, due to the use of the of the Open Data paradigm by local governments. Local authorities must enforce ICT accessibility problems, ensuring that Smart Cities digital programs and services are accessible to people with disabilities and the elderly. According to estimates of the United Nations, 15% of the world population, or around 1 billion people, live with one or more disability’s conditions. Furthermore, more than 46% of seniors with age 60 and over have disabilities and more than 250 million of the elderly experience moderate to severe disabilities (G3ict 2017). In June 2016, G3ict (The Global Initiative for Inclusive Information and Communication Technologies) launched an international initiative to define the current state of ICT accessibility in Smart Cities throughout the world and the level of digital inclusion of people with disabilities and the elderly. The initiative included a survey with more than 250 international experts from municipal governments, industry, civil society, and academia, as well as a series of round tables in global Smart Cities (Quito, Barcelona, London, San Francisco, and New York) and interviews with administrators and technicians of those cities. The initiative confirmed that most cities are not fully accessible, and as a result, there is a growing digital divide affecting disabled people and minorities. This has a negative impact in a variety of areas, including independent living, transportation, e-government, employment, security and justice, voting and elections, emergency response, and financial services.

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International standards are the backbone of our society, guaranteeing the safety and quality of products and services. In the case of ICTs, they ensure that all types of ICT products and services are accessible to people with disabilities. Accessibility, in general terms, is defined by the standard ISO TC 159 as “to what extent products, systems, services, environments and facilities can be used by people from a population with the most varied characteristics and abilities to achieve a particular goal in a context of specific use.” In the context of ICT, accessibility is generally accepted as the quality of a technology product or service that allows it to be used by a wide variety of users, regardless of their abilities or disabilities. Accessibility makes it easy for anyone to see, hear, and use a device, to personalize their digital environment according to their own preferences, needs, and capabilities. For many people, accessibility is what makes it possible to access the digital programs and services offered by Smart Cities. Beyond accessibility, there are currently several standards development efforts made in the context of the Smart Cities. Some international organizations as ISO/ IEC JTC1, IEC, IEEE, ITU, etc., are working in this area. These standards for Smart Cities have not yet converged and are creating some uncertainty and confusion between the stakeholders. The National Institute of Standards and Technology (NIST) and its associates have created an international public group to define a consensus framework of common technological and architectural features, which enable Smart Cities meet the needs of modern communities. In addition, the American National Standards Institute (ANSI) has compiled and regularly updates a list of standards and development activities of Smart Cities. In general, technological issues for Smart Cities are evolving rapidly, but there is a lack of standardization. This is not the case for ICT accessibility related rules that, in fact, they have been published and are easy to use. The European standard ETSI EN 301,549 defines a set of functional accessibility requirements for a wide range of ICT products and services. The standard was finalized in 2014 after a development period of more than 10 years. It specifies user accessibility needs for ICT products for people with impairments (e.g., poor vision, handling problems, or limited strength) and allows them to locate, identify, and access the information provided. The ICT standards solving accessibility needs are essential to make Smart Cities programs and solutions available to all citizens.

Smart Mobility and Social Inclusion Transportation and others services play a major role in the effort for making a sustainable and inclusive Smart City. Public transport plays a crucial role for mitigating the social exclusion of vulnerable and disadvantaged groups, affecting their access to basic services and their social and employment relationships. Currently, there are identified specific policies, research priorities and recommendations for local transport, long distance transport and tourism. They address problems such as the need to combat low awareness of disabled passengers’ rights; lack of information on accessibility of local transport; information presented not in accessible

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formats or not concise and reliable; low use of mobile apps and social media in the sector; low accessibility in suburban and rural areas; and major access barriers in interchanges and intermodal hubs (Verebes 2013). In April 2018, European Commission published the document “Transport in the European Union. Current Trends and Issues,” which highlights the importance of social aspects in the development of an advanced European transport system: “From a social perspective, affordability, reliability and accessibility of transport are key. However, this has not been achieved across the board. Addressing these challenges will help pursue sustainable growth in the EU” (European Commission 2018). The scientific literature in relation to the technological systems used in the transportation systems and their main elements is wide (Bekiaris et al. 2018). In addition, there is an important volume of scientific literature describing the use of IoT (Internet of Things) (Mohanty et al. 2016), and others technologies for improving the quality of life of citizens in Smart Cities through measures that leads to a healthy, green, and sustainable environment. In this context, the EU defines social inclusion as a tendency to enable people at risk of poverty or social exclusion to have the opportunity to participate fully in social life, and thus enjoy an adequate standard of living considered normal in the society in which they live (European Commission 2012). Regarding mobility, social inclusion concerns especially with people or groups of people who are in risk of poverty, segregation, or marginalization. Special attention deserves the situation of women, as some studies reveal that different travel patterns from men and that public transportation play a crucial role in empowerment, access to opportunities, and independence. Some studies highlight the importance of mobility in modern cities as a key factor in people’s lives, noting that insufficient mobility can cause less accessibility to opportunities, services, or social networks. Therefore, social inclusion and the digitalization of transport have to be harmonized in terms of accessibility, affordability, reliability, and inclusiveness. As mentioned in the study about “Social inclusion and EU public transport” (European Parliament 2015) for the European Parliament’s Committee on Transport and Tourism.

Interconnected Public Spaces and Social Inclusion The Interconnected Public Spaces or IP-Spaces is a novel concept. It is a mixture of physical and virtual environments that can be used to attract the elderly and minorities for collectively sharing experiences outdoors in public spaces (parks, squares, or bus stops), generating interconnections at a planetary scale, in order to increase their physical form and stimulate them mentally, socially, and emotionally. Promoting participation in cultural and sports activities contributes to emotional, physical health, and social cohesion (Bacon et al. 2010). The positive impact of participation in cultural activities – no matter what the level of “artistic competence” of the people involved – on the perception of one’s own psycho-physic well-being has been acknowledged for around 40 years and confirmed by a scientific

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measurement scale, the psychological general well-being index. The connection between culture and subjective well-being, especially for elderly, may often seem obvious, although scientific evidence is much harder to get (Diener 2009).

Related Projects About Inclusion in Smart Cities In the consolidated and historical urban areas we find numerous barriers to accessibility, which are sometimes difficult to solve due to heritage conservation laws or because it does not seem feasible to modify a large urban area, its already established buildings and free spaces. Many of these problems often have easy solutions. • Barriers to physical mobility. Wheelchair users and people with reduced mobility would see these barriers resolved with ramps, elevators, accessible toilets, or the elimination of access steps to public establishments. • Barriers to audio and visual capacities. The absence of operational visual and audio signals in public areas, such as transport hubs. • Barriers to those with learning disabilities. The hustle and bustle of urban areas can be an obstacle for those on the autism spectrum. Fortunately, some cities are leading the development of accessible and inclusive proposals. They have seen in the use of technologies the opportunity to improve the lives of people with some type of disability. There is a greater awareness of accessibility problems, either due to an increase in the number of people with disabilities or due to an aging population as shown in Table 1. Table 1 shows some examples of cities that have implemented innovative solutions based on the inclusion of their entire population.

Inclusive Accessibility in Smart Cities In 2017, Microsoft together with World Enabled and G3ict launched the “Smart Cities for All” initiative. It is a practical guide to develop digitally inclusive Smart City programs for the elderly and disabled through the use of ICT. This initiative includes a set of four tools aimed at ICT accessibility. The first tool ensures that installed technology promotes inclusive accessibility. The second tool defines three priorities for ICT accessibility standards and the actions for their implementation and use. The third tool focuses on communication and helps spread the need and commitment to digital inclusion. The final tool is a database of Smart City solutions that, when implemented, could lead to a substantial improvement in the quality of life of vulnerable citizens (Ruby 2017).

Melbourne Making Life Easy For The Disabled Melbourne, one of the best cities to live in Australia, set out to improve by making the city more accessible to those with some form of disability. The city prepared an

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Table 1 Selected projects related to social inclusion and their main features Project Melbourne Making Life Easy For The Disabled Smart Cities Addressing Homelessness And Isolation Opensidewalks Alma Houses One Atlanta Yingtan – 5G-enabled Digital Twin City Blindsquare Safe & The City (SatC) app City4Age Safetipin Gender and smart cities Kiosks in case of emergency Kiosks deployed as smart street furniture

Main features Accessibility for disable people Predictive analytics to prevent risks for homeless Sidewalks as first choice for transportation networks Assistive technologies for people with dementia 5G. Improving connecting technologies for citizens 5G. IoT elements and Artificial Intelligence for einclusion GPS technologies for visual impaired people App for decreasing sexual harassment Big Data for stimulate physical and mental activity for elderly Inclusive infrastructure for increasing women’s safety Information and Communication technologies for decreasing gender violence Information for guiding citizens in case of emergency Improve the user experience with urban furniture

action plan and one of its initiatives was to launch a technological innovation contest, promoting the integration of data-driven and technology-based approaches. In the 2018 edition, the goal of “Making cities more accessible to people with disabilities” was established. Among the finalists, we find Melba, a collaboration between Melbourne Open Data with smart assistants such as Siri, Google Assistant, and Amazon’s Alexa to provide real-time information through text, voice, and screen readers. Another finalist was ClearPath, this is a step-by-step navigation system designed to facilitate the mobility of blind and visually impaired people, to navigate in unknown places, including permit events, construction sites, touch surfaces, and locations with heavy traffic of pedestrians (Copp 2018).

Smart Cities Addressing Homelessness And Isolation The population of the cities grows more and more every day. New strategies are emerging to prevent homelessness and that isolated or mentally unstable people become part of the vulnerable groups. New York is an example of this, implementing a pilot data analysis program and proving that vulnerable people can receive assistance well before the personal catastrophe. The project reveals that the traditional approaches (political and economic) can be improved using new technologies and data analysis. This allows more effective use of the resources for homeless people and using predictive analysis to help those at risk of homelessness. This standpoint can moderate pressure on finances and resources related to health and society, and prevent misfortune. Isolation or loneliness is one of the main factors that make people vulnerable. Age UK talks about how harmful loneliness can affect

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health, comparing it to smoking 15 cigarettes a day, and remembers that more than 2 million people in England over the age of 75 live alone. Fortunately, Smart City technology and data are in the best position to face up to loneliness. Especially, since the elderly population will increase in the coming years, this is a problem needing immediate attention. The City of London is making an effort to deal with this issue through Civic Innovation Challenge. The initiative challenged 14 tech start-ups to take forth innovative answers out to some of the high-priority environmental and social issues in London, such as isolation, housing, and dementia (Smart City Press 2018a).

Opensidewalks Out of all the possible means of transportation within the city, this project focuses on making sidewalks our first choice within the transportation network. The OpenStreetMap (OSM) project has made extensive open user-contributed data on transport networks available (Open Sidewalks 2020). OSM emphasizes the sidewalks, as the first option of an Open Data transportation network and thus generates a pedestrian network with global coverage. The OpenStreetMap project provides the basis for many use cases and subsequent activities, including extensive analysis, optimization of travel routes, city planning, and help in case of disasters. It is worth highlighting the objectives in the different phases of the project dedicated to inclusiveness: • Provide tools for integrating public data into the OSM standard automatically. • Provide tools with intuitive and inclusive interfaces for the individual contribution of sidewalk data to the OSM standard. • Extension of the current OSM standard, seeking innovation for pedestrian routing applications with a focus on accessibility. • Routing. Facilitates pedestrian routing with support for different accessibility features. • Basic visual map display. Supports meaningful visual interpretations of pedestrian data by capturing key features in a standardized format (ideally with existing rendering software). • Annotation of significant accessibility features. • Ground. Supports pronounced grade annotations that meet OSM standards and accessibility needs. • Crossing views. Supports detailed annotations of intersections and crosswalks. • 3D objects, landmarks, points of interest. It supports detailed annotations of the characteristics and objects of the pedestrian path. • Tools to facilitate the annotation of users of pedestrian routes.

Alma Houses Smart Cities services can help improve the mental health of its citizens. Alma’s house is a 50 m2 flat that shows dementia-friendly solutions and is part of the Oslo municipality’s resource center on geriatrics, dementia and old-age psychiatry. The staff has skills and competence in cognitive functioning, dementia, and assistive technology (Akentun 2020).

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5G Connectivity and Social Inclusion One Atlanta Governments, private organizations, and individuals must realize that technological solutions are as decisive for the poor as they are for the wealthy classes. Atlanta, the capital of Georgia state in the USA, home to more than 2.5 million inhabitants, has focused on this problem, and facing its own gap. The city has gone through its initiatives One Atlanta to becoming an equitable Smart City. Since 2018, Mayor Keisha Lance Bottoms has made a special effort to fill the inequality gap, showing that Smart City programs must have inequality as a basis. Following her vision, she introduced the One Atlanta strategy to include all residents in a single “access to opportunity” framework. The key is to analyze how and where technology can help solve problems across the city. In doing this, the focus is also on ensuring connectivity and making data usage more sustainable and in real-time. Data plays a key role in connecting technology with everyone. Focusing on inclusion, data is collected and translated to provide information that can help citizens live better. Research from the Brookings Institution reveals that low-income families are generally the most affected by the digital divide in the United States. One of its initial priorities is to create a strategy for public-private partnerships. This includes the deployment of broadband and 5G connectivity throughout the city (Atlanta 2019). Yingtan: 5G-Enabled Digital Twin City The China Digital Transformation Award was given to the Chinese city of Yingtan for its project 5G-Enabled Digital Twin City, at the ninth Smart City Expo World Congress (SCEWC) in 2019. The project, supported by Huawei, was the first 5G alldomain digital twin city in the world. The core of the project is an Artificial Intelligence brain, along with highly accurate and widely deployed 5G-IoT networks. This allows creating a digital copy of the city (a digital twin) that serves as a foundation for its digital economy. One model called “One Centre, Four Platforms” allows the unification of city services, such as intelligent water facility, transportation, streetlights, and parking. It is not such important how smart is the project, what will really increase the differentiation of cities is how applied technology contributes to sustainability, innovation, and inclusiveness. In addressing the future prospects for universal access, one must also consider the ever-changing global situation. It is no longer primarily about how to design an interface for users with disabilities on a computer, but the “design for all” approach in an evolving knowledge and information society (Smart City Press 2019).

Apps and Inclusive Smart Cities Blindsquare Blindsquare is a GPS-based app for assisting blind and visually impaired people worldwide. It is one of the most widely used accessible applications. Through voice messages, it offers personalized and detailed information, allowing its users to move

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independently and safely both indoors and outdoors. By shaking the device, the user is informed about the current address. Then, the application searches information about the surroundings on OpenStreetMap and Foursquare, such as the nearest intersections, banks, libraries, cafes, and events. There are filters available to avoid overwhelming user with information. The app oversees the route and periodically reports the direction and distance traveled as the user moves. To find the way back to a given point, the user only needs to mark his position in the app. Blindsquare uses an Acapela text-to-speech engine to guide the user in more than 25 different languages. For the user convenience, the phone can be held by hand or even stowed and used in hands-free mode (Blindsquare 2020).

Safe & The City (SatC) App Safe & The City is an app founded by Jillian Kowalchuk in 2018. It was specially designed to decrease incidents of sexual harassment. Kowalchuk, a public health expert, has an episode, one night on a dark street of London, of verbal abuse. Although there were similar apps for women safety, Kowalchuk contributed to decreasing the number of victims of this type of crime with the use of police data, crowdsourced information, and the use of GPS. Although the app was designed particularly for women living in London, Kowalchuk is planning to expand it globally. She launched a partnership with United Nations Women U.K. for the UN “Safe Cities and Public Spaces” global program (Butcher 2019). City4Age (Elderly Friendly City Services for Active and Healthy Aging) City4Age focuses on the use of smart phones, sensors, Smart Cities’ infrastructure, and Open Data services to improve early detection of risks related to cognitive impairments and frailty in the elderly population. Based on the collected data, this Ambient Assisted Cities system promotes a healthy behavior, and stimulates physical, mental activities, and social interactions. The system collects data of the participants’ activity when moving around city (number of steps, distance covered, and walking average speed), weekly visits pattern and daily transport usage pattern (Abril-Jiménez et al. 2020). For professionals and politicians, City4Age supports a more citizen-centered and personalized approach that enables citizens and patients to take control of their well-being. The collected data will be used for creating new models for cognitive impairment and frailty detection and the designs of interventions to foster healthier behaviors (City4Age 2018).

Smart Cities and Women Although women represent around half of the world’s population, their absence in collective efforts to develop Smart Cities has become a topic of discussion in recent years. The technological advances of Smart Cities, unfortunately, do not stop gender inequality and discrimination against women. Therefore, a prominent theme of the World Summit on the Information Society (WSIS) Forum was Sustainable

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Development Goal 5, where gender equality issues related to Smart Cities were discussed. In this meeting, Caitlin Kraft-Buchman (CEO, Women@theTable) highlighted the dual nature of cities was analyzed, on the one hand, as “places of economic opportunity, liberation and reinvention,” and on the other hand, as places of “fear, danger and violence for women, from the dark streets from the city to public transport.” She argued that Smart Cities technology should focus on how people experience these problems, rather than solely on technological advancements (Baskaradas and Reilly 2019).

Safetipin Researchers at the University of New South Wales in Sydney are working with an India-based social startup, Safetipin, to use Open Data and help make public spaces safer. Safetipin uses large-scale data collection to engage people in women’s and children’s safety issues. Citizens and trained professionals use SafetiPin apps to rate public areas according to security criteria and point out characteristics such as visibility, lighting, density, transportation, and more. This information is represented on a map and constitutes an aid for the design of the city. Safetipin with the support of the United Nations achieved a global reach being applied in 30 cities around the world. SafetiPin information is used by local and municipal investment in new routes for better accessibility and increasing security improving lighting and identifying best locations for CCTV cameras. The introduction of an inclusive infrastructure could accelerate the development of Smart Cities, shorten the gender gap, and facilitate the integration of women and children (Hawken et al. 2020). Gender Smart Cities “Gendering Smart Cities” is an international research network focused on goals to engender today’s Smart City agendas through the everyday experiences of young women who navigate and live in the city. It will present digital stories of mobility and security of young women living in urban peripheries: border cities, resettlement colonies, and urban villages. Through photographs, videos, participatory maps, and social media diaries held by women over a lapse of time, this project tries to show how these women perceive and navigate the city by accessing ICT resources from low-cost mobile phones. Through the convergence of architecture, digital media, and artistic practice, this network will co-produce a novel visual language about their relationship, as women, with the city. It will reveal the ability of this language to go beyond existing gender data on violence against women to share knowledge and experiences and ensure that Smart Cities’ agendas include a gendered point of view (Gendering the Smart City 2020).

Unmanned Kiosks: The Best Way to Join Citizens with Cities Unmanned digital kiosks are key tools to contribute to Smart Cities’ services without forgetting inclusion.

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Smart City kiosks have countless possibilities when configured as communication junctions. They can be integrated with 24
7 IP surveillance cameras and smart sensors, provide free Wi-Fi and video calls, mobile charging points, etc. Kiosks can provide visitors and citizens with information about places, retail stores, restaurants, events, etc.

Kiosks in Case of Emergencies Daegu, the third-largest city in South Korea, has deployed unmanned kiosks as key components for disaster management. They transmit crucial information during disasters (earthquakes, floods, or fires) and provide local guidance to people. City authorities consider these kiosks as essential instruments to maintain communication with citizens even in the most complex circumstances while guiding and informing citizens and visitors on a daily basis (Smart City Press 2018b). Kiosks Deployed as Smart Street Furniture Smart kiosks can offer many free services that allow citizens to get more and better involved in the city. Philadelphia, one of the largest cities in the USA, created the LinkPHL infrastructure, 100 smart kiosks in central areas with high pedestrian traffic. These kiosks provide free services such as recharging points, Wi-Fi service, emergency services, city social services, and cultural information (news, weather, events, public initiatives, etc.). Besides, its interactive maps will help locate the closest available LinkPHL access points while guiding visitors with routes. Installation and maintenance costs are offset by revenue earned from electronic advertisements. LinkPHL shows how digital kiosks can connect communities, cities, people, and businesses (City of Philadelphia 2018). The range of smart kiosk applications shows how this technology can play a leading role in improving urban environments. Perhaps, it could result in a perfect solution to help improve services, optimize resource management, address environmental issues, and reduce costs. The possibilities are endless, and so are the benefits!

Interconnected Public Spaces (IP-Spaces) In this section, we present demographic facts related to the elderly and comment on the importance of investing resources in this segment of the population, which is, in general terms, vulnerable and economically powerful. Then, the concept of Interconnected Public Spaces (IP-Spaces) is reviewed and the extended IP-Spaces concept is introduced. The latter is very useful to reduce costs and facilitate the implementation of these spaces. Next, elder people’s activities, preferences and needs are analyzed, to continue to set a framework for the research on elders’ activities in the context of IP-Spaces. Finally, a study of IP-Spaces’ advanced interfaces suitable for the elderly and disabled people, as well as their corresponding legal issues, are analyzed.

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Elders’ Demographic Facts and Their Connection to Smart Cities Increasing population aging is a common scenario in developed countries, according to World Health Organization. Elderly people suffering from chronic diseases are the leading cause of death worldwide, as they cause more deaths than all other causes together. This affects low- and middle-income people the most (Gachet Páez et al. 2018). Furthermore, from the point of view of vulnerability, the elderly appear as one of the groups with the highest risk of poverty and social exclusion (Social Inclusion in EU Public Transport 2015), as shown in Fig. 1. Currently, there were 703 million persons aged 65 years or over in the world. The number of elder persons is estimated to double to 1.5 billion in 2050, growing from the current 9% to 16% of the population (Department of Economic and Social Affairs, United Nations 2019). Figure 2 shows the projections of population proportion aged 60 years or older, by country, in 2050 (WHO 2015). This growing sector of the population has special needs to maintain good physical and mental well-being. These needs include physical exercise, social interaction, and mental stimulation. Research on aging and cognition has shown the close relationship of sensory functioning and social communication to maintain cognitive performance and mood in the elderly, but in modern societies older people are increasingly isolated and less stimulated, both physically and psychosocially (Waterworth et al. 2009; Rabbitt 2005). This situation causes accelerated cognitive decline and the suffering associated with loneliness and confusion. Social interaction and intellectual stimulation may be relevant to preserve mental functioning in the elderly (Wang et al. 2002). Some studies report that subjects, who participated in older people’s clubs or senior centers, may have a lower risk of cognitive decline, especially if this

Fig. 1 Vulnerable and disadvantaged groups of citizens

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Fig. 2 Proportion of population aged 60 years or older, by country, 2050 projection (WHO 2015)

interaction is with young adults (Lee and Kim 2016). Other studies highlight the potential of video games for the development of physical skills, creating mental and social interactions for elderly people. Particularly, if these video games or computer games are designed with attractive content and provided with an easy and pleasurable interface (Ijsselsteijn et al. 2007). Countries’ investments in health systems, long-term care, and age-friendly environments can lead to social benefits, such as a healthier population, greater older people’s social inclusion and cohesion. It is key in the new era of Smart Cities to remove current structural and cultural barriers that impede social interactions and provide elder people with safety, independence, and comfort using technology and social innovation. There are many reasons for investing resources to improving the health and wellbeing of older populations. Aging in good health conditions is crucial for the sustainable development of societies with the current demographic transition. In the long-term, these investments make possible to minimize the expenditures associated with population aging, while maximizing the many contributions that older people make (WHO 2015). Examples of these contributions are: (a) direct participation in the formal or the informal workforce; (b) taxes and consumption; (c) transfers of cash and property to younger generations and many tangible benefits to their families and communities. Many older people are financially independent by the wealth accumulated during their lives. There is a very interesting fact about elder population. On one side, elderly people are rightly considered as vulnerable, but on the other hand, some detailed analyses show that in many countries cash flows run from older family members to younger members until people are well into their 80s (Diener 2009). In the United States, people older than 55 controlled 70% of all disposable income by 2017 (Nielsen 2012). While in France, people older than 65

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will be responsible for 49% of all increased consumption up to 2030 (Desvaux and Regout 2010). Elderly people can be essential to create an impact in society by acquiring a leadership role in terms of sharing their knowledge with the community, gained from years of experience, sharing their time (so volatile and scarce in modern societies) and sharing their financial advantages, reinvesting in future generations. Contributions made by these individuals can be enhanced using new emerging accessible technologies, which strengthen social communication and engagement; provide opportunities to improve inclusion; participating in stimulating activities; using age-friendly technological environments; and improve individual’s well-being (WHO 2015).

Elders’ Oriented IP-Spaces An “Interconnected Public Space” or IP-Space (Padrón-Nápoles et al. 2020) is an outdoor space provided with an ICT equipment that can only connect with a similar space in another part of the world. That means that an IP-Space is a node in a network of IP-Spaces. An IP-Space node can be established at a bus stop, in a park or any other outdoor spot in the city, in which a group of persons could potentially interact with it. Therefore, IP-Spaces allow the sharing of collective experiences. These nodes can be used to interconnect persons from other regions or countries, using the same or different languages. Nodes can be used to participate remotely in different sports, physical, cultural and playful activities (e.g., interesting games or video games, physical exercises, and dancing competition) engaging elderly people in remote communities and stimulating them physically, socially and intellectually. Sometimes economic and technological resources’ constraints can prevent the implementation of IP-Spaces in its original concept as an outdoor space. For those cases, the concept of “extended Interconnected Public Space” is introduced in this work, that is, a public outdoor or indoor IP-Space. Indoor public implementation reduces the requirements on electronic system capability to withstand harsh environmental conditions (reduced costs) while keeping the requirements of being outside home and sharing collective experiences. This extended concept or implementation allows using existing non-digitalized locations as dancing clubs, elder people associations’, etc., as extended IP-Spaces. Interconnected public spaces can help to create the conditions within which wellbeing seems more likely to increase.

Elder Activities Studies show that elders in cities had great enthusiasm for outdoor activities as they increased their perception of happiness (Dong et al. 2020; Ragheb and Griffith 1982). Nevertheless, two factors can be considered obstacles for going outdoors: elder people increasing frailty and environmental barriers. Neighborhood

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environment adaptation can facilitate older people’s outdoor activities and has a positive effect on their well-being (Sugiyama and Thompson 2006). Other studies show that even when the amount and variety of leisure activities are reduced, leisure satisfaction remains high, due to continued participation in a more limited but still valued number of activities (Griffin and McKenna 2009). A research study performed about outdoor and indoor leisure activities of 3950 elder persons, in Germany, Finland, Hungary, The Netherlands, and Italy, classified activities as home activities, hobbies, social activities, and sports activities. “Home activities” mainly comprised indoor activities, but the other three dimensions involved more physical mobility. The study concluded that sports activities and hobbies were performed more often by younger men, by those with good physical functioning and by those who drove cars. Social activities were performed more by women and those who used public transport. Home activities were more frequently performed by those with low physical function and women (Gagliardi et al. 2007). Another classification is provided in (Zhengying et al. 2020), which divides the elders’ activities into 3 groups (Table 2). The study, based on data collected among 363 respondents aged 60 or older in Dalian, China, suggests that regularly participating in short- or medium-duration leisure-time physical activity could be more attainable than performing higher amounts of utilitarian physical activities for older adults with physical limitations. The study highlights that environmental interventions should optimally match the physical activity patterns of different groups of elder adults. Increasing perceptions of crime-safety would be more useful to support the elderly with a college/university-level education to maintain their high-frequency leisure-time physical activity patterns while improving neighborhood social cohesion should be targeted to support older adults with physical limitations. A study about leisure activities of elder people in the city of Kunming, Yunna Province, China, showed that leisure purposes were significant in a person’s decision about how many activities to involve, activities’ duration, and companionship. Relaxation, visiting friends, entertainment, and hobbies were more appreciated activities. Leisure motivation triggers desired activity features and location, how and when to travel were secondary decisions. The qualities of locations are also highlighted: beauty, fresh air, cleanliness, and easy access. The results suggest that community parks and neighborhood green fields were very important for these activities due to their proximity. Understanding elders’ leisure activity features and travel behavior are crucial for local governments to make smart land use and

Table 2 Classification of elders’ physical activities Type of physical activity Leisure-time Utilitarian Sedentary

Description Leisure walking, walking the dog, Yoga, Tai Chi, jogging, physical exercises Bringing to and picking up children from school, shopping Sitting and chatting, sitting and watching TV/Internet, playing table games and video games

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transportation planning policies (Dong et al. 2020). Participation in a reduced variety of leisure activities was also linked to an absence of independent transport in other studies (Griffin and McKenna 2009).

Elder Activities in the Context of IP-Spaces Although the study of elders’ activities has a long research history, IP-Spaces set new conditions for activities. The remote connections, the interaction with bigger electronic devices and the multi-language interactions are some of them. To assess the effectiveness of activities, a non-exhaustive set of parameters is suggested in Table 3. Classification of IP-Spaces activities is also crucial, for both users and researchers. A portfolio of non-exhaustive categories and activities is shown in Table 4. Special interest deserves those activities that can be performed using remote immersion. That is when people from one or several IP-Spaces can be immersed in the environment of a local IP-Space using advanced technologies such as Virtual Reality or advanced telepresence technologies as Google Camera-46 (Broxton et al. 2020) or Microsoft VROOM (Jones et al. 2020). The application of these technologies can go far beyond IP-Spaces and allowing people from IP-Spaces communities to make virtual remote excursions to remarkable spots of cities, historical or natural places.

Technological Interfaces for IP-Spaces The variability of possible locations of IP-Spaces within Smart Cities and the diversity of the people who interact with them require the inclusion of user interfaces that allow adequate communication and interaction at all times and for each person, meeting the criteria of Design for All (Design for All 2020) for human diversity, social inclusion, and equality. Multimodality (Multimodal 2020) is a fundamental aspect in the design of the interfaces of the IP-Spaces so that their utility to the community is as wide as possible. Table 3 Non-exhaustive list of parameters to assess activities effectiveness

Parameters Physical well-being Mental well-being Social interaction Mental stimulation Cognitive performance Good mood Loneliness Confusion

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488 Table 4 Non-exhaustive list of activities related to IP-Spaces Field Education

Activities Languages Courses

Recipes Play an instrument Animal care Arts

Painting Photography Music Outdoor cinema Reading

Amusement – Games

News

Train your mind Trivial Dominos Chess Bingo Card games Video games Newspapers Just as today Weather Streaming TV Radio and Podcasts Notice board

Sports

Cultural recommendations Transport information Meditation Dances General mobility sports

Description Provides the ability to learn and practice a language individually or in groups Offers the possibility of learning different kinds of skills and knowledge, such as handcrafts, programing, culture, up to date learning pills. . . Shares different cooking techniques and the option to take group cooking classes Teaches users how to play an instrument or to perform an online concert with other users Includes recommendations for animal care and the option to connect with other animal lovers Painting lessons individually or in groups Photography lessons and the opportunity to perform a photographic exhibition with people around the world Option to create, share, or listen to music with other users Brings the opportunity to create an outdoor cinema by scheduling films periodically Allows book sharing, exchanging recommendations, and book presentations reaching people all over the globe Games that improve your memory and also that provide the opportunity to challenge other users The most famous games to play individually or in groups

Access to the different newspapers globally Offers information that happened the same day but during another year Access to the weather information and the possibility to share it with others Availability to access television information and possibility to comment it with others An opportunity to listen and create radio sessions and share it with the network A space where local businesses, people, or companies can connect with the community Recommends cultural or leisure activities that can be done in the area Provides mobility options for different types of users and needs Healthy activities that can be performed individually or in groups formed in different locations

(continued)

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Table 4 (continued) Field

Travel

Activities Yoga Gymnastics Zumba Stretching Personal trainer Personal diet Trips agency Virtual tourism (VR)

Aid

Purchasing Clubs

Volunteering

Elder abuse identification Decision-making feature Personal assistance Online purchase Club zone

Project investment

Description

Recommends new places to visit and tourism routes that can be booked and shared Allows the user to discover different places around the world, customs, and gastronomy by using virtual reality to enjoy a complete immersion into the culture Able to detect abuses and aid these citizens Helps users to review and make new decisions by using artificial intelligence solutions Voice Activated Personal Assistant (VAPA) that helps users in any necessary query or request Users can shop online directly on the system Creation of a space where different types of clubs can be created and members will be from different parts of the world Possibility to invest in new projects, social proposals, or start-ups to boost social activities

Two possible solutions not implemented in a generic way so far at a smart bus stop are proposed. The first solution is based on ultra-directional sound projection systems that allow the user to properly hear the sound coming from the IP-Spaces in noisy environments. The second application, based on gesture recognition, allows interaction with the IP-Spaces at distance, without touching any screen or panel. The recent development of new artificial vision techniques combining convolutional neural networks and cloud computing will allow the recognition of gestures to go one step further and be can be used by people without disabilities as by deaf persons using sign language.

Audiovisual Accessibility If the application that implements the IP-Spaces incorporates a screen where videos are played, in addition to the audio associated to the video, it is necessary to include, based on the specific needs of people with sensory disabilities, the following services, each one in one or several languages (Fig. 3): • Sign language interpretation • Subtitles • Audio description

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Fig. 3 Live streaming including subtitles and sign language (CEAPAT: https://youtu.be/ Y3mI27lRCKw?t¼2316)

The image shown above is one frame captured from live streaming that includes accessibility for persons with sensorial disabilities. A hearing impaired or deaf person may prefer to display subtitles only, while another person, also hearing impaired or deaf, could prefer to use subtitles and sign language interpretation at the same time. Meanwhile, a visually impaired person will need the use of the audio description for a correct follow-up of the videos, images, or presentations that are being shown. Audio description is a voice service that requires an independent audio channel from the main audio of the video. In live events, individual headphones with wireless sound reception systems are normally used by the bind, similar to those used for simultaneous translation in congresses. In this specific event’s speakers are talking in English and the majority of the public is Spanish, so, in that case, it was mandatory to include some kind of translation in real-time. Instead of using audio receptors, congress organization decided to use subtitles to include a translation. In this way, this accessibility service is used by people with or without disabilities, whenever we consider that not knowing a foreign language is not considered a disability. One of the main issues using videos with audio in noisy environments, where IPSpaces are normally located, such as transport stations or parks, is the loss of sound intelligibility. The following proposal can be a valid solution, both for people who follow the main audio channel of the video on the IP-Spaces screen, and for those who are using the audio description service on a separate channel. To solve this unwanted situation that does not allow the correct transmission of sound information, the use of a new kind of speakers, known as of ultra-directional speakers, is proposed. These speakers allow concentrating the sound in a specific and narrow

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area, improving significantly intelligibility and avoiding discomfort due to excessive volume to other people passing by nearby. Traditionally, sound propagation theory and the study of loudspeakers demonstrate that loudspeaker directivity is conditioned by the size of the speaker related to the size of the wavelength produced. A large loudspeaker will be more directive than a small loudspeaker, or a loudspeaker specified at higher frequency (smaller wavelength) will also have more directivity (Audio Spot Light 2020).

The Holosonic ® technique used by Audio Spotlight uses a different approach. Their “speakers” modulates the audio input to higher and inaudible frequencies (ultrasound waves around 60 kHz). Due to certain nonlinear characteristics of the air to the ultrasound propagation, the volume of air in front of the ultrasound generator (Audio Spotlight speaker) acts as a “virtual loudspeaker.” The result of this technique is a source of directional audio able to be pointed as the same way than a concentrated light (like a “Spot Light”) where the demodulation part is naturally created by thin air. The listener inside the Sweetspot hears a fully focused sound that cannot be localized moving the head (feeling as hearing a monophonic source). Another remarkable feature of this system is that the sound is concentrated in a relatively small space, which can go from half a meter to a meter and a half, depending on the distance from the speaker to the listener and the speaker power. If the listener leaves the area where the sound is concentrated, the sound disappears completely. This allows different sound spaces to be created in close proximity to each other without interferences between them. The comparison shown in the Fig. 4 shows, on the left, the directivity pattern of a 2D loudspeaker array, generally considered directive, whose graph shows a semicircular pattern, without clear directivity in any direction. The image on the right shows the directivity pattern corresponding to the Audio Spotlight AS24i model, where a directive and concentrated beam is shown in the propagation axis. The technology described above has been consolidated for more than 10 years and its use has been extended internationally, mainly in museums. In conjunction with a presence or motion detector, when a museum visitor stands in front of a painting or sculpture, a recording is played explaining, for example, its technique, details or history. The playback can only be heard by the person who is located in a specific physical space (Sweetspot) while the rest of the visitors can enjoy their visit in silence. Currently, due to the hygienic measures necessary to avoid virus contagion, this alternative is preferred because it is much safer than audio guides, which must be disinfected after individual use (Fig. 5). The pictures above have been taken from a real installation at a Bus Stop in Disneyland Hong Kong to promote the film Star Wars: Tomorrowland Takover. Floor indicators help people to know where the audio beam is been audible (Sweetspot). Each of these beams, associated with the holograms and the screens can be playing the demonstration loop in a different part of the video because the

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Fig. 4 Comparison of directivity: traditional 2D directional array (left) and Audio Spotlight speaker (right) (https://www.holosonics.com/)

Fig. 5 Bus Stops using directional sound (https://www.holosonics.com/applications-1)

audios are not mixing in the air. If other people are watching the screens out of the Sweetspots, they cannot hear it. The rectangular black speakers are installed on the ledge of the bus stop, pointing to the designated spaces on the floor. The technology and its installation in the IP-Spaces would be similar to that previously explained at the bus stops when the IP-Spaces play audio videos with audio but without an extra track for the audio description service. One or more points can be set in front of the screen to indicate where the sound is audible. If audio description accessibility service is been included, podotactile tiles should be installed to determine the location where the blind can stand to hear the audio

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Fig. 6 Podotactile floor (https://commons.wikimedia. org/wiki/File:CES_2012_-_ Microsoft_Kinect_Star_ Wars_Episode_1_Podracing_ (6764013293).jpg)

description track (Audio description – Sweetspot). In general, an audio description channel usually contains only the voice of a speaker with the description of what is happening on the screen because the main audio, from the main speakers, can be heard at the same time. In this case, if the main audio is also using Audio Spotlight speakers the audio description track must mix the main audio (at a lower volume), as it is not audible at the location of the audio description, and audio description voice (Fig. 6).

Gestural Interfaces for IP-Spaces User interfaces using modalities such as touch, gestures, or voice are referred to as Natural User Interfaces (NUI). These interfaces are consider easy to use because are adapted to the way humans communicate naturally. Gesture recognition is one of them. Gesture recognition applied to consumer electronics, automobiles, or medical systems are opening a new way to control applications without contact while are becoming an effective, safer, and faster way to control devices. Gesture capture is conditioned by the needs of the specific application to be controlled. The interface have to be design based on interaction range (close or long), the resolution of gestures (hands movement or full-bodied movements), duration of gestures (short or long), and conditions of the environment in which the interaction takes place (indoor or outdoor). In recent months, due to the global pandemic caused by COVID-19, the implementation of touchless systems has accelerated considerably. Many manufacturers have launched touchless interfaces in their electronic kiosks for access to work centers or to manage tickets at transport stations. For some developers, and on certain web pages, the concepts of hand tracking and gesture recognition are separated. In these cases, gesture recognition is a limited set of movements and positions of hands and arms while a hand tracking system has a greater number of interactions to capture finger movement and recreate it in a virtual environment. A gesture-based system is usually limited to a specific number of gestures, since people have a hard time remembering more than a few gestures, but for those limited number of hand poses, the gesture system will usually recognize them fairly robustly (Hand Tracking 2020).

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Some parallelism can be established with another type of interface: voice commands. In general, people do not usually remember a high number of voice commands and the same happens with gestures. Moreover, a detection system for a limited number of voice commands or gestures is going to be a robust recognition system. On the other hand, continuing with the parallelism, the processing of natural language equivalent to hands tracking requires a high computational load based, mainly, on artificial intelligence techniques. Voice recognition results are usually not as accurate as command detection. It is quite common to have experienced (with voice assistant or mobile device) a very precise recognition in quiet environments and quite the opposite in noisy environments. As previously mentioned, the IP-Spaces (Interconnected Public Spaces) are located in outdoor areas that are normally exposed to high levels of ambient noise that cause loss of quality in sound capture or voice recognition. This limitation can be avoided by use of other data entry interfaces, such us gesture recognition. The variability of light during the day, the incidence of direct sun on the IP-Spaces, or the lack of light at night are the undesired elements in image capture equivalent to high noise levels in sound capture. Therefore, a preliminary study of the exact location of the IP-Spaces, its position with respect to the sun, and the design and installation of protective panels or artificial lights are recommended. In this way, capture and inference processes of the artificial vision system will work correctly and will make it usable. Otherwise, any kind of gesture recognition will not work, with the consequent frustration of the IP-Spaces user (Fig. 7). Of the two cases presented alternative introduced before above, hand tracking is ruled out due to its difficult implementation without the use of sensors in fingers or special gloves, something that goes against the current trend of not touching or sharing any object with other users. If gesture recognition in IP-Spaces should not have any additional elements, there is no point in using 3D models based on skeletal

Fig. 7 Console based game controlled using gestures. (Pop Culture Geek taken by Doug Kline: https://commons. wikimedia.org/wiki/File: CES_2012_-_Microsoft_ Kinect_Star_Wars_Episode_ 1_Podracing(6764013293). jpg. License: https:// creativecommons.org/ licenses/by/2.0/deed.en)

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Fig. 8 A model of an Intel Realsense Depth camera (https://commons.wikimedia. org/wiki/File:Intel_Realsense, _tracking_program_in_the_ background.jpg)

or volumetric structures. Therefore, the model to be used will be appearance-based type, where the input information will come from image sequences from a stereoscopic camera, also called depth camera. As the IP-Spaces have to include a webcam for video conferencing applications, in order to take advantage of the same hardware for various applications, this camera will be used to gesture recognition. Among the options available on the market for depth cameras, There are some models prepared for outdoor installation as Intel Realsense Depth family of cameras (Fig. 8). The advantages of this device are that the development environment is crossplatform, its SDK is Open Source and is prepared to be used with multiple programming languages and software development environments: NodeJS, Python, C, C++, C#, LabVIEW, MATLAB, Unity, Unreal, OpenCV, etc. A video demonstration of the possibilities of using a gestural interface that can be implemented in IP-Spaces using a depth camera is shown in Project Prague (2017). There is still a lot of work to be done in this area to achieve full integration of gesture recognition in IP-Spaces where the possibilities of interaction based on human skills and the way of expressing oneself through movement can generate not only communicative but recreational and educational applications in open spaces.

Related Legislation with the Use of Technology in Interconnected Public Spaces The use of video cameras at Smart Bus Stops or Smart Kiosks for informational or recreational use is a complex case study from the point of view of privacy and image rights. These video systems can be recording or streaming live a person in

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foreground interacting with a specific multimedia application but also capturing in the background people who transit through a public space. There is no European law or regulation that specifically covers these use cases where image and personal data are being used in the same way that an indoor videoconference. The laws or regulations that can be applied in these special cases of video conferencing or more advance applications are mainly related to the use of cameras located in public spaces for video surveillance purposes or as web cameras accessible through Web pages.

European Legislation Over the years, according to developing image capture technology, the legislation has progressed in the protection at international and European level, to protect the rights of people, as well as the handling of data and its circulation and storage. The main texts related, in chronological order, are published on the Internet under the following designations: • The Convention for the Protection of Human Rights and Fundamental Freedoms (CEDH) of the Council of Europe (1950) • Charter of Fundamental Rights of the European Union • Directive 95/46/CE of the European Parliament and of the Council of 24th October 1995 relative to the protection of persons in regard to the handling of personal data and on the free movement of such • Directive (EU) 2016/680 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data by competent authorities for the purposes of the prevention, investigation, detection or prosecution of criminal offences or the execution of criminal penalties, and on the free movement of such data, and repealing Council Framework Decision 2008/977/JHA • Convention 108+. The Council of Europe Convention for the Protection of Individuals with regard to Automatic Processing of Personal Data (1981-20012013-2018) The interpretation of directives and laws is not an easy task, especially since some of the definitions and actions to be taken into account for video systems confront the use of technology to individual liberties. It seems quite obvious that under certain circumstances, the use of CCTV cameras in public spaces is legitimized and could generate a certain decrease in privacy, in the interest of security and the common good to avoid certain threats that may affect the well-being of the community. For this reason, it is necessary that individuals can exercise their rights of freedom of expression and privacy before those responsible for the management and custody of data from image capture systems. The right to privacy does not disappear as soon as we step outside our homes (EDPB 2019).

In the cases of cameras installed in public spaces, controlled by local authorities or by the managers of a city, citizens seem to accept them appropriately if there are

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transparency mechanisms that ensure that the recordings are used only for surveillance and control, respecting individual privacy. The first surveillance systems in public spaces can be classified as all or nothing, without the possibility of deciding what is recorded or transmitted and what is not, without any type of granularity. Nowadays, the latest generation cameras and video surveillance systems, using artificial intelligence, are able to determine when to store images or fire alarms in the event of certain triggers, for example, the occupation of a specific area of the image or detecting motion. In addition, making use of privacy masks is possible to blur certain areas of the image that may be focusing on private properties. A similar evolution has occurred in the management and monitoring of audiovisual material from the first systems to the present day. Analog video recorders stored on tapes and their monitoring had to be done using the same recording system. Therefore, the privacy of the recordings was conditioned to the physical access to a certain control room. Currently it is possible use different policies to grant temporary permissions to view a camera, or a set of cameras, in real time or under demand via the Internet. Proper management of “Who” and “What” you can be seen is another of the demands of individuals regarding their individual rights. Directive 95/46/CE of the European Parliament is applicable to video surveillance systems including other sources of information as sound (voice) and other type of video standard images, for example: infrared thermal imaging technology or vehicle registration plate. All of this information is considered personal data even if the images are used in the framework of video surveillance or even if they do not concern individuals whose face was filmed. Public webcams for tourism and advertising purposes can be used legally as broadcast live source points on the Internet, as long as people cannot be directly identified from the recordings. The fact that people cannot be identified is against the constant improvement in the quality and resolution of the cameras. In less than 20 years, from the year 2000 to the present we have gone from a PAL standard with a resolution of 625
576 pixels to 4 K super high definition cameras, with a resolution of 3840
2160 pixels. This increase in video cameras resolution means that in a recording with a 4 K camera, the face of a person that appears in the background of an image can be perfectly recognizable viewed in a big screen or zooming on it. Also, the Directive is not applicable to processing carried out by a natural person in the exercise of exclusively personal or domestic activities. The European directive is not fully implemented at national level in different countries of the European Union. Most member countries have constitutional laws and regulations regarding video surveillance. Currently, not all of them include the processing of personal data. For more information see pages 88–93 of EDPB (2019). On the other hand, the European Data Protection Agency guarantees that, when processing personal data, the EU institutions and bodies respect the right to privacy of citizens. General Data Protection Regulation (GDPR) is now recognized as law across the EU since May 25, 2018. The GDPT determines two roles:

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1. The controllers (governments, organizations, or companies). 2. Processers of data (companies or divisions who process data). GDPT-Article 7 establishes the bases and conditions for consent where the controller shall be able to demonstrate that the data subject has consented to processing of his or her personal data and shall have the right to withdraw his or her consent at any time. GDPT-Article 11 indicates that a controller is not obliged to maintain and acquire data to process additional information to identify the data subject in order to comply with the regulation (express consent).

MUSA: An Inclusive Smart Bus Stop The concept of the smart bus stop is relatively new as part of the developments related to Smart Cities (Gretzel et al. 2015). Several European cities have launched smart bus stop pilot projects. That is the case of Paris (one stop, Boulevard Diderot, 85 m2, accessible to persons with disabilities, and providing free Wi-Fi and USB charge, among other services), London (100 Clear Channel bus shelters, using Google Outside service to provide information), and Barcelona (around 10 stops, with mobile-based payment system). Other cities have incorporated some smart elements to traditional stops to supply more information to users, such as arrival time of buses or other general information, without providing more interactivity. One example is the smart bus stop prototype of Hungarian company Aquis Innovo in Budapest. The European Commission funded the design and development of the prototype (Fig. 9). This prototype includes ticket vending, parcel delivery, passenger counting, passenger information, wireless, USB charging, bike rental, air

Fig. 9 Aquis Innovo’s Smart-Stop (https://https://europa.eu/investeu/projects/smart-bus-stop_es. EU Invest)

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Fig. 10 Outdoor TOMI accessible information kiosk. (Courtesy: TOMI World)

conditioning, taxi ordering, tourist information, news, advertisements, weather forecast, reverse vending, surveillance, and other services (Aquis Innovo 2016). On the other hand, there are other smart furniture options, as the outdoor bus ticket-kiosk (Portuguese OEMKIOSK) or information providing smart furniture adapted to people with disabilities such as the Portuguese TOMI as shown in Fig. 10. Another example of an advanced pilot project regarding Smart-Stop is the case of Aizuwakamatsu city, Japan. There, the low consumption, bistable e-paper (only consumes power when the message changes) is solar-powered and communicated with low power wide area (LPWA) wireless technology to provide information to users. This allows replacing paper timetables and improving the user experience. Managed remotely through the Papercast data management platform, the multilingual displays will present live bus arrivals, timetables, route data, route transfers, service alterations (planned and unplanned), and a range of other travel advice (Papercast 2018). Despite the huge potential of smart bus stops, their penetration in many European cities is very limited and their adaptation to inclusiveness is just beginning to develop.

MUSA Smart Bus Stop MUSA (Advanced Sustainable Urban Furniture – Mobiliario Urbano Sostenible y Avanzado) is the smart bus stop under development in the city of Madrid that will also host an IP-Space. Its main characteristic is the provision of information services with a focus on inclusive and socially driven transport aspects. The smart bus stop, from the inclusiveness point of view, is:

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(a) An interactive bus stop available to the entire population. It is a public access point to a digitized transport system (DTS), which allows access to persons without apps or even without a smartphone. (b) It can function as a public access point and as a travel assistant for groups of disadvantage or low-income users. (c) It can improve the accessibility to DTS using customized interfaces, and therefore, reducing cognitive demand. (d) It can improve planning in real-time taking into account unexpected events that can improve or disrupt transport operations. (e) Through an attractive and customized interface, it can promote the penetration of travel planning apps and its use by different user groups (elderly, immigrants, etc.). (f) It can be implemented as small-sized smart furniture providing a robust, essential electronic equipment that converts traditional stops into accessible smart bus stops, minimizing the modernization cost and having a wide use in the cities and rural areas. (g) Finally, it can be used for introducing Interconnected Public Space spots of the city. This is very suitable when the smart bus stop is in a park or square, where people can participate in sports and physical and cultural activities. Sharing the same ICT infrastructure makes the system attractive from an aesthetical and economic point of view.

MUSA Smart Bus Stop System Architecture MUSA is a physical stop equipped with an interactive display (currently a 40-inch monitor provided with an infrared frame) and a computer system communicating with a set of cloud systems to provide different services to travelers at the stop. These services available at the stop are called Group Services (Fig. 11). The main point of these services is a multimodal travel planner, including options for walking, cycling, and private and public transport. The interface to this urban equipment is being customized to increase the accessibility for all citizens and particularly for those vulnerable to exclusion. In addition, a community service is available for planning and realization of IPSpace related activities. Services related to publicity, environment, and health are also planned, without excluding others that can be included in the future. Transport services and applications provided in the stop can also be available for mobile phones. These apps are called Individual Services (Fig. 11). Some of these apps are currently implemented using web technologies and accessing third parties’ services through their APIs. Other apps, still under development, will use our own developed cloud-based services. Through the use of Individual Services, the smart bus stop can help to foster transport planning in general, not only when passengers are waiting in the stop but also in any other places using mobile phones or other devices. Massive planning of transport needs can help to develop new ways of organizing transport (Padrón-Nápoles et al. 2018). The planning of users’ transport needs helps to characterize the demand on transport systems. This identified demand jointly with sensorization of transport

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Fig. 11 Main components of MUSA system architecture

means can be used to customize and fine-tune transport services to meet user requirements (Fig. 12), increasing clients’ satisfaction. A key factor in the users’ transport planning is to know or estimate real-time bus occupancy. Otherwise, this planning can be useless. This factor is taken into account in the MUSA system.

Sensorization of Buses The efficiency of planning, from users’ as well as from transport providers’ point of view, is highly correlated to the level of sensorization of transport means. In the context of the MUSA project, this led to increased sensorization of buses. Installation of Automatic Passenger Counters (APC) allows knowing the occupancy of the bus in real-time, the availability of free places for wheelchairs and baby strollers, as well as the flow of passengers at each bus stop (Padrón-Nápoles et al. 2020). The use of APC for transport providers is very important to analyze the performance of bus services in real-time. It allows detecting the most demanded routes or segments of routes and potentially re-planning them to increase service efficiency and users’ satisfaction. From point of view of users, bus occupancy (or its probability) is crucial for effective planning (it is useless, if the planned bus comes full and passengers cannot get on board). Buses normally include AVL (Automatic Vehicle Location) using GPS and SCAFC (Smart Card Automatic Fare). For a flat-rate service (where passengers do not

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Fig. 12 Factors that can encourage the use of public transport (Roma refers to Romani people)

check out when alighting the bus, as in Madrid), the next level of sensorization was the use of Automatic Passenger Counters (APC). There are different technologies for APC, for example, infrared systems and vision systems (video cameras, stereo cameras, and time-of-flight cameras). The use of the latter can be adapted to detect free available places for wheelchairs and baby strollers, thereby supporting greater inclusiveness. Though for this project, infrared systems and stereo cameras were studied, MUSA used simple video CCTV cameras from Retail Sensing, a Manchester company, and evaluated their performance in real-life conditions (Fig. 13). The cameras, located on top of the front and rear doors, use artificial vision algorithms to count in and out passengers. This information was sent through a 4G router to an MQTT server to make it globally available. First, we tested the camera system in the Lab; next, we installed it inside a bus and tested it during daily operations in the center of Madrid (Figs. 14 and 15). Currently, a massive installation of APC systems using time-of-flight and Artificial Intelligence systems is taking place on Madrid buses. Information on real-time bus occupancy and availability of wheelchairs and baby strollers is expected to be publicly available as Open Data in the near future.

MUSA Transport Services The multimodal trip planner employed in MUSA includes options for walking, cycling, and private and public transport. It is designed as a special software layer (Fig. 16) that can run on a commercial travel planner, such as Google Maps (and its

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Fig. 13 Installing and testing the Automatic Passenger Counters (APC) based on video cameras

Fig. 14 Number of daily passengers entering the bus. (Courtesy: Retail Sensing)

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Fig. 15 Behavior of passengers’ flow at one bus stop. (Courtesy: Retail Sensing)

Fig. 16 Layering of the multimodal planning application

API). This approach has three advantages: it allows the customization of interfaces for different users’ segments; as elderly people, it allows the anonymous collection of traveling data for building mobility models and developing social innovation solutions (Social Model in Fig. 16); and finally, it can be adapted to different commercial planners.

Interconnected Public Space Service in MUSA The new functionality of the Interconnected Public Space (IP-Space) is being developed in the current prototype of a MUSA smart bus stop.

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Fig. 17 IP-Space users’ interaction sequence

From the stop, which is an IP-Space node, a user or set of users can connect with persons from other regions or countries present in another IP-Space node, using the same or different languages. All can participate remotely in different sports, physical, and cultural and playful activities (e.g., interesting games or video games, physical exercises, dancing competitions and many more). The interface can transmit audio or video using cameras and microphones, although this can be restricted in accordance with current local privacy laws. This also will depend on whether the IPSpace is physically enclosed or if it is a completely open public location. However, in all cases, the interface will allow the transmission of images, graphics, and text as basic means of communication. In a first approach to the design of the prototype, the high-level operation of this service consists of three steps (Fig. 17): (a) Map. In this step, users request the realization of a given activity to other people in a remote IP-Space. They can agree to start the activity immediately or schedule it at a given date and time. (b) Menu. They select the desired or scheduled activity from the repository and access it. (c) Activity. They proceed to realize the desired activity. They will have a language assistance application and tools, so people from different countries and culture can communicate.

MUSA Smart Stop and IP-Spaces Current Developments The design of the MUSA smart bus stop is shown in Figs. 18 and 19. It includes a solar photovoltaic energy system for increasing sustainability, smart sensors for urban air analysis, the information screens for interacting as bus stops or as IP-Spaces, user’s services as Wi-Fi spot and battery recharging, directional sound systems and video cameras for implementing multiple simultaneous channels of communication, depth cameras for gestural interfaces, and provision as a logistic parcel point. Other implementations of IP-Spaces may require larger screens or imaging areas and larger interactive surfaces. The use of larger high-quality video walls or image projections and interactive floors and tables allows the development of remote group sports, physical, cultural, and recreational activities.

506 Fig. 18 MUSA design for a square

Fig. 19 MUSA design for a street

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Fig. 20 Main interface of the smart bus stop prototype

The current state of the prototype of the MUSA smart bus stop and its main interface are shown in Fig. 20. An advertisement is running in the background (in this case, for musicals in the center of Madrid), while different services are available in the lower carousel. For intuitively attracting users to different interfaces, two types of icons are available. A traditional picture is used to attract more resolute and direct users (e.g., some senior people). Let us call it the “conservative interface.” Moreover, a more playful icon is used to attract more skillful and playful users (e.g., some junior or young people). Let us call it the “playful interface.” Other future special services for assisting travelers with special needs (elderly, those with reduced mobility, easier travel with kids or pregnant women) are also included in the interface. The conservative interface of the public transport app is shown in Fig. 21. A box with the most frequently used destination from the current stop is shown in the top right corner. This helps to increase the probabilities of reducing the interaction to a minimum. Below, a box shows the distance and duration of the selected trip. In addition, there is an option to select private or other types of alternative transport without leaving the conservative mode of interaction. A third box allows users to select any origin and destination, using an on-screen touch keyboard. Destinations are dynamically ordered based on their frequency of use. This allows the smart stop system to learn from users’ interactions, the required destinations at each time of the day during the week. Typical interactive features of Google Maps are disabled, so conservative users cannot be distracted from their simple, direct interaction with the app. The conservative interface includes two additional features:

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Fig. 21 Multimodal planner using public transport

Fig. 22 Ordering a mobility service from the smart bus stop

(a) An option to promote physical exercise by walking. If the duration of a walking trip and using public transport are similar, the option of walking can be healthier for the user. This feature can be very interesting for elderly people. (b) The possibility of using mobility service providers such as Uber, Cabify, or Free Now, without having the application or even having a smartphone (Fig. 22). The private transport interface can verify the availability of these services and request

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Fig. 23 Interconnected public space access interface

a trip using a dedicated account. The user will pay in cash since this payment option is currently available in large cities. The design of the main interface of IP-Space is shown in Fig. 23. Again, there are two icons for conservative and playful interfaces, labeled “Community,” which give access to the services of IP-Space. A third icon is reserved for future services to the elderly.

Conclusions As can be noted in the section dedicated to the description of projects related to Smart Cities, there is a real interest in making cities evolve from just places of coexistence to more dynamic and sustainable environments. This must take into account the demands of citizens regarding the social inclusion of minorities and groups at risk of exclusion, such as the elderly and immigrants. In summary, both technology and investment efforts must be directed to convert Smart Cities into human Smart Cities. The novel concept of Interconnected Public Space or IP-Space seems to have a great potential for increasing (or at least maintain) quality of life of the elderly. The social effects of a global community of elder people can be huge as a way of cultural exchange, sharing of knowledge, solidarity, promotion of new projects, etc. Special attention should be paid to multilingual applications and tools for people’s communication. IP-Spaces can take advantage of the ICT infrastructure of smart bus stops.

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This is an effective solution in wide outdoor spaces, such as parks and squares, producing a faster and cheaper implementation of the IP-Spaces. Interconnected Public Spaces are at a very early stage of development and sociological research. There are several lines of research to explore as soon as first prototypes will be ready. There is a need to address sociological and psychological acceptance, legal issues in different countries and the development of the activities, among other questions. From the transport point of view, IP-Spaces addresses the inclusiveness of elderly people in smart transport systems (digitized transport systems). The customization of interfaces reduces the cognitive demand in the use of the interfaces. Gestural interfaces can be very useful in times of pandemic and contagious diseases. Effective planning of transportation requires precise knowledge about available resources and their behavior. Sensorizing buses helps to measure and estimate realtime buses’ occupancy, which is crucial for effective transportation planning, from both users and transport providers. The use of advanced sensors as time-of-flight cameras helps to detect or estimate vacant places for wheelchairs and baby strollers, increasing inclusiveness. Other features provided for elderly or low-income users (refugees, immigrants, unemployed people) is the possibility of share mobility services, such as Uber, Cabify, and FreeNow. IP-Spaces applications for transport planning can also help to promote physical exercise (walking) for elders’ users. Following an approach of the legal framework in the European Union for capturing images or video in outdoor areas and the increasingly relevant regulation of associated personal data, to ensure that a video call or conversational interaction system at Smart Bus Stops or Smart Kiosks can safeguard the rights of people who use it actively, such as those who are within the area of vision of the cameras, several aspects must be taken into account. Real-time image processing technology can blur image background or to remove part of the images that may be capturing private properties. It is also necessary to determine and justify the categories of processed data (image, audio, video, text) and the duration of the retention of the data. The processer, in case of collecting and storing personal information, has to justify the relevancy of the data. Smart Bus Stops o Kiosk could collect data from cameras or aggregate information from additional integrated sensors that can be used for scientific research or the development of future applications. Therefore, a description of the experiment must be available on-site and on the Internet. Finally, it is necessary to have a multimodal information process (text, images, sign language, audio description and pictograms) to advice the user in detail on the use and treatment of the application’s data, be it a video call, for example, or a conversation system with artificial intelligence or augmented reality. After the detailed report, there must be a decision point in the application event that does not allow its use without the express consent of the user. In this way, individual rights of Smart Bus Stops or Kiosk are safeguarded as well as the rights of other people who may walk or be in the nearness of the Smart Bus Stop or Kiosk.

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Malaysia Smart City Framework: A Trusted Framework for Shaping Smart Malaysian Citizenship?

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Seng Boon Lim, Jalaluddin Abdul Malek, Mohd Yusof Hussain, and Zurinah Tahir

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Malaysia Smart City Framework and Citizenship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Citizenship and The Nations-of-Intent in Malaysia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Findings and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . General Frames of Malaysian Smart City Policies and Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . Malaysia’s Citizenship Framing in the MSCF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overall Themes on the Relationship Between Smart City and Citizenship . . . . . . . . . . . . . . . . Suggestions in Building Smart City and Smart Citizenship in Malaysia . . . . . . . . . . . . . . . . . . . . . . Conclusion and Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

This chapter explores the relationship between a smart city and citizenship in Malaysia through text and thematic analysis of the recently launched Malaysia Smart City Framework (MSCF). This first MSCF top-down document functions as a guideline and assists in coordinating silo developments by local authorities. As this is the first attempt by authors to relate the smart city concept with citizenship in Malaysia, this chapter contributes as the first academic publication in Malaysia that zooms into the notion of how a smart city development could cultivate or compromise a good future in shaping the mold of the Smart S. B. Lim (*) · M. Y. Hussain · Z. Tahir School of Social, Development and Environmental Studies, Faculty of Social Sciences and Humanities, National University of Malaysia, Bangi, Selangor, Malaysia e-mail: [email protected] J. Abdul Malek School of Social, Development and Environmental Studies, National University of Malaysia, Bangi, Selangor, Malaysia © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_34

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Malaysian Citizenship. Suggestions for and challenges in building the smart citizenship, “nations-of-intent,” and in meeting local society needs are elaborated. As argued by the authors, this chapter has impacted the invariable normative content of the technology driven smart concept, as this smart city concept should and always needs to include dimensions in forming smart citizenship.

Introduction In September 2019, the government of Malaysia has launched its first national – Malaysia Smart City Framework (MSCF) (Loo 2019). The government believed that such techno-reductionism development would improve the citizens’ quality of life which has too often been over-generalized. From the authors’ perspective, the improved citizens’ quality of life could be achieved through an authentic citizenship regime. However, the issue of citizenship in Malaysia is the contested state of the “Malay-led plural society” and technology is not being emphasized on molding the stable multiethnicity society and region’s structure. This chapter questioned the essence of the contemporary MSCF framework, as will it be able to consolidate or haphazard on shaping the smart Malaysian citizenship, which could form an ideal nation state for “Bangsa Pintar” (lit. smart nation). The objective of this chapter is to dissect the MSCF framework on the aspect of its ability and relationship in shaping smart citizenship for Malaysian society. To answer this objective, in the following section, the authors reviewed the background of the MSCF and theoretical framing of citizenship; and the citizenship context in Malaysia. Next, the methodology is explained and followed by findings and discussions on text occurrence and co-occurrence as well as thematic analysis. Constructive suggestions and possible challenges for meeting the Malaysian society needs are outlined, and the chapter finally ends with the conclusion and contribution of the study.

Literature Review Malaysia Smart City Framework and Citizenship The purpose of the MSCF framework is to present a national document in guiding Malaysia’s smart city development across states and regions. For smart city development, as clearly stated in the top-down plan of the 11th Malaysia Plan (or 11MP) (2016–2020) (Malaysia 2015), smart city was viewed as a digital tool/strategy in urban management for improving the quality of living of its citizens. And also from other national documents such as National Physical Plan 3; National Urbanisation Policy 2; Vision 2020; Green Technology Master Plan 2017–2030; Low Carbon Cities Framework; and even the global agenda such as the New Urban Agenda and the UN Habitat (Kuala Lumpur Declaration on Cities 2030) share the same narratives of referring smart city as digital tool or technology strategy in achieving or assisting the achievement of Sustainable Development Goals.

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In MSCF, the term smart city was defined as “cities that use ICT and technological advancement to address urban issues including to improve quality of life, promote economic growth, develop a sustainable and safe environment and encourage efficient urban management practices” (MSCF 2019, p. 16). This definition was adopted from general private corporations’ and academics’ definitions. However, it excluded a critical element highlighted by Giffinger et al. (2007, p. 11), which is in smart cities, there needs the presence of the “endowments and activities of selfdecisive, independent and aware citizens.” In other words, the availability of the above citizens’ activities also plays a role in determining the “smartness” of a “forward looking” city in the making. To cultivate the abovementioned qualities/activities of citizens, the governance practice of allowing the participatory type of governance is crucial. Besides, from the ground, citizen participation is essential, especially in the democratic political participation that involves decision-making. In the 11 guiding principles in MSCF (2019, p. 15), the level 1-core criteria of building a smart city is on preparing the digital infrastructure; then the “strong political will” and “engage broad community as innovators” as the level 2-catalyst; and “gender/ vulnerable group/ community empowerment” as the level 3-plus point. On the other hand, this MSCF has adopted the popularly cited Giffinger et al. (2007)’s six smart elements, namely, the smart economy, government, people, living, mobility, and environment. The discussion here zooms into the Giffinger et al.’s framing of smart governance and people, where participatory of governance and citizen/human capital are emphasized. As in the MSCF’s policies, the term “participation” is mentioned in the seventh policy (way away from the first policy of digital infrastructure upgrading); and the focus is on the women and vulnerable groups (MSCF 2019, p. 37). In the authors’ opinion, the general citizens (or the “have-not” citizens with no power as coined by Arnstein 1969) demonstrated poor involvement in local authorities’ programs. However, this “under-represented citizens” phenomenon is too often viewed as an “unnecessary” matter by Malaysians. To discuss the citizens and citizenship matter, this study follows the theory of Jenson (2009)’s citizenship regime, and it can be examined and interpreted in terms of three intersecting dimensions, namely, (1) the “responsibility mix,” which refers to the distribution of responsibility between the individual, the community, the state, and the market; (2) the “rights and duties,” which establish the boundaries of a political community; and (3) the “governing arrangements and practices,” which including modes of citizen participation and access to the state. Joss, Cook, and Dayot (2017) have attempted to apply the above theory on assessing the British Smart City Standard and “the result confirms an explicit citizenship rationale guiding the smart city (standard), although it displayed some substantive shortcomings and contradictions” (Joss et al. 2017, p. 29). From Joss et al.’s (2017) study and other writings from Jenson (2007), and Jenson and Phillips (1996), the authors found that Jenson’s citizenship regime theory covers a wider scope, instead of the traditional narrow scope of mainly referring to the citizen’s legal rights to belong to a particular country (refer Marshall 1992). Therefore, the Jenson’s theory was chosen to apply on examining the Malaysian case, in addition to the context of citizenship discussion in

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Malaysia, such as the popular concept of the “nations-of-intent” in Malaysia by Shamsul (1996). The following subsection will continue to discuss the formation of authentic citizenship in relation to the nation’s unity building.

Citizenship and The Nations-of-Intent in Malaysia To discuss the citizenship in Malaysia, there are two important historical backgrounds that one should comprehend, namely, the 1957 “Malaya” (this is the previous name of peninsula Malaysia before 1963) independence from the Britishcolonial; and the 1963 formation of Malaysia which includes the land of Peninsula Malaysia, Sabah, Sarawak, dan Singapore. Many literatures on political science in Malaysia have concluded that the British-colonial influence between the 1800s and 1950s was significant in forming the nation state and local society (Embong 1996; Ishak 1999; Preston 2017; Saad 2012; Soltani et al. 2014). The major Britishcolonial influence was the (race/region) divide-and-concur policy which deeply influences the thereafter ethnic-based politics in Malaysia. These ethnic-based politics were led by the Malay party-UMNO (United Malay National Organisation), in coalition with two sizeable immigrant communities, namely, the Chinese partyMCA (Malaysian Chinese Association), and the Indian party-MIC (Malaysia Indian Congress). Demography wise, during the 1950s, the Bumiputera (lit. sons of the soil; consists of the Malays and the indigenous people) has accounted for 50%, and 37% for the immigrant Chinese, and 11% for Indians (Shamsul 1996). After the formation of Malaysia in 1963 (together with Sabah, Sarawak and Singapore), and Singapore exit from Malaysia in 1969, the demography of Bumiputera has grown since then and achieved 69.3%, and the other two, dropped to 22.8% for Chinese and 6.9% for Indian (DOSM 2019). Hence, from the 1950s of about 10 million to the current 32 million population, the influence of Bumiputera race is maintained and dominating. Although since the independence in 1957, and also written in the Malaysia Constitution, the said Chinese and Indian immigrants gained their status as Malaysia citizen. However, their citizenship status was exchanged with the Malays’ special privileges rights. From the traditional Keynesian perspective, if the male breadwinner was the “model citizen,” then in the authors’ opinion, the “Malay-Muslim race” is considered as Malaysia’s “model citizen” under its semidemocratic authoritarian realm. Here, the Islam religion as the national religion and as the “born-identity” for the Malays, has inevitable strengthen the status quo while excluding certain demographics from achieving their full potential as citizens (Mohamed 2017). From such a mixture of races, religions and regions, each group will inevitably have their own intention to defend their identities and that intention or ambition or vision refers to the concept of “nations-of-intent,” or “a defined idea of the form of a nation,” i.e., its territory, population, language, culture, symbols and institutions (Shamsul 1996). To date, the dominant “nations-of-intent” is defined

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Fig. 1 Typical image of “Malay-led plural society” representation, that appears in national events, such as the National Day celebration. (Source: https://www.perpaduan.gov.my/en/nationalisme/ national-unity-consultative-council-nucc)

by the government or “authority-defined social reality,” which is the “Malay-led plural society” (this notion is depicted in Fig. 1). However, this notion is constantly contested by the level of “everyday social reality,” which are the three groups, namely, the non-Bumiputera group, led by the Chinese, and two Bumiputera ones, the non-Muslim bumiputera group and the radical Islamic Bumiputera group, each offering its own nations-of-intent, i.e., its own vision of what the national identity should be, based on a particular ideological framework. In the dimension of governance practices, if (and it does) the “Malay-led plural society” is to be upheld by the community, the state will prioritize the Bumiputera (especially the Malay-Muslim) as the core of the Malaysian national identity while recognizing, if peripherally, the cultural symbols of other ethnic groups. In the dimension of citizens’ responsibilities, all ethnics or by regions have to put down disagreement and obey to such “Malay-led plural society” narrative; sacrifices if it may, and together accountably to build as one Malaysia nation state. However, in everyday practices, such ethnic politics survived and perceived as a state of “stable tension society”. In other words, the dominating narrative of a “Malay-led plural society” is constantly being challenged by other nations-of-intent but is still under the preservation or control of the government. This long-term ongoing social tension has inevitably dragged down the country’s economic development and focus towards achieving a developed nation status or becoming a modern society, as envisaged in Vision 2020. In terms of the citizens’ rights dimension, all citizens will still abide by the rights they have through the 1957 Malaya independence agreement, and 1963 formation of Malaysia Constitution. Although the constitution has never “downgraded” the nonBumiputera group or Bumiputera non-Islam group (e.g., the Christian-Kadazan in Sabah, and the non-Muslim Iban in Sarawak), but in everyday practices, their citizen welfare and benefits have always been less prioritized as compared to the protected Malay-Muslim special privileges since the era of New Economy Policy (1970–1990) until today’s smart cities development era.

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Methodology This study applied the text occurrence and co-occurrence analysis using Microsoft Excel and AntConC software, as well as thematic analysis using Atlas.ti and Mendeley. The MSCF document was analyzed in order to achieve the research objective as stated in the Introduction section. The purpose of close textual analysis was to provide a measure of relative weight given to citizenship in the MSCF document. Three text occurrence (frequency) analyses were applied to the occurrence of (1) the themes in the 16 policies of MSCF, (2) the themes in the 36 strategies of MSCF, and (3) the term “citizen(s)” and other actors mentioned in the overall body of text of MSCF. Other actors such as “community,” “business,” “user,” “people,” and “local authorities” were compared with the term “citizen(s)” to show the relative weight afforded to citizenship. As for the text co-occurrence analysis using AntConC, the co-occurrence of “citizen(s)” and associated terms, whereby five words each before (to the left) and after (to the right of) “citizen(s)” were captured. Functional words (without significant meaning) such as “and,” “the,” “of” were excluded. The purpose of this text co-occurrence analysis was to examine the frequency of words associations that harvests information about how the term “citizen(s)” is conceptualized. Other than that, the overall themes for the whole thematic analysis were summarized to form an understanding of the relationship between a smart city and citizenship.

Findings and Discussions The findings of this study were drawn from three sources. The first source is the general main discourse frames of smart cities in the MSCF document. The second source is the related citizenship findings, namely, the occurrence and co-occurrence of the term “citizen(s)” in the MSCF document. The third source is the overall themes derived to capture the implicit meanings of citizenship contained in the MSCF document.

General Frames of Malaysian Smart City Policies and Strategies Concerning the main discourse frames, 16 policies are listed in the MSCF document (MSCF 2019, pp. 34–39). The authors have summarized five occurring themes within these 16 policies, namely (from the most frequent to the least), governance reform (average of 0.38 mentions per policy), city system (0.38), sustainability/ resource efficiency/cyber security (0.31), digital innovation/infrastructure/data/economic growth (0.25), and social inclusion/empowerment/human capital (0.19) (refer Fig. 2). Even though Fig. 2 seems to indicate that governance reform and city system are given the highest attention among the themes, digital innovation/infrastructure is the most prioritized in terms of the strategy (refer Fig. 3). Moreover, as demonstrated in

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Governance reform

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City system

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Sustainability/ resource efficiency/ cyber security

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Digital innovation/ infra/ data/ economic growth

4

Social inclusion/ empowerment/ human capital

3 0

0.1

0.2

0.3

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Fig. 2 Occurrence of themes in the 16 policies of Malaysia Smart City Framework

Digital innovation/ infra/ data/ economic growth

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Sustainability/ resource efficiency/ cyber security

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Governance reform

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Social inclusion/ empowerment/ human capital

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City system

9 0.00

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Fig. 3 Occurrence of themes in the 36 strategies of Malaysia Smart City Framework

Fig. 2, social inclusion is given the least emphasis, reflecting the low priority or concern in such area in Malaysia. The 16 policies in MSCF have been further translated into 36 strategies (MSCF 2019, p. 42). As shown in Fig. 3, the theme with the highest occurrence is digital innovation/infrastructure/data/economic growth (average of 0.64 mentions per strategy), followed by sustainability/resource efficiency/cyber security (0.50), governance reform (0.47), social inclusion/empowerment/human capital (0.33), and city system (0.25).

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A comparison of Figs. 2 and 3 shows low compatibility between the strategy implementation and policy direction, where the strategy is focused on digital innovation and infrastructure instead of on governance reform and city system. However, there is one consistency between the strategy implementation and policy direction in that the theme of social inclusion is given the least priority, as evidenced by its position at the bottom of the rankings. Nevertheless, acknowledging that different cities have different sizes, population densities, and urban challenges, the MSCF document does not suggest which strategies to implement first (MSCF 2019, p. 136). Instead, the order of the strategy implementation is left to the judgement of the particular city/local authority concerned, as this document serves as guideline only and is not legally bindings on the authorities. Foreseeing the priorities of the city/local authorities, the authors predict that the authorities will probably prioritize digital infrastructure upgrading, followed by the top two suggested policies, namely: Policy 1: Primary infrastructure shall be upgraded to incorporate smart IoT elements towards addressing core urban challenges; and Policy 2: Shared digital infrastructure and Internet connectivity shall be enhanced for all cities in Malaysia (MSCF 2019, p. 39).

Malaysia’s Citizenship Framing in the MSCF It is evident that the MSCF gives little attention to the nation’s unity building. The frequently debated stable tension that exists in the nation’s unity building within Malaysia’s plural society (A’zmi et al. 2017; Pandiyan 2019; Pusat KOMAS 2017), the need for nation building highlighted in the 11MP (pp. 3–25, 4–24), Malaysians worried about possibility of ethnic clash (Atkinson et al. 2019; Straitstimes.com 2019), and the “state without nation” by Shamsul (2019) are somehow not mentioned as among the key urban challenges in the MSCF document (see MSCF 2019, pp. 20–21) or the challenges encountered in the five pilot smart city projects (see MSCF 2019, p. 178, 192, 204, 214, 222). In view of such exclusion from the MSCF, the smart city, therefore, has not been identified as a tool for the nation’s unity building. Along this line, the National Unity Consultative Council (NUCC) Blueprint (Bernama 2018; KITA 2015), which has been adopted nationwide since the year 2018, is not mentioned or referred to in the MSCF document. Similarly, the NUCC Blueprint does not mention “city” as one of the tools for nation building. This scenario is a gap in the policy direction even though the authors found that both the NUCC Blueprint and the MSCF share the same vision of aiming for better quality of life and wellbeing for all citizens, and should be in line with the nation’s unity building and incorporated as a key challenge in the MSCF. Although citizenship is not explicitly framed in the MSCF, the authors made an attempt to analyze this concept through the occurrence and co-occurrence of “citizen” in the MSCF document.

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Table 1 Occurrence and co-occurrence of “citizen” and other actors in the Malaysia Smart City Framework 1(a) Occurrence of “citizen” and other actors Actor Total count Average count per page Community 158 0.66 Business 94 0.39 User 83 0.35 People 73 0.30 Citizena 60 0.25 Local authoritiesb 56 0.23 Human 53 0.22 Stakeholder 43 0.18 Community empowerment 23 0.10 Customer 21 0.09 Resident 21 0.09 Participant 10 0.04 1(b) Co-occurrence of words around “citizen” (five words before/after) Actor Total count Average count per “citizen” Government 8 0.13 Business 7 0.12 Smart 7 0.12 City 6 0.10 Complaint 5 0.08 Senior 5 0.08 Service 5 0.08 Community 4 0.07 Information 3 0.05 Accessible 2 0.03 Authority 2 0.03 Development 2 0.03 At the center (centric) 1 0.02 Note: a“Citizen” represents both singular and plural forms (ditto other actors). b“Local authorities” includes “municipal council,” and “local government”

Within the entire 240 pages of the MSCF document, the term “citizen” (0.25 mentions per page) is ranked the fifth after the term “community” (0.66), “business” (0.39), “user” (0.35), and “people” (0.30) (see Table 1a). Even though “community” is the most frequently mentioned term, it is mostly associated with “empowerment” (0.10 mentions per page) and is a general term for all types of actors or a group of residents who hungers for help from the government and businesses. Examples of the mentions of the term are “Engagement of larger community (local authority, government agencies, businesses, communities, business districts, smart buildings, housing complexes), and individual residents. . .” (MSCF 2019, p. 15); “. . .in linking communities with local authorities in order to build the community capabilities. . .” (MSCF 2019, p. 15); “Encouraging

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community engagement in urban/community farming” (MSCF 2019, p. 59); and “Cities can effectively empower their communities to fully embrace a digital lifestyle” (MSCF 2019, p. 226). Other terms such as “user” and “people” also carry general meanings and serve as “subjects” to “business” and “local authorities.” Besides, there could be a confusion regarding the use of the terms “citizen” and “people” on page 160 of the MSCF document on “Smart city ecosystem in Malaysia,” where the term “citizen” is used to replace the smart living component and “people” remains in the smart people component. In fact, to make a clear distinction between these two terms, “people” should be used to represent the smart living component (because as beneficiaries/users, no responsibility is imposed on them) and “citizen” should be placed in the smart people component (because it requires citizens to participate in the workforce and community engagement). Furthermore, the authors noticed that the MSCF document is more businessoriented than citizen-oriented, as the term “business” is mentioned first on page 4 and “citizen” is mentioned later on page 14. The MSCF research outcome explicitly mentions the competitiveness of economies and cities, for example, “Fulfil the country’s direction to make our cities competitive. . .” (MSCF 2019, p. 2). Furthermore, this document highly favors Public Private Partnerships (PPPs), as mentioned in the 14th policy, “Public private partnerships (PPPs) shall be emphasised in smart city initiatives” (MSCF 2019, p. 38). Concerning “citizen,” the MSCF views citizens as different entities from businesses and the government, for example, “Sharing non-personal and non-sensitive data whether through open data or inter-governmental platform enables citizens, businesses and government. . .” (MSCF 2019, p. 15) and “Increasing the scope and quality of e-government will ensure that public services are accessible and convenient to citizens, businesses and even government themselves” (MSCF 2019, p. 37). The authors noticed the rights of the vulnerable groups such as women, and disable, and older persons are mentioned in MSCF document, for example, “the participation of women and vulnerable groups in decision making will be necessary in ensuring a safe and inclusive city environment” (MSCF 2019, p. 36); and “Giving recognition and acceptance of the principle that disabled persons have equal rights and opportunities for full participation in society” (MSCF 2019, p. 94); and “. . .to reach out and provide developmental services to the elderly” (MSCF 2019, p. 141). Overall, the mode of the Malaysian citizenship as mentioned in the MSCF document aligns predominantly with the socioeconomic interests. However, the Malaysian citizenship as an explicit political agency is less pronounced (superficial, mentioned generally as “public participation” or in the “mercy” of policymakers) despite making appearances as a “utopian state,” too, for example, “This (bottom-up) approach turns citizens from end-users to begin-users. . .” (MSCF 2019, p. 95); “. . .as citizens in general will have active roles in smart cities’ activities and decisions. . .” (MSCF 2019, p. 96); “Strategy 2 for Smart People - Enhance public awareness in practising good moral and civic” (MSCF 2019, p. 42, 86); “Strategy 4 for Smart People – Enhance public participation and community empowerment” (MSCF 2019, p. 42, 91); and “the proposed Smart City Council envisions participation of relevant public sector stakeholders to formulate appropriate policies. . .” (MSCF 2019, p. 162).

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Certain areas for “collaborative governance” practices such as online full council meeting; disaster risk awareness programs; and urban farming are mentioned in the MSCF document. For example, “Enhancement of public participation through online full council meeting” (MSCF 2019, p. 230); “Enhance community engagement to increase city citizen participation in disaster risk awareness programmes at local level” (MSCF, p. 73); and “Community participation in urban farming is highly related to the level of awareness on the benefits of the activity” (MSCF 2019, p. 229). Some of the citizen participation benefits (see MSCF 2019, p. 91, 230) and challenges (see MSCF 2019, p. 91, 199, 231) are highlighted. However, through the authors’ research experience on participation and literature such as Abdul Malek, Lim, and Tahir (2019); and Mariana (2008), the participation level is still relatively low in Malaysia with lukewarm welcome by government officials (compared to PPPs), as citizen participation is perceived as time consuming, “complaints,” practically needing more staffing to handle the crowd, costly, and making it difficult to reach a final decision that can satisfy everybody. A similar picture emerges from the co-occurrence count from AntConc 3.5.8 software (see Table 1b). Once again, “citizen” is closely aligned with the terms “government,” “business,” “smart,” “complaint,” “senior,” “service,” and “community.” The term “centric” or “at the centre” is aligned only once with the term “citizen,” thus giving a hint that the MSCF has a low aim of being “citizen-centric” and has little alignment with the policy direction of the 11MP: “Anchoring growth on people” (Malaysia 2016, pp. 1–1:1–2).

Overall Themes on the Relationship Between Smart City and Citizenship Following the above analysis of the policies and strategies as well as text occurrence and co-occurrence, the authors derived three themes, as follows: 1. The MSCF document is more “business-oriented” and less “citizen-oriented” This business or corporation orientation hints at the preference of the Malaysian society for the “individual-liberal” tradition that upholds the citizenship in a more passive and restrictive manner where politics is reduced to the market place, rendering the state accountable and protecting the free market, thus individual freedom and self-interests are given priority over the collective interests of the society (read more in de Waal and Dignum 2017; Jenson 2009; Joss et al. 2017). While the “civic-republican” tradition does appear superficially or in a utopian manner, such as mentioned the state to embrace active participation as one of the key constitutive elements of smart citizenship and citizens to rely on the state for the protection of civil rights, the authors did not notice any further emphasis on the collective responsibilities and duties of all citizens. Furthermore, as stressed by de Waal and Dignum (2017), one should see the potential of smart cities in building citizenship based on collective responsibilities toward society’s common goods, rather than merely promoting individual rights and based on consumer’s choices.

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Also, the MSCF does not emphasize that the individual stands in essentially shared relations with the community. The latter is, therefore, a constitutive community formed through a reciprocal relationship with individual citizens. Furthermore, important citizenship topics such as enhancement of public deliberation and political contestation as essential parts of establishing common values and goods were not discussed. The authors feel that, in order to achieve a universal common good, a pluralistic society such as Malaysia requires its citizens to accommodate diverse perspectives through open deliberation instead of relying on a homogenous community, for example, the Malay-Muslim political leaders and top-ranked government officials. 2. The technological tokenism narrative of “for rakyat” (Lit. citizen) Technology and data have typically been used to “nudge” the citizens instead of “with rakyat” and have been less inviting for the rakyat to involve in co-creation activities. For example, the MSCF only mentions G2G (Government to Government) and G2C (Government to Citizen) but has no mention of C2G (Citizen to Government) (MSCF 2019, p. 15). With no emphasis on co-creation activities, citizens will be hardly trained to be self-decisive, independent, and aware, as emphasized by Giffinger et al. (2007). Under the “smart government” component, still, the MSCF document is silent on the participatory style of governance (see MSCF 2019, p. 17, 98) and mentions “collaborative governance” only once, which is for the Kulim city project (MSCF 2019, p. 230). It is the authors’ understanding that the smart government component is mainly focused on open data and information disclosure, quality egovernment services, and inter-governmental data sharing (MSCF 2019, p. 17). This scenario is akin to an unmatched “head and tail,” where on one hand the MSCF encourages the citizens to participate in the decision-making, but on the other hand, the MSCF does not highlight the participatory governance style adequately, for example, how citizens should gain access to the state. However, the authors agreed with Costa (▶ Chap. 2, “Smart Cities Can Be More Humane and Sustainable Too”) that to bring in this diversity of stakeholders’ involvement under the participatory governance practice is experiencing difficulties in setting up priorities. Thus, this might be one of the reasons (lacking expertise) that this participatory governance style is excluded in MSCF. Furthermore, the MSCF document emphasizes on the importance of digital infrastructure, data, and new digital applications to solve urban challenges, and it requires the citizens to keep themselves up to date, learn, and provide data for the required analysis. Although life-long learning is stated as a need, the mastermind of technological solutions is still hinted to be led by businesses and corporations (see MSCF 2019, p. 15). Another example of the enhancement of public participation is through online full council meetings (see MSCF 2019, pp. 230–231), but the suggestion that attendees (can) use full council meetings to voice their opinions is mere “tokenism” because only the Head of Local Authority and the 24 councilors are eligible to both voice their views and vote in the decision-making. While other “public attendees” are only allowed to enter the meeting and voice their opinions with condition, but never have the right to vote

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in the decision-making, unless the sub-section of 26 (1) of the Local Government Act 1976 (Malaysia 2006) is to be amended. Other than that, the ways to balance the high level of technology by corporations and low level of technology by citizens (ground level innovations) for city solutions were not discussed. The MSCF should consider the potential of citizens as co-partners instead of as typical technology users or beneficiaries or people who always need to be “empowered” (see policy 13 in MSCF 2019, p. 38). The bottom-up co-creation and social innovation activities that largely involve citizens such as living labs (Bason 2010; Cossetta 2010; UCLG 2016), the city as a public experimentation (BSI-PAS184 2017; Gordon et al. 2018), and community mesh networks (Filippi 2015) could be good references to “tune” the tokenism narratives to authentic participation. 3. Lack of theoretical support and practical strategies in shaping the smart citizenship in Malaysia With the establishment of the Multimedia Super Corridor (MSC) in the early 1990s, Malaysia somehow entered early into the digital or smart city era. At that time, the 4th Prime Minister, Tun Mahathir, envisioned the notion of “Bangsa Malaysia” (lit. Malaysian nation) to achieve Vision 2020: The first of these is the challenge of establishing a united Malaysian nation with a sense of common and shared destiny. This must be a nation at peace with itself, territorially, and ethnically integrated, living in harmony and full and fair partnership, made up of one Bangsa Malaysia with political loyalty and dedication to the nation. (Mohammed 1991, p. 1)

The much contested Bangsa Malaysia notion has remained a political agenda and has yet to be achieved today (Aris and Hasbullah 2012; Ishak 2006; Nor 2016; Sanusi and Ghazali 2014). Shamsul (2019, p. 1128) commented that “Malaysia, which could be considered as “a state without a nation,” is still struggling with competing nations-of-intent among its different ethnic groups.” The authors are of the view that the Bangsa Malaysia vision has set the backbone for the smart citizenship in Malaysia. However, the MSCF has not been able to link the Bangsa Malaysia notion with the smart citizenship because as stated earlier, the nation’s unity issue is not viewed as one of the urban challenges. This scenario has resulted in the lack of theoretical support and practical strategies to truly shape the smart citizenship in Malaysia.

Suggestions in Building Smart City and Smart Citizenship in Malaysia All the three themes derived in the previous section are considered as some of the needs or inadequacy of the Malaysian society in creating the smart citizenship in future smart city buildings. Here, the authors propose six suggestions to meet the needs of the local society, as follows:

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1. Malaysia needs to learn discursively from international smart cities (and related citizenship) standards and practices, such as those developed by the British Standards Institution (BSI) (n.d.); International Organization for Standardization (ISO) (n.d., 2017); European Commission (Bosch et al. 2017); International Telecommunication Union, United Nations (ITU-T n.d.; Ubeda 2018) (see Table 2); National Institute of Standards and Technology, United States (NIST 2018, 2019a, b); or by scholars including Huovila, Bosch, and Airaksinen (2019), Marsal-Llacuna (2015, 2016), and Saunders and Baeck (2015). Although the MSCF mentions the establishment of Malaysian Accreditation for smart cities based on ISO 37122 (see MSCF 2019, p. 166), the authors are of the view that the eight volumes of BSI smart cities standards are the pioneer among the standards and have detailed definitions and explanations on building smart cities that are meant to be more citizen-centric, and hence, should be considered as a good reference for Malaysian smart citizenship buildings. Besides, the MSCF framework is ambitious, aiming at drafting 16 policies, 36 strategies, 112 initiatives, and numerous indicators of smart city components, along with providing suggestions for governance arrangements (e.g., the set-up of a Smart City Council), establishing a Communication Action Plan (CAP) or implementing a roadmap (from 2019 to 2025) with identified lead agencies, presenting examples of five pilot city projects (namely, Kuala Lumpur, Johor Bahru, Kota Kinabalu, Kuching, and Kulim), and demonstrating interface examples for the upcoming MySmartCity Dashboard. The authors found that most of the baseline data for the indicators are country-level measurements, which might not be suitable or provide little information for the local authorities. Therefore, the authors recommend that in the future, this framework should be elaborated for better implementation on the ground through separation into several topics and documents (such as the eight volumes of BSI smart city standards) with greater flexibility and more room for expansion or improvement. 2. Malaysia could learn from the democratic and participatory governance style of European countries or cities, such as the Netherland’s “do-ocracy” concept that respects the needs of the citizens and stresses on citizen co-creations (government.nl 2018) and Barcelona’s living labs/Fab Cities programmes (Diez 2012, 2016; Diez and Posada 2013; March and Ribera-Fumaz 2016). For the former, under the new concept of “do-ocracy,” the Netherland government views citizens as active and independent stakeholders, preferring tailormade solutions that require the officials to co-think and co-solve with the citizens. In other words, Malaysia needs to rethink its smart (participatory and open) governance style and consider providing space for different types of democracy (see Bari 2012; Berger 2002; Diamond 1999; Hollo 2018; Paivarinta and Saebo 2006). The current semi-democratic authoritarian realm is inclined towards individual-liberals, and for the authentic citizenship regime (Jenson 2009) to grow in Malaysia, the civic-republicans or social investment (Deeming and Smyth 2015) style of democracy should be studied thoroughly and implemented in stages.

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Table 2 Related international smart city (and citizenship) standards Organization/Standards Organization: BSI 1. The Role of Standards in Smart Cities 2. Smart Cities Overview: Guide 3. Smart Cities: Guide to the Role of the Planning and Development Process 4. Smart Cities: Vocabulary 5. Smart City Framework: Guide to Establishing Strategies for Smart Cities and Communities 6. Smart City Concept Model: Guide to Establishing a Model for Data Interoperability 7. Smart Cities: Data Sharing Framework 8. Smart City Solutions: Procurement and Business Case Organization: ISO 1. Smart City Concept Model – Guidance for Establishing a Model for Data Interoperability 2. Sustainable Cities and Communities – Guidance on Establishing Smart City Operating Models for Sustainable Communities 3. Sustainable Cities and Communities – Vocabulary 4. Sustainable Development in Communities – Indicators for City Services and Quality of Life 5. Sustainable Development in Communities – Indicators for Smart Cities 6. Guidance on Social Responsibility 7. Security and Resilience – Community Resilience – Guidelines for Supporting Community Response to Vulnerable People 8. Framework for Integrated Community-Based Life – Long Health and Care Services in Aged Societies 9. Guide for Addressing Accessibility in Standards Organization: EC 1. CITYkeys Indicators for Smart City Projects and Smart Cities Organization: ITU-T 1. Smart Sustainable Cities: A Guide for City Leaders 2.

Smart Sustainable Cities: Master Plan

3.

Smart Sustainable Cities: Setting the Stage for Stakeholders’ Engagement Overview of Key Performance Indicators in Smart Sustainable Cities Key Performance Indicators Related to the Sustainability Impacts of Information and Communication Technology in Smart Sustainable Cities Key Performance Indicators for Smart Sustainable Cities to Assess the Achievement of Sustainable Development Goals

4. 5.

6.

Reference BSI-RoS (2014) BSI-PD8100 (2015) BSI-PD8101 (2014) BSI-PAS180 (2014) BSI-PAS181 (2014) BSI-PAS182 (2014) BSI-PAS183 (2017) BSI-PAS184 (2017) ISO/IEC 30182 (2017) ISO 37106 (2018)

ISO 37100 (2016) ISO 37120 (2018) ISO 37122 (2019) ISO 26000 (2010) ISO 22395 (2018)

ISO/IWA 18 (2016) ISO/IEC Guide 71 (2014) Bosch et al. (2017) ITU-T Y.Supp.32 to ITU-T Y.4000 series ITU-T Y.Supp.33 to ITU-T Y.4000 series ITU-T Y.Supp.34 to ITU-T Y.4000 series ITU-T Y.4900/L.1600 ITU-T Y.4902/L.1602

ITU-T Y.4903/L.1603 (continued)

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Table 2 (continued) Organization/Standards Organization: BSI 7. Smart Sustainable Development Model (SSDM) Report 2018: Tools for Rapid ICT Emergency Responses and Sustainable Development

Reference ISBN: 978-92-61-26411-6

Notes: BSI British Standards Institution; ISO International Organization for Standardization; EC European Commission; ITU-T The Telecommunication Standardization Sector of the International Telecommunication Union (ITU), United Nations

The Barcelona’s fab labs/living labs are digital social innovations to solve urban challenges and promote local society’s collaboration networks at small and larger scales, such as the project of Smart Citizen Kit in designing sensors (using low-cost sensing/home-based technologies) for collecting environmental data (Balestrini et al. 2014; Diez and Posada 2013; Fab Lab n.d.; Saunders and Baeck 2015). The emphasis on open source development community plays an important role in creating responsible citizens, and such experimentation activities are recommended to be fully supported by local authorities. In other words, the adoption of “Experimentation as Local Service” (European Network of Living Labs (ENoLL) and World Bank 2015) enables the local authorities to set up a department of experimentation. Under this participatory governance, local authorities will have a better direction and are serious in creating a specific department to manage the local experimentation ecosystem. This ecosystem consists of public-private-people partnership and employ four main activities, namely co-creation (co-design by users and producers), exploration (discovering emerging usages, behaviors, and market opportunities), experimentation (implementing live scenarios within communities of users), and evaluation (assessment of concepts, products, and services according to various criteria) (The Open University 2017, p. 85). 3. Since Malaysia has an Islamic country’s identity, the authors feel that it is also imperative to learn from the smart city blueprints implemented in other Islamic countries, such as Dubai Plan 2021 and Dubai’s Happiness Agenda (Dubai n.d., 2017; Virtudes et al. 2017; Zakzak 2019). The Dubai Plan 2021, launched in December 2014, reflects the vision of the Prime Minister of the UAE cum Ruler of Dubai, and builds on the success of the “Dubai Strategic Plan 2015” (Dubai 2019; Gulfnews.com 2014). This top-down plan describes the future of Dubai from holistic and complementary perspectives, starting with the people and society. This aspect describes the shared responsibility of all citizens in Dubai to deliver on the city’s aspirations in all areas and co-examines the society’s needs (Dubai 2017, p. 8). Specifically, this plan has six themes, served as Dubai’s vision, with each highlighting a group of strategic developmental aims for the city. For example, the first theme is the People: A City of Happy, Creative, and Empowered People. Accordingly, “the theme focuses on reinforcing the feeling of responsibility each individual must have towards themselves and their families and society in pursing and promoting education and personel development, and

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maintaining a healthy lifestyle, to enable them to play an active, productive, and innovative role in all aspects of the society and economy.” (Dubai n.d., p. 9) As compared to Giffinger et al. (2007)’s six smart dimensions, the six themes of the Dubai Plan have upheld the dimension of inclusive society (and cohesive families) and silenced the mobility dimension. Clearly, this plan prioritizes citizens over cars, and meeting society needs is the utmost concern of the government. Besides, one of the MSCF research outcomes is to bring Malaysia’s smart cities on par with global smart cities, especially on the competitiveness factor (MSCF 2019, p. 2). The authors question the need to chase or follow others, which to a certain extent, is being nudged by others’ technological advancements. Instead, Malaysia should move beyond its own nations-of-intent identity of a Malay-led plural society and focus on building a united nation with an authentic citizenship regime that involves responsible citizens in the nation’s unity building, with citizens prioritizing collective rights over individual rights and the government practicing participatory governance. This responsible citizenship building is in line with the Malaysian King’s urge for the rakyat to be more responsible in using communications technology and should not exploit the issues in ways that could be detrimental to national unity and harmony (Bernama 2019). 4. As for the Bangsa Pintar or smart nation’s vision setting, the authors suggest Malaysia to learn from the best practices of Asian or the neighboring countries with similar combinations of demography and culture, such as Singapore’s smart nation vision, as Singapore has frequently ranked at the top of the smart city indexes, such as the recent IMD Smart City Index 2019 (IMD 2019) that viewed from the citizen centricity perspective. The authors noticed that the vision stated in MSCF: Quality and smart living (MSCF 2019, p. 16) is not clear, which is a reflection of the lack of a precise top-down vision from the Malaysian leaders in the creation of a smart nation, unlike Singapore’s Smart Nation or India’s 100 Smart City Vision (Datta 2015; Hoe 2016). Singapore does not publish a smart city framework like Malaysia. Instead, its smart development has been mainly guided by the Sustainable Singapore Blueprint (2015), the Digital Government Blueprint (2018), Digital (Society) Readiness Blueprint (2018), and the Smart Nation: The Way Forward (2018). Prior to these top-down documents, in 2014, the Prime Minister of Singapore had announced the city-state’s vision to be moving towards a Smart Nation. As defined by the Smart Nation document, A Smart Nation is a Singapore where people will be more empowered to live meaningful and fulfilled lives, enabled seamlessly by technology, offering exciting opportunities for all. (Singapore 2018, p. 1)

This vision has assisted citizens to live meaningful and fulfilled lives, and ICT as an enabler for offering opportunities. “Government will partner the civil society to drive the digital readiness and harness technology for stronger social

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cohesion,” is also mentioned on how to become a Smart Nation (Singapore 2018, p. 9). Hoe (2016) concluded that the Smart Nation concept opens a new paradigm in collaboration with the citizens. The authors see this vision of Smart Nation is akin to Mahathir’s Bangsa Malaysia, but with clearer ideas on linking both smart development and citizenship, and without mentioning the nation’s territory, ethnicity, and loyalty issues. Contemplating on the loyalty issue, the formation of the idea of Bangsa Malaysia, and the mentality of the majority of Malaysian could still be stuck back in the wartime, as building a nation during wartime fears the loyalty of immigrants (i.e., the Chinese and Indian races) (Jenson and Phillips 1996). However, Hussain and Muttalib (2016) showed that the level of all citizens’ loyalty in Malaysia is high and rejected the hypothesis of low loyalty among Malaysians. Therefore, the authors suggest that Bangsa Malaysia could eradicate the loyalty issue and incorporate ideas from Singapore’s Smart Nations, to form the new Bangsa Pintar, a smart vision for Malaysia, that stresses on citizenship. 5. Malaysia should rethink about moulding the state of a “modern society” and instead learn from a “just society,” as practiced in Canada. Canada has the Canadian Citizenship Act 1947, Canadian Charter of Rights and Freedoms 1982, and Multiculturalism Act 1985. These bills have consistently provided protection (political and civil rights) for its citizens by ensuring long-term social justice and is widely referred by other countries. It would be good for Malaysia to incorporate the essence of these bills in building the smart citizenship locally. For instance, concerns about citizenship replaced concerns about loyalty, Malaysia could learn from the multi-ethnic Canada society to form the Malaysian Citizenship Act, as the Canadian’s bill has provided an underlying community of status for all their people in Canada that binds them as Canadians (Jenson and Phillips 1996). Concerning society-building practices, the Malaysian government might have overlooked that the future population will concentrate in the cities. Perhaps they do not see a smart city as a kind of state-building or united society-building; instead, they view a smart city as merely a strategy in achieving an advanced industrialized country status with a fully modern society. This modern society concept emphasizes state-of-the-art ICT infrastructures but not the mentality of building Malaysia as a shared responsibility society and united nation that is inclusive of all races and regions. 6. Last but not least, the Malaysian citizens should put aside all their differences and assume the responsibility for building the smart nation. The citizens have the responsibility and right to challenge the issues, as all the different parties hold different ideologies and narratives. Based on observations, the Malaysian citizens view city building as the responsibility of the authority with minimal responsibility to be borne by the citizens. Passive actions and lack of critical thinking (in cases such as littering and turning a blind eye or showing over-kindness towards corrupted leaders) as well as low levels of local innovations and co-creating projects initiated by the rakyat together with the authorities are observed. As mentioned in the previous section on the findings, the majority of individualliberal advocates have often attempted to claim their rights, the rights of their

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races or their region’s sovereignty rights, but they rarely think collectively as civic-republicans in order to fulfil their responsibilities and duties of building together a Bangsa Pintar (or a smart and united nation) and putting aside all the differences for the sake of the state. If the Malaysians continue to be ignorant and refuse to shift their thinking towards civic-republicans or social investment (Jenson 2009), then the nations-of-intent and Malaysian ideal nation state by Shamsul (1996) will remain as a utopian state for the Malaysian society.

Conclusion and Contributions In a nutshell, the MSCF framework has much room for improvement in shaping the smart Malaysian citizenship, and consequently, forming the “Bangsa Pintar Malaysia” (lit. Malaysia, a smart and united nation). In the authors’ opinion, the smart city is a strong concept and should receive great emphasis as a nation-building, united society-building, and citizenship-building tool, instead of merely as a technological solution or a tool for becoming a knowledge-based economy. Besides the practice of PPPs, participatory governance and social innovations from the ground should be promoted as potential backups for the city, similar to the bankruptcy state cases in Detroit, the USA or Greece, to avoid the “switching-cost” born by the cities and citizens (Kummitha 2018) and even the sharing of citizens’ private data to corporations for profits. The smart technology issue in building the smart citizenship (in terms of fostering the citizens’ sense of responsibility and duty but not on fulfilling the passive needs of citizens) is another interesting topic to explore in the future. This chapter contributed as the first academic publications in Malaysia which zoomed into the notion of how smart city development could cultivate or perish a good future for shaping the mold of smart Malaysian citizenship. It has impacted the invariable normative content of the technological driven smart concept, as this smart concept should and always need to include dimensions of forming smart citizenship. The originality of this chapter lies on the new perspective of argument following Jenson and Philips (1996), Jenson (2007, 2009)’s citizenship regime, and contesting Shamsul (1996)’s nations-of-intent and Malaysian ideal nation state. It sparks discussions on linking both smart city development and citizenship in Malaysia. Acknowledgments This study has received no research funding from any organization.

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Making Smart Cities “Smarter” Through ICT-Enabled Citizen Coproduction

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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ICT-Enabled Coproduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Concept of Coproduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Adoption of ICT to Coproduce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Characteristics of ICT-Enabled Coproduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Process of ICT-Enabled Coproduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Direct Interaction Between the Coproducing Actors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Motivated Coproducing Actors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shared Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Joint Decision-Making Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Potential Outcomes of ICT-Enabled Coproduction Through the Lenses of Public Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Advantages of ICT-Enabled Coproduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Challenges of ICT-Enabled Coproduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ICT-Enabled Coproduction Initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Case of “Leuven, Maak het Mee,” Belgium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Case of “SmartBike,” Belgium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

In the context of smart cities, the role of non-state actors (e.g., citizens, private sector) in the policy design and provision of public services has been spreading out, aiming for a more open and collaborative government. Particularly, one of the key pillars of smart city initiatives is the concept of “citizen-centricity” which entails the shifting of smart public services for citizens to smart public services by citizens (Bovaird and Loeffler 2012; Castelnovo 2019; Clarke 2018). In this context, the concept of citizen ICT-enabled coproduction is seen as an attractive A. P. Rodriguez Müller (*) Public Governance Institute, KU Leuven, Leuven, Belgium e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_63

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alternative of regular delivery of public services. That means that citizens are given the opportunity to engage in the public services’ value chain (Linders 2012; Bovaird 2007). Despite the efforts of cities to lead the smart city initiatives towards a citizencentric approach, direct engagement of citizens has not yet been achieved by most of the smart cities’ initiatives (Cardullo and Kitchin 2019a). Therefore, this chapter aims at providing a thorough review of ICT-enabled citizen coproduction in order to highlight the potential and challenges of adopting such approach in the coproduction of citizen-centric smart cities.

Introduction Citizens play a crucial role in smart cities as either direct or indirect recipients of the benefits of smart cities. At first, citizens were viewed as passive recipients of smart services, so the smart city was built on a techno-centric approach. Nevertheless, in order to overcome the criticisms and challenges of this approach, scholars and cities incorporated the concept of “citizen-centricity.” Citizen-centricity concerns the prioritization of people’s needs in the design and implementation processes of public services (Berntzen and Johannessen 2016; Lee and Lee 2014). In order to achieve a citizen-centric approach, citizens are engaged as active contributors to cities instead of mere users. This approach is considered part of the nature of a city’s “smartness” since citizens’ resources, data, and information are crucial for the smart city objectives. As Berntzen and Johannessen (2016) argue, the “smartness” of cities depends on how governments will effectively promote active cooperation, collaboration, and interaction with citizens. Yet, for most of the cities, the challenge remains on how to achieve the vision of citizen-centricity pursuing the switch from citizens’ passive roles to active engagement (Cardullo and Kitchin 2019a). This challenge can be overcome by focusing on public service users who can contribute expertise, insight, and resources at various levels of public service delivery, including service planning, service delivery, and service monitoring. In this context, the concept of citizen coproduction is seen as an attractive alternative of regular public services’ delivery which encompasses a power redistribution, meaning that citizens are given the opportunity to engage in the public services’ value chain (Bovaird 2007). Citizens can engage in the coproduction of smart cities via traditional and more innovative mechanisms enabled by (new) ICTs. The implementation of technological advances has extended the applicability of the coproduction model in government service delivery, resulting in transformative changes, particularly at the city level (Cardullo and Kitchin 2019a; Townsend 2013). Nevertheless, the adoption of ICT to engage citizens as co-producers is not without controversies. For instance, the “digital divide” – referring to uneven access to or use of ICT – is a well-known

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ICT-innovations’ obstacle for inclusiveness. Additionally, it is feared that other advances, such as algorithmic manipulation may disempower citizens, causing an obstruction for the realization of democratic public values. This may be the product of the government replicating existing paradigms, shifting farther away from a citizen-centric approach (Cardullo and Kitchin 2019b; Castelnovo 2018; Osborne et al. 2016; Uppström and Lönn 2017). The aim of this chapter, therefore, is to explore the potential of ICT-enabled coproduction in the smart city context by reviewing literature on coproduction, smart city, and e-government. This chapter presents an extensive review of all the relevant elements of ICT-enabled coproduction in order to highlight the potential and challenges of adopting such strategy. The reminder of the chapter is structured as follows. Section “ICT-Enabled Coproduction” introduces the concept of citizen (ICT-enabled) coproduction. Section “Characteristics of ICT-Enabled Coproduction” presents the different aspects that characterized ICT-enabled coproduction. Section “The Process of ICT-Enabled Coproduction” discusses the main elements of the process of coproduction related to the smart city literature. Section “Potential Outcomes of ICT-Enabled Coproduction Through the Lenses of Public Values” introduces a brief. Review about the potential of ICT-enabled coproduction to enhance or obstruct the realization of public values. Section “ICT-Enabled Coproduction Initiatives” introduces two examples of the implementation of citizen ICT-enabled coproduction in the context of smart city initiatives in order to illustrate some elements discussed in the previous sections. Finally, section “Concluding Remarks” briefly poses the conclusion of the chapter and suggests avenues for future research.

ICT-Enabled Coproduction The interest in public services coproduction ─ collaboration between government, citizens, and non-state actors in delivering smart public services ─ has been increasing on both the academic and professional level. This growing attention is mainly, but not exclusively, attributed to the continuing effects of the global financial crisis, a shortage of government resources, and the decline of trust in the public sector. Coproduction initiatives are therefore presented as an innovative alternative to deliver more democratic and better smart public services (De Vries et al. 2016). Coproduction, as a way to engage citizens, is also known as one of the main components to account for smartness in cities. Smart city initiatives, therefore, are expected to enable the engagement of citizens as it has the potential “to develop citizens’ sense of ownership of their city, enhance the local authority’s awareness of their needs, and ultimately reshape the citizengovernment relationship” (Nam and Pardo 2011). The ultimate objective is the co-coproduction of sustainable environments to achieve better quality of life (Ganapati and Reddick 2018).

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The Concept of Coproduction The concept of coproduction is seen as part of the New Public Governance paradigm which acknowledges the provision of public services as a more pluralistic model focused on networks and inter-organizational relationships (Bovaird and Loeffler 2012; Bracci et al. 2016). However, its definition is still a subject of concern in the public management research agenda due to its wide-ranging applicability and its somehow unclear distinction with related topics, such as collaborative governance, co-creation, civic engagement, citizens’ participation, and so on (e.g., Bracci et al. 2016; Voorberg et al. 2014). Nevertheless, progress on the definition of coproduction was made in the last years (Brandsen and Honingh 2016; Nabatchi et al. 2017). In this chapter, the ground of understanding of the coproduction concept lies in the definition of Brandsen and Honingh (2016, p. 431): Coproduction is a relationship between the employees of an organization and (groups of) individual citizens. It requires direct and active inputs from these citizens to the work of the organization. The professional is a paid employee of the organization, whereas the citizen receives compensation below market value or no compensation at all.

In addition, Nabatchi (Nabatchi et al. 2017) suggests some further clarifications on the definition of the coproducing actors. First, the state actors or “regular coproducers” are the professionals serving directly (e.g., government employees) or indirectly (e.g., employees of a nongovernmental organization, like a private company) in the government. Second, the lay actors or “citizen coproducers” are members of the community that voluntarily serve as citizens, clients, and/or customers. Therefore, coproduction involves the activities that public servants (in any sector) and services users/members of the community contribute to design, implement, and/or deliver public services (Pestoff et al. 2012), where all coproducing actors “make substantial resources contributions” (Bovaird 2007), co-creating public value, and/or private value (Alford 2009).

The Adoption of ICT to Coproduce The ability and possibilities to perform coproduction activities have increased due to the new solutions brought by technological advances. Specifically, ICT-enabled coproduction compromises the coproducing activities that take place using varied ICTs, from web-based platforms and mobile applications to sensors and artificial intelligence (Clark et al. 2013; Fugini and Teimourikia 2016; Lember et al. 2019; Linders 2012). Moreover, ICT can indirectly affect coproduction by providing realtime access and exchange of information. At the same time, the adoption of new technological advances give government more opportunities citizens as coproducers in a transparent and open environment that provides feedback into governance (Nam and Pardo 2011).

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The adoption of ICT in coproduction processes can also transform coproduction by scaling up the collection of citizens’ data (e.g., gamification strategies). Even technologies can substitute coproduction with fully (or partly) automated processes (e.g., predictive policing). In this vein, different technologies are strongly shaping coproduction processes: for instance, communication (e.g., mobile applications, wireless communications, online platforms), sensing (e.g., smart devices), actuation (e.g., 3D printing and robots), and processing technologies (e.g., Big Data analytics and AI) (Aceto et al. 2018; Lember et al. 2019). Yet, the wide array of technologies adopted for coproducing public services achieve different outcomes and present varying limitations (see section “Potential Outcomes of ICT-Enabled Coproduction Through the Lenses of Public Values”).

Characteristics of ICT-Enabled Coproduction To further delineate our understanding of ICT-enabled coproduction, this section is drawn on the typology of smart city services developed by Lee and Lee (2014) and built around coproduction literature. As shown in Table 1, there are different main dimensions that define citizen-centric smart city services. These dimensions have Table 1 Dimensions of ICT-enabled coproduction of smart public services Dimension Approach

Definition Who initiated the coproduced service

Level

Number and type of actors involved

Service cycle

Stage of the service delivery

Provider vs. beneficiary

Distribution of power and responsibility

Mode of technology

How ICT changes the shape of services

Delivery mode Service authority

How services are being coproduced Level of citizens’ autonomy for coproducing the service Functionalities of the implemented ICT

ICT pillar

Categories Top down Bottom-up Individual Collective Group Design/Planning Execution/ Implementation Monitoring/ Evaluation Citizen sourcing Government as a platform Informative Transformative Interactive Voluntary Communication Processing Actuation Sensing

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been adapted in line with our understanding of coproduction enabled by digital technologies. The first dimension is the type of approach, either top-down or bottom-up. With the inclusion of technologies in the coproduction process, the type of approach is even more relevant since citizens have more possibilities to not only engage but to lead the coproducing activities (e.g., Living Labs). Depending on who is going to play the initiator’s role, the coproduction initiative can be clustered into the two approaches. The top-down approach refers to the (“traditional”) coproduction process led by the government or regular producers. For instance, Apps from Antwerp was an initiative launched by the city of Antwerp in 2016 and 2017 (Belgium) to encourage residents, students, companies, and visitors to develop mobile applications for a better city. The aim is to stimulate creativity and innovation while making things better for and in Antwerp (Stad Antwerpen 2019). On the other hand, the bottom-up approach highlights the “citizen power” since these initiatives are started by actors from outside government. Take the case of Rodalia.info, a real-time public transport information platform using data provided by service users regarding the local train services in Barcelona (Spain) (see www. rodalia.info). In addition, the collaboration may involve other actors, such as research bodies and NGOs to improve a public service or to create a new one (Skaržauskienė and Mačiulienė 2017) such as in the case of CurieuzeNeuzen, a citizen science project initiated by Flemish universities, and the Flemish regional government in 2018. In this project, 20.000 citizens were selected to measure the air quality near their own house. The aim was to acquire a detailed map of air quality in Flanders (see www.curieuzeneuzen.be). Furthermore, as the definition of coproduction by Brandsen and Honingh (2016, p. 431) states, “[c]oproduction is a relationship between the employees of an organization and (groups of) individual citizens,” the involvement of citizens in the coproduction of public services can be individual, in group or collective. On the individual level of coproduction, a citizen collaborates directly with a regular producer, leading mainly to personal benefits. On the group level, one or more regular producers collaborate with a specific cluster of citizens (e.g., residents of a neighborhood). In this case, the main benefits can be either personal or societal. Finally, the collective level of coproduction entails the involvement of one or more regular producers within an organization or across multiple organizations (e.g., municipal council) and several citizens. The main difference between the collective and group level is that collective coproduction specifically aims for the provision of social benefits for an entire community. Citizens can engage in the coproduction of different stages of the smart service management. Based on Bovaird and Loeffler (2012), on the one hand, the concept of coproduction reflects the activities of co-planning, co-prioritization, co-managing, co-delivery, and co-assessment. On the other hand, Nabatchi et al. (Nabatchi et al. 2017) refers to four phases of the service cycle: co-commissioning, co-designing, co-delivery, and co-assessment. In this chapter, however, we will further discuss the typology developed by Linders (2012) who specifically discusses citizen

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coproduction in a digital setting. While her study is focused on the use of social media, the classification can be applied to other types of technologies. First, Linders (2012) divides the stages of the service delivery into three clusters: design, day-to-day execution, and monitoring. The design phase involves both the planning and design of public services and is characterized by strategic decisionmaking. The execution phase includes the day-to-day activities while the monitoring phase covers an assessment process that entails the identification and correction of issues and the evaluation of the efficacy of the service with the aim to generate opportunities for improvement. Second, Linders (2012) classifies the type of coproduction based on the provider versus beneficiary dimension, resulting in “Citizen-Sourcing,” “Government as platform,” and “Do It Yourself Government.” The latter, nevertheless, would not be considered as coproduction based on the abovementioned conceptualization (see section “ICT-Enabled Coproduction”) which indicates that coproduction entails at least the involvement of both citizens and regular producers. Citizen sourcing can provide more functional services in the smart city by offering the “wisdom of the crowd” in order to deliver more citizen-centric services. Moreover, it can overcome some limitations concerning time and space through what is called “situated engagement.” Citizen sourcing is also expected to improve the relationships and communications between citizens and government by the share of knowledge (Wu 2017). Yet, to really exploit the advantages of involving citizens in the design, execution, and monitoring of public services, regular producers should allow a redistribution of power among the coproducing actors. For citizen sourcing efforts, this could mean citizens contributing not only with ideas and feedback, but also with other kind of resources such as time and behavior (Fledderus et al. 2015). In addition, citizen sourcing initiatives entail more complex processes and integrated information, and, therefore, poses challenges beyond technological aspects such as data risks and changes in governance processes (You et al. 2016). Another type of ICT-enabled coproduction is known as Government as Platform (GaaP) wherein the government encourages people to actively engage in the co-design, co-execution, and co-evaluation of public services. GaaP illustrates the potential collaboration between citizens and regular producers in which governments are the source of information. That means that regular producers provide citizens with data to allow informed decisions and to increase citizens’ trust and legitimacy. As shown in Table 2, the way governments implement Citizen Sourcing and GaaP coproduction approaches will also depend on the public service’s stage (Linders 2012). Other dimensions of smart public services relate to the mode of delivery of the coproduced public services and the authority. First, while Lee and Lee (2014) propose two modes, passive and interactive, only the latter concerns coproduction. However, with the inclusion of ICT in the coproduction process, the direct interaction is not necessarily face-to-face but remains interactive, being one of the valuable contributions of digital technologies. Second, to be considered coproduction, the involvement of citizens must be voluntary. Therefore, the “mandatory” characteristic

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546 Table 2 Examples of ICT-enabled coproduction Design

Execution

Monitoring

Citizen sourcing Consultation and ideation Examples: eRulemaking, IdeaScale, eDemocracy Crowdsourcing/co-delivery Examples: CrisisCommons, Challenge.gov, PeerToPatent, government-run wikis Citizen reporting Examples: SeeClickFix, FixMyStreet

Government as a platform Informing and nudging Examples: Crime mapping, data mining Ecosystem embedding Examples: GPS, Gov open sourcing

Open book government Examples: Data,gov, Recovery.gov

Note: Adapted from Linders (2012)

of smart services by Lee and Lee (2014) is disregarded (cf. Brudney and England 1983; Parks et al. 1981; Pestoff 2006). ICT-enabled coproduction compromises the coproducing activities that take place using varied ICTs, from web-based platforms and mobile applications to sensors and artificial intelligence. In this sense, it is important to identify what Aceto et al. (2018) called “core technology pillars.” These pillars are defined according to the different functionalities of technology, but they can also overlap: communication defines the forms of interaction and dissemination of information as well as participation through, for instance, internet infrastructure, wireless communications, and mobile applications; processing is related to large-scale processing capabilities and some examples are Big Data analytics and AI; actuation can enclose more disruptive technologies such as 3D printing and robots; finally, sensing includes wearable devices, smart devices, sensing technology which are able to provide rich contextual data. Finally, the technologies implemented for coproduction might assume different modes: automatic, informative, and transformative. The informative dimension refers to the use of ICT to improve the service by gathering information. The transformative dimension refers to the transformation of traditional processes into new services. Finally, the automatic dimension entails the replacement of the coproducing actors by automating processes.

The Process of ICT-Enabled Coproduction The different dimensions discussed in the previous section together with the implementation of digital advances to coproduce might influence the traditional process of coproduction. Particularly, the adopted ICT(s) might have an impact on the coproducing actors’ interaction and motivations, the required resources, and the decision-making process (Lember et al. 2019). Therefore, in order to understand the potential of ICT-enabled coproduction in smart contexts, it is imperative to review the main elements of its process.

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Direct Interaction Between the Coproducing Actors Regular producers need to mobilize and activate citizens to coproduce successfully as well as take care of the conditions that enable better interaction among the coproducers (Steen and Tuurnas 2018). One of the contributions of digital technologies, as mentioned before, is that the direct interaction needed to coproduce is not necessarily face-to-face. Moreover, communication technologies allow to share and access information in real-time, and to adopt more user-friendly and citizen-centric forms of interaction. For instance, in the smart mobility sector, regular producers have also included gaming options allowing citizens to gain points due to the reporting of service-related issues via the mobile application (Lan et al. 2017). Also, regular producers adopt social media channels to improve the interactivity with citizens and to gather new ideas to accomplish the governments’ goals (Rodríguez Bolívar 2016). However, declining physical interaction can also obstruct the interaction and collaboration between coproducing actors. In order to overcome these challenges, regular producers can facilitate the interaction with citizens by simplifying the coproduction tasks (Kennedy 2005), supporting the collaboration, coordinating the different actors’ interests, and more importantly, ensuring that value is co-realized (Alford 2002; Bovaird and Loeffler 2012; Lember et al. 2019).

Motivated Coproducing Actors Citizens’ engagement in coproduction is ensured by a combination of their (selfcentered and/or community-centered) motivations and capabilities. Salience of the public service and the ease of becoming involved in the coproduction process are also important factors (Pestoff 2012; van Eijk and Steen 2016). In turn, regular producers’ engagement is influenced by their work environment, such as the level of autonomy, perceived organizational support, and red tape (van Eijk et al. 2019). The adoption of communication technologies may help in motivating actors to coproduce by lowering the threshold to engage (Lember et al. 2019), since it enhances the speed and reach of communications, and promotes multilateral and rich information exchange between different actors (Fugini and Teimourikia 2016; Meijer 2016). Yet, ICTs might change the perception of personal competence by demanding new and specific skills to coproduce, which might lead to less motivation. Moreover, there is the pitfall that highly educated individuals will have better access and time to participate than other disadvantaged citizens (Rodriguez Müller et al. 2021). Therefore, when adopting ICT-enabled coproduction, regular producers need to overcome challenges related to the citizens’ willingness and capacity to coproduce. For instance, some strategies might involve the inclusion of gamification or the adoption offline activities to support the ICT-enabled coproduction initiatives (Le Blanc 2020; Susanto et al. 2017).

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Shared Resources Coproduction is about coordinating time and efforts of both regular producers and lay actors. It allows the government to combine citizens’ resources with its own, which may further enhance government cost savings and better-personalized services (O’Reilly 2010; Linders 2012). Coproduction efforts will also require investment (e.g., personnel, expertise) and support from the regular producers and the political level for ICT-enabled coproduction to work effectively (Le Blanc 2020). Citizens also provide expertise and information that is not available otherwise (Loeffler and Bovaird 2018). In this context, communication technologies can broaden up the scope of citizens’ inputs (Lember et al. 2019). For instance, in a smart bike-sharing system, it was observed that citizen-users can voluntarily report service-related issues helping the provider to improve the service in terms of regulation of bikes, technical issues, and software problems (see section “ICTEnabled Coproduction Initiatives”). Moreover, coproduction literature indicates that the coordination of expertise, knowledge, resources, technology, and processes contributes to better outcomes than when working independently (De Vries et al. 2016). However, with the growing involvement of private actors, due to their technological and financial capacity, the role of the government runs the risk of becoming ambiguous or disintermediated. This may be the product of private companies assuming government’s tasks and functions, serving as intermediaries between the government and its citizens (Klievink and Janssen 2012; Ma et al. 2018; Rodriguez Müller and Steen 2019).

Joint Decision-Making Process The last and more challenging aspect concerning the process of coproduction is the involvement of all coproducing actors in the decision-making process. As discussed before, coproduction challenges the traditional relationship between regular producers and citizens (Moynihan and Thomas 2013), while ICT may further change the game by giving citizens more independence and, at the same time, more responsibility. For instance, the mobile-app Firedepartment alerts citizens if someone nearby needs assistance, encouraging them to cooperate actively with the paramedics. They are responsible for indicating their level of training in cardiopulmonary resuscitation (CPR), and then, to provide CPR to the victim until the ambulance arrives (Paletti 2016). The potential redistribution of power will depend on the role assumed by the citizens, from active coproducers with full responsibilities to passive consumers (Lember et al. 2019). Yet, coproduction is criticized due to the possibility of the government offloading its responsibilities to the citizens-users. For instance, Linders (2012) points out that in ICT-based coproduction, the government might still hold the end responsibility. Furthermore, the implementation of ICT to coproduce may also redistribute power and control

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towards specific groups in society instead of towards citizens in general due to uneven access to/and or engagement in coproduction initiatives.

Potential Outcomes of ICT-Enabled Coproduction Through the Lenses of Public Values ICT-based coproduction of public services is alleged to improve the realization of public values in the city through the collaboration between diverse stakeholders (De Vries et al. 2016; Lember et al. 2019). Public values theory is considered one of the most significant subjects in matters of public administration and policy (Jørgensen and Bozeman 2007). It refers to “the procedural ethics in producing public services [. . .] and outcomes made possible by producing public services” (Bryson et al. 2017, p. 451). In this chapter, “public values” is understood as a normative concept used to give direction to the public action or to legitimize it (Witesman 2016), providing normative consensus about the rights and obligations of citizens, and “the principles on which governments and policies should be based” (Bozeman 2007 p. 17). As Aschhoff and Vogel (2018, p. 776) claim, “when co-production is successful, a service is “better” (in whatever terms; e.g., efficiency) than if it had been produced by a state actor alone.” In the same line, Meijer (2015) claims that “framing e-governance in terms of its contributions to society [the production of public values] is essential for its success” (p. 205), meaning that ICT-based coproduction of public services is also expected to be guided by the aim of using ICT to co-create public value. This implies that ICT is not value-neutral but instead has the potential for positive or negative impact on public values. Although technology can “follow its own logic,” ICT users, including cities, are also responsible for the value embedded on the technologies and its outcomes (Bannister and Connolly 2014; Skaržauskienė and Mačiulienė 2017). Building on coproduction literature (Jaspers and Steen 2019), coproduction is expected to co-realize different public values that can be clustered into three groups: a. Public values related to the service delivery, such as efficiency, effectiveness, quality of the service, user satisfaction. b. Public values related to the relationship between citizens and regular producers, such as trust, accountability, responsiveness, transparency. c. Public values related to the democratic quality of the service delivery process, including empowerment, equity, social capital, diversity, inclusion. In the smart city context, the implementation of ICT-enabled coproduction might entail the alteration of public expectations about the co-realization of public values. That means certain values like transparency, e-inclusion, and equality might get more relevant in digital settings. For instance, attitudes and expectations of citizens about the reliability and friendliness of a public sector platform change over time along with technological advances (Karkin et al. 2018).

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Advantages of ICT-Enabled Coproduction The technological advances have made co-production more feasible and relevant, both in terms of the way citizens can engage in the coproduction process and of the outcomes of such process (Johnston 2010; Le Blanc 2020). A classic example is FixMyStreet, a map-based website and mobile application used by people in the United Kingdom who want to report problems that need the attention of the local authority, such as potholes or broken streetlamps (Matthews et al. 2018). Another case of ICT-enabled coproduction is AirCasting, consisting of an open-source platform, which allows people to share health and environmental data using their smartphones that otherwise would be very difficult to gather. The collected data was used to inform personal decision-making and public policy (HabitatMap 2020). Service quality can also be enhanced through expertise and information provided by citizens coproducers, that is not available otherwise (Loeffler and Bovaird 2018). Linders (2012, p. 451) assessed advances of ICT (in the context of coproduction) to provide unique means for real-time, community-wide coordination, “presenting tremendous opportunities for data-driven decision-making, improved performance management, and heightened accountability.” These initiatives can also improve the efficiency of processes, fasten response times, and make them more secure/reduce human errors. Since ICT-enabled coproduction allows the government to combine citizens’ resources with its own, it is seen as a way to enhance government cost savings and better-personalized services (O’Reilly 2010; Uppström and Lönn 2017). Moreover, ICT-enabled coproduction is expected to increase inclusion, democracy, and participation as it might provide the same opportunities to different actors, empower people/foster local activism, unlash social innovation, and reinvigorate democracy (Linders 2012; O’Reilly 2010; Uppström and Lönn 2017). Some studies perceive digital initiatives as a way to bring the dispersed populations closer, allowing more citizen participation, and coproduction as a way to better democratic quality (Schwester 2009; Verschuere et al. 2018).

Challenges of ICT-Enabled Coproduction The adoption of ICT in the public sector is not without controversies. For instance, the “digital divide,” referring to uneven access to, or use of ICT, is a well-known obstacle for inclusiveness. The “digital divide” – including digital gender inequality (Choi and Park 2013; van Doorn and van Zoonen 2008) as well as education and age-related inequality – implies that the already more empowered citizens will have better access, time, and skills to participate than other disadvantaged citizens (Lember 2017). Therefore, the reliance on a small and potentially unrepresentative segment of the population risks loss of legitimacy (Bovaird 2007), unequal access to public services, “empowering only the empowered” (Linders 2012). In a recent study, we observed that the digital divide predicts user’s choice of traditional channels and e-government channels compared to new digital channels (Rodriguez Müller et al. 2021). The prevalence of using traditional channels while

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cities are increasingly investing and adopting technological alternatives is puzzling, especially when this phenomenon is observed in smart services that are typically associated with a high uptake of digital channels (Castelnovo 2019; Wu 2017). Furthermore, the adoption of technological advances might direct the power and control towards particular social groups such as highly educated, ICT-skilled citizens (Reddick and Anthopoulos 2014; Rodriguez Müller et al. 2021). This scene illustrates the risk that governments replicate traditional paradigms, moving even further away from the citizen-centric approach aimed by smart cities initiatives (Lember 2017). As Castelnovo (2018) claims, “irrespective of what the participation mechanisms implemented are and how innovative the tools that can be used are, in many cases participation is little more than a formality” (p. 115). Finally, alike traditional coproduction, ICT-enabled coproduction is feared to include conflict between values being (potentially) co-created such as efficiency and effectiveness, yet here new tensions might arise such as between privacy and openness, or between the expense of setting up a digital platform and the long-term savings it offers (Rodriguez Müller and Steen 2019).

ICT-Enabled Coproduction Initiatives The way to overcome the challenges and exploit the advantages and promises of ICT-enabled citizen coproduction will be contingent on the way the strategies are designed and implemented by the coproducing actors. As such, Webster and Leleux (2018) proposed a series of mechanisms to engage citizens in the coproduction of smart public services, including hackathons, living labs, faklabs, marker space, smart urban labs, citizens’ dashboard, gamification, open datasets, crowdsourcing, and online reporting. These types of mechanisms are examples of ICT-enabled coproduction strategies to engage citizens in the context of smart city initiatives. In order to illustrate some of the possibilities of citizen ICT-enabled coproduction, two cases are briefly presented below. The first case concerns the citizen participatory process organized by the city of Leuven (Belgium), involving citizens as co-designers of public policy. The second case is about a smart bike-sharing system in Belgium, engaging citizens as co-monitors of the smart service.

The Case of “Leuven, Maak het Mee,” Belgium In 2019, the city of Leuven (Belgium) launched their first large-scale citizen participatory initiative called “Leuven, maak het mee” (a wordplay that implies both experience and co-create the city in Dutch). The aim was to engage citizens as co-designers of the strategic multi-annual plan of the city (2020–2025) by gathering their ideas and proposals over 10 different topics. The topics were determined by the policy memorandum which was drawn up in consultation with experts and city officials. Three main goals were pointed out by the city: (a) to inform the citizens about the programs, show and explain the objectives clearly and raise

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awareness; (b) to obtain commitment of the citizens by asking “how to achieve these objectives together?”; and (c) to make it concrete by obtaining new inputs to achieve the objectives (Rodriguez Müller 2021). With the aim of reaching as many citizens as possible, the city implemented a digital participation platform as the project’s main channel. The platform was outsourced by CitizenLab, a Brussels-based SaaS start-up in civic tech. The company provides an online platform that can be used to engage citizens in a variety of initiatives, including participatory budgeting, survey and polling, voting, collection of ideas, among others. CitizenLab has been recognized as one of the Top European Social Impact start-ups since the launch of the platform in 2015 (2019, DT50 awards at the TechCrunch Disrupt conference, Berlin), and the Top “Digital and Inclusion” start-up awarded by VivaTech Paris and Métropole du Grand (2019) (CitizenLab 2020). Within the initiative, citizens could post an idea, comment, or vote during the period of 6 weeks. They could directly participate through the online platform via the link www.leuvenmaakhetmee.be, with or without an account. The account would allow the city to send them newsletters, feedback from the ideas posted or liked, related events, among other information. When the idea was posted, other citizens could read, vote, and comment on them. In order to overcome challenges related to digital coproduction, such as the digital divide, the city also offered an offline opportunity for citizens to participate. Each resident received a postcard, which could be filled out with their ideas and sent back to the city without any cost (CitizenLab 2020; Rodriguez Müller 2021). Between April 30th and June 9th, 2331 ideas were posted by citizens of which approximately 22% were collected through postcards and included later into the platform by the platform’s administrators (see more results in Table 3). As mentioned before, the ideas cover the ten priorities outlined by the political level in the policy memorandum for the 6-year governance term. The most popular topics were (smart) mobility, leading the group with 640 ideas gathered, followed by Streets and Squares (n ¼ 259) and Nature and Biodiversity (n ¼ 213). The topics with less citizens’ proposals were Technology (n ¼ 35), Service Provision (n ¼ 54), Citizenship (n ¼ 61) and Employment, Economy and Trade (n ¼ 64). Although the platform has been implemented in other cities around the globe, Leuven was one of the first ones in providing personal feedback to each of the citizens who provided an idea or commented on an idea of other citizens. More than 96% (around 2238 ideas) of all gather ideas received official feedback by the administration of Leuven.

Table 3 Results of “Leuven, maak het mee”

Indicator Registrations Citizens’ proposals Comments on proposals Votes on proposals

Total 3007 2331 2253 30,328

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A systematic assessment was implemented to evaluate all the ideas gathered. Before being approved for implementation, in the context of the ten priorities established in the memorandum, citizens’ ideas had to pass two evaluation processes. First, the domain experts of each of the ten priorities needed to approve the idea based on its feasibility. Second, after collecting all the ideas (online and offline), domain experts read and bundled all the citizens’ proposals, and then presented them to the mayor and city council who decided if they match with the city goals. The city council had to approve the decisions of the domain experts, and later, the feedback to be provided to the citizens. Ten percent of the ideas were disregarded due to unfitness with the political vision of the city. Depending on the type of citizens’ proposals, some were taken by the unit responsible (such as mobility), and some ideas were selected to be later co-created with external stakeholders, including citizens (Rodriguez Müller 2021). From the beginning of 2020 till 2025, the final ideas are expected to be implemented by the city. Therefore, conclusions concerning the actual impact of the participatory initiative on the decision-making process is still to be seen. Yet, new projects have already been started with the support of the online platform by other units of the city, such as Buurtmobipunten (Neighbourhood E-hubs) or Beweegbanken (Sport Benches) supported by the Sports Administration of the Flemish government. The continued use of the platform by the city to engage citizens shows that beyond the impact of “Leuven, maak het mee,” it has been a game changer for the role of citizens in the design and implementation of public policy and public services in the city.

The Case of “SmartBike,” Belgium The second case concerns a smart bike-sharing service (hereafter SmartBike) located in one of the major cities in Flanders, Belgium. SmartBike was launched in 2011 by the Department of Urban Development of the city and provided by a private company who offers the service internationally. The aim of the city is to provide a more sustainable and healthy form of public transportation, available 24/7 and to offer a solution to the “first/last mile problem,” filling the missing links between the bus and tram networks (Rodriguez Müller and Steen 2019). The service has been growing and becoming even more popular since day one. It is one of the most “successful” smart bike-sharing systems in Belgium in terms of the growing number of users, currently having more than 60.000 active annual memberships. According to the latest data from the city’s open data portal, more than 80% of its inhabitants live within 5 min by foot from a SmartBike station. The case is particularly interesting to illustrate the potential of ICT-enabled coproduction because of two aspects. First, the sharing feature of the service entails the engagement of multiple stakeholders and the “crowd,” while the “smart” aspect implies that the interactions between the coproducing actors and their context are redefined (Ma et al. 2018; Webster and Leleux 2018). Second, the service involves the engagement of citizens in coproduction efforts. SmartBike, with the aim to

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improve the efficiency of the service and responsiveness towards its users, engages its users in the evaluation phase of the service management through a reporting system. Citizen-users can report service-related issues they experienced concerning the bikes and bike stations. The report-service is not part of the rules of the service, meaning that citizens have the right to choose how active they want to be. Besides the voluntary nature of the monitoring system, the number of citizens’ reports reached 19,674 just in 2019. To connect with most of its users, SmartBike offers a variety of reporting channels from traditional to new digital channels, such as visiting the office or making a call, email, website, and social media. Moreover, in order to gather better insights from the citizen-users and provide a better user experience, SmartBike launched a mobile application through which citizens can indicate more precisely the issue they are reporting, location, number of the bike or station, among other details (Rodriguez Müller and Steen 2019). With the implementation of the mobile application, citizens could report issues in-situ and quickly on-the-go. This phenomenon is also known as “situated engagement” and it is one of the mobile’s participation greatest promises (Ertiö et al. 2016). For instance, a study on citizen reporting of a smart public services shows that the reports made by the citizen-users using a mobile platform increased the percentage of resolved problems, leading to a more effective service provision (Allen et al. 2020). However, it seems that traditional channels continue to be the most used by citizens over (new) digital channels to report service-related issues (Ebbers and van de Wijngaert 2020). A recent study on SmartBike examined user-reporters actual behavior and found that the digital divide determinants, satisfaction with the mobile application and users’ experience with the service can explain the users’ choice of traditional and e-government channels over the newly implemented m-governments channels (Rodriguez Müller et al. 2021). Therefore, the strategy of SmartBike to present citizens both offline and online opportunities to engage in coproduction efforts presents an alternative to overcome some of the challenges posed by the digital divide. To sum up, both cases present some of the opportunities and challenges of citizens’ coproduction in different contexts, including citizens as co-designers and as co-monitors. The cases illustrate the potential of ICT-enabled coproduction while highlighting the need of offline opportunities for less tech-savvy citizens. In addition, the cases show the need for a reconfiguration of the role of citizens as they are both users and providers of the service’ information (Docherty et al. 2018).

Concluding Remarks Smart cities around the globe, aiming to improve smart public services, are confronted with the need to adopt a citizen-centric approach in order to overcome challenges such as resource constrains and lack of information. An alternative to overcome some of these challenges is the reliance on public service users through

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coproduction efforts in the different stages of public service delivery, including service design, service execution, and service monitoring. While we have presented two cases where citizens play an active role in the process of ICT-enabled coproduction of (smart) public services, this is not the case for most smart cities. In turn, the role of citizens in the conception, development, and governance of smart cities needs to be reconfigured. In this context, ICT-enabled citizen coproduction is presented as an attractive alternative for overcoming the challenges towards the building of truly citizen-centric initiatives (Cardullo and Kitchin 2019b). Therefore, this chapter presented a thorough overview of ICT-enabled coproduction and its potential in the context of smart cities. A description of the characteristics of ICT-enabled coproduction, the different elements of the coproduction process, and its potential to enhance or obstruct the co-realization of public values was presented. Moreover, two cases were introduced as an illustration of some of the possibilities and challenges behind the engagement of citizens in digital coproduction initiatives. While we have shown the main aspects of ICT-enabled coproduction in a smart city setting, there is a need for empirical evidence and action research to uncover the potential of ICT-enabled citizen coproduction to co-realize a citizen-centric smart city. In addition, there is a need to focus on diverse factors beyond technological factors, broadening the focus to social and institutional aspects. To sum up, the chapter outlines the surface of how relevant the convergence of smart cities and ICT-enabled citizen coproduction can be, and calls for further dialogue and in-depth analysis complementing the views exposed.

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Part IV Energy Dimension

Smart Cities and the Challenge of Cities’ Energy Autonomy

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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Concept of Smart City . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Energy-Autonomous Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Need for Energy Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Urbanization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Challenges for the Transition to Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sociopolitical Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Financial Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Technological Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Environmental Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart City Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transition to Energy Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodologies and Tools in Buildings of Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . General Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Energy Saving and Management Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Energy Management and Saving in the Building Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Decision Support Systems (DSS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Existing Methodologies and Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Greek Reality: Greek Legislation – Directive 2010/31/EU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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V. Meleti (*) · V. Delitheou Department of Economics and Regional Development, Panteion University of Social and Political Sciences, Athens, Greece e-mail: [email protected]; [email protected]; [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_50

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Abstract

It is now more than ever understood that efforts to slow down climate change at all levels need to be stepped up. Characteristic are the findings presented in the recent IPCC report (October 2018) confirming that global warming by about 1.0
C above preindustrial levels is due to anthropogenic activities, with global warming reported to be likely to reach 1.5
C in the next 12–32 years based on current trends. The report’s conclusions explain the potential catastrophic impact of the 1.5
C growth scenario and underline the fact that mankind has only 12 years to reduce global net anthropogenic CO2 emissions by about 45% compared to 2005 levels, reaching “net zero” around 2050. This means that the remaining emissions should be offset by the removal of CO2 from the atmosphere (Papaioannou et al. 2015; Athanassoulis et al. 2018).

Introduction Cities, because of the rapid urbanization of recent decades, are at the center of attention to mitigate and adapt to climate change. It is a fact that about 55% of the world’s population lives in urban areas, and according to the UN, this figure is expected to rise to 68% by 2050 where the total population of the world is estimated to reach around 10 billion. In Europe, this figure is 75% of its population. At the same time, it is a fact that cities consume more than 75% of global primary energy and are responsible for 50–60% of global greenhouse gas emissions (UN, Department of Economic, Social Affairs, Population Division 2018). As presented in the work of Papaioannou et al. (2015), given that urban emissions are largely linked to energy requirements for urban transport and consumption in the building sector, criteria such as reducing urban sprawl and increasing urban density, enhancing sustainable urban mobility, saving energy in buildings and boosting renewable, etc. are critical urban planning factors to mitigate climate change (Papaioannou et al. 2015, Athanassoulis et al. 2018).

The Concept of Smart City As the majority of world’s population lives in cities and residential areas, they are responsible for more than 70% of global energy-related greenhouse gas emissions and the need for modern smart cities to become central agents of climate change is undisputed (Delitheou and Meleti 2019). The concept of a Smart City is different and its definition varies from city to city, or country to country, depending on the rate of growth, the tendency for change and the potential for evolution, the resources, and culture of each people. A clear and commonly accepted definition of the concept of Smart City has not yet been formulated. Smart City is perceived differently in Europe, Asia, America, or Australia. Smart City is a city where traditional networks and services are made more efficient by the use of digital and telecommunication technologies (EP 2014), for the benefit of

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citizens and businesses. Towards this end, the European Union has started investing in research and innovation in the field of Information and Communication Technologies (ICT) in recent years, by developing policies to improve the quality of life of citizens and make its cities energy efficient in line with its 20-20-20 targets (EC 2016). The concept of a Smart City does not only include ICTs but also utilizes alternative energy sources and policies to reduce carbon dioxide emissions (EC 2013a). Smart City means smarter urban transport networks, upgraded water and sanitation network infrastructure, energy efficient lighting networks, as well as more efficient ways of lighting and heating buildings. Among these, a Smart City should be characterized by more interactive city management by local authorities, ensuring safety and comfort for citizens (EC 2016). It is a combination of communications infrastructure, alternative energy infrastructure, social infrastructure, technologies, and services for easy access to knowledge. The concept of a Smart City is directly linked to smart development, which refers to the transition needed to achieve greater efficiency by coordinating all the entities involved in the Smart City to this end (Batty et al. 2012). For a city to be classified as smart, it is not enough to be smart only with its economy and energy efficiency, but as a basic requirement, local authorities and governments must respect and understand the social conditions in urban centers, adapting the evolution of technology to the needs of their residents, facilitating their daily lives. These conditions and the needs of the citizens are the main reasons for the way in which large ICT companies (such as IBM, CISCO, Microsoft, Oracle, SAP) approach the market and develop their products and solutions (IBM 2009). The concept of a Smart City combines technology with society and the state in order to create urban centers characterized by: smart economy, smart citizens, smart governance, smart travel, smart environment, and smart living (IEEE 2016a; Giffinger et al. 2007). The strategy and goals for the smart cities era are based on three pillars: sustainability, efficiency, and high quality of life. Governments and local authorities should invest in new infrastructure and implement the right policies to ensure adequate electricity supply networks and modern environmentally friendly transport, modernized water supply and efficient waste management, in combination with new communication and information technologies and green technologies that are environmentally friendly. At the same time, they should improve the quality of living, creating a sense of security and comfort for urban dwellers, modernizing and digitizing public services (e-government), as well as facilitating access to health and education.

Smart Energy-Autonomous Cities An in-depth understanding of available and up-to-date mitigation technologies and practices, as well as environmentally effective policies, measures, and instruments, is crucial for decision-making centers to accelerate the creation of sustainable cities/netzero carbon dioxide emissions, where the near future requires. At the same time, local and government levels require the monitoring of greenhouse gas emissions over time and, in addition, the promotion of relevant legislative frameworks and investment plans to move towards a low carbon economy (Athanassoulis et al. 2018).

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Important allies in this direction are modern information and communication technologies (ICTs), new technologies/innovations and new approaches, such as the “smart cities,” through which energy management can be more efficient (Giotitsas et al. 2015). In particular, by increasing the penetration of relevant applications and services of general interest in smart cities, such as sensors, smart meters, small-scale power generation from hybrid RES plants, smart grid distribution, energy storage technologies, etc., in combination with the latest applications and ICT innovations such as AI, IoE, big data analytics, blockchains, cloud, XaaS, etc. can have significant positive impacts on a low CO2 economy (Calvillo et al. 2016; Li and Du 2018). In addition, the global electrification trend is driving smart cities to adopt strategies for the development of electricity, the creation of charging points networks, the optimization of sustainable urban mobility, etc. further facilitating the deregulation of the energy sector (Boykova et al. 2016; Bahramirad 2018). At the same time, with the institutional liberalization of energy markets, other parties are allowed to participate in the local energy mix as they provide “clean” energy to the grid, thereby opening the way for small scale self-producers – consumers (presumes) to become active members of the energy system providing/ selling their surplus energy to the grid (Marta 2018). In this context, the “smart energy-autonomous cities of tomorrow” could consist of multiple presumes, which generally combine plug-in electric vehicles (EVs) in vehicle-to-grid/grid-to-vehicle shapes, where in combination with applications of microwave technologies and ICT services can cause local and potentially large-scale positive impacts on the network (Choi and Min 2018). At a Smart City level, modeling an integrated urban energy system is a complex task, which aims, among other things, at optimizing resources and creating synergies, taking into account various aspects, such as the temporal distribution of energy demand, the need for carbonization, the energy role of buildings, the voltage of electric power, the need for energy efficient transport, etc. (Calvillo et al. 2016). The building sector is at the heart of this work. According to official data, the home and tertiary sector at EU level accounts for 40% of total energy consumption and 36% of total CO2 emissions. As you can see, increasing the energy efficiency of buildings, coupled with the use of renewable energy, are major challenges for the future and significant investments should be made in this direction. In the European Union, accelerating the growth of buildings with almost zero energy consumption is being promoted by Directive 2010/31/EU. In this context, the role of prosumer towards the decarbonization of the energy sector and the energy autonomy of cities is important and promising. Different hybrid power systems, at all scales, have emerged in recent years, combining different energy production and storage technologies, depending on the region’s energy potential. An important issue that prevents from further penetrating at a low level – prosumer, in addition to the technical specifications – is high CAPEX, OPEX, and LOCE. In this context, the most suitable renewable energy sources are characterized by solar and wind, due to the constant downward trends in their prices. It is characteristic that during the last decade the prices of photovoltaic and wind turbines have fallen by more than 70% and 40%, respectively (Giotitsas et al. 2015).

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Need for Energy Smart Cities Urbanization Cities are the most important factor in meeting the key challenges of society and the economy in Europe. According to the 2016 Roadmap of the European Innovation Partnership for Smart Cities and Communities, 78% of European citizens live in cities and 85% of EU GDP is generated in them (EIP 2016). Cities account for about two-third of energy consumption, 60% of water consumption, and 70% of greenhouse gases produced worldwide (UN 2016). The ever-increasing urbanization trend observed in recent years raises serious problems in the structure and operation of modern cities. This is a huge challenge, as both the existing and the new cities to be created will have to be distinguished by the characteristics of sustainability, energy efficiency, and infrastructure integrity in order to provide an ideal living environment for their residents (Papastamatiou et al. 2014). In an ever-changing political and economic environment, the opportunities offered by the application of constantly evolving technology applications can make a significant contribution to upgrading the management capacity and efficiency of the state and local and regional authorities in the modernization and sustainability of cities. Climate change is an important global phenomenon. Without action to reduce global greenhouse gas emissions, global temperatures are likely to rise above 2
C above preindustrial levels and the increase could reach 5
C by the end of the century. This would have a huge impact on the landscape and sea level worldwide. Action to tackle climate change and reduce greenhouse gas emissions is therefore a priority for the EU. The EU has also set a target to reduce greenhouse gas emissions by 80–95% by 2050 1990 levels. The EU and its 28 Member States have signed both the United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol and the new Paris Agreement on Climate Change. The EU’s first climate and energy package set three key objectives for 2020 (EC 2008): (a) reducing greenhouse gas emissions by 20%; (b) increasing the share of renewable energy sources to 20%; and (c) a 20% improvement in energy efficiency. Framework 2030 proposes new goals and measures to make the EU economy and energy system more competitive, secure, and sustainable. In particular, it includes, inter alia, the following objectives (EC 2014, 2016): a reduction of at least 40% in greenhouse gas emissions from 1990 levels, a share of renewable energy in final energy consumption of at least 27%, and at least 30% energy saving. As can be seen from the above, modern cities face great challenges. Climate change, growing populations, air pollution, global competition, and the economic crisis are just some of them. These challenges combined with European Directives and National Legislation push cities to adapt to an ever-changing and complex environment. However, although there are several IT tools that support cities as well as methodologies for better energy management, research to date has shown the absence of a number of important components related to energy management and energy saving at the level of building infrastructure. Studying and evaluating the methodologies and tools developed to date in support of Smart Cities shows that

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most of them (Papastamatiou et al. 2017a; Marinakis et al. 2017): (a) do not reflect the current performance of smart cities; (b) do not monitor the process of change in a Smart City by checking the efficiency of the city in achieving the set goals; (c) do not evaluate how smart the city is based on objective indicators; (d) do not enable comparison between smart cities and possibly classification; (e) do not propose energy saving solutions; and (e) do not combine data from different sources and usually do not take into account in their calculations user data. To meet these challenges, it is necessary to create an easy-to-use tool that combines energy management and energy saving, using prototypes and practical tools that will holistically address these problems by analyzing related factors and induced interactions.

Challenges for the Transition to Smart Cities As cities are constantly evolving, the challenges created must be taken seriously so that population growth, economic growth, and social prosperity go hand in hand. The Smart Cities model can lead to better design and efficient management of modern cities with sustainable development. Identifying and analyzing the challenges arising in the transition to Smart Cities generation are needed to facilitate future policies to be implemented by governments and local authorities (Monzon 2015).

Sociopolitical Challenges Despite potential differences in how each country or city perceives the concept of the Smart City, all governments face the same sociopolitical challenges as they transition to the era of Smart Cities. These challenges focus on society, people, quality of life, governance and can be summarized as follows (Monzon 2015): unemployment and poverty, criminality, social cohesion and security, multiculturalism, health and education problems, changing the governance model, gaps between government and citizens, and emergency management. In addition, stakeholder groups (organizations, governments, citizens, businesses, NGOs, builders, etc.), involved in the evolution of cities and the creation of Smart Cities, often have conflicting interests. In most cases, there are several organizations or businesses or stakeholders involved in a project. Each of them, however, has different priorities, despite the common purpose for which they work. When a city moves to develop and create a more sustainable profile, political and private interests must be bypassed. A critical parameter for Smart Cities is changing habits and the degree to which citizens can adapt to technological developments. Citizens should be willing to use innovative technologies and to come up with new, smart-friendly, city-friendly ideas

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that greatly help their daily lives. Citizens should want to improve the environment in which they live and work (Chatterjee and Kar 2015). The importance of the strategy to be implemented must be emphasized by international actors. The steps necessary for a Smart City depend directly on the goals, resources, and capabilities of each city. The strategy and plan for a Smart City will require a detailed analysis of the city, in particular its resources, infrastructure, energy footprint, energy needs, and sociopolitical impact.

Financial Challenges Budgetary constraints have limited actions on smart initiatives, even though they may ultimately be more efficient. To address this problem, the authorities of each city, taking into account their development plans, must explore their available resources, redesign their actions, and find new sources of funding. Investing in new technologies is de facto expensive. Many urban centers, especially in developing countries, lack funding and funding and are therefore unable to invest in new technologies and expensive infrastructure. They are enough for conventional solutions. Appropriate economic policies at global level should be implemented towards Smart Cities to support developing countries from already developed economies. Smart technologies are still at an early commercial stage and have not been disseminated to the general public. As a result, there is uncertainty over how long the cost of depreciation is to be paid and city authorities are reluctant to incorporate them into their infrastructure (Chatterjee and Kar 2015).

Technological Challenges ICTs play an important role in the development of Smart Cities. Many believe that more emphasis should be placed on technologies rather than on what really matters to Smart Cities, namely improving living standards and the environment. A key issue is that technology is often followed without substantial feedback, but those technologies that are affordable and that contribute most efficiently to improving the quality of life in an urban center should be sought for (Chourabi et al. 2012). Smart City infrastructures must be related to their operational function and designed through effective management, control, and optimization. ICTs are rapidly being incorporated into modern cities as a core element of their infrastructure, while these technologies are being used to design and develop modern cities to improve the quality of life. Modern cities must be a laboratory for innovation and implementation of new pilot projects. ICTs are being developed to increase energy efficiency, improve the services of utilities, and improve communications and transport. For their proper design, cities and buildings should allow for their pilot integration so that proper

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research and development of new technologies can be tailored to the actual requirements of modern urban centers (Batty et al. 2012). There is a lack of know-how of people using the new technologies. Therefore, new technologies should be easy and citizen-friendly, should improve the quality of life and ensure widespread user interaction, and should enhance mobility and access. On the other hand, it is not enough for new technologies to be close to people and improve their day-to-day lives, but people themselves must be well-informed in order to properly and efficiently use new technologies and exploit the modern infrastructures of urban centers (Chatterjee and Kar 2015).

Environmental Challenges Urban centers need to become energy efficient in order to become Smart Cities. However, the large concentration of the world’s population in urban centers results in high energy consumption with significant environmental impacts. Urbanization is estimated to be responsible for three quarters of energy consumption and 80% of CO2 emissions worldwide (IEA 2014; EC 2013b). This is the biggest challenge in finding truly sustainable actions that will help the sustainable development of modern cities, reducing energy consumption and reducing emissions (EEA 2010). In addition to increased energy consumption in buildings, the household and industrial sectors, for heating, cooling, and electricity, as well as in transport, which pollute the environment, water pollution and water waste are another challenge of the urban areas. Misuse of water resources results in additional energy consumption for water abstraction and water supply. Local authorities in modern urban centers, together with their governments and/or international organizations, should develop policies which shall ensure the development of cities with respect to the environment. The actions and policies to be implemented should focus on sound and efficient management of energy resources, encouraging the use of alternative forms of energy, water resources management, and urban waste management. The following table shows the smart city development axes and related areas of urban life. Depend on the previews table, it is presented the dimensions of a smart city to related aspect of urban life. As it is referred, the smart economy related to developing industry can help in the developing actions that help in the support of the development of smart economy. A smart city must have smart people, that related with the education system. Additional help is the e-democracy which related with the simplify of the offering services to the citizens. Additional smart acts are logistics and infrastructures, efficiency and sustainability, and finally security and quality. In the Table 2, one can see the basic indicator and factors that developed in the smart economy. As one can see the weighting of each factor is the same for the smart economy. The basic indicators are innovative spirit, entrepreneurship, economic image and trademark, productivity, flexibility in the labor market, international

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Smart Economy indicators 3 2,5 2

3

3 2

1

2 1

0,5

A tr bil an it sf y t or o m

In em ter be nat dd ion in al gs

du

ct

iv ity

& Ec

on tr om ad ic em im ar ag ks e

rre p sh ren ip eu

En t

F la lex bo ib r m ilit ar y o ke f t

0

0 In no Sp va iri tiv t e

1

Pr o

1,5

Fig. 1 Smart Economy indicators

Table 1 Smart city development axes Dimension of a smart city Smart economy Smart people Smart governance Smart mobility Smart environment Smart living

Related aspect of urban life Industry Education e-democracy Logistics and infrastructures Efficiency and sustainability Security and quality

Source: Modeling the smart city performance, Innovation (Lombardi et al. 2012)

embeddings, and ability to transform. The most important indicators are the innovation and the International embeddings. This happens because a smart city must be an innovative city and also must be extrovert ready to be change to adopt the diversity, in the year of internalization the diversity is very important to be adopted. In the Fig. 1 summarized the results of Table 1 and showing the gradation of the indicators. In the table as a continuing of the Table 2-Fig. 1, one can see that the weighing of each indicator is the same in percentage rate, but as indicator each one has different level of importance in the smart mobility. See except the Table 3 and Fig. 2 (Table 4). The Table 3-Fig. 2 showing the importance of each indicator concerning the smart people and how affects them concerning their connection with Smart City. Here the most important indicator is the level of qualification, because through it recognized the level of education and innovation of the citizens (Fig. 3).

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Table 2 The smart economy growth indicators Factors and indicators smart economy Indicators 3 2 1 1 2 3 0 12

Innovative spirit Entrepreneurship Economic image and trademarks Productivity Flexibility of labor market International embeddings Ability to transform

Weighting 17% 17% 17% 17% 17% 17% 0% 100%

Source: http://www.smart-cities.eu/model_1.html (smart-cities.eu 2017) Table 3 Factors and indicators for assessing the development of smart mobility Factors and indicators smart mobility Indicators 3 1 2 3 9

Local accessibility (Inter)national accessibility Availability of ICT – infrastructure Sustainable, innovative, and safe transport systems

Weighting 25% 25% 25% 25% 100%

Source: http://www.smart-cities.eu/model_1.html (smart-cities.eu 2017)

Smart mobility indicators 3 2,5 2 1,5

3

3 2

1

1

0,5 0 Local accessibility

(Inter)national accessibility

Availability of ICT - infrastructure

Sustainable, innovative and safe transport systems

Fig. 2 Smart mobility indicators

Table 5 concerns about the indicators that affect the smart living. The most important ones, as the Table 5 and Fig. 4 show, are the cultural facilities and the health conditions. It is very important the citizens are healthy in order to manage occupy with innovative actions and also to have access to cultural facilities.

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Table 4 Factors and indicators smart people Factors and indicators smart people Indicators 4 3 2 1 1 3 2 20

Level of qualification Affinity to lifelong learning Social and ethnic plurality Flexibility Creativity Cosmopolitanism/open-mindedness Participation in public life

Weighting 14% 14% 14% 14% 14% 14% 14% 100%

Source: http://www.smart-cities.eu/model_1.html (smart-cities.eu 2017)

Smart People indicators 2 3 11 2 3 4 0

0,5

1

1,5

Level of qualification

2

2,5

3

3,5

4

Affinity to life long learning

Creativity

Cosmopolitanism / Open – mindedness

Social and ethnic plurality

Flexibility

Participation in public life

Fig. 3 Smart People indicators Table 5 Factors and indicators smart living Factors and indicators smart living Cultural facilities Health conditions Individual safety Housing quality Education facilities Touristic attractively Social cohesion

Indicators 3 4 3 3 3 2 2 20

Source: http://www.smart-cities.eu/model_1.html (smart-cities.eu 2017)

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Smart City Planning Designing or redesigning modern cities requires the integration of modern ICTs and should be done in such a way as to ensure sustainable development, efficiency, and high quality of living. Energy management policies and actions to reduce environmental impact are one of the key features of Smart Cities (Batty et al. 2012). Competent authorities and agencies should make decisions and invest in Smart Cities sparingly and after a thorough techno-economic and sociopolitical feasibility study. In this way, investments will be sustainable and efficient and focus on the real needs of citizens. In addition, one must first evaluate how smart a city is, to avoid unnecessary interference and unnecessary waste. The infrastructures of the major urban centers that need to be smart and modernized mainly concern the sectors: energy and the environment, transport and communications, communications, governance, health, and education (Molina et al. 2014). The integration of ICT in each of the above areas is expected to radically change the way urban centers operate, making them passive, dynamic. Smart cities need to be developed with the key elements of coordination, communication, interconnection, and integration of infrastructure and services so that the city can operate in the most efficient way. This may require new database formats, new methods of data analysis, the development of new software that interconnects sectors and components of urban functionality. New forms of organization and governance are also needed, which will allow interconnection within the Smart City effectively and fairly (Batty et al. 2012). To manage the complexity of Smart Cities, due to the large amount of data generated by the continuous integration of new ICTs (Molina et al. 2014), we need to build a completely new integrated system for data acquisition, retrieval, and extraction. To this end, this new integrated system should support the following (Batty et al. 2012):

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Collecting data from multiple scattered sources Manage data flow Integrating heterogeneous data into a relevant database The transformation of data and its preparation Defining procedures for extracting relevant information Methods for extracting scattered data and network analysis Managing exported models Tools for evaluating the quality of exported models Visual analysis to investigate the behavior of models Simulation and prediction methods Strategies for tackling the scalability issues that arise from Big Data management

Good governance is vital for coordinating the different subdivisions that make up the Smart City. A structure is needed to bring the traditional functions of government together with businesses and citizens. Citizens need to be informed about the actions of public bodies and to be actively involved in decision-making. Thus, in areas such as energy, with the right policies and the right response of energy consumers, the goals of reducing energy consumption and reducing emissions can be achieved (Molina et al. 2014). Large corporations must be specialized in providing hardware, software, and data technology solutions, allowing cities to become smarter and more energy efficient, while governments must be responsible for implementing the appropriate policies that contribute to the exploitation of new technologies, establish a regulatory framework for businesses and citizens, and improve the quality of life in urban centers. This will make it easier and easier for citizens to access services provided within smart cities, such as access to health, education, utilities, energy, telecommunications, and more (Batty et al. 2012). New forms of citizen participation in governance and decision-making need to be developed. Citizens should be able to easily communicate and interact with, be informed and share information. At the same time, local authorities can better understand citizens’ needs by adapting their policies and strategies. Easy access to information is great value as citizens can be informed through software applications about their city, transportation, travel, local market, etc. and participate in the energy market and in shaping their urban environment (Molina et al. 2014). Furthermore, local authorities can raise the social, cultural, and even educational level of citizens as they offer new ideas, create new standards, and citizens actually take personal initiative (Delitheou 2018).

Transition to Energy Smart Cities Technologies related to the transition of urban centers to the era of Smart Cities vary and include technologies that have already been developed and are available as well as technologies that are under development and are expected to be used in the future (IEEE 2016a). These technologies are mainly related to energy and the environment,

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as they are energy efficient Smart Cities (Mancarella 2012). ICTs also play an important role in the easy application and deployment of new energy technologies (Papastamatiou et al. 2016, 2017a; Marinakis et al. 2017), as well as in the efficient operation of existing conventional technologies. A Smart City is based on smart IT technologies applied to critical infrastructures and services. Smart computing refers to the new generation of hardware, software, and network technologies provided by information and communication technologies (ICT). These technologies provide extensive analysis that helps people make decisions about alternatives and actions to optimize their business processes (Chourabi et al. 2012). ICTs are key players in the development of Smart Cities. The integration of ICTs in developing projects can change the urban environment and provide significant opportunities; it can enhance urban management and their functionality (Chourabi et al. 2012). For a city to be considered smart, it should also be environmentally smart. Its main feature should be energy efficiency and the adoption of new technologies and infrastructures that will enhance its environmental footprint. On the one hand, new ICTs will make cities more interactive and smart, and on the other, cities will become more environmentally friendly. New technologies are expected to reduce emissions, save energy, and make the modern city environmentally smart, giving another dimension to the concept of a Smart City. The authorities must make the best use of the new ICTs and integrate them properly into modern cities. Several projects have been completed globally, and others are in progress, focusing on the Global Initiative for truly Smart Cities (Marinakis et al. 2017). The smart grid is otherwise called the smart electric grid. A smart grid is a sophisticated system that manages electricity demand in a sustainable, reliable, and cost-effective way. It is based on advanced technologies and infrastructures that integrate ICT, to facilitate the operation and coordination of all the units that make up ICT (Vasseur and Dunkels 2010). Traditional electricity networks provided power from centralized power stations to end-users. In the generation of smart grids, electricity supply is not one-sided, it is dynamic and automated, while all components of the grid from generation to consumption are interconnected and communicate with advanced communications technologies (Mahmood et al. 2015). Future smart grids will provide more electricity to meet ever-increasing energy demand, increase the reliability and quality of power provided, increase energy efficiency and be able to host new mild energy technologies (Farhangi 2010). Electricity systems are expected to change radically in the coming years, with the aim of reducing losses and reducing investment and maintenance costs. New flexible electricity management methods will make electricity network more efficient. For the interconnection and coordinated operation of all the components of the smart grid, Information and Communication Technologies (ICTs) are expected to contribute greatly. According to the Ministry of Environment and Climate Change (YPEKA) and Law N 2773/1999, the production of electricity from renewable energy sources (RES) is considered the electricity provided by (YPEKA 2015):

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• The exploitation of wind or solar energy or biomass or biogas • The exploitation of geothermal energy, provided that the right to exploit the relevant geothermal potential has been granted to the person concerned, in accordance with the provisions in force at any time • Exploiting energy by the sea • The exploitation of water resources with small hydroelectric stations up to 10 MW • Cogeneration, using energy sources (i) and (ii) and their combination. The 2009/28/EC European Directive sets the targets for achieving the contribution of RES to the final energy consumption of 20% by 2020. Increasing the penetration of RES will also contribute significantly to the reduction of greenhouse gas emissions. The aim is to reduce by 20% and reach 1990 levels in accordance with Directive 2009/29/EC.RES in the residential and commercial sectors, i.e., close to the consumer, will help to reduce the carbon footprint of modern metropolises. Smart Cities will become greener and more energy efficient. A major challenge in the interconnection of RES is their intelligent management with surveillance and monitoring systems, as well as communication systems for their wired or wireless remote management (Papastamatiou et al. 2016). Electric vehicles are the next big step to be taken in the field of urban mobility. The concept of Smart Cities should in the coming years make it easier to integrate electric vehicles on urban roads with the main objective of reducing emissions in the field of transport and transport. In addition, electric vehicles can be driven either by electricity, fuel, or a combination of these. The most common example of a hybrid electric vehicle is the Toyota Prius. Other examples of electric vehicles or hybrid electric vehicles are the Chevy Volt, Mini Cooper E, Fisker Karma, Nissan LEAF, Tesla Roadster, etc. (Papastamatiou et al. 2017a). The advantages of electric vehicles have made them an emerging technology that is going to play an important role in the evolution of cities into smart cities and energy smart cities. Electric vehicles are expected to penetrate the market 50 million by 2030 (IEEE USA 2007). This means that 25% of new cars will be electrically driven (IEEE USA 2007). Electric vehicles need special attention to the way they affect the electricity grid and require charging stations to be integrated into the electricity grids of modern cities and are characterized as green technology due to zero emissions. One of the most essential elements of cities is buildings (corporate offices, public services, public organizations, commercial shops, and residences), and in order to make the transition to the Smart Cities generation, buildings must also become smart (Marinakis et al. 2017). “Future buildings” should interconnect their components in a comprehensive, dynamic, and functional way. At the same time, they should minimize their energy costs, ensuring the smooth operation of their electricity grid and limiting their environmental impact. Smart buildings provide infrastructure and useful services to their users, contributing to their productivity at the lowest cost and least environmental impact (Papastamatiou et al. 2016).

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In that sense, smart buildings use ICT in their operation to interconnect various subsystems that typically operate independently. With this interface, the building subsystems can share information to optimize the overall performance of the building. The concept of smart buildings has its main objective: improving the standard of living of citizens, energy efficiency, and improving the environmental profile (Papastamatiou et al. 2016, 2017b). Modern buildings also include sophisticated control devices, advanced control systems, and a set of features that improve the safety, comfort, and productivity of their users. Smart buildings include machine-to-machine communication and require the interconnection between equipment and systems within a building. A prime example is the use of data necessary for energy improvement and security of the building. This can be achieved by automatically switching off the lighting and heating when the tenants are absent (Marinakis et al. 2017). The data come from different devices and are intended for different uses. As a result, heterogeneous data are used. In the era of smart buildings, building automation systems (BAS) and/or building energy management systems (BEMS) interconnect and control building devices and subsystems by operating them in a coordinated manner (Marinakis et al. 2017). Lighting accounts for 19% of global electricity consumption and accounts for 6% of total greenhouse gas emissions. Achieving energy savings of around 40% of the energy used for lighting will have a positive impact on reducing emissions from electricity and heat generation (KNX 2015). Smart lighting is the beginning and can give new life to modern urban centers. Intelligent lighting is dynamic and combines know-how and technology from different fields of science. The ability to integrate a wide range of sensors and control devices, in conjunction with ICT, can contribute to greater energy efficiency and less negative impacts, while at the same time providing dynamic lighting and multiple benefits to humans and modern cities (IEA 2006; Sanseverino et al. 2015b). One of the key contributors to smart lighting is new lighting technologies, which are starting to spread thanks to the development of semiconductors and the creation of light sources, such as light emitting diode (LED), organic LED (organic LED – OLED), and solid-state light (SSL) sources. With these technologies, the lighting sector marks the largest transition since the invention of the first lamp in the nineteenth century (Novak et al. 2014). The concept of Smart Cities is closely linked to the concept of smart lighting. Proper lighting can radically change a city, create a sense of security and comfort for citizens, facilitate mobility within cities and improve the image of a degraded area. A smart city does not just mean energy savings, but energy savings combined with improving the standard of living of citizens. New lighting technologies and automation will significantly help transition to the era of Smart Cities (Sanseverino et al. 2015a). Smart lighting completes its operations in four (4) interconnected levels. The levels can be summarized as follows (Sanseverino et al. 2015b): First level or embedded level. This level includes the optical drive or the light source.

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Second level or system level. This level comprises the entire body of light and does the illumination system comprise the optical unit of the previous level, the ignition and control instruments. Third level or grid level. This level includes the sources of energy for the supply of light. Fourth or communication level. This level includes all the communications technologies and protocols (wired or wireless communication) used to interconnect the electricity grid. Therefore, many companies developing control, management, and surveillance systems have focused on developing solutions focused on urban lighting, e.g., ECHELON, which has developed the LonWorks protocol, places particular importance on outdoor lighting. The development of machine-to-machine or M2M communications enables better design and efficient operation of lighting installations. The value of information through surveillance and management systems, along with new lighting technologies, will bring energy savings and multiple benefits to cities and humans (ECHELON 2015). In addition, another key element of modern cities is the movement of citizens within each city. The residents of big urban centers spend a lot of time on their commutes, which creates stress, stress, and extra fatigue in their daily lives. At the same time, their movements are accompanied by exhaust and noise, which have significant environmental impacts. Smart Cities should adopt new technologies and be designed to solve these problems by facilitating the movement of people to urban areas and limiting the environmental impacts (ECHELON 2015). Smart Transportation is the new approach in the field of Smart Cities that will significantly improve the standard of living and reduce the carbon footprint of major cities (ECHELON 2015). To implement smart travel, organizations and local authorities should invest in advanced communications and traffic management systems and urban traffic flow. At the same time, MMMs should be modernized and made more environmentally friendly by renewing the bus fleet with buses that will have lower emissions (e.g., natural gas traffic) or zero emissions (e.g., electric vehicles) (ARUP and Qualcomm Technologies Inc 2015). One of the most important factors in developing smart cities and achieving energy goals for them is Information and Communication Technologies (ICT). Smart Cities use ICT for the following reasons: improving services that support every urban environment, such as security, health, and citizens’ mobility, ensuring better efficiency in delivering energy to consumers (commercial, industrial, and residential), remote business and e-commerce for businesses, improving education and communication between people (Papastamatiou et al. 2016, 2017b; Marinakis et al. 2017). The role of ICT is considered important in creating sustainable cities, as they can be integrated at every level of the Smart Cities stratification, from local authorities, buildings, and urban infrastructure (lighting, urban transport, water supply) to the computer and/or and every citizen’s cell phone. ICTs are also very important for energy management either at city or building level, as the benefits of using them are manifold and can transform the energy efficiency of cities and buildings. Adoption

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of modern and more efficient technologies in the field of energy is not sufficient if it is not accompanied by the integration of ICT and monitoring and management systems. ICT seems to be a key factor in modern cities and for this reason, various methods and tools have been proposed for energy management in smart cities and the building sector. Their objectives range from evaluating energy upgrading solutions to diagnosing energy status of buildings and control (Papastamatiou et al. 2017a).

Methodologies and Tools in Buildings of Smart Cities General Description In the international literature, there is a considerable number of studies presenting methodologies and tools for energy management and saving in cities. ICTs are one of the key factors for the development of Smart Cities, and for this reason, various methods and tools have been proposed for energy management in cities. In particular, there are several Energy Management Software (EMS) tools. These tools can and do provide real-time measurements, control heating, ventilation, and air conditioning (HVAC) systems, as well as building lighting. They also offer the ability to manage installed equipment (IEEE 2016b).

Energy Saving and Management Tools According to the IEEE (2016a), four (4) are the features that energy management tools should have in Smart Cities: data collection, reporting, supervision, and management. Energy management software collects real-time data, while keeping a record of consumption history. The tracked information is generated by smart meters, automation systems, etc. Consumptions related to electricity, gas, oil, water, and even electricity generation are some of the data collected for analysis. The reports of these tools are mainly addressed to plant managers and are an important element in monitoring energy consumption and gas emissions. Energy consumption data can be summed up and presented in reports. Power management tools usually display the data they collect in real time and store it for further analysis. Some tools also include benchmarking functions, such as energy consumption per square meter, normalization of weather conditions, or even more advanced analyzes using energy shaping algorithms to determine “abnormal” consumption (IEEE 2016b).

Energy Management and Saving in the Building Sector Nowadays, the building sector accounts for about 40% of total energy consumption, both at national and European level (EC 2012). This consumption is distinguished

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either by electricity or thermal energy (mainly oil and gas) and is responsible for the burden of the atmosphere with CO2 emissions. To this end, the European Union has adopted a number of Directives (2012/27/EU, 31/2010/EU and 2002/91/EC) promoting guidelines for improving the energy performance of buildings (EC 2002, 2010, 2012). In order to comply with European directives and commitments, Greece has incorporated into Greek legislation Laws 3661/2008 and 4122/2013 (Greek Law, 2013, 2008). In addition, since 2010, Greece has implemented the Energy Efficiency of Buildings Regulation (KENAK) (Dascalaki et al. 2012). KENAK lays the groundwork for a common methodology for assessing the energy performance of buildings, using data on building design, building shells, and electromechanical facilities (“asset rating”). In essence, KENAK performs the energy simulation of buildings and their categorization into energy classes. One such tool is end-use energy audits already in place since 1999 (EN16247 – GG1526/1999). In addition, the role of the Energy Manager with specific tasks in public technical services has been specified since 2008 (JMD 1122/2008). Law 3855/2010 also stipulates the gradual implementation of an energy management system in all public and broader public sector organizations in order to achieve systematic and continuous improvement of energy efficiency. The principles, requirements and guidelines of the energy management system are set out in a relevant standard (EN 16001). European Directive 2012/27/EU includes even more ambitious actions. Among other things, the following should be noted: the amount of new energy savings made annually from 1 January 2014 to 31 December 2020 should amount to at least 1.5% of the volume of annual energy sales to the final consumers of all energy distributors; of all energy retailers, averaging the last 3 years prior to January 1, 2013. As of 1 January 2014, 3% of the total floor area of heated and/or cooled buildings owned and occupied by their central public administration is renovated annually to meet at least the minimum energy efficiency requirements set by it – Article 4 of Directive 2010/31/EU. The institutional framework and ambitious goals, then, exist. At the same time, there is a growing need for real end-use energy savings in the building sector, as it can help to streamline operating costs (especially for large tertiary consumers) (Marinakis and Doukas 2018). Piette et al. (1998, 2000) developed a building energy management tool, especially applicable to commercial buildings and corporate offices. The data collected are for electricity, indoor temperature, and ambient temperature. Data are also collected following an interview on the use of the building. This tool is an original surveillance and diagnostic information system that enables users to more effectively control. Priyadarsini et al. (2009) propose a system in which data such as year of construction, use, floor area, energy upgrading and number of users are input, as well as electricity and fuel consumption figures from invoices. This tool applies to complex buildings, hotels, and correlates energy consumption with surface and outdoor temperature.

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Figueiredo and Sá da Costa (2012) propose a tool to improve building users’ preferences and reduce energy consumption. This method was developed to be applied at the building hall level and the data entered relate to the geometric characteristics of the building hall and the properties of the building materials (e.g., walls). Data such as heat, indoor daylight levels, indoor temperature, and adjoining room temperature are also collected. Klein et al. (2012) proposed a floor energy management tool for university buildings and corporate offices. The system introduces the geometrical characteristics of the rooms, structural features (e.g., walls), user behavior, and collects data on heating, ventilation, HVAC, lighting, electrical equipment loads, and room temperature and the environment. Using the proposed system can result in significant energy savings. Kavousian et al. (2013) propose a tool that processes various data to derive statistics and conclusions. Data on electricity consumption and outdoor temperature are collected and data on the geometric characteristics of the building, electrical and electronic devices, demographic characteristics and user behavior are also entered into the system. The data collected for the case study involved 1628 buildings, and the data is available for a period of 10 min. Braga et al. (2013) propose a multilevel model to estimate building energy consumption profile statistics. The data used here are the electricity consumption, the geometric characteristics of the building, and the weekly movement of users in the building. The proposed tool was proposed and implemented for school buildings. Trejo-Perea et al. (2013) developed a method for exploiting the information available to propose changes that would contribute to significant energy savings. The input data are the energy consumption and the geometric characteristics of the building. O’Donnell et al. (2013) developed a tool that allows building managers to have reliable information that they can share with other building users. This way, decisions can be made shortly and regularly for better energy management. The data used by the system is collected from meters for electricity, heat, and CO2 emissions. Kang et al. (2014) have developed an innovative tool that uses demand, wind, solar radiation, and energy price forecasting data to plan long-term (1 to 15 days) energy flow scheduling between individual building components. The proposed tool also provides real-time control and feedback of the measured variables. Missaoui et al. (2014) propose a tool for analyzing the effectiveness of a building energy management system based on a general forecasting model. The proposed tool optimizes the compromise between building users “comfort and energy costs by taking into account users” desires and physical constraints such as energy price and power constraints. The proposed tool is applied at the home building level for the management of electrical appliances.

Decision Support Systems (DSS) The evolution of energy management DSSs is strongly influenced by technological progress in the ICT sector. Less complex systems have been used in the past, mainly as control systems, and the degree of dependence on the human factor was very high (Askounis and Psarras 1998). In recent years, with the development of ICT,

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intelligent models have been developed for the energy consumption of buildings, aiming to maintain comfort conditions and minimize energy consumption (Doukas et al. 2007; Marinakis et al. 2013). Their general philosophy is based on the principles of “Building Energy Management Systems (BEMS).” DSSs are a level above the “BEMS,” as they aim to leverage the data from sensors, recorders, and actuators of individual systems to guide the energy manager in developing short-term action plans. This also gives these systems intelligence, since they enable the administrator to use real-time data and to balance the indoor environment accordingly, ensuring uninterrupted thermal comfort and energy savings (Marinakis and Doukas 2018).

Existing Methodologies and Tools Consulting is a complete suite of building management services. It is designed to monitor the energy and operational performance of building installations. It includes support from the global network of automation energy experts and provides analysis of the elements used to maximize energy savings (Attune ™ Advisory Services 2012). The software offers the following three levels of service: update, upgrade, and optimization. BELIEF was implemented under the Intelligent Energy Europe program. In 2008, a guide entitled Involve Stakeholders and Citizens in your Local Energy Policy, Turn over a New LIEF was published, including a methodology for preparing and implementing a Sustainable Energy Action Plan in cities (Energie-Cites 2008). The possibilities offered by the methodological framework are as follows: identifying an existing situation, setting goals, and analyzing existing targeting, implementing an investment plan, monitoring the successful implementation of the process. ABB’s “Energy Manager” is energy management software aimed at industries and generally large and energy-intensive installations (ABB 2017a, b). In summary, the software’s capabilities are as follows: it analyzes real-time data from monitoring systems and combines the information available from electricity providers, supports energy managers in all industries in data monitoring, management, and optimization energy consumption of plants for the benefit of maximum efficiency and savings, produces energy demand plans with high precision to improve design and optimization of power supply, accurately designs energy requirements while delivering significant economic benefits, includes data analysis and evaluation tools from all processes, and identifies areas for improvement, indicates electricity costs, and provides support to plan consumption. Electricity for off-peak hours. It is fully expandable, due to the modular architecture. It can be expanded to include additional facilities (ABB 2017a, b). Honeywell’s Enterprise Building Integrator (EBI) tool is online software based on intelligent building automation systems. The most important element of the platform is that it integrates with existing building systems and optimizes energy efficiency. The “EBI” tool integrates the following tools: Building manager: Interacts with the latest systems solutions to provide monitoring and control of cooling and ventilation (HVAC), lighting, etc.

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Energy manager: Collects, analyzes, and processes data to reduce energy consumption, costs, and emissions across the entire facility, and also monitors and optimizes energy use for the benefit of the environment and saving money Security manager: Responsible for protecting data and facilities through security monitoring, and monitoring access to surveillance devices (Building Solutions 2017) “LEAP” is an integrated energy and environment modeling tool. It is based on scenarios that can be used to monitor energy consumption and production, giving energy managers a wide range of alternatives. LEAP also enables users to create energy-optimized models with econometric evaluation (Energy Community 2016). “MODEST” is a linear programming model that is used to optimize energy systems. The result of optimization gives the most cost-effective combination of equipment and fuel all year round to meet energy demand. Software can be applied to any power system that can be described by linear relationships. It even contains an energy system model consisting of nodes and energy flows. The model describes all systems including CHP plants, conversion systems, fuel supply, energy demand, and sales (Henning 1997). The European Energy Award is a specialized tool for guiding and controlling energy policy at a local level, in order to systematically review all energy-related activities. It provides support to communities wishing to pursue sustainable energy policy and urban development through rational use of energy and increased use of renewable energy sources. To this end, this tool provides a six-stage methodology designed to improve communities’ performance of their energy-related functions (European Energy Award 2007): • Setting long-term goals and forming an energy group • Initial energy report with information gathering and preparation of initial energy exposure and CO2 balance • Setting medium-term goals and preparing the annual energy plan • Implementation of measures • Success check and annual report preparation • Certification and external audit upon completion of 50% of actions At the end, the OSeMOSYS open source energy modeling system is a simple and powerful modeling tool. It is mainly used for energy modeling and optimization. It has been set up by KTH, the Royal Institute of Technology in Sweden and SEI, UNIDO, IAEA, and the United Kingdom Energy Research Center. “OSeMOSYS” is provided as a simple text file written in the language “GLPK” and works with the “LEAP” described earlier (OSeMOSYS 2015).

Evaluation Studying and evaluating these methodologies and tools developed so far shows that most of them do not evaluate the energy performance of the city or its buildings before and after the implementation of the proposed energy saving actions, do not

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exploit GVA and ICT, they do not combine energy management and energy saving, they do not take into account the users of buildings for the formulation of energy improvement actions, there is no holistic framework for energy evaluation in all areas, and therefore do not provide a comprehensive methodology for the Management and Conservation of Smart Cities. Most solutions are commercial and not scalable and adaptable to the needs of each city.

The Greek Reality: Greek Legislation – Directive 2010/31/EU An important parameter for the transition to smart energy autonomous cities, carbon neutral emissions, is the optimization of the energy efficiency of the built environment in conjunction with increased RES penetration. Greece applies specific requirements for the energy performance of buildings in accordance with PD of 1.6/1979 (Building Thermal Insulation Regulation), in CY 5825/2010 as amended by 178581/ 2017 (Energy Efficiency Regulation – KENAK) and Law 4122/2013 (Energy Performance of Buildings). According to paragraph 5 of Article 2 of Law 4122/2013 and respectively paragraph 2 of Article 2 of Directive 2010/31/EU, “building with almost zero energy consumption (CCPM)” means “building with very low energy consumption.” High energy efficiency determined in accordance with the methodology for calculating the energy efficiency of buildings listed in Annex I to the Directive and Article 3 of the Law respectively. The almost zero or very low amount of energy required must be largely covered by renewable energy, including energy generated locally or near the building. According to the “Adoption of a National Plan to Increase the Number of Buildings with Almost Zero Energy Consumption,” a number of policy measures and actions are proposed for the Greek reality, including the promotion of financing programs for the energy upgrading of buildings to the KPST (Ministerial Decision RPF/DEPAA/85251/242/2018 – Government Gazette 5447/Β/5). In Greece, according to official data for 2012, the building sector (domestic and tertiary) accounts for about 45% (7,751 ktoe) of total domestic energy consumption, for 10% (6.95 Mt. CO2) of total emissions CO2 and about 65% (33,894 GWh) of total electricity consumption (See Fig. 5). In Greece, it has been estimated that each household consumes 10,244 kWh of thermal energy and 3,750 kWh of electricity per year to meet its energy needs (Article 4, Directive 27/2012/EU). At the same time, you estimate that 74% of dwellings are in urban areas and 26% in rural areas (NTUA 2009). According to the 2011 census of ELSTAT, 6,371,901 regular dwellings are registered in Greece, accounting for 83.68% of the total building stock (or 72% on the surface), of which 4,122,088 are residential dwellings. Of the total number of regular dwellings, 39% correspond to single-family houses, 45% to duplicate houses, and the remaining 16% to apartment buildings (See Fig. 6). The previews in Fig. 6 show the different kinds of dwellings in order to show in Fig. 7 the sq.m of each of the above dwellings.Regarding the size of dwellings based

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Fig. 5 Building Sector and Energy

Building Sector and Energy 40% 50% 10% Domestic Energy

Total Emmissions CO2

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Fig. 6 Regular Dwellings

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Technology Through increased technology adaption, smart city is an enabler of human interactions. Human interaction in the digitally enabled city includes information and communication. It also involves the interaction between human – computer and human – machine. Now the evolution in digital is transformed the countless lives which was not possible many years ago. For example, today it connects billions of people through the Internet (Finbarr 2018). At the worst case scenario, it happened through social media applications like Facebook, Whatsapp. Today, we are living in a world where internet is equivalent to oxygen. The above formula now becomes, Problem
> Technology þ Internet

Where Technology Provides Lot of Opportunities It is mostly applicable to the areas which are to streamline various processes, improve efficiency, enhance customer experience, revenue growth, increase safety, and reduce risk (Optus 2017). The key factors are innovation thinking and collaboration. Innovation thinking may happens through design thinking via Ideate phase. Collaboration may happen through Agile execution, where collaboration is one of key principles in Agile. Now the revised formula is Problem ðor ImprovementÞ
> Technology þ Internet þ Design Thinking  Agile

Where Technology Makes a Difference Scenarios Zero emission cars Robots

Descriptions Purely runs with electricity or hydrocarbons Reduce the frequency of collision with the help of self-driving car Goes beyond its current boundaries with possible of having remote control operation Occurs due to evolution of AI and cloud computing Machines can reacts what is its next step as according to the environment (continued)

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Scenarios Evolution of GPS technology

Drones

Telemedicines

Descriptions Enables the use of robotics in the area of agriculture Additionally due to the evolution of AI, the computer is able to perform human tasks These revolutions lead to how can we make our life so easy with help of AI Unlike in the past, now the things are happening as usual (i.e., no stop) in the areas of agriculture, traffic spots, COVID lockdown periods, etc. Able to carry out a few tasks which is very tough for humans to accomplish Especially helps in rural and isolated areas With this technology, patients can communicate with the doctor to get the medicines irrespective of the situation

How Technology Connects with Cities It is a common myth that technology is a solution to address many problems which exists in today’s world or in our society. Smart cities are no exception on that. It helps to improve the quality of life of every citizen with the help of urban informatics. In alternative words, it raises the standard of every people. Urban informatics means to implement ICT and its data in the context of cities and urban environments. In this digital era, these data are not simply considered as number or any info, it can be inferenced like a statistical inference in Six Sigma or Statistics. Urban computing has been defined as “a process of acquisition, integration, and analysis of big and heterogeneous data generated by diverse sources in urban spaces, such as sensors, devices, vehicles, buildings, and humans, to tackle the major issues that cities face (e.g., air pollution, increased energy consumption, and traffic congestion)” (Zheng et al. 2014). A broad definition of a smart city is an urban environment where technology allows for an efficient relationship between data and its applications in order to provide an environment that is responsive, resilient, and healthy. The functional aspects of this relationship are that a smart city is more immediately responsive, predictive, adaptive and is capable of learning (▶ Chap. 1, “Smart Cities: Fundamental Concepts” by James et al.). In the context of smart cities, now the formula further becomes Smart Cities : Problem ðor ImprovementÞ ¼ Technology þ Internetþ Design Thinking  Agile
> Quality Life Today, half of the population live in cities, whereas in the next three decades, two third of the world’s population is forecasted to live in urban areas. The key goal is to

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travel efficiently from one place to another by considering traffic congestion in more dense areas. In the view of environment, it leads to a friendly atmosphere. For example, air quality sensor around the city can track air purity level. From city safety standpoint, it leads to a safer city with the help of leveraging various technologies and reduces strange activities. Therefore, smart city means leveraging technology to transform the lifestyle of citizen and the environment. The key areas are healthcare and public services. Smarty cities address the biggest problems like population, waste, and pollution which are increasing at an exponential rate. Declining crop yield is another classic example, where technology helps due to extreme weather conditions and pests, with the help of digital or precision agriculture. From technology standpoint, IoT and AI play a pivot role in this space. AI will be used for predicting events by reading several years of data and its incident. Also, the plans or roadmap with respect to smart cities are not supposed to be overpromised and underdelivered, which leads to various risks. In the lens of evolution, there is no edge for the smart cities. It continues to improve and continue to expand. Smart Cities : Problem ðor ImprovementÞ ¼ Technology ðIoT, AIÞþ Internet þ Design Thinking  Agile
> Quality Life The outcomes of a smart city include sustainable and healthy lifestyles, economic efficiency, political and social inclusivity through equitable engagement, and an ability for all public and private residents to flourish (▶ Chap. 1, “Smart Cities: Fundamental Concepts” by James et al.). But the fact is bigger opportunities exist in big problems. It means the solution needs to be tailored, based on the needs/pain problem. Otherwise, technology should keep in a shelf. Virtual technologies are opened a gate of possibilities. The key consideration when approach a technology is, “Think big, but start with a small step; alternatively think small, but create a huge impact.” From the above understanding, technology makes the impossible possible. A small technology can solve a complex problem. It redefines human live due to technology transformation (Eduucba 2020).

What Are the Keys to Unlock Digital Transformation Data and analytics are key for digital transformation. According to Gartner, these two are fundamental components in order to receive enterprise value. ▶ Chap. 1, “Smart Cities: Fundamental Concepts” by James et al. cited that cities require data to operate. In the past, the data existed on paper. But today and in the future, it exists over the Internet. The important point is it informs decision-making and shapes the decision framework.

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From business standpoint, it acts as a weapon in order to beat a competitor. The data and analytics should be incorporated within the high level plan. In simple words, data and analytics should act as an innovation catalyst to drive the digital transformation (Christy 2019). It increases the complexity of data, the number of variables to analyze, and the type of analysis. Basically, it is pushing us to go beyond of current capabilities and approaches (Gartner 2020a, b). Today digital transformation means anything from IT modernization, which will lead to an invention of new business models. The most accurate term is digitalization. However, the known term is digital transformation or digital business transformation (Gartner 2020b). It is known that digital is a top priority for any organization or government, etc. But the success is based on the degree of fulfillment between the levels of efforts across the enterprise. As mentioned above, data and analytics are the catalysts to drive digital transformation. But artificial intelligence, robotics process automation, and workflow streamline tools are potential enablers. For both enterprise and government, clearly defined digital ambition is important, since it provides direction and focus for the work ahead. Now there is a need to understand that doing digital projects does not mean we are doing a digital business today (Gartner 2020a, b). Typically digital transformation allows organizations to manage disruptive changes which is currently happening in the markets and customer base either by designing new products, services, and business models with customer centric approach or re-visiting those with Agile mindset and Design Thinking in addition to leverage digitalization. Due to rising digitization and urbanization across the world, businesses are switching to using technology driven solutions to meet the rapid pace of business growth. The use of cloud technology enables various small and medium enterprises (SMEs) to adopt modern Digital Experience Platforms (DXP) at affordable prices without the need to constantly upgrade or replace the systems. Moreover, the promoting factors in digitalization are end-to-end customer experience and improvement in operational flexibility with new revenue sources. The key to succeed in this game is the IT leaders should look at the data first, then respect them (Gartner 2019). Formula : Unlock digital transformation ¼ Data þ Analytics
> Enhance customer experience or Improvement But Forrester mentioned that digital transformation is not just about technology. It is basically reimagining our companies because not all companies started with digital. It is a must element today in our strategy, but a challenging journey which leads to create a lean operation. Resource allocation and innovation are the keys (Forrester 2020). Even though it is a promise, a business can be leveraged with the capabilities of data, analytics, and AI, but the companies are struggling to maximize the benefit. It is due to many factors. Human skills are needed to transform first from data engagement to data science and analytics to AI. It is hard in reality. At the same

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time, customer’s expectations are going up. Therefore, it requires a 360 degree approach which focuses on the human skills (Gene 2019). Now the revised formula becomes Unlock digital transformation ¼ Data þ Analytics þ Innovationþ Resource allocation=Human skills ! Enhance customer experience or Improvement

IoT The need of hour is to manage a product or process in a proper way to reduce various impacts. This section discusses a brief about how Internet of Things (IoT) can manage those environments, builds an eco-friendly with advance sustainability. Eventually it will lead us to understand the pros and cons to IoT when it comes to the particular instance.

Why There Is a Tremendous Growth in IoT It is due to recent advances in miniaturization, falling costs for sensors and communication technologies. Like other vertical, IoT could have tremendous positive impacts on environment. In the context of IoT with environment, sensors like pocket size can be carried around and it helps to monitor the airborne quality, radiation, water quality, etc. These sensors are either connected with smart phones through Bluetooth or Wi-Fi, in order to send enormous amounts of data to the network. It allows the user to have an understanding of the surroundings and eventually led the user to find a suitable solution for environmental problems. Since we attach a sensor with device, all these are able to inspect from remote location. Therefore, a separate field visit is not required. The outcomes from this processing will be helpful especially for infants, asthma patients, and people who are working in hazardous or radiation disposed environment.

Why There Is an Acceleration of IoT (Especially in the Last Few Years) A combination of factors has made it feasible both in terms of economical and technical to connect more and more devices and systems to a much wider, open network, giving rise to a rapid growth in IoT technology over the last 7 years (Liz and Robin 2016): • Wi-Fi and broadband connectivity are now much more widely available; more things and computers can come online due the emergence of the IPv6 protocol22; growing usage is 40% of the world’s population; “The cloud” or decentralized

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storage capacity is growing rapidly and at a much lower cost than previous hardrive solutions. • Sensor technology has become more sophisticated and requires less space, less power at lower costs, making it cheap enough to deploy in almost any location, or to be preinstalled into devices; attainability of larger sensors or displays due to the improvement of battery technology at all the time. • Data handling technology is able to absorb process and analyze the massive amounts of sensor-generated data at affordable cost; costs for mobile devices, bandwidth, and data processing have declined as much as 97% over the last 10 years; investment confidence – after years of anticipation, these converging conditions have led to a growth in investment in the IoT sector. Acceleration in Digital Transformation : Broadband connectivityþ Sensor technology þ Data handling technology

Why IoT Has a Bright Future Ahead The IoT has the potential to change the world, and we are just at the initial stage now. When we think of the IoT, we tend to think of its potential to affect consumers and enterprises, which is mind-boggling. It is changing how we work, how we live, how we get value from our belongings and assets, and even how government and society function. The potential of this new market for service providers is exciting. At the same time, the businesses are equally benefiting from the IoT. In fact, most IoT market potential will be found in enterprises as companies use it to drive operational efficiency and introduce new connected (smart) product, process, or service.

IoT Means Basically, IoT is a concept where everyday objects can be equipped with identifying, sensing, networking, and processing capabilities. That is, the objects are equipped with microcontroller sensor devices and various software application and suitable protocol stack enable them to talk to other objects. It will allow them to communicate with one another and with other devices and services over the Internet to accomplish some objective. This concept is the outcome of merged field of computer science and electronics. In general, IoT can be described as a combination of sensors, connectivity, people, and processes. Especially with the help of sensors and devices, a close connection is established between things, humans, the cyber world, and the physical world. IoT combines smart devices with smart services to create

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compound application. Objects become “smart” by embedding technology such as sensors, software, or Internet connections. Hence it leads to, IoT provides realtime services and helps in saving time activities, resources, and even manpower. Integration of device, sensor data, big data, analytics, and other enterprise applications are a core concept behind the emerging IoT (Chantal 2014; Prateep et al. 2014; Andrew et al. 2015; Timothy and Priti 2015; Liz and Robin 2016; Policy and Research Group 2016). IoT ¼ Sensors þ Connectivity þ People þ Processes Networked traffic cameras, radio-frequency identification tagging of shipments in the supply chain location tracking devices to find our car keys, remote monitoring of temperature, and activity in our homes are well-established examples. This IoT technology transforms agriculture, industry, energy production, and distribution by increasing the availability of information along the value chain of production using networked sensors. It also promises to transform many aspects of the way we live (Karen et al. 2015; Policy & Research Group 2016). Due to low cost and highly capable sensors and advances in wired, wireless communication technology and network protocols that permit us to better connect sensors to the Internet, it makes large difference today. But the term IoT has been in use since the large scale adoption of RFID began a decade ago. This is why the potential for business transformation is immense (Prateep et al. 2014).

How Does IoT Works The basic principle is, Sensors
> Networks
> Data
> Apps Sensors can be relative humidity, temperature, barometric pressure, distance, irradiance, and light color. Many objects and devices need separate recognizable identity or address, thus enabling more categories of things to be connected to the Internet and to each other and be locatable. The objects then become capable of sensing activity, collecting data, exchanging this and connection with other connected objects, devices, users, smartphones and remote information systems, via the Internet, mobile phone networks, Wi-Fi, or Bluetooth. At this stage, application platforms, device manufacturers, or cloud-based data analytics providers to run analytics on the data collected, design automated responses or link up to other data sets and analyses (Liz and Robin 2016).

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Differences Between ToI, M2M, and IoE Terms Internet for things or things on internet (ToI)

Machine to machine (M2M)

Internet of everything (IoE)

Differences (Chris 2014; Chantal 2014; Dave 2013) The trend is to stop hosting websites on dedicated web servers; instead put them on more flexible and scalable cloud-computing clusters; such techniques are custom-made and require a lot of development and maintenance; because these services only provide connectivity for things, the overall approach is called the internet for things or things on internet (ToI) But in IoT, devices talk directly to each other, make joint decisions, and exchange data between devices without the need for the cloud or servers M2M solutions typically rely on point-to-point communications using embedded hardware modules and either cellular or wired networks But IoT solutions rely on IP-based networks to interface device data to a cloud or middleware platform IoE (four pillars: People, process, data, and things) builds on top of IoT (one pillar: Things); in addition, IoE further advances the power of the internet which improves business and industry outcomes, and it makes people’s lives better by adding to the progress of IoT

How to Manages the Environment In the view of IoT, it has even more potential in rural environments, providing real-time data streams. It supports a deep understanding of environmental interdependencies. Hence, holistic management strategies are required. Particularly, the combination of IoT technology tied with Cloud Computing which enables a standard shift in management of the natural environment in times of unparalleled environmental change (Matthew 2011; Gaglio et al. 2014; Klint 2014; Martin 2014; Habibi and Alesheikh 2015; Jessica 2015; Gordon and Barry 2016; Cascajo 2016; CloudTweaks 2016; Libelium 2020) Solution ¼ IoT þ Cloud

How to Builds an Eco-Friendly Environment When we look at the big picture, IoT will most likely be positive overall for the environment. But there are some setbacks along the way that waste energy, before worldwide standards are set, whereas consumer devices are updated to reflect the new technology. Perhaps the biggest savings will be when entire cities are

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interconnected which allows public transport and new construction to save energy on a large scale. Since environmental benefits are a major concern for world leaders and government organizations, it is likely that IoT will be tweaked with this in mind.

Applications of IoT in Different Industries This concept provides immense opportunities which enable organizations in every industry to offer new services, based on the sources mentioned above. Areas Utilities Manufacturing

Healthcare

Insurance

Consumer goods and retail Transportation

Consumer appliance

Fire detection Air pollution Snow level monitoring

Landslide and avalanche

Earthquake early detection

Description Significant reduction in cost and resource savings those connected to the smart network Reduce field support costs, lower breakdowns, improve operational efficiency, optimal scheduling of production lines, anomaly detection and emission detect, improved quality, and lower energy cost Lower cost of care, improved patient outcomes, real-time disease management, improved quality of life for patients; other personal IoT devices like wearable fitness and health monitoring devices and network enabled medical devices Creation of newer insurance models such as dynamic premium pricing based on condition of property, premium pricing based on usage Creation of novel value-added applications for the customer, like alerts on expiry dates, to check products virtually, targeted advertising Improved service levels, lower costs, and lower carbon footprint; IoT systems like networked vehicles, intelligent traffic systems, and sensors embedded in roads and bridges move us closer to the idea of “smart cities” New IoT products like internet-enabled appliances, home automation components, and energy management devices are moving us toward a vision of the “smart home,” which offers more security and energy efficiency Define alert zones by monitoring of combustion gases and preemptive fire conditions CO2 emission controls in factories To understand the quality of ski tracks which allows security corps rush prevention in real time; this is applicable where a plant or buildings are located at hill station Detecting dangerous patterns in land conditions by monitoring of soil moisture, vibrations and earth density; this is also applicable where a plant is located at hill station Scattered control in specific places of vibrations (continued)

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Areas Positive impact on the environment

Environmental sensors Energy requirements

Connected solution

Description Detect pollution with environmental sensors Save money on electric bills with smart thermostats Reduction in city air pollutions due to smart parking Save energy due to smart street light with motion sensor Install smart objects like trash cans that can tell transporters when it should come in order to remove waste Precise information in real-time is possible in case of breathing problems and asthma affected citizens IoT curtails both energy requirements and environmental impacts The current IoT capabilities can be suppressed by ubiquitous lowpower sensors in the near future Contains a hub that collects and provides sensor fusion Refers to data collected from a variety of sensors on a worker like a heart rate monitor, toxic gas monitor, an activity detection device, nonverbal gesture device A cloud-based dashboard displays the resulting data with actionable intelligence from remotely Hence, plant managers and incident team are able to anticipate unsafe conditions in a better way Avoids a potential man-down scenario which could threaten worker safety Also, use the data to prevent equipment failure which could create unsafety conditions or downtime

How Is IoT Technology Classified Into It is classified into hardware, software, and architecture. Classification Hardware Software Architecture

Elements RFID, NFC, sensor networks, actuators Middleware, search/browsing, data processing Hardware/network architectures, software architectures, process architectures, general

Following are three steps to be considered: Step 1: In order to connect everyday objects and devices to large databases and networks and indeed to the internet, the item identification is crucial; then only data about things be collected and processed; radio-frequency identification offers this functionality. Step 2: Data collection will benefit from the ability to detect changes in the physical status of things, using sensor technologies; embedded intelligence in the things

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themselves can further enhance by having the network and information processing capabilities to the edges of the network. Step 3: Advances in miniaturization and nanotechnology mean that smaller and smaller things will have the ability to interact and connect. A combination of all of these developments will create an IoT. It connects the world’s objects in both a sensory and an intelligent manner (ITU 2005).

How Should Be Enable IoT Growth A standard group should be formed. This global organization is responsible for delivering open standards to enable the IoT, to accelerate the adoption rate. The mission of this group should be to establish a new standard and encourage its adoption worldwide. It should be aimed to promote a unified approach in the development of technical standards (recommendations) enabling the IoT on a global scale. So, the aim of a standard group is to act as an umbrella for IoT standards development worldwide. The group should have a board comprising leading players spanning processors, networks, chipsets, and innovative wireless technologies, etc. The formation of a standard group is a major step in building this, to support a rapidly expanding need to meet the demands of embedded intelligence everywhere, creating efficiencies in countless ways by giving the world access to data, analyze and then to act upon it in a managed way. With common standards, we can benefit from intelligence embedded and connected everywhere. Having agreed standard is going to accelerate the growth and acceptance of M2M communications. Owing to the absence of standardization, with convenience and cost in mind, many IoT projects were built vertically. The greatest IoT opportunities like – from the connected home, out-and-about mobile interactions, smart meters, connected car, smart grid, personal wellness, and connected health – have been driven from a vertical market perspective. Devices and connectivity were provided by a single vendor, with minor consideration for interoperability with products from other vendors, which leads to fragmentation of the market. Ultimately, it limits consumer choice in the rapidly emerging market. There are two types of standards relevant for the aggregation process: technology standards (including network protocols, communication protocols, and data-aggregation standards) and regulatory standards (related to security and privacy of data, among other issues). Challenges facing in this dimension are: handling unstructured data and security and privacy issues in addition to regulatory standards for data markets. However, the IoT requires interoperability over the Internet regardless of verticals. Everyone wants a common solution for their smart home, mobile phone, and connected car. By definition all the “things” should be internetworked. Without

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multivendor interoperability, things might simply come to a grinding halt and the user experience will be unsatisfactory. To become truly successful in IoT space, it requires an open means for allowing devices to find one another and communicate.

IoT Are Achievable in the Near Future The rapid evolution of the IoT market has caused an explosion in the number and variety of IoT solutions. Consequently, the focus of the industry is on manufacturing and producing the right types of hardware to enable those solutions. In the existing model, most IoT solution providers have been building all components of the stack, from the hardware devices to the relevant cloud services. The result is lack of consistency and standards across the cloud services used by the different IoT solutions. In the new model, there will be a presence of different IoT solutions work with common backend services, which will guarantee levels of interoperability, portability, and manageability that are not possible in order to achieve with the current generation of IoT solutions. The hurdles facing IoT standardization can be divided into few categories: platform, connectivity, business model, killer applications, deployment, and global scale. All these categories are interrelated; therefore, all elements should make work. Missing one will lead to failure of the model and hold the standardization process. A lot of work is needed in this process, many companies are involved in each of the categories, and bringing them to the table to agree on a unifying model will be a daunting task. As more and more connected devices join the IoT ecosystem, the industry needs to focus on providing safe, reliable interoperable access to services and information regardless of the vertical segment or vendor. Interdevice standardization is a vital requirement. With the help of standards setting bodies, should promote the use of global technical standards. Technological maturity happens over the time and then it will turn into most promising standardization approaches.

Growth of the Global IoT Market Over the Years and Expectations The pace of development in IoT is astonishing and represents a huge opportunity for the industry as a whole. IoT is the engine for economic growth in the next decade, much of which will come from new and innovative applications, with new business models. The overall opportunity for the IoT market is harmonizing the growing number of vertical segments which is paramount. At the same time, few countries are also exploring spectrum issues with respect to IoT. The drivers for Internet of Things growth are increased performance and reduced cost (and size) of technology. It means that connectivity can help to lower costs and make new revenue opportunities possible.

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Nowadays governments are pushing for smart solutions in all segments. Finally, there is also a strong push from suppliers looking for new markets. Solving these problems will open the market in IoT and may create opportunities in themselves. • Uncertain demand for IoT solutions. • Fragmented standards. • Business case with respect to cost savings.

Role of Government and Regulatory Authorities Areas International engagement

Stakeholder-driven policy processes

Government trying to get a handle on IoT Setting up IoT for success

Description Express the critical importance of a global free and open internet to future innovation and growth in the IoT space Maintain its robust advocacy for industry-led approaches and consensus-based standards and continue to use multi-stakeholder approaches to address policy challenges Continue to play a role in convening public-private processes to address policy challenges in the IoT arena The success of the efforts to engage with stakeholders, including civil society and the private sector, in building flexible and adaptable frameworks, codes of conduct, and best practices in the fast-moving technology policy space Identify key issues impacting deployment of these technologies, highlight potential benefits and challenges, and identify possible roles for the government to advance IoT Assistance with this process is available through, a guide for helping communities embed digital technologies into municipal infrastructure through successful public-private partnerships

Deployment and Adoption of IoT Will Be Different Across Geographical Regions Dimensions IoT solution deployment in terms of initiatives

Description IoT solution deployment timelines and initiatives vary by industry sector and geographic region Many organizations seek assistance from third-party vendors to implement various technical elements of these IoT solutions and applications These organizations represent many sectors, including retail, manufacturing, consumer products, transportation, healthcare, government, oil/gas, and hospitality (continued)

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To address various issues Progress

M. Prabaharan

Description Global organizations have implemented or are planning to deploy a wide range of IoT applications that incorporate many different features and functions IoT solutions require many technical elements Progress varies across both industry verticals and across geographies

Value Chain: A New Ecosystem The key players have to operate within a constantly evolving economic and legal system, which establishes a framework for their endeavors. The players are telecom provider, platform provider, device provider, application provider, and the end user. The Internet of Things will have a broad impact on many of the processes that characterize our daily lives, influencing our behavior and even our values (ITU 2005). The IoT will certainly drive the development of new business models that capitalize on its pervasiveness and ubiquity (Andrew 2015). The value chain defines how the service is delivered.

Data Collection Market Opportunities The technologies of IoT offer immense potential to consumers, manufacturers, and firms. Firms are embracing the underlying technologies of IoT to optimize their internal processes, expand their traditional markets, and diversify into new businesses (ITU 2005). According to IDC, $124 billion is forecasted to invest in 2020 especially in smart cities initiatives. In terms of market share, USA and Western Europe ranks 1 and 2, respectively. To explore multiple rigid use cases, it is a right opportunity for us to think from end user side especially few considerations during COVID-19 period. From Design Thinking perspective, this period helps to empathize how to leverage the various technologies which exists today to a greater extent. Design thinking is a human centered approach and the idea is to develop solutions which are tailored to need of citizens, that is, understand the core problems that the individual is facing now. According to Grand View Research, the market size will worth $463 Billion by 2027. The areas include healthcare, transport, water, assisted living, security, and energy. It also includes the services with respect to implementation. All the above quoted numbers might be changed now due to the importance given to COVID-19 from the government and various corporates. But HfS Research highlighted that an initiative like smart cities is critical enabler to fight against in a situation like COVID-19.

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Gartner Recommendation: Top 10 Technology Trends in Data and Analytics Areas Augmented analytics

Year 2020

Driver (Gartner 2020c) New purchases of analytics and business intelligence, data science, machine learning

Augmented data management

Thru 2022

NLP (natural language processing)/ conversational analytics Graph

2020

Data management manual tasks will be reduced by 45% through the addition of machine learning and automated service level management 50% of analytical queries will be generated via search, NLP or voice or will be automatically generated

Thru 2022

The application of graph processing and graph databases will grow at 100% annually to continuously accelerate data preparation and enable more complex and adaptive data science 75% of new end user solutions leveraging AI and ML techniques will be built with commercial, instead of open source platforms

Commercial AI/ ML

2022

Data fabric

2020

Custom made data fabric designs will be deployed as static infrastructure, forcing a new wave of cost to completely redesign for more dynamic approaches

Explainable AI

2023

Over 75% of large organization will hire AI behavior forensic, privacy, and customer trust specialists to reduce brand and reputation risk

Enables ML/AI – Created data and analytics. Surfacing the most important actionable insights in context The democratization of advanced analytics AI - > human synergy Data mechanics/DataOps Usage and use case intelligence Any user can ask questions in text or voice AI as the new UI Broader adoption, new users Data exploration the way people think Complex network traversal operations Emergent semantic graphs and knowledge networks Adoption and penetration will increase Hyperscale providers will increase market share Technologies in the market continue to evolve A single and consistent data management framework Operating dynamic data driven services, apps, storage, and success from a range of infra resources Exploiting metadata semantics for automation Extending governance to AI Autogenerated explanation of models Auto identification of bias and privacy risk in data (continued)

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Areas Blockchain

Year 2021

Driver (Gartner 2020c) Most private and permissioned blockchain uses will be replaced by ledger DBMS products

Continuous intelligence

2022

More than half of major new business system will incorporate continuous intelligence that uses real time context data to improve decision

Enables Blockchain will not replace existing data management technologies Blockchains are not inherently more secure than alternative data sources Simplify vendor selection Smart decision through real time data and advanced analytics Situation awareness Intelligent, automated, and outcome focused Prescribes the actions to take

Top 10 Digital Transformation Trends for Australia and New Zealand by IDC Basically these reports help companies in Australia and New Zealand, where they have to focus in couple of years to create their long term and short team goals (IDC 2020a, b). S.No 1

Areas Future of culture

Year 2024

2

Digital coinnovation

2022

3

AI at scale

2022

4

Digital offerings

2022

5

Digitally enhanced workers

2022

Descriptions The leaders of 50% of organizations listed in AXS200 and 60% in NZX will have mastered future of culture traits, such as empathy, empowerment, innovation, and customer centric Empathy among brands and for customers will drive ecosystem collaboration and co-innovation among partners and competitors, which will drive 20% of the collective growth in customer lifetime value in AUS and 25% in NZ With proactive, hyperspeed operational changes and market reactions, AI-powered organizations will respond to customers, competitors, regulators, and partners at least 20% faster than their peers will in AUS and 1/third faster in NZ 40% of organizations will neglect investing in marketdriven operations and will lose market share to existing competitors that made the investments as well as to new digital entries New future of work practices will expand the functionality and effectiveness of the digital workforce by 25% in AUS and 30% in NZ, which eventually lead to improvement in productivity and innovation (continued)

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Areas Digital investment

Year 2021

7

Ecosystem force multipliers

2024

8

Digital KPIs mature

2020

9

Platforms modernize

2023

10

Invest for insight

2023

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Descriptions DX spending will grow to over 55% of all ICT investment from 45% today, with the largest growth in data intelligence and analytics, as companies create information-based competitive advantages 75% of digital leaders will devise and differentiate end customer value measures from their platform ecosystem participation, including an estimate of the ecosystem’s multiplier effects 35% of companies in AUS and 45% in NZ would have aligned digital KPIs to direct business value measures of revenue and profitability Driven both by escalating cyberthreats and needed new functionality, 68% of organizations in AUS and 70% in NZ will aggressively modernize legacy systems with extensive new technology platform investments through 2023 Enterprises seeking to monetize the benefits of new intelligence technologies will invest over US$5.5 billion in Australia and NZD150 million in NZ, making the DX business decision analytics and AI domain a nexus for digital innovation

Strategy Digital technology has been churning markets and disrupting companies especially in the last few years, so key stakeholders are still struggling to enact and deliver on digital transformations (Prabaharan 2018).

Why Digital Strategy Is Very Important for Companies Organizations are directionless, when they do not have clear strategic goal for what they want to achieve in Digital in terms of gaining new customers or building deeper relationships with existing ones. The market dynamics will be different to traditional business with different types of customer profile and behavior, competitors, propositions, and options. If they are not devoting enough resources to digital or using an ad-hoc approach with no clearly defined strategies, then existing and start-up competitors will eat their digital lunch and gain their market share. A clearly defined customer value proposition tailored to different target customer base will help to differentiate their service encouraging existing and new customers to engage initially. Knowing customers well enough and use feedback tools to identify the weak points and then address them. They should have agile enough to catch up or stay ahead. If we look at the top brands, they are all dynamic – trialing new approaches to gain or keep their customers.

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That’s why companies require well thought-through strategy. Digital Strategy ¼ Digital ðTransformationÞ þ Strategy 6¼ IT Strategy ¼> Digital Edge Strategy The importance piece of the equation is digital strategy. What does it mean today? In one of John Godfrey Saxe’s stories, there is a situation between few blind men and an elephant. In the tale, one of the blind men felt the elephant’s body and thought the animal was similar to a wall. The other felt its tree and said it was like a trunk. In today’s organization, the same applies here. There are many ideas and initiatives about, what is a digital strategy. A marketing executive will perceive it as social media and web channels, an IT person as cloud, an operation executive as data analytics, an R&D executive as online products, a financial person as online revenue channel. Now there is a need to understand what exactly strategy means, it is prefer to use the well-known chemical analogy of elements and compounds. A compound (strategy) is a combination of two or more elements (activities).

What Is Digital Transformation New technologies such as AI, robotics process automation, and the IoT, the focus on technology can navigate in a dangerous direction. Digital is not the answer when it comes to digital transformation. But transformation is. Technology does not provide value to a business. Technology makes it possible when doing business differently. This idea is to focus on transformation. With these pieces of information, a strategic focus on digital sends the wrong message to a business. It means that, we do not need a digital strategy. We need a better strategy, which should be enabled by digital.

Which Type of Strategy and When It changes as the business requirements change. How predictable is your market? How flexible is the market? The recommendation is to have some mix of positional (long-term position), leverage (where we have some influence on how the market moves), and opportunity strategies (hard to predict when they will come or how long they will last). The key is that there are always trade-offs between the chance of success and reward. Otherwise, the position is not sustainable.

One of the Hardest Things About Strategy It is decisions (to make choices). Strategy forces us to make decisions and obviously cut off option. It resists the urge and to do. It is observed that this happens by getting

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distracted by competitors. Every product, service, or feature we add to our business has a cost of some kind. Trade-offs are a critical part of making sure our strategy is sustainable, because they protect from competitors trying to overlap multiple markets. Due to this force and urge, many people fall into the trap of trying to copy a competitor’s strategy. But this is not recommended for a number of reasons. In case of chemical reactions, different quantities of same elements which are combined in multiple ways can yield different results. When people try to copy a strategy, they are really just copying an element or activity. Even if we think we know what a competitor’s strategy is from the external (Outside in perspective), it can be very hard to copy successfully unless you know all of the individual details. It means that, when competitors copy each other, the only winner is the customer. Because over the long run, the more competitors converge into this trap, the more they look like each other (i.e., no difference between themselves). This drives prices down and squeezes margins.

Digital Strategy Is Not Equal to IT Strategy Most IT strategies consider technology in isolation. So, a company may have a business or IT strategy in place that incorporates digital technology, but it does not merely imply that, it is a digital strategy. For example, a company may be working on a cloud strategy, social strategy, or mobile strategy. Today’s customer-facing solutions rely on ubiquitous digital connections in which the individual technologies (cloud, NFC, mobile, big data, etc.) merge to deliver a customer experience that looks and feels like our natural behavior. There should be more connections between people, places, information, and things.

Is Digital Edge Different from Digital Automation Yes. An organization that focus their strategy on digital transactions based on automating, which substitutes physical resources for digital will only feel digital. But actually, a digital edge is a performance edge, when an organization looking to create revenue from digital technology needs a strategy that is more powerful than digital substitution. The need is to establish a digital edge. It should be the combination of digital information and physical resources in unique way, which creates value and revenue. Also, it results in business innovation rather than business disruption. When enterprises are seeking a digital edge, then they should transform processes, business models, and the customer experience by exploiting the pervasive digital connections between systems, people, places, and things.

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But the nature of digital technologies like mobile, social, and analytics is different. These technologies compliment rather than compete with existing or traditional systems and information, enabling greater leverage with less disruption.

Challenges These challenges must be overcome in order to ensure IoT adoption and diffusion (Prateep et al. 2014; Andrew 2015; Brain 2015; Karthik et al. 2015; Robbie 2015; Sarah 2016). Dimensions Government will face in laying down the infra for smart cities with respect to standardization

Technology

Areas to focus Aware of the exact needs for shared understanding between all stakeholders: Technological, market, and societal. Currently, there is a lack of understanding between all the stakeholders A published document (which includes a reference architecture for smart cities) can provide a firm foundation for further development teams in the longer term for a systematic and detailed technical architecture for smart cities Only a few cities have clear ideas about their precise future smart city requirements. So how can we meet the potential future requirements cost effectively when you cannot clearly define what you actually require There are plenty of standards which cover interoperability within the context of a particular system delivery system, but there is a lack of overall interoperability framework or standards that work across systems As we explore this industry further, it may increase the likelihood of confusion between parties in the overall supply chain Therefore, a range of standards and initiatives should be developed to set out approaches to developing and delivering smart city projects in a more efficient and organized way Acceleration of IoT devices and long-life spans: In the next few years from the understanding of various analyst reports and its findings, there will be lot of IoT devices which will enter into the market. Due to hardware upgrade, the next generation of IoT devices will (continued)

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Dimensions

Technical, social, and practical

Companies have been slow in adopting IoT

Organizations need new skill sets across a range of functions

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Areas to focus be obsolete. The old IoT devices (and also notIoT ready devices) may end up in landfills. By concerning the acceleration of IoT development, the existing number is expected to increase Instead of focusing on completely new devices which will be having new version, the companies or manufacturers can focus on software upgrades. It will extend the lifespan of products. For a sustainable development, we need to consider these acceleration variables into the factor Energy consumption: To achieve a particular objective in IoT, the networks require giant datacenters. These datacenters will consume a massive energy. It leads to massive burden of resources to the environment, in order to produce the energy. The energy and resources are not only utilized to achieve a specific objective, but also utilized to manufacture thousands of new devices Security, privacy, legal/accountability, standards, IoT strategy, start-up challenges, device management, device diversity and interoperability, integration of data from multiple sources, scale, data volume, performance, flexibility and evolution of applications, data privacy, stakeholders, and standardization Existing IT infrastructure is not suited to manage rapidly growing volumes of sensor data Organization\s lack real-time data which is critical to draw an insight from IoT The IoT magnifies data security and privacy challenges Organizations lack capabilities in developing and marketing internet of things services Sales enablers are not equipped to sell IoT services and especially in the segment of customer experience New demands will place on customer support capabilities Emphasizing the lack of needed skills within organizations, one third of IT projects require external support from vendors and other third party resources (continued)

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Dimensions Key concern

Areas to focus It requires hefty investment from the government and cautious steps are required towards the implementation, but it changes the lifestyle of every individuals in the country In Oct 2019, Gartner listed the top 10 technologies to be used by governments in 2020. Agile approach from the beginning is one of the items in their list For this, CIOs must create and agile responsive environment. 360 degree of a citizen view and early intervention are the keys. With agile approach, we can learn how to create pilot programs and move away from traditional governance

Agile in Digital Transformation Within agile principles, the digital transformation is easy as it simplifies the change/ complexity, with the help of a powerful framework. It enables the team to deliver better (valuable) products and enhance customer experience. Not only these two benefits, it encourages collaboration. Therefore, it increases visibility. That creates transparency. The core of Agile is to build winning products for customers (Robby 2019). Every day, organizations are challenged with new disruption, either due to new technologies or competitors coming to market with valuable product. It requires quicker innovation and faster time to market (Bizzdesign 2020). Basically, it deals with VUCA and balancing capabilities in four dimensions (Alan 2019). Dimension Velocity Time to market Process maturity Quality/customer feedback

Description Productivity of team and individuals Deliver a valuable product to the business via minimal viable product concept Through retrospective ceremony Through sprint review and demo, agile enables rapid iteration

How Design Thinking Helps Digital Transformation with Five Steps Steps Empathize

Description (Planbox 2018) Understand customer’s needs and motivation Observe how they interact with products and services (continued)

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Steps Define Ideate Prototype

Test

775

Description (Planbox 2018) Formulate a problem statement Break down into steps Brainstorm ideas – How to fix the problem Quick, creative, and collaborative Experiment with number of inexpensive/simple models to validate the ideas Observe how users interact with the prototype Collect feedback Adjust and optimize the next iteration Continue the testing the product/service on regular basis

Conclusion Future will be many more connected objects per person. Although IoT technology is in initial stage, it is our future. Just like smart phones have made a big change in the way we communicate and deliver information with the help of large number of application available. This technology will also bring big change in the way we live and interact with the world like we have never thought before soon. At overall, IoT brings both positive and negative impacts as we understood above. Engineers should be having a proper knowledge of these impacts so they can understand the pros and cons thoroughly and understand what is required in a system. Always, Design for Experiment (DoE) is the best approach to address these challenges by considering various impacts at the initial stage itself and then integrate with IoT development. The future state of the IoT will affect its current development and must therefore be considered. To celebrate the success, one needs to be groomed their lifestyle with the presentday demands. This is called as Put People First, then Technology. Imagine if everything goes well, when we are in smart city world, if we travel from one place to another, you can sit back and relax in your car, especially in a traffic congested city. That’s called True Success!!! In order to achieve the above success, an assessment with 360-degree approach is very important to build the strong foundations with respect to Digital (IoT) and its strategy, where it consist of prime elements like leadership, strategy, culture, business models, product and services portfolio, business process change, team and expertise, value chains and processes, market and customer access, delivery and deployment, and governance. One of the best approaches is to take incremental steps (MVP 1, MVP 2 etc.), rather than having a digital strategy fully rolled out or in production. So, the question becomes where to invest to achieve digital velocity, it is customer journey/digital touchpoint mapping. Also, successful digital strategies concentrate on specific business outcomes rather than implementing grand strategies. This gives the effort a clear and manageable focus.

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All of the above is to say that, there is no unified theory of digital transformation as of yet. But these are steps along the way, but the important thing is no one “right way” to become a digital enterprise, and the journey will be a gradual. The key to an effective and sustainable strategy is to focus on the integration of activities (elements). Today if these aspects are not part of your agenda, then it is the time to consider a different approach. Revenue sits on the table for those companies willing to explore how to conglomerate their physical and digital resources in innovative ways for the customer and business. Since IoT offers vast area of research in many fields, this chapter will help researchers to understand the boundary of IoT and to identify potential research areas. Like internet has transformed businesses and lifestyles in the last 20 years, IoT will disrupt our organization’s relationship with its stakeholders. IoT can help us to innovate new processes and initiatives to increase our organization’s business performance and create customer delight with new products and services. By understanding Agile in digital transformation, the primary focus area should be Agile Transformation, rather than Digital Transformation.

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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Source of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Simplex and Complex Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Process of Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Publish Cycles and Thresholds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Model and Converters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Private and Public Data Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anonymization of the User Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anonymization Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Mining Capabilities Over Anonymized Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Geolocation Data Anonymization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Extensibility of the Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Microservice Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scalability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Continuous Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Event Communication via Webhooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Extend a Smart City with a Smart Health Care Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Med-i-hub System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Med-i-hub Sensor Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Med-i-hub Service Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . HL7 Standard and FHIR Support in Med-i-hub System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Personal Assistant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Measurement Data Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Role of the Med-i-hub System in the Smart City Ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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E. Enler · I. Pentek · A. Adamko (*) Department of Information Technology, University of Debrecen, Debrecen, Hungary e-mail: [email protected]; [email protected]; [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_93

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Abstract

Novel solutions surrounding the Smart City Architectures derived from personal data have always been a challenging topic in recent decades. Both the volatility of the subject technologies implementing the architecture and the sensitivity of human-sourced data can be considered as the key contributors to these challenges. In this research, the goal was to identify antipatterns related to the possible architecture for a Smart City Solution by applying innovative software development methodologies and questioning proposed architecture’s efficacy in the fields of scalability, maintainability, autonomy, testability, and security via provisioning resolutions to the identified antipatterns. Research then focuses on applicable data management, sharing, and anonymization concepts that comply with the General Data Protection Regulation. The core concept of Data Protection allows the architecture to utilize a high-level abstraction business definition and extensibility. The envisioned characteristics of the aforementioned architecture solely rely on the disciplinary fields of Service Oriented Architecture and Component Oriented Development methodologies. This approach has been followed to issue a trend compatible solution which provides a well-defined foundation for advancements required by the evolving technology dependencies. After the architectural proposal, research carries on to the possible utilization of Smart City Solution as a Health Care service and consumption of the data that is provided by this service. The utilization gives an example of a healthcare workflow where the service abstraction plays a key role.

Introduction Today, researches point out how technological advancements of smart devices change daily routines of the vast majority. The Internet of things (IoT) produces valuable data by recognizing the user interaction with distributed systems or smart sensors that are embedded in these devices (Wortmann and Flüchter 2015). This integration of technological devices to human-life expands the possibilities of data analysis and comprehension of individuals in order to better align the human-centric habitats with the already existing living environments. The aforementioned statement solely defines the beneficial behavior of such devices for individuals. It is necessary to understand the possibility of an integration of the smart devices to a Smart City architecture to further design an everevolving living environment for the inhabitants with respect to the topics of health care, education, working environment, transportation, social life, and privacy. For agile and robust Smart Cities, the definition of the solution should carry certain attributes. These attributes describe the architectural, ethical, and tangible connections between the mentioned aspects of the solution. Architectural definition addresses domain-oriented requirements of the solution such as agility,

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accessibility, scalability, maintainability, and extensibility of the proposed architectural structure with the foundation and experience of software development methodologies, while providing an agnostic integration utilities for the smart devices and sensors (Adenuga et al. 2015). The ethical aspect of the Smart Cities tries to build legal foundation for the consumption of human-sourced data, related to the questions of what the data is, how the data is produced, why the data is collected, and in terms of data consumption the concept is also required to answer concerns related to the privacy standards that are enforced by the legal entities as regulations such as General Data Protection Regulation (GDPR). Integration of both concepts allows practical implementation of physical workflows that can be accepted within the Smart City architecture in order to be utilized by the user devices for consumption and production of the data which then increases understanding of the user habitat.

Source of Data Data is a piece of information that is represented in a physical form in order to easily communicate through a digital media definition or easily interpreted by humans or automated processes. Although the previously mentioned definition presents a broader perspective of the data, a Smart City architecture is required to provide an ontology of data classification. In the Smart City architecture definition, the given data has to carry and identify certain attributes. Vast variety of the data types makes the assumption of homogenous data definition almost impossible. Thus, it is necessary to address the requirement of unique data conversion. The unique data structure can implement conversion algorithms in order to satisfy service contracts for sensor data publication. The conversion aims to output a data structure which is understood by the architecture services. The public application programming interface (API) acts as a regulatory component while providing generic utilities in order to decrease the variety of conversion solutions to put forward a tangible workflow that is defined by the API (Ayres et al. 2012). Data classification is required to strategize a methodological approach for the data mining processes. This classification allows better understanding over the consistency and trust factor of the data while enabling an easier application of privacy methodologies. This classification categorizes the data in two types and two different characteristics.

Simplex and Complex Data Sources Data consistency and reliability can be considered as the most important concepts of the Smart City Solutions due to the necessity of having reliable samples in order to apply data mining and workflow algorithms with successful results. Even though the solution can provide tools for the public access in order to minimize production and

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consumption of inconsistent and unreliable data, due to the volatility of the humansourced data, semantic categorization is necessary. Having such volatile or sensitive data in a public environment impacts the trust factor and violates the privacy requirements of the architecture services. To avoid mentioned issues data is categorized as complex or simplex depending on the data source. The simplex data sources can be considered as city-wide devices which produce environmental values that are accurate and comparable with similar devices. Examples of such data sources can be Closed-circuit Television (CCTV) or measurement sensors which can measure pollution of air within the city. Meanwhile, the complex data types are produced by the individuals via consumption of the client applications, distributed services, or custom services that are deployed within the Smart City architecture as peripheral agents in order to do data harvesting from environments such as search engines and social media platforms in order to carry out cognitive operations. Aside from the aforementioned categories, data is also marked with attributes such as sensitive or public. The classification via attributes allow architecture services to cognitively decide what anonymization processes to follow while securing the privacy of the sensitive data and usefulness of the data for the analysis and research workflows.

Process of Data Collection Today, wearable or mobile devices accommodate significantly high computational power, while the other sensors like cameras and such do not. This variation of computational prowess introduces a requirement to follow different methodologies to collect data. Third-party complex data sources do not share a standard definition for the procedure of data collection or data definition out of the box. Therefore, it is architectures’ requirement to provide a loosely coupled contract for the complex sources for data publication. The complex data source can publish the data of individual sensors or can follow a sequence of workflow definitions to provide an environment sensitive data. These workflows are identified as a Business Process Model and Notation (BPMN) (Guidelines of Business Process Modeling 2002) entity which falls into the scope of Workflow Engines. The wearable or mobile devices are computationally capable of deploying lightweight workflow engines. Such deployment allows the architecture to produce workflow definitions for the clients to comply with. These workflow definitions can be generated for the purpose of creating analysis data for health, education, or analogous topics. The use-case of the utilization of workflows can aim to provide means to further generate analytic data by providing environmental data of the specific workflow process throughout the duration of the workflow. Such collective data models can provide insight into the relationship between the environmental parameters with respect to the processes.

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Data Publish Cycles and Thresholds The behavior of data publication varies between the types and the urgency of the data. In that sense, the data should be identified semantically to recognize the importance of the timely data belonging to the user or user environment. Healthrisk specifications can be obvious examples of such an itinerary data publication where the time is essential. In the aforementioned cases, the data can be set to be published by given parametric thresholds that are defined by Expert Services (Waterman 1985). The minor datasets that do not require any urgency can be collected in a circular fashion or by notifications when the data is needed. These methodologies of data collection require different frameworks for the architectural design of a public API. First and foremost the API for the data that is produced in a threshold definition should be readily available for expert services to determine the urgency and provide the necessary resolutions for the task. Secondly, the public API for the data that is produced cyclically should provide the framework for certain semantic segregation of the data in order to provide researchers a means of data source that can be studied and transferred into solutions that can be applied for the betterment of the living environment of the inhabitants. The architecture of data publication should provide an interface for further integration of data harvesting utilities such as access to the sensor data on-demand in compliance with the individuals’ privacy doctrine and GDPR. This extensibility is further analyzed in the following chapters.

Data Model and Converters The third-party sensory data has to be cleaned, separated, and converted into a media type that is accepted by the Smart City API. This provisionary setup is required to define a loosely coupled contract for the data producers and consumers. The idea of having a contract to define a data model itself introduces a complexity over the availability and consumability of the data. Therefore, the characteristics of the data model should be in a generic and minimalistic format which later can be easily wrapped or related to other data model entities. Definition of minimalistic data models can follow the normalization rules of Relational Databases by discarding tightly coupled relations or even the most recent service architectural approaches that are followed by the Entity Services (Aho et al. 1979). The minimalistic data model is an essential utility to build a scalable, maintainable, and extensible microservice architecture that defines the Smart City architecture and peripherals. The inconsistency of the data representation between sensors is an undeniable fallback for the public API of the architecture. Due to the sheer amount of available sensor types and variations, it is almost impossible to recognize each individual data format. This issue can easily be addressed by the introduction of a converter layer that can be deployed by the client library wrappers or an intermediary layer that can register converters as services by the Smart City. The architectural definition of the converters can be separated into two. First, the converter agnostically receives a data

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entity that represents sensory data in a raw format that is produced by the client as it is and deploys the conversion formula that is defined by the client device and normalizes the data as required by the API. Then, the converter proceeds to publish the data agnostically to the API. The term agnostically is used to diminish the architectural understanding of the communication layer dependencies in order to describe a more generic structure for the communication. The data models should be able to generate relational attributes of the data in a way that can be anonymized to serve it publicly. Such a formula can provide solutions to complex workflow or data entities for individuals by providing userspecific suggestions related to the environment. This practice can be utilized as a Recommendation Engine for the Smart City architecture. The aforementioned statement is used in order to open an opportunity to further relate the data to the user, over a duration or, namely, different cycles while trying to answer problems of such behavior under the topics of privacy and GDPR compliance in the following chapters.

Private and Public Data Solutions The storage of the data should be defined by the user requirements and its ethical directives. The first-hand data should not be available or traceable back to the individual who produced the data. It should always be contained in private data services in order to prevent any violations of the users’ privacy. The definition of the private data service shall be determined by the legal entities and only be accessible by the individual and the autonomous and expert services which are to be regulated and maintained in accordance with the legal entities mentioned before to carry out computational tasks to further assist the individual or inhabitants of the Smart City. The expert services can carry out the formulations that are defined by the field experts to make suggestions to the users. As an example, in a health-care scenario, the expert service can inform a health-care professional to provide an insight into an individual’s data without violating privacy just by providing the necessary data. In accordance with the privacy specifications of private data services, it is necessary to point out another definition of an important data solution of the Smart City architecture, public data services. Public data services are derivatives of private data service entities in a different format. Since it is following the guidelines of a distributed architecture, it is quite necessary to put a manuscript for the consistency of the data between services while keeping it synchronous with private data services. Aim of the public data services is to construct an environment for the consumption of the data produced by city-wide peripherals or individuals as statistical data repository in a well-defined framework architecture so as to provide researchers or user clients to connect and further expand on the use-cases or relational patterns of user behavior in a fashion where it provides the means of assisting inhabitants and lay the foundation of a human-centric living habitat. In order to acquire an extensible architecture, Smart City solution shall follow entity-oriented service definition which follows the guidelines of microservice architecture’s concepts on granularity of data. Such approach enhances the

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distribution capabilities in regard to the business domain, due to its nonexistent interdependence between other services which provides an abstraction of the data while providing the necessary tools to create new data models without affecting the already existing systems. The segregation concept of these services removes any side-effect during the expansion procedures and maximizes the capability of scalability and maintainability. Aforementioned characteristics are necessary qualifications which have to be fulfilled by the Smart City solutions in order to guarantee the adaptation aptitude for ever-growing technology trends which is required to answer the inhabitant domain with generic application interfaces.

Anonymization of the User Data The Smart City solution centralizes data architecture and consumption methodologies. The importance of valuable data determines the success of the architecture’s usability requirements, meaning it is necessary to obtain relevant data to improve the analytic capabilities of the agents that are connected to the Smart City solution. However, due to the audience of the data collection, it is necessary to point out that not all data can be publicly accessible. In order to provide a transparent process and accessibility to the API, it is significant to determine the methodologies to follow for the anonymization of the user-related data. The anonymization process can be described as formulas that can transform the data into the untrackable entities back to the data producer in the public environment. This is an important feature of the Smart City solution that should be defined by the legal entities and GDPR compliance.

Anonymization Process The user-related data can never be published publicly without the utilization of a novel and regulated anonymization process. All the user-related data have to be stored in private data services so as to avoid any conflict of privacy-related issues. The data that can be stored has to be identified and categorized by the user and regulatory services to comply with necessary legal decrees before the persistence of the data takes place. The privately stored data then can be anonymized by following the regulatory steps before publishing the data to the public data services. The anonymization process should be producing relevant data that cannot be related back to the data producers while providing necessary information entities for the public data consumption to further apply data mining formulas.

Data Mining Capabilities Over Anonymized Data The data anonymization process should produce relevant data, while keeping the privacy constraint intact, to work with as a means of data mining dataset.

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Anonymized data has to represent relationships between data models and environment parameters. For example, the relationships of the sensory data can be bundled by the workflow definitions, then the workflow definition can be represented as a data model that provides the necessary framework for data mining formulas. Usage of workflow definitions can be means of anonymizing the data while preserving certain data relationships without making any physical connection to the user data producer to identify or make further progress with previous user data. This characteristic which is provided by the workflow definitions opens a door to unconventional abilities to produce and process the data and the data flow architecture of Smart City solutions. It is necessary to point out the computational requirements of smart devices to deploy lightweight BPM engines so as to achieve such workflow data persistency and state management.

Geolocation Data Anonymization Geolocation is a valuable dataset type that can be used in various research topics to provide beneficial information about the environment to the users. These topics can be related to health care, transportation, and further many other titles. As an example geolocation data can be processed to determine the pollen count and polluted sections within the city to inform users. In order to comply with the anonymization definition “in such a manner that the data subject is not or no longer identifiable” (GDPR Recital 26) (European Parliament and Council of European Union (2016) Regulation (EU) 2016/679). K-Anonymity method can be used as a means of anonymization of the geolocation of inhabitants (Samreth et al. 2018).

Extensibility of the Architecture Monolithic applications have been the most traditional method of software architecture for decades due to their straightforward traits provided by the tight coupling of the domain requirements (Fan and Ma 2017). The mentioned coupling also provides an environment which makes testing and monitoring easy while avoiding complex log aggregations and end-to-end testing requirements. Despite the fact that a monolithic approach for building up Smart Cities seem to be beneficial with its traits, when it comes to the topics of scalability and maintenance, it is virtually impossible to support technological trends and user requirements in an application that size in long-term scenarios. As a software design Service-Oriented Architecture supports all the necessary requirements of a Smart City solution. These requirements can be identified as scalability, maintainability, extensibility, and multitenancy. Implementation of the Smart City prevents interdependency-related issues with focusing on domain specifications and proposes an abstraction which allows services to be deployed agnostically. Such agnostic deployment further enhances the capabilities of the service communication since it removes protocol level boundaries between services.

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The deployment specification reduces the requirement of complex communication architecture by defining generic endpoints or APIs which then resolves dependency injection on the communication layer. This allows applications to be only existent as containers as a service rather than compiled within the services. Current technological status allows these applications, in other words SOAs to be independently integrable without specifications rather than protocol-oriented communications. Importance of the self-governing behavior for the Smart City solution can be observed within the technological advancements and volatility of the data types that are introduced via new sensory devices which are used by the individuals. Previously mentioned characteristics of the Service-Oriented Architecture allows a proposal of a foundation for a novel Smart City solution.

Microservice Architecture The microservices are a novel methodology of implementing service architecture in modularized fashion (Wolff 2016). This philosophy can be detailed as the following features: • Microservices can carry out a single domain aspect. • Microservices can be deployed autonomously without any dependency requirements with other microservices. • Microservices manage their own data models or private data models in a shared data management environment. • Microservices can utilize a communication architecture with the entity, utility, and task services (Merson and Yoder 2020). • Microservice communication can be agnostically described without any technological dependencies. The argument of such agnostic behavior can be ascribed by the independence of standard protocol definition such as Hypertext Transfer Protocol (HTTP) or Advanced Message Queuing Protocol (AMQP) for communication layer architecture to initially build a communication between services where both technologies can exclusively be used. The comparison between the monolith and microservice architecture can be based on the capability of a microservices regarding the easy assessment of modification, where it is almost impossible to make changes to the monolithic applications without introducing any side effects. This concept is realized by the modularization capability of the microservices and avoided by the domain segregation and utilization of explicit interfaces to communicate with other services. Such architectural aspects provide a certain level of abstraction to allow service to handle unique domain problems in an encapsulated manner while avoiding any introduction of technical definition to be leaked into other service definitions. A catastrophic event may lay any application inoperable. In a Smart City architecture, high-availability is an important aspect and downtimes cannot be afforded. In order to avoid availability issues, Smart City architectures should provide failure

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handling scenarios where design patterns such as circuit breaker or auto-scaling are utilized. Due to the microservice characteristic of autonomous structure, these services are replaceable without any impact to the system. An identical microservice contract can easily be replaced with another without affecting the orchestration and requiring any dependency specification. This autonomous characteristic of the services enables the software components to be technology independent via providing means of technology agnostic environment as well.

Scalability As a domain-oriented solution, microservices’ approach to the scalability problems involves the segregation of user-requirements. Segregation of these requirements makes it possible to scale individual services effectively. Existence of such services requires an agnostic approach throughout the resolution of the domain problem. Compared to the traditional monolithic applications, microservice approach for the Smart City solution allows services to be agile and robust. Size of the service following microservice methodology makes it easier to scale which allows nondependent environments of the architecture to overcome technical boundaries on the concept of scalability (Adamkó et al. 2015).

Continuous Integration The continuous integration is a requirement of Smart City architecture. It is a necessity to provide the utilities to adopt new sensory devices and integrate them into the architecture without introducing any side effects (Hideg et al. 2016). Microservice architecture and client library definitions of such will provide data producers the tools that are required to implement new converters for new devices. The new sensory data type should be recognized by the architecture maintainers and introduce new services to the architecture if necessary. This does not require any converter definition since this solely depends on the client library definition. Such new services are also implemented by following the disciplinary definition of microservice architecture.

Event Communication via Webhooks The communication architecture-related definitions, for both the client and the services, establish prerequisites for a concrete definition of communication flow. In an agile architecture, it is not always possible to provide the definitions of such terms or introduce a generic communication layer that comprehends the innovative devices of the future out of the box manner. The most common concepts of communication between services correspond to the HTTP and AMQP standards. Even though it is a standard and generic

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architecture to define the communication of services over HTTP, it lacks the asynchronous and logging aspects of the AMQP, neither of the definitions better than others exclusively but can be utilized to fulfill certain requirements of the communication. Distributed computation of services can easily deploy an AMQP layer as communication means but it does not necessarily address adaptability of new services and distant messaging topics. It is essential to provide the framework of communication for the newly integrated services and clients. The usage of webhooks as the main method of communication between services and clients which can increase the capability of reactive application definition for the Smart City architecture. The webhooks are used in order to communicate with event triggers between applications to achieve and integrate a certain level of augmentation of the applications by modifying the behavior of the application in orchestration (Webhooks: Events for RESTful APIs (Biehl 2017). Their highly abstract structures allow them to easily adapt the interface definition of the microservices to provide data to the third-party client libraries, and introduce new services to the orchestration. Furthermore, clients can register to the services which support webhooks to get notifications from the Smart City architecture. The idea of webhook utilization can construct a bridge between standard protocols like HTTP and AMQP behind the scene. High abstraction of webhooks can further initialize new technologies of data flow automation to combine different standards as communication architecture. Such technologies of such implementations will be discussed in the following chapters.

Extend a Smart City with a Smart Health Care Solution Smart cities have different types of sensors and the smart city systems are collecting the sensor data to use the insights gained from the processed data. The information, extracted from the sensor data, can be used to manage the assets, resources, or to develop the services. Smart cities could share the raw data or the extracted information with other smart cities or smart institutions in the city. Based on the extracted information, the professionals and the cities’ services can make much better decisions. Modern smart homes are not individual systems; they are part of smart cities, they can collaborate with other cities or institutions to make more efficient decisions. Modern smart homes are equipped with a lot of sensors to collect different types of data. Most of these sensors are to collect data to help the smart home system to operate the building. There are some types of sensors in a smart home that can generate helpful data to smart cities or their facilities too. In this way, the smart cities can be consumers of the modern smart homes, and modern smart homes are producers of the smart cities. Smart homes can use other devices and sensors from their environment, e.g.: the residents’ wearable devices. These types of devices and their sensors are not so helpful to smart homes but are more useful to healthcare institutions in the city. For example, health-related measurement data collected by the smart homes from

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external sources could be useful to the health-related institutions in the smart cities to improve their services. Healthcare services must also be as intelligent as possible in a modern smart city. To achieve this, the smart city systems must forward data to their healthcare institutions (Garai et al. 2017). To improve health care services, the smart city system must filter data, the non-health-related data is worthless to these institutions, so these should be ignored. However, the health-related sensor data transferred to healthcare systems from smart homes through the smart city systems help to improve the health-related services and can be created personalized monitoring processes by the health-related systems. On the other hand, a simple medical examination can be more precise if the specialist performing the examination uses medical information from external sources as well. Although the sensor data is not from an authenticated source, it should be taken into account when using it, but they still carry very useful information (Kadadi et al. 2014). Personalized remote health monitoring can be reliable enough if the configured monitoring system accepts information continuously about the patient (Asada et al. 2003). Most people have one or more devices equipped with biosensors, most of them wearable with multiple sensors (e.g.: fitness trackers, smartwatches). The other type of tools is not wearable, but produces useful health-related data, e.g.: smart scales. These devices continuously produce sensor data about the individuals that can be useful sources to the smart healthcare institutions. Unfortunately, there are a lot of mobile applications for different wearable devices. In most cases, they have a unique communication protocol and communication channel, the most devices can transfer the measurement data only to their mobile application. Mostly these applications forward the measurement data only with their private application servers, so the measurement data and the extracted information are enclosed into a closed system. It is a common problem with the wholesale assets and their software. A health-related measurement record must have a well-defined context to understand its value. The individual measurement values are not significant without context. By context, it is meant that the previous measurement values and other health-related attributes about the individuals are known. A unique measurement value can be a valid measurement or an outlier value, measurement error. The integrator system can efficiently use the collected data with its context, so the smart city system must hand over the most detailed data possible. The different types of measurement data have variant descriptive data context. The Med-i-hub system we developed is an easy-to-integrate hub system that allows healthcare systems to easily integrate biosensory data from an external data source (Adamkó et al. 2016). The system collects, processes, and transmits the data recorded by the sensors. It is able to analyze and even interpret the data, it can also send reports or alerts according to predefined conditions. The system can transmit the data collected by smart homes to smart cities or their institutions, thus helping them to operate effectively. The advantage of the system is that its integration is simple, and the integration system does not have to deal with communication with hardware devices with different interfaces. The Med-i-hub system takes over these tasks and the data processing only needs to be solved by the integrated system.

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Med-i-hub System One of the most difficult challenges for the current health-related systems is the adaptive and forceful scalability as the load raises. Many old-fashioned healthrelated systems are not or not so scalable and have an old-fashioned architecture that is not suitable for involving additional system resources as the load increases (Fengou et al. 2013). These types of systems are able to manage to use cloud services, but due to the lack of scalability, they miss the advantages of supporting cloud services, so for the most components they do not even operate them but require individual system components with dedicated physical resources (Abdel Khaleq and Ra 2019). It is often the case when several services of a resource are shared, which can degrade the efficiency of each service because a significant part of the system resources is used by other system components, but the used ecosystem is not scalable. Scalability must be considered during the designing of the system; it is only possible to make the different system elements for scaling by transforming quite a few expenses. Scalability has several conditions, the most critical of which must be taken into account when designing the system: • The commitment of replacement: it is highly necessary to involve further resources, it is essential to determine where and exactly how they will come into the system. The elements of the current system and the ongoing operations will be influenced by the involvement of the supply. • Predefined route selection: the specified route is served in a defined method, through a well-defined route, it can be simply followed by system operation and scalability is an easier change. • Capacity planning: based on the number of transactions, it is easy to estimate how much resources need to be involved with a given number of transactions. • Premature screening: it is critical to explain observing processes already in the design phase so that we can detect and react to an increase in load-in time. By constructing the Med-i-hub system, the previously described principles were the most important to make the result an adaptive, dynamically scalable system architecture. As a result, the system can be architecturally divided into two independent parts, which differ in the technologies used as well (Fig. 1). The responsibilities of the two parts are also different. The two logically separated parts are the Sensor layer and the Serving layer.

Med-i-hub Sensor Layer In the Med-i-hub system architecture, the sensor layer is responsible for communicating with the health-related sensory devices, and filtering, cleaning, and possibly transforming the received measurement data before the data is placed in data storage. This layer is not responsible for serving requests in the classical sense, but for almost

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Fig. 1 Med-i-hub reference architecture

real-time data processing, its task is to keep on the response time of the sensor data as short as possible and to guarantee that the data stream joins the system smoothly. The sensor layer can fulfill the described requirements if it uses technologies that allow new resources to be brought to work as the load increases without any impact on ongoing tasks. This layer also stores data, so it is necessary to use a database. In the case of classical relational or NoSQL databases, we have to imagine with the fact that the query time increases in comparison to the increase in the load, the same is true for recording data in the database. So it is advisable to avoid these technologies if real-time operations are required. The Med-i-hub sensor layer operates with a memory-based database. Memory-based databases do not work on the hard drive directly rather they persist the content directly into memory. Data can be accessed from memory orders of magnitude faster than from a hard drive, and these types of databases build on this (Kabakus and Kara 2017). The implemented Med-i-hub system uses the VoltDb memory-based database. VoltDb is a memory-based relational database management system written in Java and C++ that runs on Linux and is based on NewSQL principles. NewSQL is a classification of relational database management systems that seek out to give the scalability of NoSQL systems for a load of online transaction processing (OLTP) while keeping the ACID guarantees of a traditional database system (Pavlo and Aslett 2016).

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While VoltDb is retaining the ACID property of transactions, it can be used as a standard SQL language as well. The database can be scaled horizontally by adding additional servers that meet the design goals. There is no dedicated master node, it partitions the information between the nodes involved in the service, so the requested information is always available as quickly as possible from the cluster. The advantage is that it can be integrated with many other solutions, even open-source, such as Apache Kafka, Amazon Kinesis, or RabbitMQ, thus further expanding the possibilities of later use. Thus, the sensor layer uses a memory-based database for data storage, which in turn requires more memory than other data storage technologies. Thus, in the case of scaling, we can speak of memory-intensive scaling in the case of the sensor layer.

Med-i-hub Service Layer In addition to the sensor layer, the architecture has a service layer. This layer is responsible for serving web requests in the classical sense over the Internet. This layer can also be scaled horizontally depending on the amount of the load. The role of the server layer is to keep in touch with integrated systems, serving the data needs of systems and users as much as possible. The following services are found in this layer: • Data Visualization Services: presents the data gathered by the sensor layer through a web user interface. • Analytical services: the sensor layer operates with the data collected and encourages various analyzes. • Monitoring services: interprets and analyzes the data collected by the sensor layer. This feature allows you to operate various interpretations and alerts. The service layer publishes data through two different interfaces, both with different purposes. One is a standard web user interface where registered users can browse the data set they provide and configure various monitoring services from browser applications. Classical authentication and authorization are required to use the web interface. Each user can only see their dataset, but not others. The other one is an open web application programming interface-based communication interface that provides two-way communication between the Med-i-hub and integrated health-related systems. This part of the service layer supports the common health-related standard the HL7. Using this standard, the layer is able to pass information to external health-related systems in a standardized way.

Data Storage For the system to be able to process the data efficiently and serve the data requested by the systems and users, the data must be stored accordingly. The

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earlier described layered architecture model uses a health-related data warehouse when serving the requests. Data stored in the persistent storage is simplified to keep data service, reporting, and other services performing well and easy to manage. In the Med-i-hub system architecture, the data storage in the persistent storage is object-oriented, which means that we plan the data storage in a datadriven way with the object area in mind. The type of individual measurement data, such as heart rate data, can be considered as the object of data storage. In the persistent repository, all data is grouped around specific subject areas and collected accordingly. A persistent repository can thus be interpreted as a data warehouse used by the system to serve requests from outside, which are mostly for analytical or reporting purposes, possibly for a set of data generated in a time interval. The data in the repository is no longer modified, it is read-only, but data requests or information retrieval can affect millions of records. Taking these into consideration, it is advisable to organize data storage as well. The Med-i-hub system stores data placed in persistent storage arranged in a star schema. Visually illustrated, the fact table is located in the center of the star schema, which stores the actual measurement data and the dimension tables are located around it. Based on an example from the Med-i-hu system, the heart rate measurement data is located in a fact table in the middle of the star scheme, and the dimensions are as follows (Fig. 2): – – – –

Environmental data (e.g.: temperature, humidity) Location (e.g.: GPS coordinates) Activity during measurement (e.g.: running, swimming, reading) Personal information (e.g.: age, gender, weight)

Fig. 2 Database schema of the heart rate measurement values

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HL7 Standard and FHIR Support in Med-i-hub System Health Level 7 (HL7) is an internationally used standard for the transmission of health-related data and patient files used in the transmission of electronic patient records. The name of the standard refers to the 7-layer communication model of the OSI model. In the HL7 model, the uppermost, i.e., the seventh, layer is the application layer, which is responsible for health-related data exchange, which determines how the system can interpret the sent or received health-related data. The HL7 standard is the result of a private initiative run by the Health Level Seven consortium, also recognized as a standardization body by the American National Standards Institute (ANSI). The common purpose of the standard is to establish a more widely used health standard. More than 55 countries are members of this organization (Dolin et al. 2006). At the time of writing this article, the most common version is v2, but the v3 includes several important changes that may help spread the standard. The spread of the new version is hampered by the fact that healthcare systems are evolving more slowly than other health-related systems and the transition to newer standards and technologies is costly. Most systems that can use the HL7 v3 standard are backward compatible and support the v2 version. HL7 standard uses the Systematized Nomenclature of MEDicine (SNOMED) grading system. SNOMED is one of the most comprehensive medical code systems that takes to deliver the most detailed potential medical description of patient role (Sedano et al. 2009). Its ancestor is the Systematized Nomenclature of Pathology (SNOP), created in the 1960s, which was originally created to represent pathological concepts in a multidimensional system. Fast Healthcare Interoperability Resources (FHIR) is a healthcare standard based on the HL7 standard. The HL7 standard was created by the Health Level Seven care organization with the aim of describing the data format and a standard programming interface for the efficient and clear exchange of health data (Bender and Sartipi 2013). FHIR builds on HL7 standards’ data format, but it is more modern and therefore easier to use, using up-to-date web technology such as Representational State Transfer (REST) and HyperText Markup Language (HTML). The external systems can use the Javascript Object Notation (JSON) or Extensible Markup Language (XML) message format to represent the data. The primary purpose of the standard is to facilitate collaboration between existing health systems by standardizing data exchange. Thanks to the standard, data can be easily accessed by healthcare providers and individuals from a variety of devices, be it a mobile device or a personal computer. The Med-i-hub system uses the FHIR standard to implement the distribution of health data to the integrated healthcare systems. The standard is extensible and describes the format of messages for the exchange of observations, and measurement data.

Smart Personal Assistant A pilot application has been developed for the Med-i-hub system to assist in the integration and analysis of sensor data from the activity monitoring devices and

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fitness trackers used by the user. The application transmits health-related data from the connected activity monitoring devices or fitness trackers to the Med-i-hub system. The pilot mobile application also attaches descriptive data available from the user’s device to the measurement data, which can be calendar entries or other data recorded by the user (e.g.: weight, age, underlying diseases). The Med-i-hub server can evaluate the data much more efficiently using the metadata received from the pilot application. The pilot application requests permission from the user to use the descriptive data. With the help of the application, the previous measurement data and their descriptive data can be traced, and alarms can be configured in the system. The conditions of the alarms are evaluated by the system based on the measurement data and, if necessary, an alarm is sent on the configured channel. The channel can be: email, push notification, SMS, or even an automated phone call. With the help of the application, the previous measurement results can be easily visualized. The pilot application builds a separate connection to the devices through the programming interface published by the device. This usually means a Bluetooth data connection with the device’s custom message formats. Most devices have their format and do not publish measurement data based on a common standard format. The application transmits the data to the server if there is an active Internet connection. If there is no active Internet connection, the measurement data is stored in the device’s local memory. When the device gets an active Internet connection again, it sends the measurement data to the Med-i-hub server. In case the time between measurements is small enough, the mobile application opens a socket and continuously streams the data to the server. If this is not necessary, it communicates the data to the server using a simple REST API. The Med-i-hub publishes data through an open programming interface(open API). Open API is publicly available and clients can use it to retrieve data (Qiu 2017).

Measurement Data Classification Only a few devices equipped with certified bio-sensors, most of them equipped with general hardware for personal use only. There is no medical supervision during the use and the devices are not certified. There is also a margin of error for devices certified for medical use, which must be calculated when evaluating the measurement data. When applying general-purpose biosensory devices, incorrect measurement can occur for several reasons: – Improper wearing of the device or the device has moved – Interference – Incorrectly calibrated or user-modified settings on the device The Med-i-hub system evaluates the received data regardless of the device type and authentication. The system does not delete or modify measurement data, but it assigns a confidence value to each measurement value. The confidence value is a number between 0 and 1.

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Take into consideration the most common data sets that portable devices can provide. Most of them available on the market are capable of measuring heart rate and mostly use the same optical-based technology. The measurement in this case must be made on the wrist. This measurement is based on photoplethysmography (PPG) and, in simple terms, works by illuminating the wrist with an LED and using a light sensor to measure changes in the size of blood vessels, converting these measurement results into pulse data. It is easy to see that the technology carries in the possibility of erroneous measurements (e.g.: if the device moves because it is not properly fixed) that need to be addressed. Measurement errors or outliers can be easily filtered using simple mathematical operations, such as a moving average. In this case, only the maximum deviation from the average should be calculated. The moving average is sensitive to outliers, so we can efficiently estimate the probability that each measurement value can be erroneous or outliers based on past measurement data (Cockcroft et al. 2005) (Fig. 3). Another good way to filter out erroneous values is to group the possible values into zones. Zones should be designed so that each zone has a minimum and a maximum value and there is no overlap between the zones. An activity is assigned to each zone. If the user activity and other descriptive attributes of the measurement data are known, the measurement value can be easily assigned to a specific zone (She et al. 2015). The disadvantage of this method is that the classification of measurement data with an unknown context may not be efficient enough. The descriptive attributes of the measurement, on the other hand, raise questions from a GDPR perspective. In some countries, this data is protected by strict regulations. If the user grants permission to use the data, then knowing the context, the Med-i-hub system is able to apply it to classify the data when calculating reliability. Descriptive attributes can come from other systems in smart homes or data provided by smart cities.

Fig. 3 Moving average with outlier value

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The effectiveness of the zone method depends on the quantity and quality of the descriptive data, while simple mathematical methods do not handle the descriptive attributes of the measurements and can be overly sensitive to outliers. The efficiency of these methods alone is not perfect, but if we use them in combination, we can produce a classification method that allows the data to be cost-effectively and quickly assign a reliability number. This number can serve as a basis for each system to use the data.

The Role of the Med-i-hub System in the Smart City Ecosystem In the case of smart cities, it is especially important that the city provides as many smart services as possible to its residents. Smart cities usually bring with them the concept of smart homes. Modern smart cities are even less concerned with integrating smart homes into smart cities, although there are several useful sensors available in smart homes that can further improve city services. Such sensors can provide not only health data but also other data that are essential for weather forecasting. The Med-i-hub system is able to improve the city’s health services by collecting health data. The system can receive data from the user directly or from smart home systems. Smart homes are able to make sensor data available in their own ecosystems available to the system using the REST API. The system is able to transmit the collected data to hospital information systems or other types of healthcare systems after analysis, transformation, and integration. The systems can build on a variety of health services based on sensor data and can even perform real-time monitoring. Integrated health systems can formalize the knowledge revealed by their own cognitive processes based on the data obtained (Garai et al. 2016). By transferring the formalized knowledge to the Med-i-hub system, the system is able to more accurately classify the new measurement data. External systems or even users can set alarms or scheduled reports in the system. Alerts can initiate additional processes by using additional services in smart cities. For example, in the event of a cardiac arrest, rescue units may be disrupted, or in the event of a persistently high heart rate, the medical team may be notified by the Med-i-hub system.

Conclusion The abstract capabilities of smart cities give a new concept where the changing environment responds to the needs of its inhabitants. It is not easy to determine the corresponding architecture, as in traditional practices, for a smart city system to deliver solutions for an always-evolving requirement definition of entities. The architecture of existing systems is difficult to change in some cases, so over time, these systems will run into problems or perhaps disappear, leaving room for new solutions. Smart cities and smart homes work with a lot of sensor data, many of them are sensitive according to the regional regulations. The big challenge for the systems of

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the future is to obtain and use the sensor data for the benefit of the community without infringing on people’s privacy rights. The integration of smart homes and smart cities is an interesting task in the future that will allow both smart homes and smart cities and other institutions in the city to gain a lot. There is a need to rethink current regulations so that the use of sensor data is efficient and of interest to everyone. Handling the sensor data is not a straightforward mission, so it is essential that handling requires a lot of raw hardware resources. Applying simple and effective mathematical methods, data communicated by biosensors can be evaluated and handled more efficiently with fewer system resources. Smart cities can grant the explained solutions with capabilities that establish new ways for improvement of cognitive health services using classified measurement data. Smart cities can be expanded in a sense that does not need serious architectural change, biosensory data lives in separated systems. Smart city solutions only need to achieve an interoperability link between health systems and biosensory devices available from smart homes through the Med-i-hub. The described personal assistant pilot application supports users to monitor their well-being and evaluate their measurement results by the users’ mobile devices. The contextualized data make it available to evaluate it much more accurately. Compared with the available applications, the personal assistant permits the user to assign context to the measurement data. As a next step, the introduced smart personal assistant could be enhanced and be integrated with more useful data sources that are offered in mobile environments. The enhanced personal assistant can underline the evidence of the described architecture when applying it as a data source by external services. Acknowledgments The publication is supported by the EFOP-3.6.1-16-2016-00022 project. The project is co-financed by the European Union and the European Social Fund.

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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ontology Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ontology Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ontology Developing Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Direction of Taxonomy Hierarchy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Source Type of Ontology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ontology Languages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . RDF Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . OWL Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ontology Design Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mobility Ontologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Foundation Ontologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transportation Physical Network Ontologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future Directions in Smart Mobility Ontology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mobility Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MaaS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Autonomous Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Connected Roadways and Internet of Vehicles Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Ontology is the explicit and formal representation of the concepts in a domain and relations among them. Transportation science is a wide domain dealing with mobility over various complex and interconnected transportation systems, such as land, aviation, and maritime transport, and can take considerable advantage from ontology development. While several studies can be found in the recent literature, there exists a large potential to improve and develop a comprehensive A. Yazdizadeh · B. Farooq (*) Laboratory of Innovations in Transportation (LiTrans), Ryerson University, Toronto, ON, Canada e-mail: [email protected]; [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_66

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smart mobility ontology. The current chapter aims to present different aspects of ontology development in general, such as ontology development methods, languages, tools, and software. Subsequently, it presents the currently available mobility-related ontologies developed across different domains, such as transportation, smart cities, goods mobility, and sensors. Current gaps in the available ontologies are identified, and future directions regarding ontology development are proposed that can incorporate the forthcoming autonomous and connected vehicles, mobility as a service (MaaS), and other disruptive transportation technologies and services.

Introduction Ontology is the explicit formal representation of the concepts in a domain, the relations between them, and their properties and constraints (Gruber et al. 1993). Ontology, as a discipline of philosophy, explains the nature of existence and has its roots in Aristotle and Plato studies on “metaphysics” (Welty and Guarino 2001). However, the word ontology originated from two Greek words: ontos (being) and logos (word), and conceived for the first time during the sixteenth century by German philosophers (Welty and Guarino 2001). From then till the mid-twentieth, ontology evolved mainly as a branch of philosophy. However, with the advent of artificial intelligence since the 1950s, researchers perceived the necessity of ontology to describe a new world of intelligent systems (Welty and Guarino 2001). Moreover, with the development of the World Wide Web in the 1990s, ontology development got to be common among different domain specialists to define and share the concepts and entities in their fields on the Internet (Noy et al. 2001). During the last three decades, ontology development studies have evolved and shifted from theoretical issues of ontology to practical implications of the use of ontology in realworld, large-scale applications (Noy et al. 2001). Nowadays, ontology development focuses mainly on defining machine interpretable concepts and their relationships in a domain. However, ontology development also pursues other goals, such as providing a common conceptualization of the domain on which different experts agree (Métral and Cutting-Decelle 2011) and enable them to reuse the domain knowledge (Noy et al. 2001). It also enables researchers to easily analyze the domain knowledge and eloquently express the domain assumptions. Furthermore, ontology facilitates sharing a common understanding of the structure of concepts among people or software agents (Noy et al. 2001). Transportation science is a wide domain dealing with a variety of complex and interconnected transportation systems, such as land, aviation, and maritime transport. Figure 1 shows the different disciplines and fields of science involved in smart transportation. The image has been generated by searching for publications and conference papers that include the “Smart Mobility” phrase on Web of Science. The image shows around 20 different fields of science involved in smart mobility. Such a multidisciplinary domain can take a considerable advantage from ontology

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Fig. 1 Different fields of science and disciplines involved in transportation studies. (Generated by Web of Science search engine)

development. Several reasons justify the demand for a comprehensive mobility ontology. First, since the last two decades, the technological innovations in data collection techniques, from smartphone and WiFi technologies to the different roadside, on-vehicle and on-body sensors, monitoring cameras, etc., have provided transportation/mobility scientists and planners with huge amounts of data, summarized under the term “Big Transportation Data,” which consequently have boosted the demand for cloud platforms and storage systems during the last years (BaduMarfo et al. 2019). Such cloud platforms and storage systems usually suffer from heterogeneity issues, as they are required to encompass a variety of services and data, each of which possesses different features, definitions, and details without relevant standards and conventions to enable interoperability between various environments (Al-Sayed et al. 2019). Second, in the realm of mobility, the data is collected by different public and private parties and often stored and accessed in separate datasets that are extremely hard to organize and form into an integrated management or monitoring system. As ontology has first been employed in the artificial intelligence domain to conceptualize some real-world elements (Falquet et al. 2011), it is a rather new concept in the transportation/mobility domain. While several studies exist in the literature, there exists a large potential for improving and developing mobility ontology. The major goals of this study are to present the different aspects of ontology development, review the existing mobility ontologies, and identify the gaps in the current literature to address future development. This chapter begins by explaining different components of ontology in section “Ontology Components.” Afterwards, section “Ontology Classification” describes different ontology classification approaches. Next, section “Ontology Developing Approaches” explains various ontology development approaches and points to some examples in mobility ontology. section “Ontology Languages” gives a brief background on different

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languages for ontology development. Next, section “Ontology Design Procedure” explains the general procedure and steps for the development of an ontology. Afterward, section “Mobility Ontologies” categorizes different sectors in the transportation domain to identify the parts covered in the current literature and the parts requiring more attention. It also reviews the current ontologies in the domain. Afterward, section “Future Directions in Smart Mobility Ontology” explains the gaps in the literature, specifically in disruptive transportation technologies and services, and discusses possible solutions to fill these gaps in mobility ontology development. Finally, section “Conclusion” concludes and summarizes the future directions.

Ontology Components Here we elaborate on the main components in an ontology, regardless of its formality and domain granularity. Every ontology is at least composed of three main components (Noy et al. 2001): • Concepts (classess) • Properties (also called roles or slots) of each concept • Restrictions on properties (also called restrictions on slots or facets) Classes represent the entities or concepts in a domain and are the core elements of any ontology. For example, in the domain of transportation, a class of vehicle defines all types of vehicles. Specific vehicles are instances of this class. For example, bus can be considered as an instance of the class vehicle. In an ontology, a class can have subclasses. For example, we can divide vehicles into personal and public vehicles. Alternatively, we can divide vehicles into motorized and unmotorized vehicles. Each class in ontology has some attributes or characteristics. For example, an instance of class vehicle has a name, brand, and fuel type that describes it. In ontology, the characteristics or attributes of a vehicle are referred to as properties. Indeed, the classes are described by their properties in more detail. Restrictions on properties or facets define the data type of properties, the range and domain of values a property can take, and also the number of values (referred to as cardinality of properties) a property can have. For example, in vehicle class, the fuel type property is of data type “string.” Moreover, fuel type can only take specific types of fuel, for example, gasoline, diesel, electricity, ethanol, biodiesel, and hydrogen. Furthermore, regarding the cardinality, a vehicle can run on just one type of fuel, such as gasoline, or on two types, like gasoline and electricity. Hence, the minimum cardinality of fuel type in this case can be defined as 1, and the maximum of it equals 2. However, some properties may only accept one value, for example, the color property of the class vehicle. From a practical point of view, ontology development mainly focuses on the following four steps (Noy et al. 2001):

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Define the classes Define the taxonomic hierarchy of classes Define properties and their allowed values Specify the instances and set the values for the properties of each instance

The term “taxonomy hierarchy” refers to the subclass/superclass hierarchy in an ontology and will be explained in section “Ontology Developing Approaches.” Also, section “Ontology Design Procedure” explains the above steps in detail and describes a framework for ontology development. Before going to more details about ontology development approaches and procedures, the next section explains different types of ontology and how they are classified in the literature.

Ontology Classification Ontologies can be classified based on different dimensions (Roussey et al. 2011): • Language expressivity and formality • Scope of the ontology, or domain granularity Based on language expressivity and formality, there are four types of ontologies, ranked from less formal languages to more formal ones (Roussey et al. 2011): • Information Ontologies: used only by humans, the goal of information ontologies is to clarify and organize the ideas and plans in the development of a project using visual languages, i.e., diagrams and sketches, such as a mind map. • Linguistic/Terminological Ontologies: focus on terms and their relationships, linguistic ontologies can be any type of glossaries, dictionaries, lexical databases, Web metadata, etc. For example, the Resource Description Framework (RDF) is a general-purpose language to represent concepts, terminology, and information on the Internet (Roussey et al. 2011). • Software ontologies: used for software development projects, provide the schema for databases, and data manipulation to guarantee data consistency. During the software design procedure, usually, a conceptual modeling language, such as Unified Modeling Language (UML), is used. • Formal ontologies: usually developed using formal logic (e.g., first-order logic or description logic) to describe the rules about how to define the concepts and relationships. The most well-known formal language is OWL, which will be introduced in section “Ontology Languages.” With respect to scope, or domain granularity, ontologies fall into four different categories (Roussey et al. 2011): • Application/Local ontologies: are developed according to a user or a developer’s viewpoint to give a representation of the particular model of a domain. Application/local ontologies can be deemed as a task ontology, combined with domain

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ontology, to define concepts and their relationships regarding a specific purpose of an application. Core ontologies: can be considered as a basic and minimal ontology that defines the minimal concepts in a domain that are essential to understand the other concepts. Domain ontologies: represent concepts and relationships regarding a specific domain of the real world, for example, transit network. Domain ontologies show how a group of users/experts perceive a specific domain, what are the main concepts in the domain, and what their properties are. General ontologies: define concepts and relationships related to a huge area of knowledge. Foundation ontologies: are top-level generic ontologies applicable to various domains (Roussey et al. 2011). Every core or domain ontology usually includes a foundation ontology, such as Basic Formal Ontology (BFO) (Arp et al. 2015). Domain ontologies developed based on the same foundation ontology are more interoperable and can be easily integrated.

In the domain of transportation, various ontologies can be developed based on the two dimensions explained above. However, in the context of transportation ontologies for smart cities, it is more effective to follow a formal ontology approach. Information or linguistic ontologies are appropriate when the goal is to prepare sketches or diagrams for a small project or when a glossary of words is required to define different concepts in the domain of mobility. In the context of smart cities, a more advanced ontology development approach is required that goes beyond just diagrams or glossary of words. Regarding the scope and granularity of ontology, transportation ontologies have been generated at various levels. For example, some ontologies have been developed for specific functionality of autonomous and connected vehicles (ACVs) (Viktorović et al. 2020), including the functionality of sensors in autonomous vehicles, and can be considered as application ontologies. Some other works define the core ontologies related to a specific mobility domain. For example, Zhao et al. (2015) developed a core ontology for Safe Autonomous Driving that consists of minimum concepts and relationships between them for designing a safe autonomous driving system. Moreover, some other ontologies have been designed for a specific domain of transportation, such as freight transportation or city logistics (Anand et al. 2014), and can be categorized as domain ontologies. Finally, there are general transportation ontologies (Katsumi and Fox 2017b; Bellini et al. 2014) consisting of several sub-ontologies describing a large transportation knowledge base. In the context of smart city and smart mobility, ontologies have mainly followed a general formal approach. The reason is that the smart mobility domain consists of a huge number of entities, concepts, and the relationships between them. Hence, any ontology regarding smart mobility is usually an integration of several ontologies, sometimes from other domains of knowledge. The next section explains the most well-known ontology design approaches.

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Ontology Developing Approaches While various approaches have been used in the literature to develop ontologies (Falquet et al. 2011), to the best of our knowledge, an all-purpose methodology for ontology development does not exist. Indeed, no one can claim that there is a unique way to define the ontology of a domain. The best method to develop an ontology highly depends on the domain and application of ontology, as well as the extent to which a domain overlaps with other fields of science. However, this section explains some general issues worth considering when developing an ontology. Also, different design approaches are categorized in this section. Ontology development approaches can be categorized based on two criteria (Falquet et al. 2011): • Direction of taxonomy hierarchy • Source type of ontology Each of the criteria has been explained briefly in the following sections.

Direction of Taxonomy Hierarchy The direction of taxonomy hierarchy describes three different approaches to develop class hierarchy (Noy et al. 2001; Hogan 2020a): • Top-down development process • Bottom-up development process • Hybrid development process The top-down development approaches begin with the definition of the most general concepts (classes) in a domain, and subsequently, the subclasses are defined. For example, we can start with the class vehicle, and then subclasses can be defined as motorized and unmotorized vehicles. Bottom-top approaches initially focus on defining the most specific classes and grouping the subclasses to generate superclasses (Falquet et al. 2011). For instance, the car, bus, motorcycle, bike, and scooter are defined first, and then the car, bus, and motorcycle are grouped into class motorized, similarly bike and scooter as unmotorized class. Finally, a class vehicle is defined as the superclass of both motorized and unmotorized classes. The hybrid approach combines both top-down and bottom-up approaches (Hogan 2020a). For example, one begins with defining two middle-level classes, such as unmotorized and motorized vehicles, and subsequently defining more specific subclasses within each, i.e., car, bus, bike, etc., and finally adding a superclass vehicle to the ontology. Also, one can generate more mid-level classes, such as passenger and cargo vehicles as the subclasses of class vehicle.

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None of these three methods can be considered superior or possesses an inherent quality over the others. Indeed, the preference among them is a matter of domain expertise and how an individual would rather view and define a domain. However, as the concepts in the mid-level are usually the most descriptive ones, researchers tend to pick the hybrid approach to begin the ontology development (Hogan 2020a).

Source Type of Ontology While the ontology development in some domains can be achieved via domain expertise and knowledge, identifying and defining the classes in vast and intricate domains is a time-consuming, high-cost, and contentious task (Falquet et al. 2011). Hence, researchers have used techniques to elicit knowledge from different resources, such as corpora, thesaurus, or relational database. Such knowledge elicitation has led to the rise of learning methods in ontology development. The learning methods in ontology learning are mainly Natural Language Processing (NLP), text mining, and Information Retrieval methods (Falquet et al. 2011) to elicit concepts from text documents. The resources for knowledge elicitation can be categorized into different types: • Text documents • Schemata Each category is explained in the following sections.

Text Documents Text documents include corpora, dictionaries, and thesauruses. Corpora are large and structured sets of texts on a specific domain that contain the concepts in the domain. Ontology learning based on corpora includes several steps, such as elicitation of the relevant terminology, identification of synonym terms, the establishment of concepts, organizing the hierarchical of the concepts, and inferring relationships between concepts and their properties (Falquet et al. 2011). Thesaurus refers to a lexical ontology containing the definition of domain terms in a manner that the concepts and the relationships (e.g., the synonyms of main terms, the more general and more specific terms) are explicitly defined. Thesaurus is mainly used to generate the first draft of a formal or software ontology (Falquet et al. 2011). Dictionaries, particularly the domain dictionaries, are also suitable for extracting the domain concepts and their relationships. All of these text documents can be analyzed by learning algorithms to extract the classes, relationships, and properties of a domain ontology. Schemata The second main source for ontology learning is schemata, which are relational database models, entity-relationship (ER) diagram, Unified Modeling Language (UML), object-oriented models, or unstructured data (such as XML documents) (Hogan 2020b; Falquet et al. 2011). Ontologies elicited from schemata are usually

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referred to as model-driven ontologies, as the ontology has been developed based on a previously available data model. Researchers have investigated different methods to convert schemata to ontologies. Such methods consist of a set of rules to map the structure of a source schema to an ontology, usually refers to schema mapping techniques. Due to the structured form of relational databases, ontology development can benefit more from them than the unstructured text documents. The structure of the database is not only useful for defining the concepts and classes of ontology, but the data stored in the relational database is also beneficial for defining the class hierarchy and properties (Falquet et al. 2011). The main difference between relational databases and ontologies is that the latter has an object-oriented schema, while relational databases follow a structured schema. Hence, while using relational databases for ontology development, the first step is to design a set of rules to transform the schema of relational databases into an object-oriented schema. As mentioned above, construct ontology from the database usually follows certain mapping rules and principles (Desmond Mogotlane and Fonou-Dombeu 2016). Some tools have also been designed to automatically construct ontology from relations databases, although the quality of resulted ontology may not be satisfactory always. Desmond Mogotlane and Fonou-Dombeu (2016) have reviewed the tools and algorithms to convert databases to ontology and compared them. While ontologies have been primarily developed within the artificial intelligence community, the UML diagrams have been mainly utilized by the software engineering community as a standard language to visually represent a software system and its main actors, concepts, and the relations between them. Ontology and UML share similarities in many aspects. The main resemblance between UML and ontology is that both focus on objects and their properties; in other words, they are both following an object-oriented paradigm (Mejhed Mkhinini et al. 2020). Moreover, both UML and ontology first define the classes (concepts) and their properties, and afterward, they create instances of classes. Hence, there is great potential in ontology development using UML, and the field has attracted attention recently. Mejhed Mkhinini et al. (2020) have done a comprehensive literature review on the methods and tools to transform UML to OWL ontology. Ontology can also be derived from different file formats, such as geospatial Shapefile, CSV, or XML files. W3C organization (Berners-Lee 2007) listed converters developed to convert from different file formats (around 44 different file formats at the time of this writing) to Resource Description Framework (RDF), that is, a language for describing concepts and resources on the Web (RDF is explained in section “Ontology Languages”) (W3C is the World Wide Web Consortium and provides the main international standards organization for the World Wide Web.).

Ontology Languages The previous section explained the main approaches for ontology design. Despite the availability of many methodologies and languages for ontology development, it is not straightforward to choose a proper method or tool for

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developing an ontology (Katsumi and Fox 2017b; Kalibatiene and Vasilecas 2011). Moreover, some methodologies have been developed based on specific ontology languages. Also, some ontology languages are highly dependent on a specific ontology development tool. Such a situation leads to the fact that there are not too many practical options to select for ontology development. Moreover, every ontology language or tool should be weighed up against its pros and cons to see if it is an appropriate tool or language for the target domain application (Kalibatiene and Vasilecas 2011; Hogan 2020b; Katsumi and Fox 2017b). Ontology languages are the languages mainly developed in computer science used to construct ontologies and encode the knowledge in a specific domain and apply reasoning rules to process that knowledge (Smith 1998). While there are many ontology development methods and languages, it is not easy to choose the appropriate language, mainly due to the fact that many of the existing ontology languages have been built for domain-specific ontology development. Moreover, developing ontologies requires an ontology development tool (Kalibatiene and Vasilecas 2011; Bellini et al. 2014). However, deploying of some methodologies is restricted to certain tools. Hence, there are limited options to select from, for ontology development methodology. At the top level, the ontology languages are categorized into two categories (Kalibatiene and Vasilecas 2011): traditional ontology and Web-based ontology languages. The traditional ontology languages are mainly developed based on first-order logic. The second category has the languages developed based on Web standards to facilitate the interchange of data on the Internet (Kalibatiene and Vasilecas 2011; Falquet et al. 2011). However, some languages belong to both categories. According to Kalibatiene and Vasilecas (2011), there are five main ontology languages used in the literature: • • • • •

RDF (Resource Description Framework) OWL (Web Ontology Language) KIF (Knowledge Interchange Format) OIL (Ontology Interchange Language) DAML (DARPA Agent Markup Language)

The early works on Web ontology languages started in the early 1990s, including KIF (Genesereth and Fikes 1992), OIL (Fensel et al. 2001), and DAML+OIL (Mcguinness et al. 2002) languages. Work on OWL and RDF development began by the W3C in 2001. Since then, RDF and OWL have been widely used in mobility ontology development projects and other ontologies across different fields of science. As most transportation ontologies have been developed using either OWL or RDF, these two languages are briefly introduced in the following section. The reader may refer to Kalibatiene and Vasilecas (2011; ?), Mcguinness et al. (2002), and Genesereth and Fikes (1992) for more information on other ontology languages.

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RDF Language World Wide Web Consortium (W3C) designed and developed RDF as a standard data modeling language to define and describe resources on the Web. Resources here refer to any sort of entity in the real-world, abstract, and virtual entities (Hogan 2020a). The resources are defined using a standard expression of subject-predicateobject, referred to as triple-based format. Figure 2 demonstrates an example of triple format as a sentence. RDF uses a similar triple format to describe the resources, as shown in Fig. 3. RDF language has been mainly designed to be understood and read by computers in Extensible Markup Language (XML) (Kalibatiene and Vasilecas 2011).

OWL Language OWL is the most well-known Web-standard language for the semantic web, also developed by the World Wide Web Consortium (W3C). Like RDF, OWL has been used to formally classify comprehensive and complicated knowledge about concepts and entities, groups of entities, and relations between them. W3C has introduced OWL as a logic-based language designed to express knowledge so that computer programs can exploit the knowledge on the Web. Indeed, OWL can be considered as an extension of RDF language enhanced with more core vocabulary to provide a more comprehensive range of new terms (Hogan 2020b). Hence, today, OWL is considered a much richer language to define the semantic web, making it a more appropriate language to integrate data from different sources automatically (Hogan 2020b). In the mobility domain, most ontologies have been developed in OWL language (Katsumi and Fox 2017b; Bellini et al. 2014; Codescu et al. 2011), since OWL plays the role of a de facto language in the Semantic Web domain. Moreover, the majority of ontology software and editors have been developed using the OWL language. Hence, it is more straightforward for an ontology developer to choose among

Fig. 2 An example of subject-predicate-object sentence. (Image adapted from Hogan (2020a))

Fig. 3 An example of subject-predicate-object in RDF language. (Image adapted from Hogan (2020b))

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software designed for OWL language, rather than searching for specific software developed for other languages in a particular domain.

Ontology Design Procedure While there are several approaches for the design and development of ontologies, some general rules and steps have been suggested in the literature to make ontology development an efficient procedure. Before explaining the steps required to develop a mobility ontology, some general considerations should be noted (Noy et al. 2000; Falquet et al. 2011; Lorenz et al. 2005). First, generally speaking, ontology is inherently an iterative procedure and should be revised and updated regularly. Notably, as the transportation domain keeps changing fast due to technological advances and innovations, the ontology development should be considered an ongoing project continuously updated to reflect the new concepts and entities introduced by emerging technologies. Ontology development usually begins with an initial version, further extended and evaluated by domain experts against its pros and cons. For example, in the mobility domain, many disruptive technologies, such as connected and autonomous vehicles (CAVs), will gradually set foot in the urban spaces in upcoming years. Accordingly, any mobility ontology should be revised to incorporate such new technologies. Second, it would be easier for an ontology developer to consider the classes and relationships as nouns (classes) and verbs (relationships) in a sentence that describes some concepts in a domain. Figures 2 and 3 show how an example sentence describing an entity in the domain of mobility converts to classes and relationship in an ontology language. While there are many approaches to design ontology, as explained in the previous section, one can benefit from a set of general-purpose rules applicable to ontology development regardless of the domain and data source. Noy et al. (2001) proposed a set of rules applicable for various fields of science, based on Protégé software (Protégé is an open-source ontology editor, which is indeed a platform with a suite of tools to develop ontology and visualize it. Protégé also contains a rich library of ontology across different fields of science.). The procedure is well suited for mobility ontology development either through domain expertise or by applying learning methods. Mobility ontology development may include the following main steps (Noy et al. 2001): • Step 1. Identify the scope of mobility ontology and list important concepts and terms in the domain of mobility. • Step 2. Consider reusing the existing mobility ontologies. • Step 3. Choose the direction of ontology development (as explained in section “Direction of Taxonomy Hierarchy”); define the classes of mobility ontology and the class hierarchy. • Step 4. Specify the properties of concepts (classes). • Step 5. Determine the facets of the properties of each defined class. • Step 6. Generate instances of mobility ontology entities.

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The first step plays a crucial role in mobility ontology development, as the domain is vast and multidisciplinary. For example, for vehicle ontology, the scope and context in which the ontology will be developed largely impact the next steps in the development of mobility ontology. As an example, a car manufacturer views a vehicle with more details than a researcher who aims to develop vehicle ontology in the context of smart mobility. A car manufacturer may be involved with the detailed mechanical and electrical aspects of a vehicle or its performance and appearance, while such properties of a vehicle may not be in the focus of a transportation engineering researcher. After identifying the scope of mobility ontology, the researcher needs to list all the concepts and terms in the domain. For a mobility ontology, all the concepts, such as streets, intersections, vehicles, walkways, traffic devices, etc., should be enumerated. Also, detailed relations between the concepts should be listed. This step is important, as the classes and properties will be further defined based on the concepts listed in this step. Step 2 recommends to search for the available ontologies in the literature and consider reusing them. Especially in a vast domain like transportation, some ontologies already developed across different domains that one can benefit from. For example, Linked Open Vocabularies for the Internet of Things (LOV4IoT) Website consists of more than 550 ontologies (Noura et al. 2019), including smart city and mobility ontologies, at the time of writing. Protege Ontology Library is another source of developed ontologies across different science fields and a good source to find the available ontologies and consider reusing them. However, although the idea of ontology reusing may sound appealing, it is not straightforward to implement due to several reasons. Obrst et al. (2014) have discussed “Mismatches and Misunderstandings,” “Finding the Right Ontology,” and “Integration” as the major hurdles in reusing an existing ontology. Mismatches between the domain coverage and requirements are caused when the domain scopes are entirely different or while the ontology developers look at the same entities from different perspectives. Sometimes an available ontology is very specific, such as an ontology developed for defining the autonomous driving decision-making procedures, and may not be straightforwardly reusable in a broader context, such as including autonomous vehicles ontology in a smart city ontology. Some existing ontologies may have classes and properties utterly irrelevant to other domain concepts and hard to integrate. If not overcome, such hurdles may finally discourage ontology developers from reusing existing ontologies, and instead, they may decide to develop it from scratch. Furthermore, ontology reuse usually requires integrating several existing ontologies, which sometimes may be more challenging than developing a new ontology. Moreover, although there are mobility ontologies available, some of them are not well documented or reusable (Katsumi and Fox 2017b). Despite these facts, standard foundational ontologies, such as time or geospatial ontology (explained in section “Foundation Ontologies”), have been developed and well documented by some organizations, such as World Wide Web Consortium (W3C), and are used widely across different domains.

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Steps 3 and 4 are the most important steps as the classes and properties are defined, and the relationships between them are specified. The class hierarchy is also defined in Step 4, based on the approaches explained in section “Direction of Taxonomy Hierarchy.” Classes can include four types of properties (Noy et al. 2001; Hogan 2020b): • Intrinsic properties such as “number of doors” or “fuel type” of a vehicle • Extrinsic properties such as vehicle “brand” or “production date” • Parts can be physical or abstract parts, for example, different parts of a road network, such as road links and intersections. • Relationships define the relations to other entities. For example, a road link is connected to an intersection. Another issue regarding class hierarchies is the notion of subclass and superclass. Any class can have one or more superclasses. While a class has more than one superclass, the superclasses can be mutually exclusive. For example, the owner of a vehicle can be an individual or an organization. Hence, one can define a class owner, which is a subclass of two superclasses: class organization and class individual. However, class owner can have only one superclasses, either organization or individual. It should be mentioned that any subclass of a class inherits all the properties of that class. In Step 4, the facets of properties are defined. Facet definition refers to three issues: the values and data types of properties as well as the range and domain of a property. For example, the class vehicle can have make as a property. The property make is of data type string and accepts the make of the vehicle as values, and its range is all the available make of vehicles in the market. Occasionally, the value of a property can be the instance of another class. For example, in transportation ontology, one can define two classes: vehicle and organization. Class organization refers to a group of individuals who work together toward a goal. Class vehicle has a manufacturer, and its values can be the instances of class organization. The last step in ontology development is creating instances of classes in the hierarchy. Individual instances of a class are first created, and then the properties of the class are filling out by their values. For example, for the class vehicle, one can create an instance Tesla, to represent a specific vehicle. This instance can have the following properties: • • • •

Manufacturer: Tesla, Inc. Fuel type: Electricity Number of doors: 4 Owner: Peter

Figure 4 demonstrates an example of the vehicle, organization, person ontology, and the properties of class vehicle. The organization ontology has been explained in section “Organization Ontology.”

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Fig. 4 An example of vehicle ontology, with classes shown in blue and properties and instances in red

Mobility Ontologies Smart mobility includes many different dimensions and sectors. It also overlaps with other domains such as urban and land-use planning, Internet of Things, sensors, etc. To show various aspects of smart mobility and the extent of the domain concepts, two scientific platforms, i.e., Web of Science and Scopus, were searched for “smart mobility” phrase. Figure 5 shows various fields of science, with the most number of publications on “smart mobility,” based on Web of Science data analysis. The inclusion and exclusion criteria for selecting publications and analyzing their data are presented in Table 7. The search resulted in 1,779 and 1,812 publications from Scopus and Web of Science, respectively. Smart mobility itself includes many parts and sectors, as shown in Fig. 5. The image shows the co-occurrences of keywords in Web of Science publications on smart mobility. While there are numerous publications on smart mobility, they are much less when searching for “smart mobility” and “ontology” in Web of Science or Scopus databases. Keeping the same inclusion criteria in Table 7, there were less than ten publications in each database, which shows the lack of studies regarding the ontology of smart mobility in the current literature. However, one should notice that not all the available ontologies on transportation or mobility are presented in conference or peer-review papers. Indeed some of them have been published in the form of metadata data models using OWL or RDF frameworks. Moreover, some ontologies have been published in other domains related to smart cities, such as connected and autonomous vehicles, that are not included in the search criteria. The transportation system can be categorized into two general categories: transportation physical network and transportation cyber network. Each of these categories includes several foundation or domain ontologies. The transportation physical network has been explained in section “Transportation Physical Network

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Fig. 5 Most frequent words in “Smart Mobility” Publications on Web of Science database

Ontologies” The foundation ontologies (which are generic top-level ontologies used to explain basic concepts in a domain ontology and usually are reused from previously developed ontologies) are explained in section “Foundation Ontologies.” Due to innovative and advanced technologies in cyber network ontologies, a separate section, i.e., section “Future Directions in Smart Mobility Ontology”, is devoted to explaining the cyber network and future directions in transportation ontology in the concept of the smart city.

Database Publication period Document type Source type Subject Area Language Number of documents

Inclusion criteria Scopus Web of Science 1984–2021 1991–2020 Article, Book/Book Chapter, Review, Editorial Journal, Book/Book Series, Conference Proceeding Engineering, Computer Science Shown in Fig. 1 English 1,779 1,812

Foundation Ontologies Foundation ontologies cover the basic ontologies used across different domains. For example, the ontology of time and space has been initially developed in Geography domain; however, such ontology has been reused by another domain studies, such as

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transportation or urban planning. Foundation ontologies can be categorized into the following basic ontologies as suggested by Katsumi and Fox (2017a): • • • • • • • • •

Geospatial Time Weather Trip Units of measure Change Household and dwelling Organization ontology Stakeholders (public/private actors)

In the following, each of these ontologies is explained, and best practices to reuse are mentioned.

Geospatial Ontology Every entity in the smart city or smart mobility domain requires a spatial property to be located on a map. An intersection or a street cannot be shown on a map without their geospatial information. The location of entities can be presented in different forms: • • • •

Longitude and latitude coordinates Point Linestring Polygon

Longitude and latitude coordinates are designed to point to any location on the earth’s surface and widely used in many GPS positioning devices. Point is mainly used for showing the location of vehicles, individuals, or dwelling. Linestring is used to show any line shape entity (either a straight or curved line), such as road network links or railways. Polygon is usually used to present the boundaries of a city, a municipality, a transit agency, etc. It also can be used as the shape of a vehicle in selfdriving cars, where the geometric shape and boundaries of a vehicle play an important role in designing the autonomous driving algorithms. For example, in iCity ontology (Katsumi and Fox 2017b), every entity has a spatial feature. The spatial feature has been defined by reusing the WGS-84 Ontology, defined by W3C and Linked Open Vocabulary as in RDF format.

Time Ontology Time ontology defines the required classes for describing the temporal properties of real-world assets or resources on the Web. The most well-known time ontology is the “Time Ontology in OWL” (Hobbs and Pan 2006) developed by W3C, which is widely included in many ontologies across various domains. Time has also been encoded into upper-level or domain-specific ontologies using RDF languages

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(Gutierrez et al. 2006). To equip ontologies with time, one should take considerations to avoid duplication of the original ontology, specifically when the entities of an ontology have properties that change over time. Krieger (2010) has proposed a general methodology to equip ontologies with time ontology. The transportation ontology can take advantage of the proposed methodology as the properties of many transportation entities, such as vehicle or passenger GPS location, are changing over time. Today, many ontologies in OWL use the time ontology developed by W3C.

Weather Ontology Transportation systems are significantly affected by weather conditions. Road weather management decisions require access to data on environmental conditions from observing systems and forecast providers. Moreover, the adverse t effect of weather conditions on traffic accidents is a well-known phenomenon in traffic accident analysis and prevention studies. Furthermore, weather conditions play an important role in the functionality of autonomous vehicles. Indeed, to conceptualize the environment in which an autonomous vehicle evolves, the weather ontology is essential. Chen and Kloul (2018) developed weather ontology for advanced driver assistant in OWL language. A more sophisticated weather ontology has been developed by KM4City (Bellini et al. 2014) in the context of smart cities. They have developed the weather ontology based on the real-time weather forests. The ontology has been published in RDF format using SPARQL query language. Weather ontology may consist of a different number of classes or slots depending on the specific domain requirement. For example, the weather ontology for aviation and air transportation is more comprehensive and detailed compared to the ontology used for road transport. Moreover, in road transportation and traffic monitoring systems, the weather ontology can possess different classes and slots based on data types collected by different meteorological instruments and sensors. Units of Measure The ontology of units of measure refers to a set of classes defined to describe the different measures or values of a given quantity (Katsumi and Fox 2017a). For example, a vehicle with 4 m length can have a subclass called length, which describes the nature of the length quantity, its numerical value (i.e., 4), and the unit that length is measured in, which is meter in this example. In this case, instead of defining length as a property of the vehicle, defining length as a subclass of vehicle provides a more comprehensive description of length. Moreover, the class length can be used to describe the length of any other entities. Change Change ontology firstly proposed by Welty et al. (2006) includes fluent properties of concepts in OWL. In mobility domain, several concepts have changing properties. For example, the location of a vehicle is a changing property (Katsumi and Fox 2017a). Other example is the number of passengers on a shuttle at different times of a

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day. Even the transportation network itself changes over time, for example, the number of traffic lanes or pedestrian lane width may vary over time. Change ontology indeed enriches an ontology by adding a fourth dimension to the concepts and entities which have a changing property (Welty et al. 2006). In the domain of transport, the iCity ontology (Katsumi and Fox 2017b, a) has included change ontology in their general ontology of transport. Indeed, they have followed the change ontology suggested by Krieger (2008) and adopted it for the domain of mobility. Change ontology also makes some complex data inferences possible and ensures that some competency queries are correctly answered. For example, if the result of a query is to answer whether a vehicle has taken a trip, it can query about the change property of the vehicle location to see if it has changed over a period of time.

Household and Dwelling Ontology In transportation planning, demand modeling is usually conducted for individuals in a household, as the household members usually depend on each other for commuting, as they usually share a car; also they adopt similar mobility habits. A household also occupies a dwelling unit. In transportation ontology, a dwelling may have a market value, a location or address (Katsumi and Fox 2017b), and type of dwelling. Other detailed properties of a dwelling, such as the building sensors, are not in the scope of transportation ontology. Organization Ontology An organization usually refers to a body of individuals, in private or public sector, who follow the same goal(s) (Katsumi and Fox 2017a). Organization ontology, like the stakeholder ontology, has been developed mainly in e-Government and project management ontologies. Stakeholder Ontology In the field of transportation, the concept of “stakeholder” is mainly used in freight transportation studies or in transportation externalities assessment, i.e., the study of negative impacts of transportation activities on other sectors of society. Stakeholder ontology in logistics and freight transportation is described in section “Freight Transportation System.” Trip Trip describes the mobility of persons or goods from an origin to a destination via a transport mode. In the transportation domain, trip ontology has been developed within iCity ontology (Katsumi and Fox 2017b). Trip ontology has also been studied in other domains, such as Recommendation Systems (Choi et al. 2006). However, the class trip can consist of different trip segments. Also, the trip itself can be considered as part of a tour. Hence, the trip definition in mobility ontology highly depends on the domain application, i.e., whether the domain experts aim to consider tours in their ontology.

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Transportation Physical Network Ontologies Mobility ontology can be categorized into several sectors. However, at the top-level mobility, sector can be categorized as physical and cyber network. Physical networks itself include different types of transportation concepts: • • • • • • • •

Road transportation network Pedestrian network Cycling network Railway network Transit system Freight transportation system Road service area and parking Vehicle

The cyber network in the transportation domain consists of different innovative and disruptive technologies, such as sensors and Internet of Vehicles (IoV) communications. Moreover, some innovative technologies, such as autonomous robotics, Connected and Autonomous Cars (CAVs), or Unmanned Aerial Vehicles (UAVs), will flood into smart cities in succeeding years. Hence, section “Future Directions in Smart Mobility Ontology,” Smart Mobility and Future Directions, has been devoted to four major innovative technologies: • • • • •

Sensors Autonomous robotics Mobility as a service (MaaS) Connected roadways and Internet of Vehicles The next sections explain each of the abovementioned ontologies.

Road Transportation Network Road network ontology is among the essential ontologies in every transportation ontology development. The road transportation network consists of links (in some studies (Lorenz et al. 2005) referred to as edges) and nodes. The road links are streets and roads on which motorized vehicles move. Nodes represent the connections between the links in the transportation network. Nodes can be located on intersections or in the middle of a street when the characteristics of the street, such as speed limit or the number of lanes, change. One of the best practices of road transportation ontology has been implemented by INSPIRE (INS 2014). The INSPIRE transportation network ontology scope is vast and includes five major transportation networks: road, railway, water, air transportation, and cableways. The INSPIRE ontology defines the details of each network and the connections between them. Road network ontology has also been developed by the Ontology for Transportation Networks (OTN) (Lorenz et al. 2005). Both INSPIRE and OTN ontologies have been developed as part of a geospatial information project, and the data

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specification in both is based on a common template used for different domains, such as transport, geoscience, etc. However, OTN ontology has not been updated since 2005, the date it has been published first. Another ontology developed in the smart cities domain is the km4City ontology (Bellini et al. 2014), which includes road network ontology. The Km4city ontology has defined several entities regarding road transportation network, such as Road, Node, RoadElement, StreetNumber, RoadLink, Junction, Entry, and EntryRule Maneuver. All the above-mentioned road network ontologies are comprehensive and include all the details related to the road network. Moreover, all of them have mainly generated from the encoding of Geographic Data Files (GDF) into OWL language. A road network can possess different classes. However, the simplest road network consists of road link sequences, road links, and nodes, as shown in Fig. 6. In this figure, the classes are presented by the blue boxes and properties by the white ones. A road link sequence consists of road links, usually having the same properties or is part of a route. Figure 6 represents only a few numbers of properties. Apparently, road links, for example, can have other properties, such as pavement marking, capacity per direction, right of way, etc.

Railway Transportation Network Railway transportation network includes railway links and nodes, and railway yards and their properties. It should contain all types of railways for both

Fig. 6 An example of classes and properties in a transportation road network

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passenger and freight transportation. All the mobility ontologies developed in the previous section have defined railway ontology, such as INSPIRE, iCity, OTN, and Km4City. However, Network Statement Checker Ontology (NSO) (Verstichel et al. 2011) is another ontology developed mainly for railway networks. The study’s goal is to design a system to check the feasibility of running a train on any specific railway line. While the core of the NSO ontology is the railway network, the classes are defined as very generic and applicable to other transportation networks (Katsumi and Fox 2017b). One of the distinctive features of the NSO ontology, compared to the other mobility ontologies, is that it has distinguished between physical tracks and transit lines. Their approach is first to define the railway network elements and then collect information about these elements provided by other transit and railway authorities. This approach is useful and more practical when the ontology is developed for a specific application, which is a railway network checker in the case of NSO ontology. An example of classes and properties in railway transportation ontology has been shown in Fig. 7. Again, it should be mentioned that this writing does not present all the possible classes in a domain ontology; instead, it provides just the most important classes in a category as examples. Railway Line, Link Sequence and Link, and Railway Node and Railway Yard Node are the classes in Fig. 7. The Railway Yard Node is a subclass of Railway Node, but with a restriction (shown as a red box in Fig. 7) on the form of node. Indeed, for a Railway Node, form of node can be defined

Fig. 7 An example of classes and properties in a railway network

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as junction, level crossing, pseudo node, stop, etc. However, the form of node property for a Railway Yard Node is always Railway Stop.

Cycling and Pedestrian Network Inclusion of cycling and pedestrian networks in an ontology is achieved via two approaches: first by including a “form of way” or “road type” property for road transportation network and distinguishing motorized road segments from cycling and pedestrian segments. The second approach is to define an independent cycling network and make connections between the road network and cycling network wherever is required. While many transportation ontologies (Katsumi and Fox 2017b; Bellini et al. 2014) defined pedestrian and cycling network as part of the road transportation network, however, due to the unique characteristics of pedestrian network or cycling network, it is better to define them as separate entities. For example, parts of the pedestrian network in today’s cities are extended to underground and in-building walkways, distinctive from road networks where motorized vehicles can travel. Bike and pedestrian trails are usually located on areas not accessible to motorized vehicles and may not be included in the road network. Moreover, cities and government authorities usually publish geospatial data of pedestrian or cycling network independently from regular road network used by cars and buses. Transit System The transit network includes the bus and metro routes, their associated stops and schedule, and their geospatial information and access method, i.e., the payment method for accessing the public transit services: iCity ontology (Katsumi and Fox 2017b), OTN ontology (Lorenz et al. 2005), as well as Km4City ontology (Bellini et al. 2014). They have similarly defined the main concepts in transit systems, as mentioned above. However, all of them lack considering static and real-time GTFS (General Transit Feed Specification) data (GTFS 2017) in their ontology. Indeed, GTFS data is considered as Linked Open Data and can enrich the transit system ontology by the inclusion of a detailed transit schedule and its geospatial features. Figure 8 shows an example of classes, properties, and relationships for transit network ontology. Transit route and transit stop are the main classes in transit ontology. One may also define a transit schedule class as the property of a transit route or node. The data for the transit schedule class can be acquired from GTFS data. Every transit system has a fare collection method that determines the transit access method (the values can be cash, transit pass or mobile app, etc.), the fare type (monthly pass, weekly pass, pre-purchased tickets, etc.), or validity period. Moreover, the transit system may include a transit shelter that kind of street furniture serves transit users. Transport Disruption Ontology (TDO) (Corsar et al. 2015) has also incorporated transit events in its ontology. The TDO ontology’s focus is to design a data model to recognize events with a major impact on travel patterns. The ontology is used to query and extract social media data, in this case, any event reported on social media, and subsequently provide travel information to transit users.

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Fig. 8 An example of classes and properties of a transit system

Fig. 9 An example of classes and properties in urban freight transport

Freight Transportation System The urban freight transportation system plays an important role in today’s cities’ logistics and economy. The most well-known ontology regarding freight transportation is GenCLOn ontology (Anand et al. 2014), which is an ontology for city logistics. Urban logistics is a domain dealing with freight transportation issues that different stakeholders in urban areas are faced with. Different concepts (classes) should be included in the development of freight transportation ontology, such as warehouses, fleets, and products. Figure 9 shows an example of classes and their properties and relationship in an urban freight transportation system. The stakeholders in the domain of urban logistics can be categorized into two general classes as public stakeholder and private stakeholder. Any private stakeholder is also a subclass of either organization or person class. Here, we say class organization and class person are mutually exclusive superclasses of class private stakeholder. Also, class private stakeholder itself has three subclasses as shipper, carrier, and receiver, defined by Anand et al. (2014).

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The class public stakeholder is also classified into three subclasses: national, regional, and local authorities classes. Each stakeholder can possess several properties, for example, a stakeholder has an objective and a location. Also, each shipper and carrier may have a logistics fleet, mainly a list of vehicles used by a stakeholder to deliver products. While there are some ontologies developed in the field, in the foreseeable future, freight transportation will stand to benefit from innovative delivery methods, such as unmanned aerial vehicles (UAVs) for goods delivery. Moreover, UVA application in goods delivery not only changes urban logistics but also introduces new concepts to the urban mobility, such as human-robot interactions. They occur when robots require some kind of human intervention to function properly. For example, assigning a fleet of UAVs to different delivery tasks requires a robust system to efficiently manage the UAVs fleet by a human and maintain them in the case of a fault detection (Kumar et al. 2019). Such requirements of UAVs also will change the future of freight transportation ontology. Section “Future Directions in Smart Mobility Ontology” explains more on innovative autonomous robotics.

Road Service Area Road service area refers to any surface annexed to a road network that usually offers some services to road users. Some ontologies (INS 2014) in the literature have defined road service area as an independent class, which can have different road service types, such as gas stations, rest and drive through area, toll area, or parking. However, the majority of current mobility ontologies lack the road service arearelated classes. For example, three ontologies, i.e., Ontology for Transportation Networks (OTN) (Lorenz et al. 2005), iCity ontology (Katsumi and Fox 2017b), and Open Street Map Ontology (Osmonto) (Codescu et al. 2011), have only considered parking area in their ontology.

Future Directions in Smart Mobility Ontology This section points to the cyber-physical transportation networks as well as innovative and disruptive technologies and services in the domain of mobility that have not been covered extensively by the current general ontologies in the literature. The innovative or disruptive transportation technologies and services can be categorized as the following areas: • • • •

Mobility sensors Mobility as a service (MaaS) Autonomous robotics Connected roadway technologies and Internet of Vehicles

All of these categories have been developed in recent years and are either currently available or forthcoming in the market. However, no ontology has been developed yet for these technologies. This section explains each of the above

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innovative areas. Furthermore, it introduces some ontologies developed in other fields of science related to each area above. Moreover, the main concepts or categories in each of the above areas are introduced. Ontology developers who aim to cover disruptive and innovative technologies in the smart mobility ontology will benefit from this chapter.

Mobility Sensors Generally speaking, sensors are electronic devices that detect and react to changes in the sensor’s environment. The change usually reveals the property value, and the process to determine this value is referred to as observation (Janowicz et al. 2019b). Observation usually includes the sampling process from the feature of interest, for example, a loop detector observes the presence of vehicles, and it takes samples from a specific area of a road. Observations can trigger some actions, called actuations, and the devices or entities that perform the actions are referred to as actuators. Today, a variety of sensors collect data in smart cities, from Bluetooth or WiFi sensors to thermal and 3D cameras, which lead to the huge amount of sensors data published on the Web, which consequently has given rise to the reuse and fusion of such data (Janowicz et al. 2019b) in smart mobility studies. However, such data are mainly published as raw data without any context or information required to interpret or analyze the data. Hence, to facilitate the integration and fusion of sensors data, a standard framework and linked vocabulary that include sensors, observations, samples, and actuators are essential part of many smart mobility studies. Currently, the most comprehensive and updated ontology regarding sensors is Sensor, Observation, Sample, and Actuator (SOSA) ontology Janowicz et al. (2019a) jointly developed by W3C and OGC (Open Geospatial Consortium) Spatial Data on the Web (SDW) Working Group. SOSA is a flexible, comprehensive, and coherent ontology to define any entity, relationship, and activity (Janowicz et al. 2019b) regarding sensors, samples, and actuators. SOSA also has been designed as an extendable vocabulary easily being combined with other ontologies. Figure 10 shows an example of classes and properties in SOSA sensor ontology. Regarding on-vehicle sensors, two ontologies have been developed based on SOSA. One is Vehicle Signals and Attribute Ontology (VSSo) (Klotz et al. 2018), which has been developed to understand and define the vehicle-specific signals, based on Vehicle Signal Specification (VSS) taxonomy. Another ontology called Connected Traffic Data Ontology (CTDO) has been developed based on the foundation of SOSA ontology. The focus of CTDO is on the on-vehicle sensors. On-vehicle sensors refer to any type of sensor installed on modern vehicles, such as cameras and radar sensors. There are several types of radar sensors on today’s vehicles, like blind spot detection (BSD), autonomous cruise control (ACC), rear cross traffic alert (RCTA), rear cross assist (RCA), and safer lane changes and overtaking sensors. It should be mentioned that a modern vehicle may possess several other sensors; many of them control the vehicle engine’s performance and probably are out of scope for smart city ontologies.

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Fig. 10 An example of classes and properties in SOSA sensor ontology

While on-vehicle sensors play an important role in smart mobility and traffic safety, none of the currently available transportation ontologies have incorporated such sensors in their vehicle ontology. Furthermore, besides on-vehicle sensors, there are other two types of sensors in the smart mobility domain: intrusive on-road sensors, which are mounted on or under the road surface, and non-intrusive roadside or above-road sensors, which are sensors mounted on the poles at the side of the road or mounted above the road on sign bridges (Klein 2017). Inductive loops and magnetic sensors are among the intrusive sensors, while video detection systems, Lidar (laser radar), passive infrared, and ultrasound sensors are categorized as non-intrusive sensors. As mentioned above, sensors ontology has been developed mainly in the traffic management system domain or connected and autonomous driving. However, the current mobility ontologies, such as Km4City, OTN, iCity, or OSMOnto, lack the sensors ontology. In the context of the smart city, all the mobility-related sensors should be defined in the smart mobility ontology, and the relation between them, as well as the connections between on-vehicle sensors and road sensors, should be defined using the same ontology paradigm. Moreover, the current ontologies can be extended to integrate mobility sensors. For example, the current vehicle ontologies can be extended to include a new property representing all the safety and traffic sensors installed on a vehicle.

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MaaS MaaS refers to multiple mobility services, usually integrated into a Website or smartphone app that enables the individuals to plan, book, and pay based on their mobility needs and preference. Maas includes several innovative services, such as ride-hailing, car-pooling, or bike-sharing systems, to name but a few. Ontologies in the realm of transportation have not yet defined MaaS concepts, though, in recent years, there has been a major interest in Maas services. In general, MaaS includes two major parts: fleet and infrastructure. Fleet refers to different types of vehicles, as shown in Fig. 11. Each fleet type can be defined as individual new classes or as subclasses of superclass vehicle. MaaS ontology may borrow some classes and properties from vehicle ontology, especially from the autonomous and connected vehicles (ACV). Also, micromobility services, such as electric bikes and electric scooters, have gained attention in recent years and will play an important role in future smart cities’ mobility. Autonomous shuttles and on-demand transit are the other types of MaaS services, usually addressing the problems with which the current urban transit agencies are faced, such as the first/last mile problem. As shown in Fig. 11, MaaS infrastructure includes the following: • Physical infrastructures, such as parking areas devoted to MaaS vehicles in smart cities, or charging stations, for electric cars, bikes, and scooters • Cyber infrastructures, such as mobility apps or Websites to reserve and access to the MaaS services While some of MaaS physical infrastructure elements, such as parking areas, have been covered in the current mobility ontologies, researchers have not yet defined ontologies for other important elements, for instance, charging stations, on-street parking spots allocated to electric vehicles, mobility apps, or cloud platform services.

Autonomous Robotics Autonomous robotics is another domain that has rapidly advanced in recent years with potential impacts on the future of urban mobility. In mobility, robots

Fig. 11 Different concepts in Mobility as a Service (MaaS)

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can transport humans and goods on land, air, or water. Regarding land-based mobility, connected and autonomous vehicles (CAV), such as Waymo or public transit shuttles, will be introduced to the cities in the near future. The autonomous trucks also will soon play an important role in urban and interurban logistics. As mentioned in section “Freight Transportation System,” one of the transportation disciplines that will benefit majorly from robotics is freight transportation, where the UAVs will start delivering goods in the near future. While there are numerous advances in the robotics domain, the transportation ontologies have not included the robotics systems and autonomous vehicles. For example, Katsumi and Fox (2017b) have reviewed around 15 mobility ontologies, and none of them defined classes or properties regarding robots or autonomous vehicles. One of the most important issues in mobility robotics, such as UAVs, is the interaction and information exchange between robots and humans or between multiple robots (Olszewska et al. 2017). In other science fields, such as robotics and automation, ontologies have been developed to provide knowledge representation and reasoning for autonomous robots. For example, autonomous robot architecture ontology (ROA) has been developed to explain the important concepts and their relations regarding robot architecture (Olszewska et al. 2017). The ontology defines formal concepts in the robotics domain, such as task, function, and behavior, along with the spatiotemporal relationships between different objects. In the mobility domain, the interaction and information exchange between CAVs and humans, i.e., pedestrians and cyclists, or between multiple CAVs can benefit from ontology development, where ontology is used to develop intelligent decision-making systems to improve the driving safety of CAVs (Zhao et al. 2015). Moreover, the CAVs require a sophisticated map to perceive the driving environment and make decisions. Hence, the road, pedestrian, and cycling network should be defined in more detail where the connection between them, the right of way, turns, and directions are accurately described in the ontology. Furthermore, all the sensors and cameras of CAVs should be defined in the ontology. For example, Toyota Technological Institute (TTI) team has developed three ontologies for the ADAS system: map ontology, control ontology, and vehicle ontology. These ontologies, along with a reasoning language, referred to as Semantic Web Rule Language (SWRL), have been used to analyze scenarios in the traffic ecosystem and to make driving decisions. Furthermore, autonomous driving requires integrating different and high volume sources of data from CAV sensors, roadside sensors, and road infrastructure. Hence, the ontology of autonomous robotics or CAVs is associated with sensors’ ontology, connected roadway technologies (described in the next section), and geospatial ontology. While some efforts have been made to develop CAV-related ontologies, however, the literature lacks an ontology that incorporates all aspects of CAVs, from safety and decision-making issues to sensors and traffic data integration as well as connected roadway technologies (as explained in the next section). Hence, future smart mobility ontologies need to encompass all the aspects of CAVs, as mentioned above.

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Connected Roadways and Internet of Vehicles Technologies This section introduces the new technologies related to connected roadways and Internet of Vehicles (IoV) or vehicular communication networks (VCNs) (Khan et al. 2018). It helps the ontology developers to have a general view of the domain and identify the main concepts and their relationship and how these technologies will affect the future transportation networks in smart cities. Connected roadways refer to a set of communication devices and technologies allowing connected vehicles, travelers, and traffic management systems to communicate with each other and exchange information securely in real time. Connected roadways mainly rely on VANETs (vehicular ad-hoc networks). VANETs are used for four main vehicular communication (Gasmi and Aliouat 2019), i.e.,: • • • •

Vehicle-to-Vehicle (V2V) Vehicle-to-Infrastructure (V2I) Vehicle-to-Personal (V2P) devices or Vehicle-to-Human (V2H) Vehicle-to-Sensor (V2S)

Vehicle-to-everything (V2X) communications are the most generic form, which covers all types of communication to everything surrounding a vehicle. As the number of connected vehicles is increasing and will rise sharply in the future, network services to connect the huge number of vehicles need new requirements, such as robust and scalable information exchange between vehicles and other devices. Also, VANETs share many similarities with Internet of Things (IoT) domain, which has caused researchers to give a new naming for vehicular communication technologies as Internet-of-Vehicles (IoV). The IoV technologies can be divided into three general classes based on their coverage area range (Jawhar et al. 2018; Gasmi and Aliouat 2019): • Personal Area Network (PAN) • Local Area Network (LAN) • Wide Area Network (WAN) As shown in Fig. 12, each of these three network categories has different types of services that are briefly explained further. The PAN services support low bandwidth and energy consumption communications. ZigBee is a low-cost communication technology that supports short-range information exchange between a vehicle and its internal sensors (V2S). Bluetooth is also a short-range communication network that mainly supports V2P or V2H communications in many of today’s vehicles. Infrared is considered a PAN technique that transfers data at a lower rate compared to Bluetooth. However, it has some advantages, such as its large bandwidth that enables high network traffic in V2V applications. However, as infrared signals are highly affected by obstacles, their usage is limited to very short distances (Anwer and Guy 2014).

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Fig. 12 Different network technologies in Internet of Vehicles (IoV)

Regarding LAN services, the most well-known technology is wireless access in vehicular environments (WAVE). A WAVE system, also known as dedicated short-range communications (DSRC), refers to a system designed for efficient and reliable radio communications for V2V, V2R, or V2I direct connections (Jiang and Zhu 2019). Generally speaking, WAVE technology is achieved via wireless access through two types of WAVE devices: vehicle on-board units (OBU) and roadside units (RSU) (Chhabra et al. 2015). RSUs are usually installed on roadside infrastructure, such as poles, road signs, or traffic signals, or electronic cabinets on the roadside. WAVE devices support a communication range of approximately 300 m to 1000 m for moving vehicles with speeds of 10 to 200 km/h (Chhabra et al. 2015). WiFi technology for vehicular communication consists of roadside units, as wireless access points, to support vehicular communications inside their coverage area. WiFi services provide V2I and ad hoc V2V communication (Zekri and Jia 2018). WiFi technology coverage range is up to 100 m. However, it does not support vehicles moving at high speed. Another type of LAN service is WiMAX (Worldwide Interoperability for Microwave Access). WiMAX supports vehicle communication to the Internet at a maximum distance of 50 km. It is considered as a fast and high bandwidth connection providing V2X communication. Cellular networks can provide different vehicular communications based on radio waves at long distances (Anwer and Guy 2014) and high mobility speeds. It includes different cellular services such as 2G, 3G, and 4G/LTE technologies that differ in their bandwidth, latency, and data transfer rate. Today’s cellular networks, specifically 4G/LTE networks, are considered as a practical communication technology for V2V communications (Gasmi and Aliouat 2019). Satellite communications are mainly transferring data via Microwaves or low-power radio at long distances. They can be used to set up long-range wireless networks to connect multiple cities. Moreover, satellite communication plays a crucial role in vehicle positioning in VANETs, through Global Positioning System (GPS).

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Today, IoV is mainly considered as heterogeneous vehicular communications that integrate cellular, WiFi, and DSRC technologies to provide seamless and efficient communication. Moreover, with the advent of the 5G cellular networks, the performance and capability of IoV and connected roadways keep improving drastically, which consequently has given rise to new concepts and technologies in smart mobility, such as cloud computing, fog computing, and vehicular fog networks (VFN). Besides different communication technologies, IoV and connected roadways also rely on physical devices, such as sensing or actuating devices, CAVs, or RSUs, OBUs. Hence, to develop an ontology for IoV and connected roadways, one should consider other innovative ontologies, such as CAVs or mobility sensors ontology. For example, the IoV technologies mentioned above are implemented through some physical RSUs or OBUs and connected to different sensors on vehicles, robots, infrastructure, or personal devices (smartphones, smartwatches, etc.). As a consequence, it is recommended that any effort to develop ontologies for innovative mobility technologies should encompass all of them, as they are mainly interrelated together. IoV ontology development can also benefit from ontologies developed for the Internet of Things, as indeed the concepts and technologies are pretty similar between the two domains. IoT-Lite (Bermudez-Edo et al. 2016) is an ontology developed for the IoT domain, consisting of three main classes, i.e., objects, system, and services. Objects are any entity in IoT environment. A system is a unit of abstraction for all the physical entities for sensing. The system has components and subsystems. Service refers to any service provided by IoT devices. The IoT-Lite ontology can be used for IoVontology. For example, all the vehicles, RSUs, and OBUs can be defined as instances of class object. Some of the objects, such as RSUs and OBUs, provide a service, for example, a DSRC service. Such devices have a coverage property. The coverage of a device is of geospatial data type, which shows the area covered by a device. Moreover, any mobility sensing device can have a connects property to a service, such as a device that provides a DSRC communication. Moreover, the ontology of transportation road networks should be connected to the IoV ontology. Also, vehicle ontology should be extended to include the innovative OBUs and other sensors used for V2V, V2I, V2P, or V2S communications. While ontology studies have used VANET data in some specific mobility applications, such as vehicle routing (Chhabra et al. 2015), the concepts and entities in connected roadways and IoT technologies have not yet included in transportation ontology in the context of smart cities. Future efforts need to focus on defining concepts and entities in IoV and sensors technologies and defining them according to future connected roadways requirements.

Conclusion This chapter aims to give a general overview of ontologies in the domain of smart mobility, which will be of interest to researchers and practitioners. It will be of help to not only define the current concepts and entities in today’s cities, but also to include the innovative and disruptive mobility technologies and services in the future

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smart cities. We explain what an ontology is and why it has gained attraction in recent years across different fields of science. Also, we cover the main components of ontology and different ontology design approaches and languages. While there are several languages and approaches in the literature for ontology development, OWL has proven itself as the formal language for ontology development. Today, numerous mobility ontologies have been developed using OWL language, and due to its flexibility and coherence, OWL seems to be the best option to develop formal general ontologies in the domain of smart mobility. However, the transportation domain is changing fast and will keep on changing in the upcoming years. Future transportation road networks do not consist only nodes, links, and street furniture but also include a wide spectrum of communication technologies, sensors, and autonomous vehicles. Future streets and highways will not only differ in the number of lanes or speed limit but also vary depending on different V2X communication technologies they offer and various types of on-road or roadside sensors they have. Hence, future road network ontologies need to be extended considerably to cover these innovative technologies. Moreover, the current mobility ontologies, such as freight transportation, vehicle ontology, or transit system ontology, will change considerably due to the different types of autonomous passenger and freight vehicles that will surge to the urban environments in the near future. Besides the effects on physical and cyber networks, many concepts in the current transportation domain, such as vehicle ownership, driving behavior, parking requirements, etc., will vary dramatically due to the disruptive technologies, which consequently call for new ontology development efforts. Considering the fact that different innovative technologies finally interact with each other in future smart cities environments, stress the importance of a “general” smart mobility ontology that reflects the effects of future technological disruptions in the domain of transport. Currently, some applications or core ontologies have been developed according to the different aspects of connected and autonomous vehicles, for example, for safe autonomous driving. However, no general mobility ontology, in the context of smart cities, has yet included such innovative technologies. Moreover, as mentioned by Katsumi and Fox (2017b), only when an ontology is well documented, maintained, and accessible on the Web is it reusable by other domain experts. Hence, any ontology development effort not only should deploy extensive and relevant domain expertise but also requires choosing an appropriate approach, following standard procedures, and is written in a standard language using the right ontology editor. Hence, the chapter can serve as a useful starting point for transportation researchers and ontology developers who aim to generate a formal general mobility ontology in the context of smart cities.

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Part VI Data Dimension

Towards Autonomous Knowledge Creation from Big Data in Smart Cities

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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Big Data in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Cities and Big Data Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Vision of (Autonomous) Knowledge Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Example Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anomaly Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Activity Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Remaining Useful Life and Survivability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Desired Solution Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wisdom of the Crowd Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Examples and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fault Detection and Failure Prediction for a Fleet of City Buses . . . . . . . . . . . . . . . . . . . . . . . . . . Transfer Learned Knowledge Across Diverse Fleets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SAFARI Framework for District Heating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Outlook and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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The notion of smart cities is inherently connected with the notion of Big Data. It is Big Data that allows more and more intelligence to be added to our existing urban systems. This intelligence then, at least as a goal, is used to serve the needs of the citizens better, making the everyday operations more efficient and adaptive. Many recent successes of supervised machine learning make it an auspicious tool; however, the long-term vision of smart cities clearly requires technology that goes beyond that. The data collected based on the current operation of the system does not in itself contain information about possible improvements. The next S. Nowaczyk (*) · T. Rögnvaldsson · Y. Fan · E. Calikus Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden e-mail: [email protected]; [email protected]; [email protected]; [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_38

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generation of smart cities undoubtedly lies with the systems that build towards autonomous and semi-autonomous “knowledge creation.” They can self-improve and adapt to changing conditions and expectations. They must handle situations that were not anticipated during their design. Such construction of knowledge can be illustrated with the Data, Information, Knowledge, and Wisdom hierarchy. It requires collecting and representing the data; creating relevant “events” from this data; generating rules that can combine information from different sources; and finally, the ability to project into the future and reason back into the past.

Introduction The notion of Smart Cities is inherently associated with the concept of Big Data. Cities become smart by adding intelligence to existing urban systems, intelligence that is based on the digital infrastructure of collecting and analyzing data. To become and be a smart city is a continuous and never-ending process; this ideal is something one can strive to get closer to, but never reach. New technologies and solutions make the city smarter every day, but getting smarter is not the goal in itself – it is a means to an end. The goal is to address the needs of the citizens in a more efficient and adaptive way, capturing their preferences and conforming to their ever-changing desires. To this end, smartness means using new services and technology to optimize various aspects of the city. This includes the infrastructure and the policy, mainly in terms of the limited resources – primarily the physical space, since it is the most scarce, but also others such as energy, money, human resources, skills, and so on. We are facing what is often referred to as a new industrial revolution. It is characterized by a significant increase in the importance of concepts such as sustainability, enhanced quality of life, and intelligent management of natural resources. With 60% of the world population expected to live in cities by 2030, we need novel approaches towards resilience and governance. While the smartness of a city encompasses all aspects of life, some areas are explicitly mentioned more often than others. They include mobility, energy, education, and healthcare – all centered around crucial areas of people’s daily life. The definition of a smart city is still somewhat fuzzy. However, certain aspects are becoming consensus key factors. First of all, smart cities are built on technology, primarily Information and Communication Technology (ICT), related to collecting and analyzing data. Second, they are characterized by different actors that employ this technology in a distributed and only loosely controlled fashion. Finally, they share a goal of making better decisions that lead to a better quality of life for their citizens. Better decisions are made possible by the unprecedented access to data and information, and our newly found ability to aggregate it into useful insights. Developments in areas of network communication and data storage have made it possible to collect the necessary data. Another critical factor is the progress in the area of Artificial Intelligence (AI) that makes it possible to analyze all the data.

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However, it is not the technology that forms the core of the smart city. A smart city must be centered around the people and address their needs. It always starts with the desire to make better decisions, and the first step is understanding the needs and desires of those people. To this end, the technology, be it data collection or new services, necessarily requires a human-centric design approach. Cities exist because they are the main engines of innovation and wealth creation (Bettencourt et al. 2007). As they grow, they require more and more frequent innovation cycles to sustain economic growth and remain attractive (Ibid.). It is not obvious how to manage the trade-off between adapting cities to their inhabitants’ desires, to generate wealth and quality of life, and at the same time be, for example, sustainable. A very topical and poignant example of such trade-offs is the covid-19 pandemic, where people’s idea of quality of life (bars, restaurants, theatre, travel, etc.) is in conflict with reducing risks for exposure and very high costs for the city. Smart cities, with their Big Data, enable a tight “feedback loop” to manage this trade-off. Well used, they provide an unsurpassed possibility to measure, observe, and adjust the operation of this complex ecosystem.

Big Data in Smart Cities The ability to describe, predict, understand, and prescribe things that happen around us is uniquely enchanting. However, with it come many dangers. One of the biggest, possibly, is the lack of clear understanding and overview of the consequences of possible actions. The desire to optimize city operations and all the processes within it is powerful. However, optimization requires an explicit value function, and today we lack enough understanding of our society to measure such a value function on the overall level. Thus, we can only optimize on a particular narrow aspect of the city, for example, traffic flow or efficiency of different city operations. There are clear dangers with suboptimization, which are beginning to show in organizations that use AI-driven optimization to gain market advantages (Dzieza 2020), and this can lead to less human well-being in the long term. The most successful (even if still early) attempts at building smart cities are based on concepts of open innovation and free ecosystems of many collaborating actors. In a sense, this agrees with Adam Smith’s argument that many actors acting in their own self-interest make a positive systematic change (Smith 1776). Every person who, for example, uses travel data to pick a less congested route, or to change their travelling schedule to avoid a traffic jam is not only improving their own situation, but also reducing commute time for everybody else. Cities become more efficient as they allow every single one of their citizens to be more efficient. However, and in line with John Nash’s game theory (Nash 1950), acting in “blind” individual selfinterest is not necessarily enough, there is still a need for a central authority to ensure that proper incentives are in place and harmful behaviors are discouraged. All these developments are based on using data, and the amount of data is growing exponentially; more than 80% of the data available today have been created

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in the last 2 years, with a projected growth of 40% per year. This data tends to be unstructured and disorganized, and we have never before collected and stored as much useless data. To find interesting and relevant pieces of insight or wisdom in all that noise is a very challenging task. Inherently, this data is distributed and partitioned – in several different aspects, for example, geographically, by ownership, purpose, management, quality, rate of generation, etc. From commercial companies’ point of view, smart cities are incredibly exciting new markets, providing unique opportunities to both understand the needs of individuals and to fulfill them – disrupting the current status quo. From the citizen’s perspective, the new developments promise to vastly improve services and quality of life. Clearly, all cities are interested in becoming smart, but it is critical not to go for just the technology; active participation by citizens is a prerequisite for success, since only by understanding them can city managers deliver genuine improvements. Current technology offers the potential for unprecedented efficiency increase in how we manage our cities. To deliver on this promise requires understanding the requirements. First and foremost is the data collection and processing infrastructure; without the data, and the ability to analyze it, no city can become truly smart. Second is understanding the particular requirements on the technology – for example, some services require real-time access to the data, while for other near real-time access is enough; or even batch processing can be used. Inherently, smart cities data is streaming, continuous, and never-ending – which is challenging for, for example, machine learning solutions, since there are no clear training and exploitation phases. In addition, there is almost always concept drift so that models must be able to adapt over time to varying conditions and goals. The core value the smart city can deliver to its citizens is individualization and personification. The “dumb cities” were based on understanding the needs of the majority and fulfilling it really well – beyond that, though, every individual needed to adapt. Today all our services, from smart energy and smart healthcare to smart transportation, can be tailored to the individual’s needs. In mobility it includes controlling traffic flow, providing different modes of transportation (including micromobility) as well as vehicle and road control to ensure safety, and more. However, in many cities, operations are still uncoordinated today. It is generally believed that a smart city is based on three layers: sensors and communication for data collection; deployment of smart applications that extract insights from this data producing alarms and actions; and open and collaborative innovation connecting users. Citizens must become “prosumers” of both data and services. One of the biggest problems is that of scale – most solutions only become valuable once the critical mass of adoption is reached, making bootstrapping a challenge. Big data is usually described using several “V” words: (1) Volume refers to the size and amount of data that has been created and collected across all the different sources. (2) Velocity refers to the speed at which data is generated, as well as the speed at which it must be analyzed. (3) Variety refers to how different types of data must be combined, with a focus on the fact that most data is unstructured and cannot be easily categorized. (4) Variability refers to the constant change, both in the structure and meaning of data, but even more importantly, in the underlying reality.

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(5) Value refers to the benefits that are potentially hidden in the data. (6) Volatility refers to the inability of storing all the data forever and the fact that data once seen may not be accessible again in the future. (7) Veracity refers to how accurate, truthful, and trustworthy a data source may be, and accounts for issues such as bias, abnormalities or inconsistencies, duplication, and volatility. (8) Validity refers to how accurate and correct the data is for a particular, intended use. On top of all these challenges is the issue of lack of efficient automation for data capture and preprocessing. Despite the recent impressive progress in the Internet of Things, preparing all the data necessary for any given task is still a tedious, quite manual process. Within the Smart Cities context, all the information must be treated as time series, since it changes over time, but this is where the consistency ends. One can encounter all different types of data: some data streams will be structured and highly processed, others will contain raw sensor measurements from various devices, while yet another will be multimedia, like images and sound. Additionally, complex data, such as graphs and spatial information, are common. All of them need different approaches and methods for analysis and modeling, which means the overall process ends up being very time-consuming. There is much work done today on infrastructures and platforms to make all these tasks easier and more automated. The success of large scale deployment of smart cities lies, to a great extent, in improved tools for management of Big Data – tools that would allow us to build rich ecosystems of solutions, with all the different actors contributing. Today’s model of building a smart city, one application at a time, is suitable for doing the proof of concept, but clearly does not scale. Moving forward requires progress in architectures, policies, practices, and procedures that properly manage the full data life cycle – expanding from solving a single, well-defined, and specified task to “autonomous knowledge creation.” Big Data processing platforms and smart network infrastructure is the prerequisite, and this is where a lot of today’s attention focuses. The next step, still very much unsolved, is developing new open standards for technology and advanced algorithms that can interact with each other and build on top of each other. In this aspect, we will face new challenges for the government role, in particular related to security and privacy, as well as citizen awareness. The centralization of today’s smart cities, in the form of “operation centers” that provide data integration, is a way to get practical results. However, it puts quite strong constraints on what is possible. Those centers inherently introduce bottlenecks in terms of performance, and also in terms of innovation capabilities. In most cases, they focus on “quality of service” provided to the citizens. The next step, though, is to focus on “quality of experience” – and that requires more distributed and decentralized models, where the actors can contribute and collaborate in building the smart city. As the ultimate goal is better quality of life for the citizens, citizens need to be included more throughout the process. The technology can provide more efficient resource utilization – however, the “optimization criteria” for this efficiency is not obvious. Additionally, it is not static and changes over time. Our smart cities need higher levels of transparency and openness. Today we are still facing essential limitations on capabilities of Big Data for smart cities, but the source of those limitations has changed in recent years. The technology

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itself is no longer a constraining factor. Incredible advances that have made it possible are what we have seen in areas such as connectivity, including the unprecedented smartphone revolution or the Internet of Things, and data processing, including machine learning or artificial intelligence. New applications are being created continuously, with expanding capabilities and new features, making the tools more and more sophisticated. Still, all new technologies take time to become accepted in our lives, not least because they generally require behavior change on the side of individuals and change of processes on the side of organizations. It is clear that our cities will continue to grow smarter and smarter in the years to come, even if the pace of change is slower than many of us would like. This change will also reach deeper and deeper, in ways that we are not quite able to predict. A large part of what the smart city is about is the discovery of the unknown – the complexity of cities is too high for us to identify the “correct” way they should evolve. There are too many variables affecting each outcome for us to phrase the development of these cities as an optimization problem. Supervised machine learning is only a partial answer. We can use existing training data to improve efficiency incrementally, but we do not have the labeled data that could show us the new possibilities emerging from the ground-breaking changes. On the other hand, fully unsupervised machine learning is unable to cope with the complexity of smart cities. There is no way we can, for example, cluster citizens in a meaningful way – there are too many different aspects to consider. What is relevant necessarily depends on the task to be solved and thus requires subjective assessment. Research in smart cities is today moving more and more towards semisupervised approaches, potentially supported by reinforcement learning aspects, where the benefits come from combining “software” and “wetware” in the right way. Using tools such as visualization and interaction allows us to combine knowledge and understanding of human experts while still harnessing the power of data-driven Big Data algorithms. To summarize, at the core of smart cities lies a social contract that defines how communication will, or should, look like between governments, citizens, and businesses side effects of using technology. Alignment of all those actors in terms of the goals to reach and trade-offs to be made on the way towards those goals is the single most important factor defining success. The complexity of the problem arises from the inherently dynamic environment that is a city, with ever-changing conditions and situations, combined with the under-defined goals and key performance indicators that come from our own lack of understanding. Research and implementation in this area are severely hindered by the lack of realistic benchmarking and our inability to make comparisons between different solutions due to their fundamental specificity.

Smart Cities and Big Data Challenges The increased availability of data, both in terms of amount and variety, usually comes with unrealistic expectations. Many people and organizations expect data scientists to extract from the sheer volume of data, regardless of its nature and origin, significant but often very vaguely defined, forms of value. There have appeared

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notions such as “the end of theory,” essentially claiming that “data can speak for itself” without the need for an underlying model, and that “weak signals” can be found everywhere by merely considering more and more of the data. This has caused significant and well-deserved skepticism against much of the Big Data movement. The same pitfalls that existed in previous waves of AI and machine learning popularity still exist in the context of massive data sets today. If anything, they appear on an even larger scale. A model, whether implicit or explicit, is needed to make sense of the data; the generative process must be taken into account, especially with regard to noise, bias, and uncertainty. Correlation should not be mistaken for causality; the more data one has available when searching for patterns, the more spurious patterns one will find. At the core of Smart Cities must be the concept that it is not data itself that is interesting, but the underlying reality. The data merely, when appropriately treated, provides traces of that reality. The idea of starting from the raw data and seeing “what can we find in it” is just one way to approach it – usually a good starting point for an exploratory study, but it rarely leads to a successful and useful service. Instead, the designer should always start with a question or a task which requires information about reality. Based on the data, we must build as good a model as possible, making sure it captures the relevant aspects of this reality. The crucial concept here is this “relevant” part – no model can ever be elaborate enough to capture the complete, inherent complexity of the reality we live in. The usefulness of every model lies precisely in the simplifications it makes. Such a model then can be used to draw conclusions of interest. Critical for success is, of course, both that the model represents the relevant aspects, and that the data actually contains indications about those aspects. Ultimately, it is the end user who has to be the final judge of both those aspects. Within smart cities, there are different potential actors that can take this role, which often muddles the responsibility, but these core requirements remain valid nevertheless. In the context of Big Data for Smart Cities, there are several specific, technical challenges that need to be addressed. Below we list a subjective selection of the most important ones. • Data volume makes it difficult and expensive to both store and process all the collected measurements, while also increasing the amount of information that is irrelevant for any given task. • Data velocity, that is, the speed with which data arrives, puts constraints on the analysis tools. However, it is also related to the need for making decisions faster and faster. • Data variety requires ways of combining all the different kinds of available information. Doing it automatically is still an open research problem, which increases the amount of manual work that goes into handling Big Data. • Data veracity means that with more and more data, we are also getting more and more noise. Ensuring that the data is accurate, precise, and trustworthy is often difficult. • Data privacy is becoming a critical challenge for smart city applications, as public awareness of this topic increases. Hidden inside all the data collected

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within a smart city is a large amount of personal information that is easy to misuse. Data labels are lacking. A tiny fraction of the streaming data will be expert labeled. Any AI system for smart cities must build on a large amount of selforganization and unsupervised learning. Data drift, or concept drift, addresses the fact that our cities and society are everchanging. We need AI systems that can adapt to these changes, even if they are not immediately visible in the Big Data. Data representation, which is related to “curiosity” that today’s AI algorithms often lack. In order to improve over time and adapt to novel challenges, smart cities systems need to institute human-like ability to actively look for new information. Data benchmarks, that is, open data sets for benchmarking studies, are lacking, which makes it nearly impossible to fairly compare different solutions in a standardized way. The smart cities research community needs to develop consistent ways of measuring and contrasting competing solutions.

The Vision of (Autonomous) Knowledge Creation The previous sections should have made it clear that smart cities require intelligent systems that go beyond solving “simple,” well-defined tasks – which is where the majority of current solutions stop. In particular, those next generation AI systems must go beyond the assumption that human experts are there to specify the “tricky” parts of problem formalization in a precise and simplified manner. Smart cities require systems that can improve and adapt to changing conditions and expectations, both in terms of, for example, what data format they use and which performance measure to optimize. Overall, they must handle situations that were not anticipated during the design. Within the smart cities research community, we are striving towards AI solutions with the ability to (semi-)autonomously construct knowledge from streams of data and observations. The construction of knowledge from data in an AI setting can be illustrated with the Data, Information, Knowledge, and Wisdom (DIKW) hierarchy (Rowley 2007), shown in Fig. 1. Although it certainly presents a simplified model of knowledge creation, it is useful for visualizing and reasoning about the core abilities that a knowledge-creating system needs. The bottom level, data, relates to collecting and representing data. A pertinent question here is how to autonomously select what data to observe and collect. This decision is mostly taken by humans today, which simplifies the learning problem immensely, but it is one of the most relevant questions for autonomous learning. A related and more researched question is how to construct general features and representations from the raw data, features and representations that are useful in solving many, potentially diverse problems. Furthermore, with endless streams of data (i.e., in the Internet of Things era), it is impossible and uninteresting to save all the data. Instead, it must be possible to save snapshots – compressed or aggregated

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Fig. 1 The DIKW hierarchy

representations of the data. These representations should be learned and general so that they apply to many different tasks. The information level relates to creating “events” from the data collected by the layer below. Examples of operations that are required for this are classification, rearranging/sorting, aggregating, and selection. This stage of knowledge creation has been addressed by parts of the machine learning research field. A prominent example of research question here is the autonomous clustering of events. How can events be autonomously grouped into categories, without knowing how many categories there are? Another research question is how to optimize the categorization when an initial idea of the categories is provided. The knowledge level is about creating rules from the information. This requires combining information from different sources. Is an observed event from one data source associated with another event in another data source? Can such associations be formulated in a formal way? Much machine learning research is devoted to this; the obvious example is the supervised learning setting, where events (input patterns) are matched to correct responses (targets) provided by a human expert and encoded into a model. An interesting open question deals with knowledge representations (knowledge structures); how can knowledge be represented so that it can be used for reasoning and prediction? Another is how to autonomously select which data sources to combine. Handling uncertainties in data is very important, since essentially all data sources have uncertainties, missing values, erroneous values, and do on. The top level, the wisdom level, relates to the ability to project into the future and reason back into the past. Ultimately, a knowledge creating system needs to be capable of extrapolating information into the future in order to be able to estimate the

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consequences of various actions. It should also be able to look into the past and explain why decisions were taken (or recommended). A knowledge creating system for a smart city must be able to handle and combine these levels as autonomously as possible. Even if full autonomy may be unachievable, this goal should always guide the design process. After all, humans use social interaction for effective knowledge creation (Nonaka and Takeuchi 1995; Leonard and Sensiper 1998). So, AI-based knowledge creating systems will most likely form parts of joint human-machine learning systems. However, just as with good co-workers, smart city managers will expect AI systems to show a high level of autonomy in the knowledge creation process. As a side note, the DIKW hierarchy builds on ideas presented by Russell Ackoff in his 1988 address to the Presidents of the International Society for General Systems Research. Ackoff discusses what is required to generate knowledge and wisdom and is quite critical towards computer-based methods that claim to generate knowledge. He ends his address by stating that “wisdom-generating systems are ones that man will never be able to assign to automata” (Ackoff 1989). There is much less research devoted to (semi-)autonomous knowledge creation than the subject deserves. A case in point is AI for predictive maintenance of equipment. This field is vital for smart cities and also an excellent application domain for autonomous knowledge creation methods. The number of scientific publications on predictive maintenance with AI methods has grown rapidly recently, with the annual output increasing more than fourfold from 2016 to 2019, and the total number of papers over the last 30 years is somewhere close to 1000. However, only a tiny fraction of this body of work is directed towards autonomous knowledge creation – the vast majority of the works are static setups where faults and inputs are defined a priori by human experts.

Example Scenarios There is no single set of requirements that Big Data applications within Smart Cities setting should necessarily fulfill. The multitude of different imaginable scenarios necessarily enforces different trade-offs between possible constraints and quality criteria. Aspects such as accuracy, predictability, real-time demands, transparency, privacy, and personalization are often conflicting, and their relative importance needs to guide the design choices. Depending on the application, Big Data can be used for solutions that lie across all the different levels of the DIKW hierarchy. The smart city as a whole is, of course, located at the very top, but getting there requires many smaller building blocks. Some of these building blocks are relatively simple, occupying the lower levels, and only their combinations reach the Knowledge or Wisdom levels. Throughout this chapter, the application of predictive maintenance is used as a vehicle to demonstrate the ideas and concepts introduced above. As mentioned earlier, this area is a rapidly growing field of both research and implementation,

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popular within smart cities, and using multiple different AI methods. Predictive maintenance means understanding the operation of various machines, parts of the infrastructure, and systems to anticipate their future service needs, proactively prevent failures, and optimize their operation. Interest in this area has snowballed in the last years, due to increasing access to Big Data and recent developments in the field of machine learning. Predictive maintenance includes, among others, condition monitoring and fault diagnosis. Predictive maintenance lends itself excellently to scientific study, and for demonstrating knowledge creation approaches. A successful predictive maintenance platform needs the ability to select the right data, to learn useful representations, to connect on-board observations such as sensor reading with off-board information such repair records, to provide explanations for faults, and to provision a prognosis for the future. The state-of-the-art AI and ML techniques available today are not capable of overcoming all these challenges, driving new developments in these areas. Once created, such solutions will undoubtedly have much broader applicability. At the same time, the practical implications of good predictive maintenance are apparent, since ensuring proper operation of the technology in a smart city is of paramount importance.

Anomaly Detection Anomaly detection is the problem of identifying data points or patterns that do not conform to the normal behavior (Chandola et al. 2009). Anomalies – also known as rare events, abnormalities, deviants, or outliers – contain useful information about abnormal characteristics of the systems. The identification of such unusual characteristics provides (often critical) actionable information in many real-world applications, including predictive maintenance across various domains such as production, finance, security, IT, and energy of today’s cities. For example, in the monitoring of a running engine, anomaly detection techniques can be applied to automatically discover faulty sensor readings or abnormal changes on a variety of parameters such as rotor speed, temperature, pressure, performance, and more. The same principle can be used, in more complex systems such as traffic flows or healthcare, to reveal interesting patterns such as usage changes, atypical activities, and misbehaving equipment or infrastructure. In most applications, it is necessary to detect abnormal operations as soon as possible, in order to react appropriately without delay; in predictive maintenance, this means being able to fix them before they result in a failure. The data in this scenario are usually in the form of sensor data, in which continuous measurement values are tracked over time. Thus, methods should be able to cope with both the challenges associated with anomaly detection itself and those associated with learning from temporal streaming data. A smart city is undoubtedly a complex environment where one can find a massive volume of heterogeneous data. Defining the exact notion of the normal (i.e., healthy) operation in such complex environments is a difficult task since it can differ significantly across various domains and settings.

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Furthermore, what is considered correct today may not be so in the future, because of the dynamic nature of smart cities. Most of the current methods in the literature attempting to solve the anomaly detection problem can be categorized into two major approaches (Pimentel et al. 2014): general or ad hoc. The general approaches assume the one-size-fits-all solution model and look for a single “perfect” anomaly detector that can detect all anomalies in any scenario. The alternative, ad hoc approaches are specifically tailored to a particular target application, often using complex built-in assumptions based on deep domain expertise. However, anomaly detection is an inherently subjective task, where it is often challenging to quantify how “abnormal” a single observation is based on the global context of the application. Any fixed metric to measure deviations may not be suitable for addressing the situation of the user, or the deviation levels may not directly correspond to the desired levels of “strangeness.” As demonstrated in earlier studies (Calikus et al. 2020), no anomaly detection algorithm is ultimately superior in all cases; it is even difficult to define generally what “superior” means in this context, without knowing the ground truth. Many tasks in predictive maintenance can be seen as applications of anomaly detection since symptoms of emerging faults and inefficient operation often materialize as unexpected or surprising measurements. Smart cities context combines the challenges of defining the acceptable range of variability, before a data point is considered abnormal, with those related to learning from streaming data. The difficulty of capturing the dynamic nature of normal behavior, which evolves over time, motivates the need for developing algorithms with more focus on knowledge creation aspects.

Activity Recognition An essential piece of the smart cities puzzle is to know the usage of different subsystems, be it equipment, infrastructure, or services. It is especially critical if this equipment can be used for many different tasks. Is it possible to, directly from data, create high-level descriptions that summarize the operation of machines? Making this knowledge easily accessible would allow, for example, better interaction with users (customers) and more well-designed maintenance plans. A smart cities (sub)system should be able to learn and visualize the core characteristics of its own behavior, identify the core aspects of it, as well as how to compare itself against other similar systems. The state-of-the-art in the industry on quantifying and describing equipment usage is based on quite straightforward measures, for example, fuel consumption, time use of motors, engine rotation speed and torque, and similar. However, the recent years, research on human activity recognition has inspired some interesting scientific work on machine activity recognition, and this is a field that certainly will become more active. Perhaps the earliest work on machine activity recognition with AI methods was published by Vachkov et al. (2004), who used self-organizing maps (or variants of this), and also showed how activities related to wear (Kiyota et al.

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2006). Two very recent papers are, for example, the works by Rashid and Louis (2019) and by Jakobsson et al. (2020), who use accelerometers mounted on the vehicles and deep neural networks; the latter also shows how the activities relate to machine wear. In smart cities context, examples of innovative services to be developed include showing different ways in which long operation sequences can be segmented into individual actions. Such processing can be applied to complex machines, such as fork-lift trucks, buses, construction equipment; to pieces of infrastructure such as roads, roundabouts, building; or to abstract entities such as healthcare and education systems. Different types of actions (lift, drive, load, unload, and similar in fork-lift trucks) can be discovered automatically or learned based on relatively few examples of actions provided by a human operator. In one sense, this corresponds to developing an additional sensor modality for measuring actions, thus making the city even smarter and allowing even more advanced models to be created on top. Another example is to show that two customers who operate fleets of vehicles, or two subfleets operating in separate regions of the city, have measurable variability in how they are utilized when viewed in action detail while they are identical in use when described with the current industrial state-of-the-art. This can be followed by demonstrating that such different uses translate to contrast in maintenance needs, efficiency, energy usage, environmental impact, or any other metric – which in turn enables significant economic and productivity gains (lower maintenance costs, shorter workshop visits, extended maintenance intervals, among others).

Remaining Useful Life and Survivability The third example scenario relates to prognosis and understanding the future consequences of current actions. It includes identifying early symptoms of unwanted behaviors, conditions, and usage patterns that are particularly harmful in the long run, and other cases where the long-terms effects are neither obvious nor immediately visible. Ultimately, prognosis in smart cities is closely related to analyzing and understanding the underlying causal models, and recent advancements in combining causality reasoning with Machine Learning (Schölkopf 2019) are going to play the crucial role here. Once more using predictive maintenance as an example, the main point is to transform unplanned downtimes and repairs into planned downtimes and repairs. Detecting a deviation or a symptom is just the first step. It is essential also to provide reliable prognostics on how urgent the problem is. Is it possible to wait with fixing it, can the repair be done at the next planned maintenance, or is it necessary to revise the maintenance schedule and recall the vehicle to workshop immediately? Prognostics is a field of engineering focusing on predicting survivability or the remaining useful life of the equipment, or piece of infrastructure. Modeling the survivability of complex machines, based on operations data, is difficult, and much research on remaining useful life prediction instead focuses on individual subcomponents. One example is wheel bearings (El-Thalji and Jantunen 2015; Rai and Upadhyay 2016); it appears that roughly about one-quarter of all papers on

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predictive maintenance (including diagnostics and condition monitoring) with AI methods deal with wheel bearings. However, smart cities require prognostics of a very different kind. The target is highly complex equipment, and it is questionable whether building accurate prediction models for all subcomponents is possible due to the scale of the problem. Instead, one should be approaching the sophisticated equipment as a whole, based on the system-level analysis. Some examples of approaches for predicting remaining useful life of complex components, using field operational data and maintenance records, are the works by Voronov et al. (2018), Fink et al. (2015), and Prytz et al. (2015). Accurate, detailed, and interpretable survivability models allow vehicles or pieces of infrastructure to be stratified into risk groups, and to identify factors that affect these risks. In turn, this enables decision support systems that advise operators on improving efficiency, for example, to extend drive battery life – which is one of the biggest challenges for electromobility. A central challenge with survival analysis, c. f., survival analysis in the medical domain, is the lack of failure data. Many types of equipment are seldom allowed to fail, which results in censored events. Additionally, machines are rarely used across the full spectrum of possible conditions and settings, which means the exploration of the alternatives is limited. Due to economic reasons, very few systems are equipped with a wide range of sensors that would provide complete information about all relevant aspects of the operation. Usually, the suite of measurements is designed in a minimalistic way, only to satisfy control and regulatory constraints. Those limitations need to be compensated by exploiting available modalities that are somewhat correlated with the hidden information. In such a setting, there are often other, confounding patterns that could explain possible failures and direct causes of inefficiency. To handle these challenges, the current research frontier explores the use of transfer learning. In many cases, it is also possible to identify suitable proxy events (frequently occurring) for the (rare) real events. In the end, with the prognosis that is connected to usage, one can envision smart city services that provide feedback on better operation, including how to actively improve efficiency and life expectancy.

Desired Solution Properties Solutions suitable for smart city real-world applications should exhibit important properties to meet the requirements and challenges of today’s society. We list the following properties concerning autonomous knowledge creation: Data-driven: Most tasks involving data in today’s cities are still performed by human experts, in an often time-consuming and expensive manner. It is, however, infeasible for analysts to monitor a large number of components in smart cities (e.g., people, machines, systems, buildings) simultaneously. Solutions need to be datadriven and require as little manual labor as possible. Streaming: Data sources are becoming increasingly ubiquitous and fast. We now have many applications with sensors that produce data that continuously stream and change over time. These characteristics motivate the development of systems in

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smart cities that can efficiently process large quantities of data streams in (near) “real-time.” Data streams are infinite sequences of information that arrive sequentially and often need to be forgotten immediately afterwards – only the most recent observations are available at the same time. This limits the applicability of offline algorithms since all the data is never accessible at the same time. Methods that require many iterations over the “full” data sets are of limited applicability. Instead online, on the fly, processing is necessary. A decision on what to do with a data point should be made essentially immediately as this data point arrives. Furthermore, streams inherently include the notion of time (each data point is associated with a timestamp), and solutions should also consider the temporal context of the data for decision-making. Adaptive: Smart cities continuously produce data from heterogeneous sources. Most data come from systems that operate in dynamic environments, where conditions frequently change and evolve. For example, the distribution and behavior of traffic in a city may differ between peak hours and nonpeak hours. The behavior of people, for example, going out, may shift dramatically with a virus epidemic. This phenomenon is known as concept-drift (Gama et al. 2013; Lu et al. 2019). Smart city environments are subject to different types of concept drifts, including changes in the values of a feature, their association with different classes, the relevance and availability of features, and more. An autonomous knowledge generation process should be designed to track and adapt to such changes continuously, accounting for, among others, shifts in data distribution and feature importance, the emergence of new classes, or changes in optimal parameters. Flexible and extensible: Smart cities consist of complex systems with various needs and specifications. On the one hand, the highly heterogeneous and dynamic nature of smart cities prevents designing a one-size-fits-all solution that can solve all the tasks for diverse domains and use-cases. On the other hand, methods that are tailored to their target application and deliver specific functionalities are often designed based on hard constraints that come from deep domain expertise. If the domain or the requirements for the particular domain changes, it is not trivial to modify the previous settings or adapt to new ones, even if there is a considerable degree of commonality between the existing and the new functionalities. The solution must, therefore, support flexibility, which means that the components of the system can be easily mixed and matched to construct application-specific models consistent with the objectives and the requirements of the different tasks. Furthermore, it should be extensible, allowing easy integration of new models into any of its components or modifying the existing ones without harming the rest of the system. Human-in-the-loop: Most tasks building up the smart city environments are inherently subjective, in a sense that both the characteristics of the data and the notions of the optimality criteria vary significantly across different domains and scenarios. It is often precarious to automatically capture knowledge that corresponds to semantically meaningful information from an application perspective. As an example, consider a system that monitors the heating operations in a city and has the goal to flag anomalies and faults. A substantial temperature increase caused by an operation to disinfect pipes may show up as a statistical anomaly. However, it is not

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an anomaly for an analyst who knows that temperature is sometimes used for killing bacteria. Such gaps between statistical anomalies and real operational deviations can easily render a monitoring system unusable. Domain expertise plays an essential role in bridging this gap – an analyst is needed to give clues, for example, by creating features that are more likely to produce relevant anomalies or by providing feedback that separates useful results from noise. Therefore, it is crucial to develop approaches that actively involve users in the learning loop. Knowledge creation needs to be a collaborative process between AI systems and human experts, in which people provide domain knowledge, guidance, or feedback, as well as observe, interpret, and learn from the AI system. Decentralized processing: As mentioned above, smart cities inherently combine perspectives, data, and solutions from multiple actors. There is a certain level of collaboration across these actors, but they do not necessarily agree in all aspects and often have partially conflicting incentives and goals. In such a setting, it is not realistic to assume that all the data can be gathered in a single, centralized location for processing. Instead, different forms of decentralized processing are necessary, such as federated machine learning (Yang et al. 2019). Thus, frameworks for smart cities must account for the possibility that some of the data, and some of the intermediate results, will not be shared with any central authority. At the same time, the ecosystem as a whole must be able to achieve results that are, from a global perspective, close to the optimum.

Wisdom of the Crowd Framework An excellent general framework for smart cities solutions for (semi-)autonomous knowledge creation can be based on “the wisdom of the crowd” (WotC). It builds on a very straightforward idea to draw knowledge from a peer group, for example, compare the behavior of any system to its “peers.” Large fleets of similar systems are common in smart cities, take for example city buses or subway trains, and using them as peers allows automatically identifying interesting patterns, detecting anomalies, and adapting to changes in external conditions. Among the work on autonomous knowledge creation using Big Data in smart cities is a WotC approach for unsupervised deviation detection: Consensus SelfOrganising Models (COSMO) (Byttner et al. 2011; Rögnvaldsson et al. 2018). The method assumes that the majority of the peer group is healthy, and individuals that consistently deviate from this typical behavior are considered anomalies that require further investigation. The COSMO method (Bouguelia et al. 2019) has four conceptual steps. First is search for informative data representations in the streams of data. Second is to encode and capture characteristics of the signals with these data representations. These two steps can be done in a distributed fashion, on each piece of equipment individually. The third step is to measure distances across units in the fleet. Finally, the fourth step is to find deviations using the WotC concept, for example, for each unit, compute the similarity to the fleet average.

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The COSMO encodes sensor data stream into models, that is, data representations, and detect deviations in the model space. The term “self-organized” emphasizes that the model or the data representation can capture characteristics of the signal without external supervision or a teaching signal. This is an important aspect, the ability to capture and encode the nature of various signals by using model-space representations. Autoencoders, nonlinear principal components, and density estimators are examples of such self-organized models. These can be as simple and straightforward as a histogram that approximates the probability density function of the signal and can be utilized for capturing the differences in the spread. Histograms are memory efficient, robust against noise, and easy to store as well as to compute on-board. On the other hand, a complex representation such as a recurrent neural network can capture dynamics, that is, temporal information, of the signal. Given the representation, the differences between individual assets are measured in the model space, with a suitable metric. The inherent variation within the fleet can be used to estimate the expected similarity of any healthy system to its peers. This way, any individual that falls outside the acceptable range can be flagged and inspected further. The COSMO method computes deviation levels, based on p-values, that reflect how likely it is that the data samples from one of the systems are sampled for a different distribution than those of the reference group (a peer group, ideally composed by nominal samples). In the end, this approach can provide several useful outputs. Besides the primary measure of the severity of the deviation, one can learn the expected time to failure or remaining useful life. By comparing the deviation against historical data, it is possible to identify the most likely causes and the similarity to past faults. The relative importance of different features provides a means to reason about the root cause and potential consequences. It has been demonstrated that the distance to peers from COSMO is a useful feature for understanding the equipment, and it relates to its relative health condition within a crowd (Fan et al. 2020). In a statistical sense, the larger the distance from the peers, the closer the equipment is to its end of life (EOL). Thus, the distance to its peers is an indicator for estimating the remaining useful life (RUL), which can be utilized for decision support to optimize maintenance schedules. If the crowd has comprehensive coverage on nominal behaviors from various operating profiles of the equipment, with a right mix-and-match to a subfleet, this WotC approach allows autonomous adaptation to varying external conditions. In that sense, it differs from the conventional methods of estimating the equipment condition, which are often based on a reference model built from data collected in well-controlled experiments. In the streaming setting, a meta-framework SAFARI makes it easy to create different unsupervised anomaly detectors adapted that can learn from time-evolving streaming data. The framework provides a generalized procedure for streaming anomaly detection, with self-contained, cohesive building blocks that address the fundamental tasks of this problem as separate concerns. SAFARI’s flexible and extensible schema helps to overcome the limitations of one-size-fits-all solutions. One can compose a collection of different algorithms by integrating new methods into SAFARI’s components or have more accessible algorithm adaptations by

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modifying the existing components without the need for altering the rest of the framework. The generalized procedure provided by SAFARI also provides a basis to conduct more elaborate evaluation experiments. By unifying and separating key concepts in existing methods, SAFARI allows one to study commonalities and differences of the algorithms more thoroughly. Most of the existing experimental studies evaluate their algorithms by reporting performance scores on application-specific case studies or synthetic datasets without characterizing the nature of the anomalies in these benchmarks, or other factors that can affect the performance, such as noise or concept drift. Even though many methods included in the comparison studies share similarities, their unique properties get concealed in the design of the algorithm. Therefore, the current evaluation practice fails to answer whether an algorithm performs better or worse because of, for example, the novel distance function or the new features that it uses. This makes it difficult for practitioners to pick algorithms that are suitable for their data and use-cases. The work (Calikus et al. 2020) integrates several different methods into the SAFARI framework, producing 20 different detectors. An extensive evaluation study of these detectors compared their performances using real-world benchmark datasets with different properties. The performances of different combinations vary depending on the characteristics of data sets and discussed the benefits and drawbacks of each method in-depth. It provides guidelines for future practitioners of SAFARI on choosing appropriate algorithms for their problems. Furthermore, formalizing state-of-the-art anomaly detection methods within the SAFARI framework led to noticing a critical research gap in streaming anomaly detection. It has been found that there is no general approach to learning data streams designed explicitly for the case of anomaly detection. This motivated the proposal of a novel learning strategy that extends the weighted reservoir-sampling schema considering the constraints of the anomaly detection problem.

Examples and Results This section presents some recent successes in different areas of smart cities and discusses how Big Data related to their specific needs and design choices. While those examples could come from many different areas, we focus on predictive maintenance.

Fault Detection and Failure Prediction for a Fleet of City Buses A fleet of equipment with connected Electronic Computing Units (ECUs) and sensors are a good example of the IoT. As a case in point, an advanced modern commercial heavy-duty vehicle has over a 100 ECUs, which transmit data on the controller area network (CAN). It is tempting to mine this data to predict maintenance needs better and to understand how the vehicles are used. A series of studies

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Fig. 2 Two examples of deviation level in the Engine Oil Temperature (left) and Temperature Catalyst Upstream (right) signals: (left) deviations indicated engine cooling fan running at full speed all time, which costs extra fuel – the fault is due to oil that leaked into the contact so that the circuit of an ECU was shorted; (right) deviations indicated a case of engine cylinder jam. In both cases, the deviation disappeared after the problem was fixed in the workshop (red vertical line)

(Byttner et al. 2011; Rögnvaldsson et al. 2015, 2018) on a commercial fleet of city buses have demonstrated that with the COSMO approach it is possible to detect deviations autonomously, indicate faults, and build up new knowledge in multiple systems on-board buses. Examples are runaway engine cooling fans, failing emission sensors, jammed cylinders, and failing air compressors (see Figs. 2 and 3). The strength is that the approach is the same, regardless of the system that is being monitored. Moreover, the faults that get detected are those that happen but have no dedicated fault detection on-board, that is, no effort is wasted on faults that almost never occur. The created knowledge has also been made more concrete in terms of approved patents. An illustration of applying the COSMO method applied to a city bus fleet is shown in Fig. 4. A back-office server hosts the algorithm for detecting deviations and capturing abnormal operation of the fleet using representations, learned onboard vehicle, collected from each unit, based on the WotC idea. The probability of a vehicle deviating is computed by comparing compressed representations of the subsystems’ operation on each vehicle against the rest of the fleet. The approach defines the “nominal behavior” from the fleet, and individual deviations from this

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Fig. 3 Deviation levels of Wet Tank Air Pressure signal. Red vertical lines correspond to air compressor failures and blue ones correspond to other air system related repairs. The first two cases of deviation levels match the compressor failures quite well, that is, bus A in July 2012 and bus B in March 2014. For the case of bus M in February 2012, the deviations start when the compressor is replaced, due to the fact that a very new compressor behaves differently from what is typical for this (quite old) fleet

Fig. 4 An illustration of applying the Consensus Self-Organising Models Method to detect deviations and monitoring the condition of a city bus fleet: selected representations of each vehicle are computed on-board and sent to a back-office server for analysis; deviations discovered are reported to the fleet operator

reference behavior are considered to be anomalies. Several studies have shown (Fan et al. 2015a, b) that the COSMO method is able to detect vehicle air system related faults and can achieve a performance similar to an approach based on expert-defined features (Fig. 5).

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Fig. 5 ROC curves and comparison of AUC values of different methods, with 95% confidence interval, in predicting air the compressor failure, based on the Wet Tank Air Pressure signal

The deviation detection using COSMO is purely unsupervised, depending on the proposed crowd selection method, and can be performed without access to labeled training data.

Transfer Learned Knowledge Across Diverse Fleets Transferring information across different settings, that is, utilizing knowledge gained from solving a relevant yet different task is an emerging research topic. It is a very promising direction for enabling high potential fleet-based approaches for predictive maintenance, for example, prognostics for fleets of commercial vehicles. Although commercial fleets of vehicles are usually comprised of similar units, that is, similar physical structure and configurations, they are often operating under different external conditions, by different technicians or drivers and deteriorate in different ways. Compared to controlled experiments performed in a laboratory setting, scenarios encountered in real-world applications are almost always more complicated, for example, including unforeseen new operating conditions and novel faults. As an example, Fig. 6 illustrates some run-to-failure trajectories of turbofan engines from the C-MAPSS dataset. It showcases that data under phases B and C, collected from a subset with multiple operating conditions, possess a greater complexity compared to phase A, which only contains a single operating condition. There is a need for adaptive methods capable of dealing with future data samples that may come from unseen distributions, accounting for new faults and different progression of deteriorations.

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Fig. 6 Real-world applications (phases B and C) are often more complicated compared to lab experiments (phase A). The signal shown in the first row is the seventh feature of engine units from a different subset of the C-MAPSS dataset; t-SNE visualization of 10 run-to-failure trajectories from different subsets is shown on the second row, where blue dots mark out observations that have RUL larger than 50 cycles and the rest (yellow to red) are observations from RUL 50 to 0 cycles

A recent study (Fan et al. 2020) shows how to use COSMO method to predict remaining useful life (RUL) of equipment in fleets under different operating conditions, demonstrating the benefits of sharing learned knowledge across different fleets for prognostics. Dealing with a group of heterogeneous units is challenging since the assumption behind WotC methods requires a crowd with comprehensive coverage of all the possible operating profiles. Therefore, a reference group was collected across all units in a fleet. The COSMO method is able to compute distance to the peers (Fig. 7a, b), in addition to the probability for deviation (which is bounded). The former was demonstrated to be a significantly more transferable feature – with higher predictive quality for RUL estimation. The hypothesis is that both the source and the target data are projected into a latent space where distances of each sample to a reference group are preserved, that is, the feature is transferable. If the crowd (i.e., selected peer group) is representative of nominal conditions across all operating profiles, the proposed COSMO feature is expected to generalize samples from different domains into a common latent feature space where the

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Fig. 7 Example of COSMO distance-to-peers features and experiment result: (a) distance to the most central pattern; (b) distance to k nearest neighbors; (c) comparison of using the proposed feature compared to other methods – it is significantly better in dealing with new operating conditions and faults that occur in the target domain

discrepancy of marginal distributions between domains is reduced and deviating (or near EOL) samples are projected onto the edge of the majority distribution. The main requirement is to build a crowd that provides comprehensive coverage of the variability expected in the healthy samples. To achieve this, one needs a mechanism for continuously updating the healthy behavior as new data arrives. Then, for any testing instance, distance or the degree of deviation can be computed based on a “subfleet” drawn from such a crowd.

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The proposed approach (Fan et al. 2020) was verified on the Turbofan Engine Degradation Simulation Data Set, which is generated by C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) (Saxena et al. 2008). There are four subsets of engines, with different numbers of operating conditions and faults. The most simple case, that is, the first subset, X1, includes engines with a single operating condition and one fault, whereas the fourth subset, X 4, contains engines with six operating conditions and two faults. Assuming that X1, the simplest case, corresponds to the data from laboratory experiments and X 4 is the data from the realworld application, the crowd should be updated with samples of healthy behavior from X4, merging it with healthy samples from X1. After COSMO features, for each dimension of the signal, are generated based on the selected crowd, a mapping function using a random forest (RF) regression model is learned between the COSMO features and RUL for the prediction task. A diagram illustrating the proposed approach for RUL prediction is shown in Fig. 8. It is indicated in Table 1 that the discrepancy in the marginal distributions between the source data and the target data is reduced in the COSMO feature space. The experimental result, in Fig. 7c, shows that the proposed COSMO

Fig. 8 Transfer learning for remaining useful life prediction using COSMO method

Source domain DS Unit 49 from X1 xS  X1

θS  X1

Approach with sensor data x

Approach with COSMO feature θ

θT  X2

Target domain DT (DT 6¼ DS) Unit 49 from X2 xT  X2

y^T ¼ fS (θT)

RUL prediction y^T ¼ fS (xT)

Table 1 Illustration of sensor data (the seventh feature) and COSMO feature of unit 49 from X1 and unit 20 from X2. Red dots on the third column correspond to RUL prediction (^ y T), which shows that using COSMO feature θ is more accurate compared to using sensor data under scenario C1 where DS 6¼ DT

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features, that is, distance to the k nearest neighbors (kNN) and the median (m-kNN) of it, achieve better performance. It is better at dealing with new operating conditions and faults that occur in the target domain, compared to well-known domain adaptation methods such as TCA, CORAL, and SCL. It also has an error approximately four times lower than a conventional, nontransfer approach. Other methods for handling such problems include multitask dimensionality reductions (see for example, Vaiciukynas et al. 2018).

SAFARI Framework for District Heating The third type of crowd selection method utilizes feedback from domain experts on whether queried deviating samples are anomalies of interest (correctly identified deviations) or healthy samples with atypical usage. Leveraging the implicit or explicit involvement of domain experts helps to produce more effective and practical fault detection, which better serves the application context. One successful application example that uses this approach is deviation detection in a district heating (DH) network. District heating (DH) is a system for distributing heat generated in a centralized location through a system of insulated pipes. It is the most common form of heating for residential and commercial premises in Sweden. District heating plays a vital role in the implementation of future sustainable energy systems (Lund et al. 2010; Connolly et al. 2014; Münster et al. 2012) by diversely incorporating recycled and renewable heat sources and contributing to a decrease in carbon emission. However, the current generation of district heating technologies has high supply and return temperatures, which leads to significant heat losses in the network and inefficient use of heat sources (Rezaie and Rosen 2012; Fang et al. 2013). It is vital to reduce distribution temperatures to achieve the target of a 100% renewable energy supply system (Gadd and Werner 2014). Achieving low temperatures in the network requires intelligent systems and elaborated strategies for continuous identification of anomalies and faults, causing high return temperatures. To design such strategies, it is crucial to have in-depth knowledge of the customers, and a better understanding of their heat use as even a single substation can have a significant impact on the global efficiency of the system. This work builds and evaluates approaches for district heating systems to discover how the typical and atypical load patterns look like and how different crowds (i.e., customer groups) use the heat by incorporating feedback from domain experts. The baseline for detecting deviations and atypical usages is SAFARI framework (Calikus et al. 2020) that allows one to create use-case specific anomaly detectors easily. In this setting, the overall SAFARI’s original structure is generalized to actively exploit available feedback from the user and incorporate into the anomaly detection process. SAFARI itself consists of four main components, that is, data representation, discovering reference group, anomaly detection, and active learning, as illustrated in Fig. 9.

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Fig. 9 An overview of the SAFARI framework that shows four components along with flow the overall procedure

The first component, data representation, is concerned with automatically transforming raw input data into informative representations, or features, so that it can be effectively exploited in anomaly detection tasks. Useful representations can capture essential clues about the past and the current state of data as well as the key characteristics of the monitored system that are relevant for anomaly detection. This work mainly uses the domain-specific representation of district heating data – which is referred to as heat load profiles – to summarize individual heat consumption behavior of each building (Gadd and Werner 2013; Calikus et al. 2019c). A heat load profile is the average hourly heat load of a single building as a function of time. The heat load is the quantity of heat per unit of time that must be supplied to meet the demand of a building. Given a building b and seasons S ¼ s1, s2, s3, s4, let Ms  ℝh  w be a matrix of hourly heat load measurements of b recorded by a single meter, where h ¼ 24  7 ¼ 168 is the number of hours in a week and w is the number of weeks in season s. Heat load profile Pb ¼ fA1 , A2 , A3 , A4g is the set of vectors derived from the four seasons S, where As ¼ as1 , as2 , . . . , as168 is a vector of averages of columns such that asi ¼

j¼w 1 X s M w j¼0 ij

We define the four seasons in a calendar year as winter (12 weeks of December, January, and February), early spring and late autumn (18 weeks of March, April, October, and November), late spring and early autumn (9 weeks of May and September) and summer (13 weeks of June, July, and August). Intuitively, heat load profiles capture the recurrent behavior of a building over the whole year, with the hourly variations during the day, the changes across weekdays, and seasonal differences (Fig. 10).

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Fig. 10 An example showing how to extract a heat load profile. In each week w in matrix Ms, there are 24  7 heat load measurements, {Mw1, Mw2,. . ., Mw168}. The average weekly heat loads of four seasons form the heat load profile. Then, the heat load profiles are concatenated to single sequence and z-normalized for clustering

The second component, discovering reference group, is responsible for capturing the norms in the structure of real-world data from a large collection of diverse domains. This work specifically focuses on discovering the most typical behaviors that provide a standard for normal behavior in the district heating domain. The goal is to effectively capture similarities on heat load profiles showing the individual behaviors in a district heating network and represent them as a set of patterns that can serve as a reference group. o n b b Let N b ¼ P1 , P2 , . . . , Pbn be a set of n different heat load profiles in a district P

heating network. Let N b be divided into k different clusters (C1, C2,. . ., Ck), where P

P



Ck  N b and C i \ C j ¼ 0. Then, ci, a heat load pattern, is defined as the centroid of a cluster Ci and R ¼ c1, c2,. . ., Ck, a collection of all heat load patterns, as the reference group of the district heating network. A heat load pattern is the representation of the central behavior in a group of buildings. Intuitively, clustering heat load profiles and extracting cluster centroids provides a set of heat load patterns that capture the most typical behaviors in a DH network.

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Heat load profiles reflect how heat is used in an individual building over a year by containing information on changes during the day, differences among weekdays, and seasonal variations. Therefore, it is essential to consider the shape characteristics of these profiles, that is, the timing and magnitude of its peaks when looking for similarities between them. For this purpose, we apply the k-shape algorithm (Paparrizos and Gravano 2016), which is a centroid-based clustering algorithm that can capture the similarities in the shapes of time-series sequences. Figure 11 shows four example heat load patterns captured by this study, which present different typical behaviors in the DH network. In each figure, heat load patterns (i.e., centroids) are visualized with opaque colors, while heat load profiles (i.e., cluster members) of the buildings with transparency.

Fig. 11 Cluster examples showing different heat load patterns in a DH network

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The third component, anomaly detection, aims to detect anomalies by first measuring how “much” different these observations are from the reference group and then, by scoring them using the global context of the application and the user feedback into account. The measure that quantifies how “different” a single observation is from the norm is referred to as nonconformity measure. Nonconformity measures are solely used to find anomaly candidates, that is, those samples that deviate too much from the normal behavior. Various approaches can be given as examples of nonconformity measures – such as measuring the average distances to nearest neighbors, the local density, the likelihood fit to a generative model, or the difference between actual and predicted (i.e., expected) values. In this setting, the distance from the nearest cluster centroid is used as a measure of nonconformity, which means the less a heat load profile is similar to its heat load pattern (centroid), the more likely it is to be abnormal. Let R ¼ c1, c2,. . ., ck be the reference group containing k heat load patterns and Pbi is a heat load profile in a district heating network, the nonconformity score of Pbi is computed as follows:    ai ¼ min d Pbi , R :

ð1Þ

where d is the distance function. Using this approach, we have identified buildings whose heat load profiles do not conform well to their expected heat load patterns. Figure 12 shows three examples that are marked as abnormal to be further investigated by the domain expert. These examples clearly show characteristics different from the typical groups in the DH network. However, it is essential to note that not all “abnormal” profiles indicate an actual “anomaly” or “fault” in the system. In many other cases, those profiles look much different just because activities and operations in those buildings are rare or unique. For example, the first building, shown in Fig. 12, has a strange trend where demand increases from Monday to Saturday and also has inconsistent daily variations where some days have higher night loads. Further analysis revealed that this is a building used as a restaurant and a nightclub. The nightclub is open on Fridays and Saturdays, which explains the high heat demand in those days. The low heat loads on Sundays also indicate that there are not many customers on that day. Even though this building can be considered as “nonconformant,” it cannot be considered as an actual anomaly from the application perspective. The fourth component, active learning, focuses on incorporating feedback from domain experts so that the anomaly detection algorithm can better distinguish between these “irrelevant” anomalies and “anomalies of interest.” The general approach works as follows. In the beginning, one assumes not to have any labeled instances, and therefore, the initial deviation detection is done in a purely unsupervised fashion. Then, the commonly used uncertainty sampling active learning strategy (Calikus et al. 2019) is employed. In each step, SAFARI selects the example with the least certain predicted label and asks the user to label that example. In our setting, this entails selecting the example whose anomaly score is closest to

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Fig. 12 Examples of abnormal heat load profiles

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the threshold that separates anomalies from the normal samples. After receiving the feedback, SAFARI updates its model parameters, and the loop continues until a specific budget of queries is spent. The goal of this approach is to maximize the total number of “anomalies of interest” to be presented to the expert.

Outlook and Conclusions To summarize, there is much fascinating development happening in Big Data for smart cities. As more intelligence is being added to existing urban systems, the needs of the citizens are being addressed more efficiently and adaptively. The smart city encompasses not only the physical infrastructure but is mainly built on human factors and centered around social interactions. The new key to smart cities is the smartphone. Those relatively new devices that we can now barely live without form the interface for the citizens to access and control the digital world around them. At our fingerprints, we can have free access to all the information that the city makes available and can make use of all the services different actors provide. Still, the core of the smart city is not the technology itself, but rather the desire to provide better support for citizens. In this context, it is clear that supervised machine learning and solutions designed to address a single, narrowly defined task does not allow us to achieve the long-term vision. The unprecedented availability of data is the enabler for smart cities, but it is also critical to understand the limitations of this data – and the main limitation is lack of labels. The data described the current operation of the city, not the operation that is desired. Based on these observations, it is clear that the next generation of smart cities requires systems that can build toward autonomous and semi-autonomous “knowledge creation.” They can self-improve and adapt to changing conditions and expectations. They must handle situations that were not anticipated during their design. Such construction of knowledge can be illustrated with the Data, Information, Knowledge, and Wisdom hierarchy. It requires collecting and representing the data; creating relevant “events” from this data; generating rules that can combine information from different sources; and finally, the ability to project into the future and reason back into the past. As a final note, at Halmstad University we run a research center (CAISR) that focuses on autonomous knowledge creation, using AI and machine learning for predictive maintenance for both humans and machines. There are strong similarities between these two superficially different application fields. Some things are easier in the medical domain, for example, getting more accurate health estimates and diagnostics from experts, others are easier in the machine domain, like listening and collecting onboard data for a considerable fleet. The COVID-19 pandemic can be seen as an analogue to what vehicle manufacturers call “a quality issue,” an unexpected increase in poor health (or death) of some “systems.” The task, in the context of smart cities, is similar whether we are talking about people or equipment:

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to detect it as quickly as possible, build up knowledge about the details (e.g., who gets severe problems and why) so that it can be contained, and start deploying a fix. There is still plenty of room for improvement since our current techniques for Big Data analytics are not capable of coping with a problem of that complexity.

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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Characterizing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data in Vs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Characterizing Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Applications and Urban Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Integration, Interoperability, and Coupling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Application Interactions in Big Data Contexts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interoperability Frameworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The NIST Big Data Interoperability Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Layered Interoperability Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Big Data Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Where Should Be Interoperability Headed? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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The world is experiencing an explosive data growth, making big data an increasingly important field. Particularly in complex contexts such as smart cities, big data is no longer limited to analytics on large data sets. Small data produced by a large number of devices (Internet of Things) also lead to large data sets, with the added difficulty that most of these data can be produced, and may need to be processed, in real time. In either case, the interoperability problem (enabling several applications to interact and work together) is one of the hardest challenges to overcome. This chapter characterizes big data, with variety and variability as the most important characteristics for interoperability, and interactions between applications, distinguishing data integration from application interoperability. J. Delgado (*) Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_48

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This chapter also presents two interoperability frameworks, one focusing on architectural issues and the other concerning different abstraction levels of application interaction. Several of the most relevant standards in this area are listed, with a discussion on their limitations and the possible evolution of interoperability, in particular, in big data contexts.

Introduction A smart city (Mohanty et al. 2016) can be defined as a high-density conglomerate of people and infrastructures that use software services, based on information and communication technologies, to improve the efficiency and competitiveness of urban operations and services, the sustainability of social and environmental aspects, and generally the quality of life of its inhabitants. At the end of 2018, IDC (International Data Corporation) published a forecast report on the digitization of the world, encompassing the period from 2018 to 2025 (Reinsel et al. 2018). This is a very important report for smart cities, outlining pertinent predictions. As a side note and to clarify terminology, IDC uses “digitization” in the sense of digital transformation (Vial 2019), which can be defined as the use of digital technologies to transform a business model to provide more efficient processes and improved customer experience. Others, such as Gartner (2020), prefer the term “digitalization,” reserving “digitization” for the conversion from analog to digital form. This may include business processes (digital enablement), as long as the changes made are more adaptations to the digital format than a real transformation to support new business models, made possible only by the possibilities of digital technologies. The IDC report divides the global datasphere (the data created, captured, and replicated in 1 year across the world) into three areas of influence: • Core: Cloud computing providers (public, private, and hybrid) and other onpremises, enterprise datacenters. • Edge: Computing infrastructures regionally distributed, including cell towers, branch offices, and gateways, to support aggregation and dissemination of data, as well as to provide better local response times and improved end-user experience. • Endpoint: Terminal devices, such as personal computers and smartphones, sensors, actuators, connected cars, and wearables. The network of devices not used mainly for user interaction is generally known as the Internet of Things (IoT) (Qian et al. 2019). One of the key findings of this report is the increasing importance of the role of the edge and endpoints, gathering, and processing data to support the analytics, intelligence, and real-time decisions to provide faster and better services. This is one

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of the main objectives when designing smart city infrastructures. With the digital transformation of the enterprises and society, in general, progressing at a fast pace, increasing demand for real-time services, IDC estimates that roughly 30% of the global datasphere will be real-time by 2025. If data are created/captured at the endpoints and processed locally at the edge, the core is becoming increasingly important not only to store data (as users rely more and more on the cloud for storage), but also to process it for data analytics and to support management and governance decisions. IDC estimates that by 2025 about 49% of the world’s stored data will reside in public clouds. Another fundamental prediction of this report is the exponential evolution of the size of the global datasphere from 33 ZB (zettabytes) in 2020 to 175 ZB by 2025. One zettabyte is 1021 bytes, or 1000 exabytes, one million petabytes, or 1 billion terabytes. There is only one byte multiple left in the International System of Units (SI), the yottabyte (1000 zettabytes). Soon, some more will be needed! As world population rises, the percentage of people actively using the Internet increases, and the quality of data (such as image and video resolution) improves, the global datasphere can only enlarge exponentially. The 2020 version of “The Internet Minute” (Lewis 2020) presents staggering numbers concerning the worldwide interactions of Internet users in just 60 s! Internet World Stats (2020) estimates the world population to be around 7.8 million people by the end of Q1 2020, with approximately 4.65 million Internet users (a penetration rate of 59.6%). IDC (Reinsel et al. 2018) estimates the number of Internet users to rise to around six million by 2025, or about 75% of the world population. IDC also predicts that in 2025 there will be, on average, one data interaction per capita (Internet user) every 18 s. This will have a large contribution from the billions of IoT devices globally connected, with are expected to create over 90 ZB of data in that year (roughly half of the datasphere). Therefore, connected people increase data, and the availability of data and of the services they support bring additional users to the Internet, increasing the penetration rate, in a virtuous circle. Largely due to IoT and the services it enables, the most active part of the datasphere, where most data is created, is located close to people, which means (smart) cities. According to the United Nations (2018), 55% of the world’s population was residing in urban areas in 2018, up from 30% in 1950, with a projection of 68% by 2050. The most populated urban area in 2020 is Tokyo, with more than 37 million inhabitants, and the top-10 world’s most populated urban areas have more than 20 million people, or almost (World Population Review 2020). Datasphere density is therefore not uniform across the globe, but highly correlated with population density. Data are created where people live, increasingly concentrated in urban areas. This is the reason why it is so important to make cities smart and to provide better and more efficient services to their citizens. The data explosion acknowledged by the IDC report has been going on for years and gave rise to the designation “Big Data” (Berman 2018). Although there is no single universally accepted definition, it entails very large quantities of data, usually

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produced at a very high rate and/or from a vast number of sources with a wide variety of formats. The sheer size of the problem is a problem in itself. MarketsandMarkets (2020) forecasts that the global big data market will grow from USD 138.9 billion in 2020 to 229.4 billion by 2025, with a compound annual growth rate (CAGR) of 10.6% in this period. The IoT in smart cities market is projected by MarketsandMarkets (2019a) to have a CAGR of 22.5% in the 2018–2023 period, from USD 79.5 billion to USD 219.6 billion. MarketsandMarkets (2019b) also expects the global edge computing market to grow from USD 2.8 billion in 2019 to USD 9.0 billion by 2024, with a CAGR of 26.5% in this period. While these numbers are impressive and highlight the importance of all these issues, it seems that estimating the number of devices connected to the IoT is a much more difficult task. The IDC report (Reinsel et al. 2018) predicts 150 billion devices by 2025, without clarifying which types of devices are included. IDC (2019) made a more recent forecast, in which the estimate for 2025 is 41.6 billion devices, generating 79.4 ZB of data (instead of the 90 ZB of the previous report). By reading blogs and experts’ opinions, other estimates appear for the number of IoT devices in 2025, such as 21.5 billion by Statista, 64 billion by Gartner, and even a mention of 200 billion by Intel. It is apparent that nobody knows exactly how this number is going to evolve (or even specifically how many IoT devices are there today), particularly, since not all analysts count the same types of devices in the same way. One thing is clear, though. The number of IoT devices exceeds the number of human Internet users by a factor of four or most likely greater, which means that today’s Internet no longer belongs to people, but rather to computer-based systems that need to interact to cooperate and to produce added value to the society. All these numbers and developments are both exciting, for the opportunities, and frightening, for the challenges, since they anticipate that there are many problems to solve. One of the main challenges is how to make all these IoT devices interoperable, especially in big data and smart city contexts, so that cooperation is possible and effective. Interoperability (the ability of systems to exchange data in a meaningful way) constitutes the focus of this chapter.

Characterizing Data Data in Vs The first reference to the term “Big Data” was made by John Mashey in a talk given in 1998 at Stanford University, presented the following year at the 1999 USENIX Annual Technical Conference (Mashey 1999). In 2001, Douglas Laney introduced the first 3Vs by which big data is usually characterized: volume, velocity, and variety (Laney 2001). This means too much

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data to process, created too fast, and with too many different formats. In a sense, a big problem caused by the data deluge of today’s highly digital world. Dealing with all these data presents many challenges, including capture, fusion, wrangling (format transformation), integration, curation (organization for preservation), storage, processing, indexing, search and retrieval, analysis, visualization, and management. These challenges must be overcome to be able to cope with all the raw data and to extract value (information, knowledge, and insight) from them, to support from basic day-to-day operations to governance-level strategic decisions. More V-words appeared to express these challenges (Kapil et al. 2016; Khan et al. 2018). There is no universally accepted set, but it may include the following: • Volume: This is probably the most obvious characteristic, given the exponential growth of data created by the digital society (Dufva and Dufva 2019). Most of these are text, images, video, and other unstructured data, but still needs to be processed. Smart cities are data generators by excellence, given the sheer size of infrastructures and sensors (Sobin 2020), all producing data that needs to be analyzed and acted upon. • Velocity: This indicates how fast data production is and how fast data needs to be analyzed. While volume may be the main concern in data mining, taking the time that analytics require, a context such as a smart city involves many real-time data producers and consumers. IDC (Reinsel et al. 2018) predicts that by 2025, with the increasing digital transformation of businesses and of people’s lifestyles, nearly 30% of the data created will be processed and consumed in real time. The challenge is then how to process large volumes of data so fast that it can be considered real-time. • Variety: This is another major challenge to add up to volume and velocity. It concerns the fact that not all data are created equal but rather in a plethora of different formats. There is a huge diversity of data producer types, models, versions, manufacturers, and so on, not only from physical devices such as sensors but also from software applications and services. Given the interactions needed for functionality, this correlates with a probably even higher diversity in the consumer side (actuators, applications, services). Heterogeneity if therefore a natural and intrinsic characteristic of big data, constituting a true obstacle that conspires to hamper the smooth interoperation of the multitude of systems and subsystems that constitute a smart city. Unfortunately, without interoperability, a city cannot be smart (Sharma 2019), which is the main motivation for this chapter. • Veracity: This indicates how trustworthy data is, in terms of providing complete, accurate, reliable, consistent, and timely data. In human terms, fake news come to mind, but in a more computer-oriented perspective, involving a sensor or a business process, the quality of data with regard to the above aspects is fundamental. Great care must be undertaken to ensure that decisions, either at the operations, management, or governance levels, are based on factual, high-quality data. • Value: This expresses the relevance of data to some subsystem or process. Data are fundamental to support decisions. Some data will be crucial to some

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subsystems or processes, and irrelevant or less valuable to others. Data will usually lose value with time, in particular, those with high velocity. Value can also increase with time, if it is derived from analytics applied to an increasing number of data that help to consolidate the perception of reality. Variability: This expresses how fast data change, not only in terms of variety (new or evolving formats), but also in terms of all other aspects, namely, volume and velocity. This will most likely have impact on provisioning of resources (problem that cloud-computing platforms may help alleviate) and on applications and processes, which may need to adapt to changing data. This characteristic, therefore, indicates the speed of data changes, or, if applied to data production and consumption, the acceleration (with velocity expressing speed). Variability and velocity are thus different, albeit related, concepts. Validity: Data are valid if applicable to some application or service, by fulfilling its requirements. This is not the same as veracity. Some data may be factual and trustworthy, but fail to comply with a given set rules. Regulatory compliance (ElGhalayini and Al-Kandari 2020) is an obvious example, but at a lower level other examples spring to mind, such as a thermometer sensor with readings in a Celsius scale sending data to an application requiring a Fahrenheit scale, or outside the supported range. Validity is crucial for interoperability. Volatility: Time is essential to the lifecycle of data, since several of their associated characteristics, such as veracity, value, and validity, may change with time. Highly volatile data, typically those with higher velocity, may lose these characteristics quickly, since the new data produced will gain focus of interest, in detriment of previous data. Volatility will also influence the retention time, i.e., the period during which data will be stored before being discarded or eventually relegated to a data lake (Khine and Wang 2018) to feed data analytics (Jane and Ganesh 2019). Data lifecycle management has to take the volatility characteristic into account (El Arass and Souissi 2018). Visualization: This characteristic entails reducing the complexity of big data, focusing on important patterns, trends, or conclusions and presenting relevant information and knowledge in a pictorial or graphical form, in order to maximize the readability and perception by key stakeholders. This is more a human-related need (to trade complexity by perception) than an intrinsic characteristic of data, although nevertheless, it needs to consider the other characteristics to optimize the gains of visualizing data macroscopically. Venue (or Vastness): This is variety in cyberspace. Data can be produced, processed, and consumed in a wide diversity of platforms and geographical areas. This can affect the data context, including data ownership and legal issues, possibly including regulatory compliance (El-Ghalayini and Al-Kandari 2020) and GDPR (General Data Protection Regulation) (van der Sloot 2020). Vagrancy: This is variability in venue. Complex systems such as smart cities are constantly changing, in systems, applications, platforms (such as clouds), and actors. If interoperability between smart cities is considered, the size of the problem increases further. Changes in where data is dealt with, in its various slants, will hardly be transparent, which means that this is a characteristic of data

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that simply cannot be ignored. Fortunately, these changes are likely to happen at a much slower pace than in data variability. • Viscosity: Some authors (Kapil et al. 2016) describe this characteristic as the lag time between data production (e.g., an event) and its consumption (viscosity in time), but others (Khan et al. 2018) describe it as the degree of correlation and interdependencies in big data structures (viscosity in space), which software engineers know as coupling. Although coupling seems to receive little attention in the big data field, it is actually one of the main problems in data integration. If coupling between different data is significant, a small change in some data format may very well imply many changes in other data formats and in the applications or services that process them, in a domino effect. • Vagueness: Data should not be vague, but the fact is that data can be incomplete, ambiguous, or not always consistent, at least from the point of view of the consumer’s requirements, thereby making validity difficult. Specifications, even standards, can be vague in some aspects, leading to different interpretations on the part of implementers. This is another item to add to the challenges to overcome in order to achieve interoperability. There are even more big data characteristics than these (Khan et al. 2019; Sundararajan et al. 2020), such as vocabulary (data terminology or ontology), valence (expressing the degree of data connectedness in a graph), virality (how quickly data spreads between their producer and consumers), viability (in the sense of relevance for analytics, or some other pertinent purpose), vulnerability (susceptibility to attacks) verbosity (data/information ratio), and versatility (the ability to be used in different contexts, although this could be considered more an interoperability issue than a data characteristic). It seems that finding additional relevant words starting with V has become a challenge on its own. The meaning of these characteristics is also not entirely consensual, particularly in the case of the latter ones, since they depend on the purpose and context in which big data processing is used. In any case, these are just different perspectives on data, focusing on specific aspects. They may be useful for systematization, but they do not define what a smart city is, nor how it should produce, process, consume, and manage data. In spite of all these aspects, data is just one slant of the life of a smart city. This chapter concentrates on variety, since it influences heavily interoperability between systems and applications, particularly with the complexity and diversity that characterize a smart city.

Data Structure For a consumer to process data, it has to understand the information they convey. Nontrivial data are not atomic, or monolithic, but have a structure, recursively composed of less complex data until primitive data (e.g., numbers, strings) are reached. In addition, there are rules, or constraints, that prevent all possible primitive

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data combinations from being generated by a data producer and from having to be expected and dealt with by a data consumer. These constraints, which define what data are and how they should be interpreted, are known generally as metadata (data that describe data) (Gartner and Gartner 2016) or, in a more technical designation, as schema (a description of the constraints that specifies the set of admissible data values) (Guha et al. 2015). A database schema specifies which data structures and relations it can describe. The same is true in message-based interactions between data producers and consumers. For example, if a sensor in a smart city sends a reading to a controller, or if the latter sends a command to an actuator, both sender (the data producer) and receiver (the data consumer) need to know the schema (structure and constraints) of the message’s data to be able to interoperate. Otherwise, the consumer will not be able to understand the information that the producer is trying to convey and interoperability will not be possible. Figure 1 expresses a typical interaction scenario, in which both interacting parties share the schema of the data they exchange. Message and network should be interpreted liberally, since a data interaction does not have to involve sending an actual message. Data can be produced, stored somewhere (e.g., a database) and then read and consumed sometime later. In any case, producer and consumer must know the data schema. The classical, and still prevailing, perspective on data structure entails the following categories: • Structured data: Primitive data types such as strings, names, and dates organized in tabular form (tables, rows, and columns) in a database or spreadsheet. This is the realm of SQL databases, in which data are tightly controlled under a relational schema. • Semi-structured data: Data with a flexible and arbitrarily complex structure, usually tree-shaped rather than tabular, expressed in data description languages such as XML (Fawcett et al. 2012), JSON (Bassett 2015), or RDF (Kaoudi and

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Producer (e.g., sensor) Fig. 1 A typical producer-consumer data interaction, sharing a schema

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Manolescu 2015). These data will be accompanied by a self-description, expressed in schema description languages such as XML Schema (Walmsley 2012), JSON Schema (Pezoa et al. 2016), or RDF Schema (Kaoudi and Manolescu 2015). NoSQL (“not only SQL”) databases (Meier and Kaufmann 2019) fit here, as well as schema-based messages exchanged between interoperable systems in a smart city. • Unstructured data: Data with a very low metadata/data ratio, typically files, such as text, audio, images, and video or a mix (e.g., emails, news, social media). Industry analysts estimate that only roughly 20% of the produced data is structured, (Giudice et al. 2019), with 80% left as a challenge to exchange, integrate, and process. It is important to notice that truly nonstructured data is noise, since there is no recognizable structure that a consumer can act upon. In this context, even unstructured data has a defined structure. A JPEG image file, for instance, has a well-known format (a standard, in fact). The distinction provided by the categories above is not absolute and expresses only decreasing ratios of metadata/data in the context of the envisaged application. For most applications, a JPEG image will be just an image, with little interesting metadata apart from file size and image resolution, and therefore no externally visible structure. However, a vision recognition application can recognize and make use of a complete set of details regarding the image’s internal structure. Structure can be an intrinsic property of data but, at the end of the day, its usefulness lies in the eye of the beholder. In the context of smart cities, all types of structure are important. The setting is not as tight as in an enterprise information system with a relational database at the heart, since a smart city is a complex, dynamic, and ever-changing system, but it needs a good level of recognizable data structure, so that smart applications can process data and provide the desired functionality. In a smart city’s information system, NoSQL databases (Meier and Kaufmann 2019) must be contemplated, and data from sensors and other IoT devices, most likely expressed in JSON (Bassett 2015) and exchanged through RESTful APIs (Subramanian and Raj 2019), must be central to the design.

Characterizing Interactions Smart Applications and Urban Computing An enterprise information system is designed around a database and deals essentially with data (costumer records, product details, invoices, etc.) stemming from operational business processes. A smart city is a much more complex ecosystem, centered on citizens, on the services they need and on the data useful for improving the quality of life.

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The interaction between citizens and a smart city is both virtual, in the usual browsing sense, and physical, taking advantage of the concentration of people and infrastructures to provide services such as smart government and governance, automated traffic management, connected vehicles, smart public transportation, environment control, smart street lighting, smart homes and buildings, smart surveillance, and, in general, city data analytics (▶ Chap. 5, “Smart Cities Data: Framework, Applications, and Challenges” by Bilal et al.). All these services are made possible by the data generated by the myriad of installed sensors and used to control many actuators and interactive displays, all in a number and density only possible in a city. There is no unique and central database. Actually, a substantial part of the state of the system is held by citizens themselves, simply because they move and interact with the system in varied locations at different times. This is why the processing of data in this type of contexts is usually referred to as urban computing (▶ Chap. 4, “Urban Computing: The Technological Framework for Smart Cities” by Bouroche and Dusparic). Smart city applications need to be much smarter and more adaptive than the classical and rigid business logic, since a smart city is almost a living being, constantly changing its goals, needs, and requirements, precisely because it is focused on people, thereby making continuous adaptation mandatory. In addition, a smart city encompasses a huge variety of applications and of the organizations that create and manage them. All these issues contribute to turn the integration of heterogeneous data and the interoperability between different applications into a true challenge that must be overcome to ensure smooth performance of a smart city.

Integration, Interoperability, and Coupling Although these terms are related, they have different meanings and should not be used interchangeably. In addition, they can be applied to different contexts. Berman (2018) defines data integration as the process of gathering data from different sources, in a way that preserves the identities of data objects and their relationships, to form a common dataset. This is a typical operation when merging data from different datasets, or databases, under a single view. Still according to Berman, interoperability allows two applications to work together. It makes no sense do talk about data interoperability, since they are passive and do not (inter) operate. However, applications can also be integrated. Panetto and Whitman (2016) define application integration as the act of instantiating a given method to design or adapt two or more applications, so that they can cooperate and accomplish one or more common goals. These applications then become integrated into a larger application, even if only virtually. Integration can in fact be seen at all levels of abstraction and complexity, from low-level cyber-physical systems (Garcia Alvarez et al. 2019) up to high-level enterprise value chains (Kanade 2019), targeting capabilities such as those required by smart cities (Jamous and Hart 2019).

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Interoperability (Leal et al. 2019) is defined by the ISO/IEC/IEEE 24765 standard (ISO 2010) as “the ability of two or more systems or components to exchange information and to use the information that has been exchanged.” This means that merely exchanging data is not enough. Interacting applications must also be able to understand the information conveyed by those data and to react according to each other’s expectations. Several frameworks have been proposed to systematize interoperability (Chituc 2019; Delgado 2019). Just like integration, interoperability can be considered from low up to high levels of abstraction. Interoperability is therefore a capability of a set of two or more interacting applications, whereas integration is a process that can yield or support interoperability as a result, or increase its level of abstraction. To be interoperable, both producer and consumer of the data exchanged need to agree on the schema of those data, as shown by the basic interaction of Fig. 1. The producer needs it to know which data format to generate and the consumer needs it to be able to read the individual data components (its structure). The problem is that this entails dependencies and constraints between producer and consumer. If one of them tries to evolve and change the schema, the other one needs to change it too. Software engineers know this interdependency as application coupling. Coupling (Fregnan et al. 2019) provides an indication of how much applications depend on each other. Some degree of coupling is unavoidable, since some form of mutual knowledge is necessary to make interoperability possible. However, it should be as low as possible, as long as it does not hinder the interaction capabilities necessary to support the required functionality. This is particularly true in contexts such as the IoT in smart cities (Qian et al. 2019), in which the number of interacting entities (applications and devices) is huge, variability is high (many different and evolving types of devices), and limited computer power does not allow resorting to complex interoperability solutions. The greater the degree of connectivity, the more relevant the coupling problem becomes. Decoupling translates into higher changeability, adaptability, reusability, and reliability. Tuning it to the right degree in practice, however, is not an easy task. In general, the fundamental problem of distributed application design, in terms of interaction, is how to provide (at most) the minimum possible coupling while ensuring (at least) the minimum interoperability requirements. Each interacting application should know just enough about the others to be able to interoperate with them but no more than that, to avoid unnecessary dependencies and constraints.

Application Interactions in Big Data Contexts There are two main perspectives with regard to the interaction between big data producers and consumers, by using a persistent store or a transient message: • Repositories: Large data sets, used not only to support analytics, in which data are analyzed using statistical, machine learning, and data mining techniques

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(Soomro et al. 2019) to provide a basis for high-level planning and decisions, but in many cases also to support operational processes. • Messages: Individual data, or small nonpersistent data sets, sent as they are produced and processed as they are received, to provide immediate or near realtime functionality, or to enable fast management or administration decisions. Repositories are usually centralized and able to store persistent data for long periods, according to their lifecycle, whereas messages are highly distributed and inherently transient. Repositories are large but limited in number, whereas messages are usually small but are produced and consumed in very large numbers and/or at a very high rate, particularly in complex contexts such as smart cities. Both types of data can be classified as big. There are several types of data repositories, including the following: • Data warehouse: Filled with high-quality (structured) data, typically obtained by ETL (Extract, Transform, and Load) operations to clean and integrate data from operational systems of an organization, it is used mainly for data analysis and reporting to support business intelligence. • Data lake: Used to store data in its natural format, including structured (relational databases), semi-structured (with a self-description schema), unstructured, and raw binary data. More flexible and less structured than data warehouses, data lakes are rather adequate to feed big data processing systems, such as Apache Hadoop (Wiktorski 2019), and to support analytics and machine learning algorithms. Since a data lake stores any kind of data, its needs good management to not grow uncontrollably, becoming a data swamp. • Data hub: A collection of organized and harmonized data from multiple sources, combining the high quality of data in data warehouses with the flexibility of data lakes of supporting multiple data formats. With a hub-andspoke architecture, its main usefulness is efficient data sharing and distribution across an organization, offering operational efficiency gains and driving agility. Typically smaller than data warehouses and data lakes, a data hub gathers key data from multiple sources and provides de-duplication, homogenization, and secure access. The main goal of analytics is to extract high-level information, such as patterns and trends, and to support strategic (governance-level), tactical (management-level), or even operational (administration-level) decisions. Real-time requirements, in terms of time needed to produce results, increase from governance to administration levels. Gartner defined a model with four types of analytics with increasing levels of maturity (Prabhu et al. 2019): • • • •

Descriptive: What happened? Diagnostic: Why did it happen? Predictive: What will happen? Prescriptive: How to make it happen?

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However, in the context of smart cities, analytics is just one slant of the big data field. Much of the data is created by sensors at the endpoints in the datasphere, processed by applications at the edge and consumed by actuators, again at the endpoints (Reinsel et al. 2018). For analytics, including large datasets collected from sensors, data integration is fundamental, since data from a wide variety of sources must be merged and transformed so that analytics know how to process them. Governance-level decisions use the result of analytics, typically without real-time requirements, whereas management-level decisions may need real-time analytics. However, in the applications and services that support the operational functionality of a smart city, what counts are the individual interactions between sensors, gateways/controllers, applications, and actuators, where real-time interoperability is of crucial importance. This is where the variety and variability characteristics of big data have its most impacting effect. With a wide variety of sensors, actuators, controllers, gateways, and so on, coupled with a huge diversity of services, which on top of all this tend to evolve rapidly, interoperability in big data has become a big problem on its own. All data have producers and consumers. In repositories, the main problem is data integration, since typically data come from various sources, with different schemas. Messages are mostly used in operational data interactions (such as in Fig. 1), which means that the main problem is producer–consumer interoperability, with two main conceptual approaches: • M 3 N: M producers need to interact with N consumers or point-to-point interactions. Interoperability needs to be setup for each producer–consumer pair, with potentially M
N different combinations. • M + N: A mediator performs the necessary adaptations and schema mappings, so that each producer and consumer has to deal only with another entity (the mediator), with M + N interoperability relations. The mediator can be centralized (data hub, in a hub-and-spoke architecture) or decentralized (service bus). Point-to-point seems a poor solution, since the number of relations grows quadratically with the number of different data schemas. In fact, for quite some time, mediator-based solutions, such as the ESB (Enterprise Service Bus) approach (Aziz et al. 2020), were preferred in the field of enterprise application integration, with relational databases as the core data and SOA (Service-Oriented Architecture) as the main architectural style (Niknejad et al. 2020) to support integration. With the advent of big data, largely unstructured, the REST architectural style (Fielding et al. 2017) and RESTful APIs (Subramanian and Raj 2019), cloud computing, small-grain services (microservices) (Di Francesco et al. 2019), and lightweight deployment techniques (containers) (Casalicchio 2019), decentralized solutions, without a central mediator, became attractive again. As usual, there is no one-size-fits-all solution. In the case of smart cities, analytics and database-style applications will most likely continue to use centralized, repository-based solutions, but the interaction between the myriad of sensors, actuators,

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and the applications that manage them, with the inherent variety of schemas, will need point-to-point approaches. In addition to data integration and application interoperability, coupling is affected by the approach taken in matching data schemas between producer and consumer, so that integration and/or interoperability become possible (Janković et al. 2018): • Schema-on-write: The data schema is the responsibility of the producer, which must first define a schema, then conform the data to that schema and finally write those data in a repository or send them in messages. Consumers need to use that schema, if they want to read those data. This is the classical approach, namely, in relational databases, guaranteeing high-quality, structured data. The drawback is poor support for data variety or variability, since the data schema tightly couples both producers and consumers. The interactions in heterogeneous systems, involving different schemas of several producers, is also difficult. This approach is preferable when one producer and many consumers are involved in repetitive interactions without frequent schema changes. • Schema-on-read: The data consumer is now the responsible for the schema it uses. The producer creates data in raw form and it is up for the consumer to decide how it wants to interpret those data, by applying its own schema. This approach appeared as a consequence of the increasing relevance of semi-structured and unstructured data, big data, analytics, and machine learning. Most data are no longer as structured as in a relational database, variety has increased, and flexibility became a necessity. Schema-on-read is adequate to explore data lakes, or when consumers deal with data from varied sources, with varied formats, and/or with frequent changes. Many big data tools and frameworks support combining the two approaches, by using schema-on-write to deal with structured data with a known schema, and schema-on-read for semistructured and unstructured data, thereby tuning the approach to the available data and envisaged applications.

Interoperability Frameworks A framework can be defined as a set of principles, assumptions, rules, and guidelines to analyze, to structure, and to classify the concepts and concerns of some topic or domain. This section illustrates two frameworks, shedding light on big data interoperability essentially from two different perspectives, the architecture of applications interacting in a big data system and message-based application interaction.

The NIST Big Data Interoperability Framework The National Institute of Standards and Technology (NIST), one of the leading organizations in the development of standards, has produced the NIST Big Data

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Interoperability Framework (NBDIF) (NIST 2019), with the collaboration of more than 600 experts from industry, academia, and government. The NBDIF intends to help systematize the underlying concepts of big data and data science, with the aim of enhancing communication and tool interoperability between big data producers and consumers, tools, and platforms, in a vendor-neutral perspective and in a technology- and infrastructure-independent fashion. It is organized in nine volumes, covering different aspects of the framework: • • • • • • • • •

Volume 1: Definitions Volume 2: Taxonomies Volume 3: Use Cases and General Requirements Volume 4: Security and Privacy Volume 5: Architectures White Paper Survey Volume 6: Reference Architecture Volume 7: Standards Roadmap Volume 8: Reference Architecture Interfaces Volume 9: Adoption and Modernization

The focal point of this framework is the NIST Big Data Reference Architecture (NBDRA), described in volume 6. A reference architecture (Behara 2019) is a template specification, including a common terminology, reusable designs, proven architectural principles and patterns, standards, and generally industry-tested best practices. It serves as a guide for architects, designers, and implementers. By following the reference architecture guidelines, not only they benefit from proven best practices but they also contribute to systematize a given domain and to foster interoperability between actors, tools, and platforms. These are precisely the goals of NBDRA, in the field of data science, specifically targeted at the implementation of tools and platforms capable of dealing with big data. Figure 2 depicts the NBDRA, in a simplified way, adapted from NIST (2019). Since the NBDRA is just a reference architecture, it needs to be implemented to yield an actual big data system. This can be done in many ways depending on the design configuration and implementation of architectural components. The NBDRA definition followed an extensive survey of several big data architectures (volume 5), from both industry and academia, trying to identify common features, and identifies the following main roles performed by the actors involved: • System orchestrator: Provides the requirements that the system must meet at several levels (including policy, governance, architecture, resources, and business), the high-level design guidelines, and monitoring activities to ensure that the system complies with those requirements and guidelines. • Data provider: Produces raw data or makes available transformed data created by another source. It can be part of the big data system or belong to another organization. • Big data application provider: Executes the manipulations of the data life cycle required to comply with the requirements established by the system orchestrator,

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by encapsulating the business logic and functionality that the architecture must perform. This includes activities such as data collection, preparation/curation, analytics, visualization, and access. • Big data framework provider: Provides general resources or services to be used by the big data application provider in the creation of the specific application. It consists of one or more hierarchically organized instances of infrastructure frameworks, data platforms, and processing frameworks, for which it provides support functionalities such as messaging and resource management. The various instances at a given level in the hierarchy need not be of the same technology. Hybrid implementations can combine multiple technological approaches. • Data consumer: Receives the data output by the big data system, mirroring the data provider. For the latter, the entire big data system appears to be a data consumer, and to the data consumer the entire big data system appears as a data provider. The data consumer can perform activities such as data search and retrieval, local analysis, reporting, and visualization. In Fig. 2, the data arrows show the flow of data between these roles, the SW arrows show transfer of software tools for big data processing in situ, and the use arrows represent software programmable interfaces (services).

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In addition to these five main roles, the NBDRA includes two axes, representing value chains: • Information value chain (horizontal axis), along which value is created by data collection, analysis, and transformation. • Information technology (IT) value chain (vertical axis), along which value is created by providing networking, infrastructure, platforms, application tools, and other IT services for supporting data applications. Implementations of the NBDRAs can be stacked along the vertical axis or chained along the horizontal axis. For example, one big data system can be the data provider, or the data consumer, of another. A big data system can implement the NBDRA with multiple instances of elements performing any of the roles described above, except the system orchestrator. Different applications can use different frameworks, with, for example, one application focusing on streaming analytics and another one performing data warehouse-style batch analytics. Overall, many variants are possible when implementing the NBDRA. This gives ample room for enterprises to compete and produce different big data systems, for application domains such as smart cities, while exhibiting the same underlying architectural principles. This is not a guarantee of seamless interoperability, but certainly constitutes a step forward in facilitating the interoperation of different systems and applications.

A Layered Interoperability Framework Rather than looking at a reference architecture, or an ecosystem of applications with a recommended global organization to make interoperation easier, interoperability can also be seen in terms of the data exchanged between applications, particularly in message-based interactions. Each interaction can be defined and expressed at various levels of abstraction, with a classification detailed by Table 1, which in practice corresponds to a simple, layered interoperability framework (Delgado 2018). The first column of Table 1, as well as its subdivisions in the second column, should be interpreted as follows: • Symbiotic: Expresses the purpose and intent of two interacting applications to engage in a mutually beneficial agreement. This can entail a tight coordination under a common governance (if the applications are controlled by the same organization), a joint-venture agreement (if the two applications are substantially aligned), a collaboration involving a partnership agreement (if some goals are shared), or a mere value chain cooperation (an outsourcing contract). Smart cities applications are usually at the topmost level in application interaction complexity, since they extend up to the human level, with governance and strategy heavily

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Table 1 Levels of interoperability in a message-based interaction Category Symbiotic (purpose and intent)

Level Coordination Alignment Collaboration Cooperation

Relationship Governance Joint-venture Partnership Outsourcing

Pragmatic (reaction and effects)

Contract Workflow Interface

Choreography Process Service

Semantic (meaning of content)

Inference Knowledge

Rule base Knowledge base Concept

Ontology Syntactic (notation of representation)

Structure Predefined type Serialization

Connective (transfer protocol)

Messaging Routing Communication Physics

Schema Primitive data Message format Message protocol Gateway Network protocol Media protocol

Description Motivations to have the interaction, with varying levels of mutual knowledge of governance, strategy, and goals Management of the effects of the interaction at the levels of choreography, process, and service Interpretation of a message in context, at the levels of rule, known application components and relations, and definition of concepts Representation of message, in terms of composition, primitive components, and their serialization format

Tacit

Empiric

Lower level formats and network protocols involved in transferring a message from the context of the sender to that of the receiver

involved. Therefore, they map mainly onto this category, although the same principles apply (in a more rudimentary fashion) to simpler applications. • Pragmatic: The effect of an interaction between applications is the outcome of a contract, which is implemented by a choreography that coordinates processes, which in turn implement workflow behavior by orchestrating service invocations. Languages such as Business Process Execution Language (BPEL) (Juric and Weerasiri 2014) support the implementation of processes and Web Services Choreography Description Language (WS-CDL) is an example of a language that allows choreographies to be specified. • Semantic: Interacting applications must be able to understand the meaning of the content of the messages exchanged. This implies interoperability in rules, knowledge, and ontologies, so that meaning is not lost when transferring a message from the context of one application to the others. Semantic languages and specifications, such as OWL (Cardoso and Pinto 2015) and RDF (Kaoudi and Manolescu 2015), map onto this category. • Syntactic: Deals mainly with form, rather than content. Each message has a structure, composed of data (primitive resources) according to some structural definition (its schema). Data need to be serialized to be exchanged as a message,

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using data description languages such as XML (Fawcett et al. 2012), JSON (Bassett 2015). • Connective: The main objective is to transfer a message from the context of one application to the other’s, regardless of its content. This usually involves enclosing that content in another message with control information and implementing a message protocol (such as SOAP or HTTP) over a communications network, according to its own protocol (including MQTT, CoAP, AMQP, XMPP, DDS, and at lower level ZigBee and Bluetooth) (Ejaz and Anpalagan 2019) and possibly involving gateways. It is important to recognize that all these levels are always present in all application interactions, even the simplest ones. There is always a motivation and purpose in sending a message, an effect stemming from the reaction to its reception, a meaning expressed by the message, and a format used to send it over a channel under some protocol. However, not all levels are dealt with explicitly, but rather only a range of the relevant levels between two thresholds (defined by the designers of applications), depicted in Table 1: • Tacit: This is the highest level, above which concepts are too complex or too difficult to describe in detail. It encompasses the tacit knowledge and know-how (Castro 2017) of the application designers. They express their insight and implicit expectations and assumptions about the problem domain in documentation, not actual software; • Empiric: This is the lowest level, below which details are not relevant. The application designers just settle for something that already exists and is known to work, such as a standard or a software library. The most frequent case in interoperability is to cater only for the syntactic category (or at most semantic or even pragmatic, with processes), with the connective category as empiric and the symbiotic category (or even lower) as tacit.

Big Data Standards Big data is a relatively new field, and standards are still scarce. Many organizations and standardization bodies have working groups in this area, including the following (Miloslavskaya et al. 2018): • • • • • •

The International Organization for Standardization (ISO) International Telecommunication Union (ITU) National Institute of Standards and Technology USA (NIST) Institute of Electrical and Electronics Engineers (IEEE) British Standards Institute (BSI) The International Electrotechnical Commission (IEC)

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Internet Engineering Task Force (IETF) The World Wide Web Consortium (W3C) Open Geospatial Consortium (OGC) Organization for the Advancement of Structured Information Standards (OASIS).

Although interoperability is not the single focal point, the following standards are the most recent and relevant in the context of this chapter, well worth mentioning: • • • •

ISO/IEC 20924 (ISO 2018a) – Internet of Things (IoT) – vocabulary ISO/IEC 30141 (ISO 2018b) – Internet of Things (loT) – Reference Architecture ISO/IEC 20546 (ISO 2019a) – Big data – Overview and vocabulary ISO/IEC 21823 (ISO 2019b) – Internet of things (IoT) – Interoperability for internet of things systems • ISO/IEC 20547–3 (ISO 2020) – Big data reference architecture – Part 3: Reference architecture. Note that the NBDIF (NIST 2019) is a framework (descriptive, advocative), not a standard (prescriptive, normative).

Where Should Be Interoperability Headed? Interoperability is one of the main challenges in big data processing applications, in general, and in smart city contexts, in particular, boasting a large body of literature on this topic. Yet, it remains an elusive goal, seemingly further apart from the status of a solved problem each time new applications appear and society evolves toward an increasing digitalization in all domains. Ideally, there should be only one universally accepted set of specifications, rendering interoperability a trivial problem. Alas, the world does not work that way. Two heads mean at least two opinions, and variety and variability are the norms. Standards are the best next thing, creating order and providing common ground, as long as they are widely adopted. There are two main types of standards: • De jure standards: Specifications defined by standardization bodies, such as the International Organization for Standardization (ISO) • De facto standards: Specifications defined by an innovative and/or heavyweight market player or consortium, which other players follow suit De jure standards are preferable, since they typically involve the collaboration of many experts, from industry and academia, and try to avoid sectorial interests. However, they take too long to define and tend to be the ashes of the fire of innovation, benefiting from previous attempts and experience in their domain, but frequently leaving little room for continued innovation. Unfortunately, sometimes they come too late, after the de facto standards have had time to set in.

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Another fundamental problem with standards is that, although very useful for interoperability, they can constitute a straightjacket and hamper innovation by, and differentiation between, different vendors. When a standard is successful, it will take not much long before vendors start offering new features and variants fueled by obvious marketing arguments and technological advances. Therefore, de jure standards are continuously playing catch-up, and even de facto standards have a growing difficulty to succeed in a world with increasingly faster technological innovation lifecycles. The standardization effort must continue, but it will never constitute a complete solution for interoperability. Where standards seem to be truly useful is below the empiric level (Table 1), since this involves established specifications, well below marketing arguments and innovation interest, and in which standardization of details is of primordial importance to software developers. This the case of communication protocols, for example. Frameworks, such as the NIST Big Data Interoperability Framework (NBDIF), also take long to develop. The NBDIF took 6 years to reach its actual stage (NIST 2019). However, since the main purpose of a framework is to guide, not to prescribe, it can start producing useful guidelines very early in the process, as well as continuously getting input and feedback from industry, academia, and government. Frameworks are particularly useful above the tacit level (Table 1), where the specifications of a system are fuzzier and hindsight, insight, and foresight are needed the most. In the middle, between the empiric and tacit levels, interoperability aspects are not established well enough to just follow a standard and frameworks do not supply enough details to just implement the guidelines. There are several approaches to tackle this gray area, namely, the following: • Schema sharing (Sharma et al. 2017): The applications exchanging data need to share the respective schema (Fig. 1). This is the most basic option but the most coupled one, typical of structured (relational) or semi-structured (XML- or JSONbased) data. • Mediation (Benaben et al. 2018): There is a middle application, or platform, that reads data from the producer and translates the data to the consumer, performing the role of a mediator. This is done by mapping the data schema of the producer to the data schema of the consumer. This is the most flexible option, but it can easily lead to an M
N problem. By using independent adapters on each side of the mediator, an M + N problem can be achieved, but this assumes that an underlying common schema is possible (the mediator’s own schema). The existence of a central role can also pose problems of performance, reliability, and coupling. • Compliance and conformance (Oemig and Blobel 2017): Every data schema has mandatory and optional characteristics. The schema used by the producer is structurally compared (characteristic by characteristic) with the schema of the consumer. If the former has all the mandatory characteristics required by the latter, they are compatible (the producer complies with the consumer) and interoperability is possible, by mapping the relevant characteristics of the producer’s

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schema onto the corresponding ones of the consumer’s and ignoring the others. If another consumer has a variation of the schema of the first consumer, including all its characteristics and exhibiting additional optional characteristics, interoperability is still possible and the new consumer is able to read the data meant for the first one (the new consumer conforms to the first one). When applications use a large dataset as the interaction data, interoperability may be preceded by the reduction of data, involving data fusion (integration of data from multiple sources to produce a more consistent and organized dataset), data wrangling (transforming the data to another, more appropriate format), and data cleansing (elimination of inconsistencies, errors, and duplicates). After this, however, the interoperability problem remains and an approach such as those described above needs to be applied. It is important to recognize that interoperability must be ensured at all categories and levels of Table 1, in particular, the syntax, semantics, and pragmatics categories, which typically sit between the empiric level provided by communication protocols of the connective category and the tacit level provided by the cooperation agreements of the symbiotic category. Schema sharing and mediation are the most classical and most used approaches, either when accessing a simple Web Service (Halili and Ramadani 2018) or RESTful API (Subramanian and Raj 2019). However, they rely on strict mapping rules, namely, at the syntax and semantics (ontology) levels. As variety and variability of data increases, interoperability must evolve and rely on more flexible mapping mechanisms, so that application coupling is reduced and the implementation of changes becomes easier. The schema-on-read approach described above (Janković et al. 2018), used to explore data lakes and data from varied sources, is a symptom of that trend. The underlying fundamental idea is to decouple the producer’s schema from the consumer’s schema. Unlike schema sharing, schemas can differ on the production and consumption sides. Unlike mediation, there is no intermediary application to make the schema conversion, or mapping, since the consumer uses is own schema, without having to know the schema of the producer. Ontology mapping (Ramar and Gurunathan 2016) needs to be considered. Two schemas may map structurally if they use the same ontology (terms) in the schema, but if the ontologies are different the schema terms must be mapped, and mediation is needed again. This is the recurrent scenario today, in systems that go as far as including semantic interoperability. The problem is that ontology standardization is also very difficult, particularly considering the huge variability of domains, contexts, and use cases. There have been advances, but since the size of problem (variety and variability of data) is continuously increasing, the goal of interoperability seems as elusive as ever. Since the advent of the Universal Turing Machine (Haigh and Priestley 2019), programmers know that arbitrarily complex tasks can be decomposed and mapped onto a set of primitive operations (the instruction set of the processor) by a set of

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structuring rules, valid for a given programming language and implemented by a compiler. The secret is to divide and conquer, mapping complex data into a set of primitive objects by an arbitrary complex structure that obeys a set of primitive structuring rules. In the interoperability field, standardizing or at least agreeing on high-level definitions (including ontologies) is elusive. Variety and variability will not go away, all these definitions will have to be mapped onto the others to ensure interoperability, and the problem will grow in complexity instead of becoming simpler and easier (Rahman and Hussain 2020). An alternative to defining a plethora of ontologies for each application domain or use case, at is being done today, could be to invest on the set of structuring mechanisms at various levels (e.g., syntax, semantics, and pragmatics), to build/ decompose complex data types from/into primitive data types. This could allow to use the lowest common denominator as the basis for interoperability, rather than trying to do it at higher levels. This would require data producers and consumers to be able to compose and decompose data based on this, i.e., an M + N problem. Easier said than done, naturally, and this would still not guarantee interoperability, since compliance and conformance would still have to hold (the producer and consumer would need to be compatible), but, much like a reference architecture, this approach could provide a common architectural ground to make interoperability simpler and to reduce application coupling at the same time. Only future will tell how interoperability will actually evolve.

Conclusion Application interoperability remains an elusive goal and an unsolved problem. The relentless and explosive data growth worldwide (Reinsel et al. 2018) does not help, and new complex systems such as smart cities also contribute to make interoperability not only more needed but also harder to achieve. There are two main perspectives on big data production: • Large datasets of operational and social data, structured and unstructured, from a relatively small number of sources and used essentially for business intelligence and analytics. In terms of interoperability, the most relevant issue is data integration, trying to organize data under common definitions, using transformations and data reduction operations. • A large number of small transient data, coming from a large number of producers, such as sensors and other IoT devices, and used for operational and management purposes. Message-based application interactions constitute the main interoperability problem. Naturally, combinations of both situations are not only possible but actually the usual case in smart city contexts.

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Standards are contributions to organize a domain, but are not enough to ensure a solution to application interoperability. Although not prescriptive, frameworks can also help, since they contain important guidelines and recommendations that, if followed by implementers, contribute to limit the diversity in specifications, methods, and techniques. This chapter presented two interoperability frameworks, one emphasizing architectural issues and describing how the components of a big data system should interoperate, and the other focusing on message-based interoperability at various levels of abstraction. Although these frameworks do not constitute interoperability solutions per se, the more the big data domain is conceptually structured, the greater the number of tools, platforms, and applications that will follow the recommendations, and the easier interoperability will become.

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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . From Privacy To Data Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Territorial Scope of The General Data Protection Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . General Data Protection Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What Is An EU Regulation? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Structure of the Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Definition of Personal Data and Processing of Personal Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Choosing the Basis for Processing in the Context of Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . Rights of the Data Subject . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart City Service Provider or Equipment Vendor as a Potential Controller or Processor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Protection Impact Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Designation of the Data Protection Officer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart City, IoT, and Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Compliance: Where Do We Start? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

General Data Protection Regulation, the recent update of the world’s most developed legal framework of personal data protection, has been fully in force in the European Union since May 2018. As a new chapter in European data protection development, the Regulation is an instrument of uniformization replacing the older Data Protection Directive framework and its national G. Vojković (*) Faculty of Transport and Traffic Sciences, University of Zagreb, Zagreb, Croatia e-mail: [email protected] T. Katulić Faculty of Law, University of Zagreb, Zagreb, Croatia e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_28

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transposition measures. GDPR also addresses the transfer of personal data outside the EU and EEA areas when required for electronic commerce, information society services such as cloud services, on-demand streaming, and other content services or for more traditional purposes such as civil aviation traffic, cargo and passenger shipping, etc. Compliance with the Regulation in the context of Smart City development strategy now includes analyzing the impact of several evolving technological trends such as the Internet of Things, Big Data and biometricsenabled video-surveillance, utility payment systems, and all other kinds of services based on gathering large amount of personal data. GDPR has adopted a significant number of compliance mechanisms already familiar from information security standards and infosec legislation. For Smart City data controllers, this means adapting their advancements and deployment of new services and products to meet data protection standards.

Introduction General Data Protection Regulation, the recent update of the world’s most developed legal framework of personal data protection has been fully in force in the European Union since May 2018. As a Regulation, it is directly applied to all EU Member States. As a new chapter in European data protection development, the Regulation is an instrument of uniformization replacing the older Data Protection Directive framework and its national transposition measures, the national personal data protection laws the Member States adopted in late 1990s. Development of international, cross-border commerce, as well as the rise of mobile communications demanded the uniform rules for the whole European common market. Computers, tablets, smartphones, and a myriad of smart Internetof-Things devices are becoming increasingly efficient at collecting, processing, and storing seemingly an ever-increasing amount of data. As we transition further into a postindustrial information society, personal data is becoming a valuable resource and commodity. More efficient control over its use and firmer affirmation of rights and freedoms of data subjects is one of the key regulatory challenges of our time.

From Privacy To Data Protection The right to privacy has been recognized in various legal traditions, from ancient texts to modern legal systems. Many if not all modern legal systems also recognize its natural sibling, the right to protection of personal data (Charter of Fundamental Rights of the European Union (2000), Treaty on European Union and the Treaty on the Functioning of the European Union 2008). The purpose of privacy regulation has traditionally been to restrain governmental and private actions that threaten the various aspects of personal privacy of individuals, from physical to communication and information privacy. Today many national

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constitutions regulate the right to privacy, and several important international conventions have been adopted to interpret and develop the right of privacy to the changing society, especially in economic and technological sense. Personal data protection in the European Union, as a means of ensuring privacy of individuals, has historically been considered as a means instead of ends before finally asserting itself as a separate fundamental right with the Treaty of Lisbon and the Charter of Fundamental Rights of the EU (Fuster 2014). Replacing the old framework of established in mid-nineties by the Data Protection Directive (Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data), the new General Data Protection Regulation (in following text: GDPR or Regulation) (Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation)) represents the general legal framework of data protection in the European Union. The importance and the effect of this Regulation on the field of data protection, not just in Europe, but globally, is significant. The Regulation lays out a framework of obligation for data controllers and data processors and other entities participating in personal data processing. For smart city projects and services, this means a potentially extensive and exhausting work to identify and properly manage processing activities in line with data protection principles now enshrined in law and enforced by potentially crippling administrative fines. The nature of such processing, the potential to influence a large number of individuals over large public spaces, with regard to many different kinds and categories of data and for purposes potentially incompatible with the principles of processing enshrined in the Regulation. The primary goal of all data controllers, regardless of the purpose and scope of processing, should be to address the risks and protect the rights and freedoms of data subjects, and in this regard public entities, service suppliers, and equipment vendors that participate in smart city efforts are no different.

Territorial Scope of The General Data Protection Regulation GDPR application is not restricted only to EU Member States. The GDPR applies to non-Member States such as those included in the European Economic Area (EEA, extension of the EU single market to non-EU member parties: Iceland, Liechtenstein, and Norway). Additionally, there are many territories of the European Union outside Europe. There are territories of EU member states which enjoy special status outside the European Union, like Greenland. Others are territories of a member state situated in a great distance from European continent, but inside of European Union, like French Guiana. European Union law applies to these territories, with possible derogations, according to “structural social and economic situation” (Article 349 of the Treaty on

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the Functioning of the European Union) (Consolidated versions of the Treaty on European Union and the Treaty on the Functioning of the European Union). GDPR also addresses the transfer of personal data outside the EU and EEA areas when required for electronic commerce, information society services such as cloud services, on-demand streaming, and other content services or for more traditional purposes such as civil aviation traffic, cargo and passenger shipping, etc. According to the Art. 3 of the GDPR, the Regulation applies to the processing of personal data in the context of the activities of an establishment of a controller or a processor in the Union, regardless of whether the processing takes place in the Union or not. Also, GDPR applies to the processing of personal data of data subjects who are in the Union by a controller or processor not established in the Union, where the processing activities are related to the offering of goods or services, irrespective of whether a payment of the data subject is required, to such data subjects in the Union or the monitoring of their behavior as far as their behavior takes place within the Union. GDPR also applies to the processing of personal data by a controller not established in the Union, but in a place where Member State law applies by virtue of public international law. From that, we can conclude, the purpose, goals, and effects of GDPR are not limited only to effecting adequate data protection rules for legal systems of Member States. Its ambitions are far wider, and leverage the strength and political influence of one of the world’s largest markets in promoting what the EU has recognized as a fundamental right of its citizens and all other individuals in its territories – a right to protection of personal data, this precious new commodity or resource that fuels new services and products that use information technology and develop as quickly as IT does. Just like the Data Protection Directive of 1995, which has had a far greater reach and effect than just transposition into legal systems of 27 Member States, some of those like Germany and UK already had data protection laws reaching far back in 1970s, but also influencing legal systems of faraway countries in South America (Argentina), North America (Canada), and South East Asia (Republic of Korea), the GDPR has already spurred a new wave of data protection laws being developed around the world from Japan and Brazil to India and Australia. Even the United States, the birthplace of the first modern legal definition of privacy (Brandeis and Warren 1890) is reexamining its decades long opposition to adopting federal level data protection legislation in the wake of the GDPR and state laws it inspired like the California Consumer Privacy Act (CCPA). This is especially underlined in the provisions of Chapter V of the Regulation concerning the transfer of personal data to third countries or international organizations. The Regulation demands in Article 45 that any transfer of personal data which is undergoing processing or is intended for processing after transfer to a third country or to an international organization shall take place only if controller and processor comply with the conditions set forth in Articles 45 to 50 of the Regulation. Third countries can apply for adequacy decision (Art. 45), effectively allowing unimpeded transfer of personal data to third countries as if they were EU Member States provided they meet a complex set of criteria ranging from general legal

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standards such as ensuring the rule of law, the respect for human rights and fundamental freedoms, adopt relevant legislation and data protection rules, professional rules and security measures, effective and enforceable data subject rights and effective administrative and judicial redress for the data subjects whose personal data are being transferred, existence and effective functioning of one or more independent supervisory authorities with responsibility for ensuring and enforcing compliance with the data protection rules including adequate enforcement powers for assisting and advising the data subjects in exercising their rights and for cooperation with the supervisory authorities of the Member States etc. At this point, it is safe to conclude that the Regulation has already had a profound effect on the international development of data protection regulation. The recognition of principles, data subject rights, legal basis of processing, and specific technical and organizational measures that help ensure safe and secure processing of personal data have made their way across legal traditions and systems into national laws of countries around the world. Even if their national legal systems do not recognize the European concepts of data subject rights, the data controllers and processors, as well as hardware, software, and service vendors established in countries that lack adequate data protection laws will have to meet the Regulation requirements in order to be able to process personal data of data subjects in the EU and participate in the common market. This is especially the case concerning the real issues facing the smart city projects in development both in the EU and worldwide. As an incredibly layered and nomotechnically complex legislation, the application of Regulation has raised issues concerning recognizing the role of controllers, joint controllers, and processors; identifying proper legal basis for processing and potential reuse of data; the role and liability of representatives; the role and position of data protection officers and many other issues that national supervisory bodies; the European Data Protection Board and the now defunct Article 29 Working Party identified in numerous guidelines and opinions. The purpose of this chapter is primarily to identify these issues and to offer at least partial guidance in recognizing the legal obligations of providers of smart city services and technology with regard to the Regulation concepts (Table 1).

Table 1 GDPR compliance for Smart Cities? Country is a member of the EU Country is a Candidate Member State Passenger traffic with EU Not a Member State but has strong economic or cultural ties with the EU Not a Member State but interested in economic cooperation and receives tourists from the EU Not a Member State, no significant economic, cultural, or political ties with EU

Mandatory Probably Mandatory May be applicable Convenient Convenient No immediate importance but may be of comparative legal interest.

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General Data Protection Regulation What Is An EU Regulation? GDPR is a European Union regulation. Historically, most of EU legal instruments were directives which require transposition into national law. In some Member States, this may require adopting or amending one or more national laws. Member States have significant leeway to interpret and transpose directives into their legal systems which in turn may create different outcomes in different legal systems and significant differences between Member States. In the case of Data Protection Directive, over the course of the quarter of the century it has been in place, this has resulted with an unacceptable variety of transposition results allowing some Member States to become a sort of data havens for personal data where unscrupulous data controllers would create EU establishments to take advantage of the common market without fear of supervision by national supervisory bodies of Member States that transposed the Directive more faithfully. This created a fragmentation that jeopardized the efforts to create the digital single market and develop new innovative information society services, a field where Europe traditionally lags far behind US and East Asian economies. Unlike directives, regulations become immediately enforceable in all Member States outranking existing national law. In theory, they create a common legal framework for all Member States and should result in same legal practice. The European legislators usually use regulation as an instrument when they want to ensure equal legal standards in all Member States allowing only minor considerations and options for Member States to regulate in accompanying implementation acts. Replacing the Data Protection Directive with the General Data Protection Regulation was motivated with a goal to achieve a much higher level of harmonization and to ensure adequate protection of data subject rights and freedoms in a consistent manner in all Member States regardless of their own national legal traditions and societal and cultural attitudes to privacy and data protection. Regulations, however, seldom completely remove all obstacles to harmonization. In the case of the GDPR, some issues such as the exact way of implementing administrative fines, the organization of the national supervisory bodies, issues of national security, media exemptions, and other matter falling outside of scope of EU legislative competence. The Regulation thus provides room for Member States to adapt the application of the Regulation to their national legal systems. There are over twenty situations where Member States have chosen to do so, ranging from regulating the status of personal data of deceased persons, imposing specific rules for legal basis of processing and consent of children, processing of sensitive personal data, data relating to criminal offences or convictions to exemptions, restrictions of data subject rights, carrying out of data protection impact assessments, additional conditions in naming DPOs, etc.

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Structure of the Regulation The text of the Regulation is divided into two main parts, the Preamble and the Articles of the Regulation. By the general definition, the preamble is an introductory statement of the legal text. In EU laws, the preamble is usually contained by a number of recitals that explain the purpose of the regulation and accompany the main text containing Articles. There are 173 recitals and 99 Articles in the main text of the GDPR. Many of these recitals are important when discussing the effect of the Regulation on the development and use of smart city solutions and technologies as they further explain the provisions of the Regulation, especially those containing definitions of key concepts and principles of processing, obligations of data controllers and processors, and other provisions with recitals often providing additional context and insight. Some of the recitals deal with the interaction of data protection right with other fundamental rights, while some with the scope of application of the Regulation such as: • Recital 4: “. . .The right to the protection of personal data is not an absolute right; it must be considered in relation to its function in society and be balanced against other fundamental rights, in accordance with the principle of proportionality.” • Recital 14: “. . .The protection afforded by the Regulation should apply to natural persons, whatever their nationality or place of residence, in relation to the processing of their personal data. This Regulation does not cover the processing of personal data which concerns legal persons and in particular undertakings established as legal persons, including the name and the form of the legal person and the contact details of the legal person.” • Recital 15: “. . .In order to prevent creating a serious risk of circumvention, the protection of natural persons should be technologically neutral and should not depend on the techniques used. The protection of natural persons should apply to the processing of personal data by automated means, as well as to manual processing, if the personal data are contained or are intended to be contained in a filing system.” • Recital 22: “. . .Any processing of personal data in the context of the activities of an establishment of a controller or a processor in the Union should be carried out in accordance with this Regulation, regardless of whether the processing itself takes place within the Union.” Others introduce new concepts, such as pseudonymization or expand on the basic principles of processing such as transparency or lawfulness: • Recital 26: “. . .The principles of data protection should apply to any information concerning an identified or identifiable natural person. Personal data which have undergone pseudonymization, which could be attributed to a natural person by

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the use of additional information should be considered to be information on an identifiable natural person.” • Recital 39: “. . ..Any processing of personal data should be lawful and fair. It should be transparent to natural persons that personal data concerning them are collected, used, consulted or otherwise processed and to what extent the personal data are or will be processed. The principle of transparency requires that any information and communication relating to the processing of those personal data be easily accessible and easy to understand, and that clear and plain language be used. In order for processing to be lawful, personal data should be processed on the basis of the consent of the data subject concerned or some other legitimate basis, laid down by law, either in this Regulation or in other Union or Member State law as referred to in this Regulation, including the necessity for compliance with the legal obligation to which the controller is subject or the necessity for the performance of a contract to which the data subject is party or in order to take steps at the request of the data subject prior to entering into a contract.” Newly defined data subject rights, such as the right to access data, data controller obligations such as maintaining the records of processing are also further explained in the Recitals. • Recital 50: “. . .The processing of personal data for purposes other than those for which the personal data were initially collected should be allowed only where the processing is compatible with the purposes for which the personal data were initially collected. Personal data which are, by their nature, particularly sensitive in relation to fundamental rights and freedoms merit specific protection as the context of their processing could create significant risks to the fundamental rights and freedoms. A data subject should have the right of access to personal data which have been collected concerning him or her, and to exercise that right easily and at reasonable intervals, in order to be aware of, and verify, the lawfulness of the processing.” • Recital 82: “. . .In order to demonstrate compliance with this Regulation, the controller or processor should maintain records of processing activities under its responsibility. In order to maintain security and to prevent processing in infringement of this Regulation, the controller or processor should evaluate the risks inherent in the processing and implement measures to mitigate those risks, such as encryption.”

Definition of Personal Data and Processing of Personal Data Regulation in Art. 4 defines personal data as any information relating to an identified or identifiable natural person; an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier, or to one or more

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Fig. 1 GDPR – indentified and indentifiable natural person

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identifiable natural person

identified natural person

personal data

factors specific to the physical, physiological, genetic, mental, economic, cultural, or social identity of that natural person. This definition clearly focuses on data regarding natural persons only – legal persons obviously can have “personal” data in a sense – data that distinguishes them from other legal persons, data received through the process of establishment or other pertinent data; however, this is not personal data in the sense of the Regulation and data protection law. Another issue is personal data of the deceased. This data is still personal data, however, the Regulation in Recital 27 explains it does not apply to such data, instead leaving this issue to be addressed by the Member States in their national legislation accompanying the application of the GDPR (Fig. 1). Getting back to the definition of personal data, the meaning of the phrase “identified natural person” is clear, but what can we discern from the other – “identifiable natural person”? An individual is directly identifiable if he can be identified by using nothing but the information available in possession. By knowing his name and location, data controller can identify a natural person. Further, the name is not always necessary. Had the data controller not known the name of the person, but collected the data about his location and physical characteristics such as height, voice, hair color, it could still be possible to identify the data subject. This question has been a subject of several WP29 and subsequently EDPB guidelines and opinions, as the understanding of this provision, as defined in Data Protection Directive in 1995 has been interpreted by Member States law makers in drafting the transposition measures in late 1990s and through case law from then on (WP29 or Art. 29 WP – former (before 25.5.2018) advisory body made up of a representative from the data protection authority of each EU Member State; EDPB – The European Data Protection Board is an independent European body composed of representatives of the national data protection authorities, and the European Data Protection Supervisor (EDPS). With this development in mind, the Regulation has provided several examples in Recital 30 that explain how the definition of personal data may include data such as Internet protocol (IP) addresses, cookie identifiers, and other network and communication identifiers such as radio frequency identification (RFID) tags. There are more factors to consider with indirect identification. Indirect identification means you cannot identify an individual through the information you are

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processing alone, but you may be able to by using other information you hold or information you can reasonably access from another source. A third party using your data and combining it with information, they can reasonably access to identify an individual is another form of indirect identification. These identifiers refer to information that is related to an individual’s tools, applications, or devices, like their computer or smartphone and the examples mentioned above should by no means be considered an exhaustive list. Any information that could identify a specific device, like its specific and unique codes such as the MAC address for integrated network interfaces or IMEI numbers of GSM compatible mobile phones (virtually all mobile phones made in last two decades), are such identifiers. These identifiers can be personal data, but not always and not in all circumstances. According to applicable ECJ case law, such as Breyer (ECJ Case C-582/14), the criteria here would be whether the data controller or data processor, as they go about performing personal data processing, have a legal basis of establishing who uses the device or network interface such as an IP address at any point in time. An Internet Service Provider (ISP) that logs activity of users commenting content on a media portal that is published by the company or a subsidiary inside the business group has a legal way of establishing the identity of even unregistered commenters on the said website. An independent media outlet logging the comments from its readers has no legal way to access information about the users using the exact IP address – only their ISP has that. These two simple examples show when an IP address is considered personal data – and when it is not. The phrase “other information you hold or information you can reasonably access from another source” is very important for data controllers participating in smart city projects and services. Many local communities or local self-government bodies usually own or control various utility companies. These bodies can enact communal or local taxes that require precise data about the local population. It is reasonable to expect that information systems underlying these activities will facilitate the possibility that various personal data can be accessed from connected companies (such as those that provide water supply, electricity, waste management) and local government. This is already the case where local government offers, in cooperation with these utilities, consolidated monthly bills for all utilities. In that case they can make an accurate picture and indeed profile of the customers and their habits. The established definition of personal data in European legislature is intentionally very broad and is meant to encompass all information that may help to identify a person. This is in stark contrast with particular, sectorial legislation found in legal systems in other parts of the world. Intention behind this is easy to understand – the law has to be applicable to the rapidly changing and developing economic and technological landscape and facilitate an effective protection and enforcement of individuals rights and freedoms. While the opposite approach with effectively numbering categories of personal data recognized to receive protection may be more pragmatical and practical in everyday application (i.e., such as those recognized by the US Health and Insurance Portability and Accountability Act – HIPAA),

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it limits the application of data protection principles only to recognized data ignoring everything else. Information can identify a data subject as a distinct individual person, even without being necessarily attached to an actual personal name. Records that contain information that is clearly about a specific individual are considered to be related to that specific person, such as their medical history, educational or sport track record, or criminal records. Records that have information that describes an individual’s activities may also qualify, such as a credit record or other bank statement. Any data that relates to an identifiable natural person is personal data. The general, vague definition of this basic term has encouraged many national supervisory bodies in Europe to publish their own guides and opinions around the most vague or unclear topics. One of the most active of those is the UK’s Information Commissioner’s Office (ICO), which is also one of the best funded and resourced supervisory bodies. Its Guide to General Data Protection Regulation discusses the different cases where there is doubt if certain information can be considered personal data. The ICO suggests criteria – if a living individual can be identified from the data, or, from the data and other information in the possession of, or likely to come into the possession of, the data controller then yes – the data in question is personal data. The same document also provides insight into what is considered under the meaning identified – an individual is identified if the controller can distinguish that individual from other members of a group. In most cases, an individual’s name together with some other information will be sufficient to identify them, but just because the controller does not know the name of an individual does not mean it is unable to identify that individual and that the data it collects is not personal data (Determining what is personal data 2012). These criteria are analyzed through several examples similar to these: • A short, middle-age woman with blond hair who works in a bakery at number 12 and drives a metallic blue Toyota Yaris. • An individual caught jaywalking on traffic camera overseeing a busy crossroad. • Discerning an individual not previously known to the city government that operates a CCTV camera system connected to a biometric scanner and a social standings/credit system similar to system currently employed by China It is quite apparent that second and third examples could be very important for current and future smart city projects and development. Some countries have already started to develop mass traffic and public space surveillance systems usually associated with sci-fi literature and Orwellian dystopias. Unfortunately, the current state of technology, especially mass production and availability of key components of such systems, such as high-speed optical networks, cheap and abundant processing power, sophisticated biometric screening equipment and recognition systems, as well as application of scoring algorithms known from credit ratings and other financial services to insurance, health, and public safety systems has created the perfect conditions to advance such wide-scale invasions of privacy.

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There is very little doubt that these systems could eventually produce results unacceptable under the principles of processing recognized by the General Data Protection Regulation and the European judicature. The third example is especially interesting from the perspective of smart city services. Data showing an individual owns a Toyota Yaris can be personal data in a small town or a village, but not in an area of greater population density such as urban areas where dozens or hundreds of people own every model present in the market. On the other hand, a research paper explaining a rare genetic abnormality or a rare complication of an even rarer disease may contain enough personal data to allow the subject to be distinguished on the basis of described medical history and physical characteristics. Of course, given the abundance of data created by the new IoT technology, it is no wonder that in its guide the Information Commissioners’ Office reflects on a few more situations relevant for Smart City models, such as regarding the valuation of property used to determine an individual liability for Council Tax or to determine the assets of an individual or individuals in proceedings following legal proceedings such as succession or divorce or an example based on taking almost identical photographs of New Year’s celebrations two separate photographers and stored in electronic form on computer, one for media purposes and use in a stock photo library and the other by a police officer taking photos of the gathered crowd to identify potential troublemakers. GDPR does away with the old concept of data collections present in the old Data Protection Directive and the national laws based on it and instead as a basic concept takes processing which it defines as any operation or set of operations performed on personal data or on sets of personal data, whether or not by automated means, such as collection, recording, organization, structuring, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making available alignment or combination, restriction, erasure, or destruction. Several other concepts and terms defined by the Regulation are important for smart city services, especially the role of the providers of such services, their vendors, and all the players participating in the process of collecting, processing, storing, and communicating of data: • Principles of processing, as set by the article 5 of the GDPR, especially principles of lawful, fair, and transparent processing; purpose limitation; data minimization and integrity and confidentiality of processing; and their realization through various provisions of the Regulation. • Data subject rights, such as the right to be informed, the right to access own personal data, to restrict processing, etc. • Technical and organizational measures such as pseudonymization and encryption. Pseudonymization is the processing of personal data in such a manner that the personal data can no longer be attributed to a specific data subject without the use of additional information, provided that such additional information is kept separately.

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• Data controller, defined by the Regulation as the natural or legal person, public authority, agency, or other body which, alone or jointly with others, determines the purposes and means of the processing of personal data. • Data processor, defined as a natural or legal person, public authority, agency, or other body which processes personal data on behalf of the controller • Data breach, a breach of security leading to the accidental or unlawful destruction, loss, alteration, unauthorized disclosure of, or access to, personal data transmitted, stored, or otherwise processed. • Any form of automated processing of personal data consisting of the use of personal data to evaluate certain personal aspects relating to a natural person, in particular to analyze or predict aspects concerning that natural person’s performance at work, economic situation, health, personal preferences, interests, reliability, behavior, location, or movements For understanding the obligations, the Regulation imposes on data controllers, smart city services providers will also have to consider the implications of the following: • Recipient means a natural or legal person, public authority, agency or another body, to which the personal data are disclosed, whether a third party or not. A recipient, in the context of smart cities would be any entity receiving data from data controllers participating in processing for the purposes of smart city services, regardless of their own role in such processing. A third party, on the other hand, is defined as a natural or legal person, public authority, agency or body other than the data subject, controller, processor and persons who, under the direct authority of the controller or processor, are authorized to process personal data. • Consent of the data subject means any freely given, specific, informed, and unambiguous indication of the data subject’s wishes by which he or she, by a statement or by a clear affirmative action, signifies agreement to the processing of personal data relating to him or her. Consent is just one of the six legal bases underlined by the Regulation in Article 6. A common misconception is that it is the most important one or the one that most processing situations require. Neither is true, and while there certainly are situations where consent would be preferable basis even in the context of smart city services, these are in minority, and most situations will see the use of other options, notably the processing on the basis of performance of tasks in public interests or exercise of official authority, compliance with legal obligations, the fulfilment of contracts, etc. Consent, its legal interpretation especially considering its quality, form, and documentation with regard to rights and freedoms of data subjects now extensively elaborated in the Regulation has been a subject to many opinions and guides over the last two decades. Both the EDPB and various national data protection authorities such as the ICO have published detailed guidelines and sample documentation on this topic (EDPB Guidelines on Consent).

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• A representative helps data controllers with establishment outside of the EU to remain compliant with the Regulation, communicate with data subjects in the EU market, and respond to their requests and with the national supervisory bodies. • Data protection officer (DPO) is an important concept originally missing from the Data Protection Directive, although well established in national laws of some EU Member States even before the adoption of the Directive. The Regulation now extensively regulates the position, mandate, responsibilities, and competences of the DPO, as well as adopting qualitative criteria with regard to the data controller obligation to designate the DPO.

Choosing the Basis for Processing in the Context of Smart Cities All personal data processing activities need to be compliant with the principles of processing, starting with choosing an adequate legal basis and ensuring the lawfulness, fairness, and transparency of processing. These basis for processing is regulated by the Art. 6 of the Regulation.

No. 1. 2.

3. 4. 5.

6.

Legal basis for processing The data subject has given consent to the processing of his or her personal data for one or more specific purposes. Processing is necessary for the performance of a contract to which the data subject is party or in order to take steps at the request of the data subject prior to entering into a contract. Processing is necessary for compliance with a legal obligation to which the controller is subject. Processing is necessary in order to protect the vital interests of the data subject or of another natural person. Processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller. Processing is necessary for the purposes of the legitimate interests pursued by the controller or by a third party, except where such interests are overridden by the interests or fundamental rights and freedoms of the data subject which require protection of personal data, in particular where the data subject is a child.

Yes/ No

Processing shall be lawful only if one of the possibilities above apply. The controller and processor need to identify the applicable basis of processing for each processing activity and note it in their records of processing activities.

With regard to the processing conducted by the public authorities in performance of their tasks, their processing cannot be based on legitimate interest. The public authorities will process personal data according to the laws and regulations that regulate their activities, and these provisions need to be, from the perspective of

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protection of personal data as a fundamental right, in line with the principle of processing. Member States will, in process of adopting new legislative and amending existing laws, perform data protection impact assessments in order to mitigate risk. As commented earlier, the consent of data subjects is just one of six recognized legal basis for processing. Article 7 of the Regulation in more detail lays out the duties of the controller when using consent as the legal basis for processing, including the obligation to document and demonstrate how, when, and what kind of processing the data subject consented to. The Regulation requires that consent should be presented in a manner that data subjects can easily distinguish it from other matter, especially in the context of contracts regulating provisioning of a service. The data subjects have to be able to understand the processing they are consenting to, so the consent should be worded in an intelligible and easily accessible form using a clear and plain language (WP29 Guidelines on Transparency). Additionally, data subjects can withdraw their consent at any time. The data controller has to inform data subjects of their rights including the right to withdraw consent and has to make sure that withdrawing consent is for data subjects as easy and simple as is giving it. This makes consent potentially dangerous for data controllers performing large scale continuous processing even if no recognized category of sensitive data is being processed as data subjects may revoke consent at any time. Smart City data controllers relying on consent need to invest in consent management systems in order to be able to help data subjects exercise their rights in timely and documented manner. Current understanding on rules and best practices concerning consent as well as examples of consent that do not meet Regulation standards is well explained in Article 29 Working Party (WP29) Guidelines 2016/679 as well as in national data protection authority guidelines such as those published by the UK Information Commissioner’s Office.

Rights of the Data Subject Following the ECJ cases such as C-131/12 Costeja or C-362/14 Schrems, the need to explicitly define the content of data protection right as a fundamental right recognized by the Charter of Fundamental Rights of the European Union (Charter of Fundamental Rights of the European Union 2000) was met by the Regulation in Chapter III (Articles 12-23) that defines the specific rights a data subject has with regard to the processing of his personal data. These rights are in effect a reflection of the basic principles of data processing as regulated by the Article 5 of the Regulation. These rights include the right to be informed, the right of access to personal information, the right to rectification, the right to erasure, the right to restrict processing, the right to data portability, the right to object, and rights in relation to automated decision-making and profiling. While the detailed comment on these rights would take much more space than possible with the confines of this chapter, a short look from the perspective of smart

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city services that include or are based on the processing of personal data would focus primarily on the following: The right of access to personal data is guaranteed by the Article 15 of the Regulation stipulating that subject shall have the right to obtain from the controller confirmation as to whether or not personal data concerning him or her are being processed, and, where that is the case, access to the personal data and the information like the purposes of the processing, the categories of personal data concerned, the right to lodge a complaint with a supervisory authority, etc. The Regulation now explicitly regulates a right to rectify personal data or to complete incomplete personal data, as well as right of erasure – the right of the data subject to have data controller erase personal data concerning him without undue delay. The data subject has a right to object to processing of personal data at any time to processing of personal data concerning him or her which is based on legitimate interest or necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller. The controller then can no longer process data subject’s data unless demonstrating compelling legitimate grounds for processing that would override the interests, rights, and freedoms of the data subject or in case the processing is required for establishment, exercise, or defense of legal claims. Another interesting provision is the right of the data subject to object to automated individual decision making and profiling. According to the Regulation, the data subject shall have the right not to be subject to a decision based solely on automated processing, including profiling which produces legal effects concerning him or her.

Smart City Service Provider or Equipment Vendor as a Potential Controller or Processor Chapter IV of the Regulation details the obligations of data controllers and processors regarding the data processing, from the general obligations to the specific duties concerning the security of personal data processing, mechanisms such as data protection impact assessments and the obligation to conduct prior consultation with data processing authority and the position, competences and tasks of the data protection officer. Finally, the Chapter IV regulates the mechanisms of certification and adoption of codes of conduct. Information security mechanisms and duties feature heavily in the GDPR. The data controllers and processors need to conduct processing operations with keen regard to potential risks to security of processing and personal data, taking into account especially the nature, scope, context, and purposes of processing. In order to mitigate the recognized risks, the data controller is primarily responsible to implement appropriate technical and organizational measures to ensure and be able to demonstrate that his data processing is adequately secured against possible data breaches (Katulic and Protrka 2019).

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The Regulation introduces the concept of data protection by design and by default. Taking into account the nature, scope, context, and purposes of processing, as well as the risks of varying likelihood and severity for the rights and freedoms of natural persons, the controller shall implement appropriate technical and organizational measures to ensure and to be able to demonstrate that processing is performed in accordance with principles of processing and other provisions of the Regulation. The controllers and processors are under obligation to regularly review and update the organizational and technical measures in relation how the profile of risk changes. In order to be able to demonstrate compliance, the data controller should adopt internal policies and implement measures which meet in particular the principles of data protection by design and data protection by default. In practice, this means that data controllers should try to minimize the processing to the level required for services offered and use applicable measures such as pseudonymization and encryption to further reduce the risk for rights and freedoms of the data subject should a data breach occur. In everyday operations, data protection by design and default means that controller should choose applications, services, and products that have been developed and designed with regard to data protection. (What does data protection “by design” and “by default” mean?) Safety of processing requires that the controller implements appropriate technical and organizational measures for ensuring that, by default, only personal data which are necessary for each specific purpose of the processing are processed. This obligation applies to the amount of personal data collected, the extent of their processing, the period of their storage, and their accessibility. The data controller needs to make sure that by default, personal data are not made accessible without the individual’s intervention to an indefinite number of natural persons. The data controller is also responsible for choosing data processors. Where processing is to be carried out on behalf of a controller, the controller is required to use only processors providing sufficient guarantees to implement appropriate technical and organizational measures. The controller is responsible to check if processor has implemented these measures in such a manner that processing will meet the legal requirements and ensure the protection of the rights of the data subject. In turn, the processor is not allowed to outsource the processing to another processor without notifying the data controller. The data controller needs to authorize such outsourcing. One of the fundamental changes to the previous legal framework is a consequence of the broadening of the principle of accountability that now includes the obligation for data controllers and processors to document and demonstrate accountability. The cornerstone of this is the obligation to maintain a comprehensive and accurate records of processing activities.

Data Protection Impact Assessment The Regulation has adopted a significant number of mechanisms already familiar from information security standards and legislation. Even though the recitals and

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articles mention the term information security only once, the concept of risk is mentioned over seventy times. Any privacy legislation needs, in order to be effective in protecting rights and freedoms of individuals and to provide a stable framework for development of new products and services, a degree of technological neutrality and resilience to technological change. This goes especially for all encompassing laws such as the GDPR. New technologies and new uses of existing information technology present a great challenge to regulation. Obligations of data controllers need to cover the advancements and deployment of new services and products. One of the mechanisms of the GDPR that connects the risk management approach with technological neutrality is the data protection impact assessment (DPIA). In short, where a type of processing in particular using new technologies, and taking into account the nature, scope, context, and purposes of the processing, is likely to result in a high risk to the rights and freedoms of natural persons, the controller is required prior to the processing to undertake an assessment of the impact of the future processing operations on the protection of personal data and the potential risk to rights and freedoms of the data subjects. In a nutshell, when considering a processing that may endanger the data subject rights, the data controller needs to conduct a risk assessment with regard to the purpose and actual method of processing of personal data, and from the perspective of data collected, its sources and potential recipients, uses, technology used, and other circumstances that imply the higher level of risk (WP29 Guidelines on Data Protection Impact Assessment). DPIA is only one of the mechanisms that showcase the GDPR reliance on risk assessment. Organizations offering smart city services will have to conduct an extensive risk assessment operation usually with the help of another compliance mechanism regulated by the Regulation – the data protection officer. The Regulation prescribes data protection impact assessment is required in certain cases such as when the data controller plans an automated processing that may include profiling and which includes a systematic and extensive evaluation of personal aspects relating to natural persons on which decisions are based that produce legal effects concerning the natural person or similarly significantly affect the natural person, when the processing activities require processing on a large scale of special categories of data or of personal data relating to criminal convictions and offences and when the processing includes systematic monitoring of a publicly accessible area on a large scale. When introducing smart city models in EU Member States, conducting data protection impact assessment when planning such activities will usually will be mandatory due to the very nature of such processing as smart city services usually include monitoring publicly accessible areas or large-scale monitoring of household installations.

Designation of the Data Protection Officer While the data protection officer (DPO) has been present in comparative European data protection law since the 1970s, the Data Protection Directive did not contain

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provisions about this important data protection compliance function. However, some national data protection laws enacted as transposition measures by Member States have included such provisions, especially concerning the criteria when the controller is mandated to designate the DPO. Often, this generation of legislation did not explicitly regulate its position or the competences, instead focusing on the tasks the DPO has ensuring compliant data processing. The Regulation now explicitly regulates the designation, the position and the tasks of the data protection officer (DPO) in Articles 37 to 39, and the WP29 and later EDPB has published updated Guidelines (WP29 or Art. 29 WP – former (before 25.5.2018) advisory body made up of a representative from the data protection authority of each EU Member State (WP29 Guidelines on Data Protection Officers). Generally speaking, if the data controllers carries out certain types of processing activities or if it is a public sector authority or body, the Regulation mandates the designation of the data protection officer. This is especially true when the controller conducts processing activities which are central to its functions, which represent the core activities of the data controller and which include regular and systematic monitoring of data subjects on a large scale or consist of processing on a large scale of special categories of data and personal data relating to criminal convictions or offences. From the perspective of smart city services, it is immediately obvious that many data controllers offering these services are going to be public authorities or bodies or municipal infrastructure companies which are state owned or perform public tasks. Additionally, many of these services will be based on regular and systematic monitoring on a large scale, and some will probably collect and process personal data that may fall under the Article 9 (special categories of sensitive data). For this reason, many smart city service providers will be under the mandate to designate a data protection officer. The Regulation now demands that the data protection officer be designated on the basis of professional qualities and, in particular, expert knowledge of data protection law and practices and the ability to fulfil referred tasks. This provision basically links the complexity and the risks of data processing to the competences of the data protection officer in a sense that data controllers need to appoint data protection officers whose knowledge of data protection law and practice will allow them to perform all the necessary tasks such as informing and advising the controller, processor, or their employees of their obligations according to Regulation and other Union or Member State laws, continuously monitor compliance, engage in awareness raising and training, adequately cooperate with the data protection authorities, etc. An interesting provision regarding the position of the data protection officer is the explicit provision that the DPO may be a staff member or may fulfil the tasks on the basis of a service contract. This provision allows hiring of outsourced data protection officers and the creation of an EU wide market for data protection officer services where data controllers and processors may hire individuals with skills adequate to their processing circumstances which will help mitigate the relative lack of adequately trained data protection and information security professionals in the EU market. The data protection officer position inside the organization is important for his proper function. The DPO has to be able to report directly to the highest level of

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management and enjoy sufficient independence to perform his tasks according to the Regulation, other Union and national law. Furthermore, the DPO has to be involved in issues relating to data protection and adequately resourced to perform their tasks. Considering the speed and volume of expansion of practices and understanding in the field of data protection, and the incessant march of information technology development, the DPO has to have resources and chance to maintain his expert knowledge. At the same time, while involved, the DPO has to be independent and not receive any instructions regarding the exercise of his tasks or be dismissed or penalized by the controller or processor for not performing his tasks. While the designated DPO may perform other duties in an organization, the controllers and processors designating the DPO have to be careful to avoid potential conflict of interest. A basic criterion to establish whether conflict of interest exists is participation in decisions on how and what personal data organization will process. If a position or responsibilities include participation in such decisions, such as those usually taken by management, these positions and tasks are incompatible with those of the data protection officer as they present a conflict of interest. For more complex organizations, creating of list of incompatible jobs and positions would be a wise precaution to avoid conflict of interest. European data protection supervisor quote on web several good tips about appointing and position of the data protection officer in the organization organigramme, according experience of EU institutions. Supervisor advises that the appointment of a DPO must of course be based on her personal and professional qualities, but particular attention must be paid to her expert knowledge of data protection. A good understanding of the way the organization operates is also recommended (European Data Protection Supervisor, Data Protection Officer (DPO)).

Smart City, IoT, and Privacy Internet of things (IoT) and Smart City data protection issues have a lot of common ground and deserve a closer look. Smart city is generally a framework predominantly composed of Information and Communication Technologies (ICT), to develop, deploy, and promote sustainable development practices to address growing urbanization challenges. A big part of this essentially ICT framework is an intelligent network of connected objects and machines that transmit data using various networking technologies and storing data in the cloud. Cloud-based IoT applications receive, analyze, and manage data in real-time to help municipalities, enterprises, and citizens make better decisions that improve quality of life (Secure, sustainable smart cities and the IoT 2019). IoT is proving to be one of the most prevalent buzzwords in recent years in information technology industry, increasingly connected to the rise of information security incidents and potential personal data breaches with significant impact on the privacy of individuals around the world.

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As a mix of developing and existing technologies applied to a new context, even finding a proper definition of the term is not an easy task. As this technology spreads, its effects are being felt in areas usually reserved for big, centralized, often critical infrastructure such as energy and water transportation and distribution infrastructure, communications, and transport, but its potential is also to enable citizens using national administrative and legal services (European Commission, IoT Privacy, Data Protection, Information Security). The IoT-based sensors present a tangible risk for personal data breaches as they are vastly more efficient and present than previous sensors and monitoring technology. This translates to a higher degree of risk to rights and freedoms of data subjects. We can consider several examples to illustrate this point. Let us consider electricity consumption meters where suppliers adopt new digital meters connected to the Internet. The same principle easily applies to thermostats and water meters. IoT products and services allow the operators to collect a large volume of data, some of which may be of potentially very sensitive nature. When such services are in place and large amounts of data are collected, the current framework of data protection in the EU requires certain safeguards need to be undertaken to protect privacy of the users and prevent misuse of their personal data. This data may contain sensitive data or categories of data of interest to many data controllers, from the financial sector to the communication and content providers, market researches, and others (Vojković et al. 2019). The principles of data protection, especially the principles of purpose limitation, storage limitation, and data minimization are quoted in GDPR Recital 39: “Natural persons should be made aware of risks, rules, safeguards and rights in relation to the processing of personal data and how to exercise their rights in relation to such processing. In particular, the specific purposes for which personal data are processed should be explicit and legitimate and determined at the time of the collection of the personal data. The personal data should be adequate, relevant and limited to what is necessary for the purposes for which they are processed. This requires, in particular, ensuring that the period for which the personal data are stored is limited to a strict minimum. Personal data should be processed only if the purpose of the processing could not reasonably be fulfilled by other means. In order to ensure that the personal data are not kept longer than necessary, time limits should be established by the controller for erasure or for a periodic review.” From the perspective of using IoT devices in Smart City services, it is obvious that many of their uses require processing that may be somewhat in contrast to these principles and may represent a threat for personal data of natural persons through which third parties can monitor and use their habits. Controllers deploying such services have an obligation to adequately inform data subjects using such devices and services about the nature, volume, and scope of personal data processing. The controllers are also required to adopt proper technical and organizational protection measures, assert the level of risk to the rights and freedoms of their data subjects, and regulate their relations with data processors as regulated by the Article 28 of the Regulation: “Where processing is to be carried out on behalf of a

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controller, the controller shall use only processors providing sufficient guarantees to implement appropriate technical and organizational measures in such a manner that processing will meet the requirements of this Regulation and ensure the protection of the rights of the data subject.” Finally, the data controllers need to carefully examine their processing operations. Through the records of processing activities, they need to establish proper and applicable basis for every personal data processing activity and those controllers responsible for IoT infrastructure will need to develop easily accessible and functional ways to let users exercise their data protection rights (Vojković et al. 2019).

Compliance: Where Do We Start? Suppose you are the person in charge of designing and leading data protection compliance efforts in your local government, a smart city operator. What do you need to do first? You have already heard that data protection is complicated and read about the potentially crippling administrative fines. Just a few recent examples from various EU data protection authorities may reinforce this notion: • Austria: EUR 1500 fine for CCTV at a takeaway that covered the street and a nearby gas station without a proper privacy notice. • Germany: A fine of EUR 10,000 was imposed because the company had not appointed a data protection officer (DPO). • UK: Nationwide retailer fined half a million pounds for failing to secure information of at least 14 million people. • Sweden: The Swedish Data Inspection Authority said it has imposed its first penalty for breach of GDPR to a school that had been trying out a facial recognition to register pupil attendance. • Hungary: A fine was imposed for not providing a data subject with CCTV recordings, not retaining recordings for further use by the data subject, and not informing the data subject about his right to lodge a complaint to the supervisory authority. • Spain: Fine of EUR 3600 to a private company was issued because surveillance of the public space by video surveillance cameras against violation of the principles of data minimization, etc. Banks, other private companies, public institutions, and local government – seemingly all kinds of data controllers private and public make mistakes and pay fines, all over Europe, even in countries with a long legal tradition and practice of personal data processing. Does this mean that compliance tasks are so complicated it is nigh impossible to avoid fines? Of course not. Let’s think about the problem rationally. Do we run a big city or a small municipality? If we run a big city – we probably already have existing data protection experts in our employment. Small municipalities will probably need to

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hire professionals, especially if they control local utility companies or institutions offering various public services. Whatever the situation, the first task of all data protection compliance projects is to review and update the records of personal data processing activities, analyze the flow of personal data, the business and IT processes that contain them, and identify appropriate technical and organizational measures to ensure safety and security of processing. Some of these measures may include: • • • •

Minimizing the processing of personal data Use of pseudonymization and encryption Use of methods to secure physical access to processing systems Regular training of employees concerning best data protection and information security practices • Regular information security reviews (Voigt and Bussche 2017) A city, especially one investing in smart technology, needs to plan its processing activities from the perspective of data security risk and consider their impact on rights and freedoms of data subjects – its citizens and other individuals who frequent the public spaces. Starting with key data protection principles, data controllers need to recognize and align their personal data processing with justifiable purposes and legal basis, documenting and balancing their need for data with the interests of data subjects. The compliance efforts require a multidisciplinary approach and a good project team of various backgrounds and skills, understanding the nature of the processing, the legal, technical, and organizational processes and requirements (Fig. 2). These activities are not a one-time effort to satisfy yet another EU regulation but should be considered as a first step and a basis for future efforts by the data controllers. The use of personal data from the perspective of risk to data subject rights and freedoms and observance of personal data processing principles regulated by the Article 5 of the GDPR is now a mandatory, permanent activity of all data controllers, a necessary step to ensure the protection of personal data as a fundamental right of individuals in the EU.

Conclusion The General Data Protection Regulation is obviously an ambitious, demanding, and very extensive legal document that has many layers and possible interpretations far exceeding the confines of this chapter. Since it came into effect, many implementation issues have remained unanswered. The European Data Protection Board and the national Data Protection Authorities are hard at work at interpreting its provisions and offering detailed guidance and explanations to the public. Compliance with the Regulation in the context of Smart City development strategy now includes analyzing the impact of several evolving technological trends

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Fig. 2 Data security and compliance require a multidisciplinary approach

project manager

business process expert

financial expert

techical expet

legal expert

such as the Internet of Things, Big Data and biometrics enabled video-surveillance, utility payment systems, and all other kinds of services based on gathering large amount of personal data. Such compliance efforts, regarding Regulation requirements, may be challenging for local government. They require a multidisciplinary approach and skills ranging from understanding underlying technical processes, industrial self-regulation standards and practices, risk management principles, and adequate understanding of data protection law and practice. The new European data protection rules definitely change the game for development of Smart City models, potentially rewarding those that systematically approach data protection issues while developing their Smart City elements. The Regulation requirements can add additional quality to the Smart City development plan and foster the culture of understanding between the service providers and the citizens based on trust and ability to exercise their personal data protection rights. On the other hand, where development of Smart City models considers various forms of automation without a systematic approach to risk management and development strategy ignores data protection issues, as current enforcement track record shows there is significant risk that it will be met with potentially crippling fines, even for a minor oversight such as improper setup of video surveillance of automated traffic light monitoring system (Vojković 2018). Data controllers need to ensure the whole Smart City development process is performed in line with current legal requirements through careful planning, development, and control of Smart City functions. Mistakes and oversights in this phase may result with complex and expensive adjustments later.

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References Brandeis, L., & Warren, S. D. (1890). The right to privacy. Harvard Law Review, 4, 193. Charter of Fundamental Rights of the European Union. (2000). Official Journal of the European Communities, C, 364(1). Consolidated versions of the Treaty on European Union and the Treaty on the Functioning of the European Union. Official Journal of the European Union, C, 115, 2008. Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data (No longer in force). Official Journal, L, 281, 0031–0050. EDPB Guidelines 3/2018 on the territorial scope of the GDPR (Article 3) – version adopted after public consultation. https://edpb.europa.eu/our-work-tools/our-documents/guidelines/ guidelines-32018-territorial-scope-gdpr-article-3-version_en. Accessed 20 Jan 2020. European Commission, IoT Privacy, Data Protection, Information Security. Available at: http://ec. europa.eu/information_society/newsroom/cf/dae/document.cfm?doc_id¼1753. Accessed 20 Jan 2020. European Commission: What does data protection ‘by design’ and ‘by default’ mean?. Available at: https://ec.europa.eu/info/law/law-topic/data-protection/reform/rules-business-and-organisations/obli gations/what-does-data-protection-design-and-default-mean_en. Accessed 20 Jan 2020. European Data Protection Supervisor, Data Protection Officer (DPO). Available at: https://edps. europa.eu/data-protection/data-protection/reference-library/data-protection-officer-dpo_en. Accessed 20 Jan 2020. European Union Agency for Fundamental Rights and Council of Europe. (2018). Handbook on European data protection law, fundamental right. Luxembourg: Publications Office of the European Union. https://fra.europa.eu/sites/default/files/fra_uploads/fra-coe-edps-2018-hand book-data-protection_en.pdf. Accessed 20 Jan 2020. Fuster, G. (2014). The emergence of personal data protection as a fundamental right of the EU. Cham: Springer. Guidelines on Consent. under Regulation 2016/679 (wp259rev.01), https://ec.europa.eu/newsroom/ article29/item-detail.cfm?item_id¼623051. Accessed 20 Jan 2020. Information Commissioner’s Office, Determining what is personal data, v1.1, 20121212. Available at: https://ico.org.uk/media/for-organisations/documents/1554/determining-what-is-personaldata.pdf. Accessed 20 Jan 2020. Katulic, T., & Protrka, N. (2019). Information security in principles and provisions of the EU Data Protection Law. In K. Skala (Ed.), MIPRO 2019 42nd international convention proceedings (pp. 1420–1426). Rijeka: MIPRO. https://doi.org/10.23919/mipro.2019.8757153. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Official Journal of the European Union, L, 119(1). Secure, sustainable smart cities and the IoT. (2019). https://www.gemalto.com/iot/inspired/smartcities. Accessed 20 Jan 2020. Voigt, P., & Bussche, A. (2017). The EU General Data Protection Regulation (GDPR) – A practical guide. Cham: Springer. Vojković, G. (2018). Will the GDPR slow down development of Smart Cities? In K. Skala (Ed.), MIPRO 2018 41st international convention proceedings (pp. 1495–1497). Rijeka: MIPRO. https://doi.org/10.23919/MIPRO.2018.8400234. Vojković, G., Milenković M., & Katulić, T. (2019). IoT and Smart Home Data Breach Risks from the Perspective of Croatian Data Protection and Information Security Law. In Proceedings of the ENTRENOVA – ENTerprise REsearch InNOVAtion Conference, Zagreb. WP29 Guidelines on Data Protection Impact Assessment. (DPIA) and determining whether processing is “likely to result in a high risk” for the purposes of Regulation 2016/679, WP248

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rev.01. https://ec.europa.eu/newsroom/article29/item-detail.cfm?item_id¼611236. Accessed 20 Jan 2020. WP29 Guidelines on Data Protection Officers. https://ec.europa.eu/newsroom/article29/item-detail. cfm?item_id¼612048. Accessed 20 Jan 2020. WP29 Guidelines on Transparency. under Regulation 2016/679, https://ec.europa.eu/newsroom/ article29/item-detail.cfm?item_id¼622227. Accessed 20 Jan 2020.

Multitier Intelligent Computing and Storage for IoT Sensor Data

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Osamah Ibrahiem Abdullaziz, Mahmoud M. Abouzeid, and Mohamed Faizal Abdul Rahman

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Applicability Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multitier Reference Framework for IoT Data Processing in Smart Cities . . . . . . . . . . . . . . . . . Computing Continuum for IoT Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Regional Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Edge/Fog Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Virtualization Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Intelligence for Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Supervision in Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Generalization in Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Federated Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learning in Different Tiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Management and Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multitier Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IoT Data Security in Motion and at Rest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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O. I. Abdullaziz (*) · M. M. Abouzeid Department of Electrical Engineering and Computer Science, National Chiao Tung University, Taiwan, China e-mail: [email protected]; [email protected] M. F. Abdul Rahman International College of Semiconductor Technology, National Chiao Tung University, Taiwan, China e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_49

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Abstract

The next-generation mobile systems will soon transform cities into intelligent and well-orchestrated infrastructures. This creates a new breed of opportunities for innovative applications in various sectors such as healthcare, transportation, energy, manufacturing, agriculture, finance, and residence, to mention a few. Moreover, with the advances in wireless communication, the Internet of Things (IoT) devices will take over the world. The ratio of the IoT devices to the human population has drastically grown, and this trend will continue. This growth in the number of connected IoT devices translates to a massive amount of generated data that pose challenges in terms of transmission, processing, and storage requirements. To raise to these challenges, a multitier intelligent computing and storage framework can accommodate this spectrum shift of data growth. In this chapter, several IoT applications for smart cities are first introduced. Next, the chapter introduces a multitier framework that addresses intelligent IoT applications computing and storage requirements. Finally, the security of the IoT data in motion and at rest is discussed.

Introduction The drastically growing number of the connected Internet of Things (IoT) devices generates enormous data. In 2003, the ratio between the world population and the connected physical objects was 0.08 (Evans 2011). That is, approximately 6.3 billion people living on the planet and 0.5 billion connected devices. After the introduction of smartphones, tablets, and smart biomedical devices in 2010, the ratio raised to 1.9. According to the European Telecommunications Standards Institute (ETSI), there will be over 20 billion connected devices in 2020 (ETSI – Internet of Things – IoT Standards|Machine to Machine Solutions (M2M) n.d.). Almost every person is interacting with lots of sensors daily. Take the smartphone as an example. Smartphones come with a basic set of sensors. For example, the digital compass or the magnetometer allows your phone to always identify the north direction. Besides, smartphones have other sensors such as accelerometer, gyroscope, and barometer, just to name a few. That is how sensors are close to us. On a larger scale, another example would be the smart city or the digital city where sensors are in every field and domain of our lives. In such a deployment, millions of sensors are embedded all over the city. In addition, for smart cities, sensors play an important role in the building of any system and in the provision of any service. For instance, systems for healthcare, transportation, energy, manufacturing, agriculture, finance, and residence, to mention a few, demand the deployment of thousands of sensors. Moreover, each of these systems has different constrains regarding IoT sensor data. Healthcare subsystems that monitor patient’s vital signs require real-time data collecting and analysis. While other subsystems for transportation might need the analysis of 1 year old to infer new knowledge.

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Applicability Use Cases Healthcare and Telemedicine Healthcare technology has increasingly invested in smart and connected biomedical solutions. Now, telehealth allows caregivers and doctors to monitor their patients’ health remotely and intervene when necessary. These solutions require huge computing resources for continuous, intelligent, and ultralow latency decision-making based on massive heterogenous and scalable data. However, body sensor networks (e.g., wearables) which collect patients’ data are highly constrained in computational power, energy, and storage capacity. They neither have the capacity to store large data nor they have the resources required to process the data. Existing healthcare services solutions (such as Oberdan Rolim et al. n.d.; He et al. 2012; Hoang and Chen 2010) offer IoT-cloud-based pervasive health monitoring systems which are able to acquire patients’ vital data from a sensor network and transmit it through a gateway to a remote cloud service for processing and diagnosis. This architecture extends many advantages such as cost reduction, location independency, and on-demand services provisioning. However, transmission latency in these solutions is a critical issue. Healthcare applications with ultralow latency requirements such as emergency alerts based on multisource learning are latency intolerant. Thus, it is necessary to develop a new architectural approach to address life-dependent and time-sensitive healthcare application requirements. To rise to these challenges, software-defined edge computing can potentially overcome the existing art limitations. Edge computing is an umbrella term that refers to bringing computation infrastructures closer to the edge of the network. Edge networks inherit all the benefits of cloud and add characteristics such as low latency, location awareness, geographical distribution, mobility, and heterogeneity (Bonomi et al. 2012) (see section “Computing Continuum for IoT Data” for more details). In this architecture, sensor data is collected to a centralized smart node, a gateway, which can be provisioned at home access point (IoT from Cloud to Fog Computing – Cisco Blogs n.d.). This approach cannot only reduce bandwidth but also latency. In addition, intensive computational tasks can be offloaded from the sensor device-enabled solutions for advanced computation and services such as multisource learning, task migration, and notification service at the edge of network (Fig. 1). Public Safety and Disaster Response Today, the fifth-generation (5G) mobile systems transform cities into intelligent orchestrated infrastructures and cement a powerful relationship between services and operators. This opens up a new breed of opportunities for vertical industries such as autonomous drone surveillance and navigation. Autonomous drones will soon have a major impact in areas including agriculture, communication, and disaster response (Floreano and Wood 2015). Drones have been extensively utilized for disaster response due to their agility, mobility, and size which make them suitable to collect insight about impacted areas (Petrides et al. 2017). In Ardiansyah et al. (2020), an edge-enabled aerial disaster relief response system, called EagleEYE, is

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Fig. 1 Software-defined homecare

Fig. 2 Aerial-based response system for urban disasters

proposed. EagleEYE builds on top of an intelligent edge-enabled platform called 5G-DIVE (5G-DIVE – EDge Intelligence for Vertical Experimentation n.d.). Figure 2 shows a simplified illustration of the proposed response system architecture. EagleEYE takes drone video stream and location information as inputs.

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The video and the location data are preprocessed using an analytics subsystem to be used for machine learning training and inferencing. After a model is trained to detect a person in need for help, EagleEYE deploys the trained model in computing nodes at the edge of the infrastructure for low latency inferencing. Once a person in need for help is recognized, relevant information is instantly sent back to the mission control to aid rescue teams.

Smart Transportation Smart transport systems will soon be crucial components of smart cities. According to the 2018 Revision of World Urbanization Prospects produced by United Nations (Boukerche and Coutinho 2019), 55% of the global population lives in urban areas, and this number is projected to increase to 68% by 2050 (Boukerche and Coutinho 2019). When there is a large influx of people in urban areas, it is important that the transportation system of the cities is excellent for ease of mobility. The development of transportation systems is an indication of social progress in any country, since it connects people and eases the movement of goods and services around the world. Transportation systems in developed countries account between 6% and 12% of the total GDP. There’s a correlation between higher income levels and greater share of expenditures toward transportation. In addition, the average household expenditure toward transportation accounts to 10–15%, and people spend about 8% of their time commuting to and from work (Harbers and Snellen 2016). All this is indicative of how important transportation systems are to the development of smart cities. Thus, it’s of paramount importance that the challenges and bottlenecks facing transportation systems are quickly identified and resolved. Furthermore, the major challenges which cities face are the following: 1. Road safety: fatalities in road accidents stand at 1.35 million globally (Road Traffic Injuries & Deaths: A Global Problem|CDC n.d.). 2. Vehicle emissions have been significantly contributing to global warming (Car Emissions & Global Warming|Union of Concerned Scientists 2014). 3. Traffic congestion has always been an issue slowing down the logistics of major business and the workforce. In the context of smart cities, intelligent transportation systems or smart transportation systems aim to give several value propositions. For example, ITS enhances safety through connected cars and autonomous and semiautonomous vehicles. Also, ITS aims to increase environmental friendliness and eliminate congestion through better fleet management, besides implementing various systems like intelligent traffic control to reduce the emissions. All of these would result in better productivity and quality of life. In brief, data-driven intelligent transport systems will be expected to have the following components, some of which are already being implemented in smart city projects around the world (Ning et al. 2019): 1. Smart vehicle control systems 2. Smart travelers information systems 3. Smart transportation management systems

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4. Smart public transportation systems 5. Business models for vehicular infrastructures These would be accomplished by using multitier computing depending on the context of applications and relevant IoT systems. The choice of the tier would depend on the latency and computational power requirements. Accordingly one of the three tiers, i.e., device, fog, and cloud, or a combination of these will be chosen. As a specific use case of this emerging paradigm of ITS, connected cars are a good example; they demonstrate how the services are implemented across various tiers. Connected cars can be classified into four stages: 1. Cars are connected to the Internet for the entertainment, like streaming music movies or other media and for some basic infotainment requirements of the end user. Typically data is downloaded from the cloud of the service provider, through Wi-Fi hotspots or LTE network. 2. The second stage of connected cars aims enabling remote services assisting vehicular navigation. This involves real-time collection of vehicle location and vehicle status. Vehicles are connected to a network of service providers. Existing example is the telematics services provided from the car makers, generally handled by mobile network operators (MNOs). In this stage computing could be a combination of edge and cloud. 3. The third stage is connection to immediate surroundings with focus on safety. Cars are equipped with various sensors using RADAR, LIDAR, etc., to safely detect pedestrians and other objects. There would be systems in place to avoid collision avoidance to achieve a safer driving experience. This is where all the vehicle-to-everything protocols come into play, for example, vehicle-to-vehicle communication and vehicle-to-network depending on the situation. The processing is done in the vehicle due to the latency requirements. 4. Fully connected vehicle – in this stage the aim is to have ideally autonomous cars without the need of a driver interaction. This is an emerging technology, and with the spread of 5G networks, this is expected to gain traction. In such a vehicular system, a combination of various sensors in the vehicle collects the data and is supported by the edge and cloud computing. Google’s self-driving cars and Tesla are heading toward making cars fully autonomous. Connected cars stages demonstrate various tiers of computing coming together for the intelligent transportation systems. Another area of interest in smart transportation is video analytics to be used in the context of smart traffic management systems. There’s a lot of research being carried out in this field (Chang et al. 2019), where the goal of vehicle detection is for better traffic flow control and anomaly detection. This would fall under a broader framework of advanced traffic management systems. Other services which are already emerging in various smart cities around the world are Mobility-as-a-Service, where entire trip planning, booking, and choosing rides are all streamlined for the passengers and also public transportation and ride-sharing services with contactless payments and shared

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bicycle systems which can be rented on demand for a nominal fee in specific stations (Smart City Transportation: Benefits & Examples from Leading Cities n.d.). In summary ITS will be a driving force toward the development of smart cities.

Smart Gardening According to the World Bank, over 55% of the population lives currently in cities, and by 2050 nearly 7 out of 10 people will settle in urban areas (Urban Development Overview n.d.). As a result, the increase in population will pose challenges in several areas of people’s lives. An example of such challenges is the agricultural lands, which will suffer a decrease in its production rate. One of the urban strategies to overcome such a challenge is to exploit urban gardens for agriculture (Carrión et al. 2018). Assisted by the IoT and its technologies, smart gardens can effectively produce crops. Authors in Carrión, Huerta, and Barzallo (2018) proposed an IoT solution to plant two types of vegetables Brassica viridis and Lactuca sativa in an urban garden. The system contains three subsystems: firstly, a mechanical subsystem that is responsible for monitoring each part of the garden supported by a wheelmotor mechanism; secondly, an electronic subsystem that contains a controller and sensors to measure the humidity and temperature of the soil; and, thirdly, an information technology subsystem that connects the system to the Internet. Furthermore, the system is equipped with a feature that allows users to monitor gardens through a mobile application. To that end, the user can preset the variable related to the climatic requirement of each plant. The electronic subsystem has six sensors that acquire physical parameters. The controller analyzes the data and make irrigation decisions based on the pre-settings. Finally, the data is displayed to the user to monitor the garden in real time. In summary, new urban strategies should take into consideration the increase of the population and consider a new solution like smart gardening.

Multitier Reference Framework for IoT Data Processing in Smart Cities The vision of integrating ubiquitous and pervasive computing into the fabric of our daily lives has already become a reality. Two decades ago, Mark Weiser (1999) envisioned that computing will be intensively used in people’s daily lives till they cease to be aware of it. In fact, this is already a norm and can be seen by the IoT devices like connected cars, drones, smartphones, smart glasses, smart watches, and smart bracelets. Having a pervasive computing infrastructure which is geographically distributed with multidimensional data sources enables innovative and novel services. However, provisioning such services for the envisaged future use cases is not possible by relying on the capabilities of these devices because of their computing power limitation. One simple solution is to extend the computational capabilities of the IoT devices by offloading intensive computational tasks to a leased on-demand computing resource infrastructure such as centralized cloud, edge, and fog computing. Figure 3 shows a multitier reference framework for IoT data computing and storage.

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Fig. 3 Mult-itier computing and storage framework

Computing Continuum for IoT Data In this section, different computing paradigms and their pros and cons are briefly explained along with the virtualization techniques that make these computing technologies crucial for innovative smart city applications.

Cloud Computing Traditional cloud computing can solve the computational limitation of the IoT devices. Cloud provides on-demand pay-as-you-go service which is great for users with constantly changing service requirements. Furthermore, cloud enables flexibility and scalability in providing computing, storage, and networking resources in a centralized fashion. Equally important, cloud infrastructures facilitate big data analytics and machine learning techniques due to its powerful computing capabilities. However, cloud technology brings about issues, especially for time-sensitive applications, in terms of network latency and bottleneck because of the remote and centralized nature. Wide area networks are used to provide cloud services and applications.

Regional Cloud Computing For stringent latency vertical services, edge computing is a key technology where services are brought to the proximity of the users for reduced end-to-end latency. Provisioning pervasive computing infrastructure offers a wide range of novel vertical industries. Regional cloud provides computing, storage, and networking resources near to the edge of the network hence reducing end-to-end latency. In the regional cloud

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model, most of the high-volume and high-rate data generated by the IoT devices is processed at the local region. Only a small portion of the data can be sent back to a collaborative cloud thus reducing the networking load. Regional clouds are distributed in nature which minimizes the risk of networking bottlenecks especially when there is a huge number of connected devices with high rate of data transmission. The load of processing such data can be distributed to multiple edge clouds. However, deploying the computational infrastructures near to the edge of the network brings other multifaceted challenges, namely, networking complexity, seamless service delivery, and orchestration (Abdullaziz et al. 2018; Ibrahiem et al. 2019). To address the networking complexity, SDN has emerged as the most promising network paradigm which provides a dynamic and responsive network with programmable and centralized network control. The softwarized and global control of SDN simplifies the network management, facilitates network virtualization, and increases network capability (Abdullaziz et al. 2019). To this end, the complexity of pervasive edge deployment is abstracted from application developers and service providers. Also, SDN network intelligence is concentrated at a logically centralized controller which relieves the burden of executing complex networking tasks, such as service discovery and orchestration, from edge devices. As such, by incorporating edge tier into the conventional cloud infrastructure, the traffic generated at the edge can be routed, using the features provided by SDN, to an edge tier that can satisfy the quality of service required by the user. Although software-defined edge is capable of managing the network and providing a seamless service experience, it still lacks true orchestration automation. The increasingly growing complexity of edge infrastructure operation can no longer be managed by simple techniques that automate repetitive manual tasks. As such, there is an urgent need for real automation capabilities to elevate operational complexity. By leveraging the recent advances in data analytics and machine learning, true intelligent automation can be made possible. Inspired by the new paradigms, knowledge plane for the Internet (Clark et al. 2003) and knowledge-defined networking (Mestres et al. 2017), which rely on machine learning and cognitive techniques to operate the network.

Edge/Fog Computing Edge computing is a trending paradigm that has emerged as a key technology where services are brought to the proximity of edge devices for reduced end-to-end latency. Many terms appeared in academia and industry including multi-access edge computing, fog computing, and mist computing. In this chapter, we embrace all these terms under one umbrella conceptual term of edge computing. Because of its popularity, the multi-access edge computing (MEC) which is being standardized by the European Telecommunications Standards Institute (ETSI) (Hu et al. 2015) is described here. Figure 4 illustrates the ETSI MEC reference architecture (European Telecommunications Standards Institute (ETSI) 2019). The reference architecture consists of two levels of management, namely, mobile edge system and mobile edge host-level management.

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Fig. 4 Multi-access edge computing reference architecture (European Telecommunications Standards Institute (ETSI) 2019)

The user equipment (UE) applications and third-party customers can exploit the MEC via customer facing service (CFS) portal. The UE applications and the CFS portal interface with the MEC system through the system-level management. The system-level management includes a user application lifecycle management (LCM) proxy, operation support system (OSS), and mobile edge orchestrator (MECO). The LCM initiates, terminates, or relocates the UE’s application, while the OSS decides if UE requests are to be granted or not. The MECO, which is the core functionality of the mobile edge system-level management, processes the UE granted requests and maintains overall view of the available edge resources and services. Depending on the UE request requirements, the MECO allocates the virtualized resources to run the MEC application. The system-level management is connected to the MEC host-level management through mobile edge platform manager (MEPM) and virtualization infrastructure manager (VIM). The MEPM manages the application lifecycle, application rules and requirements, and element functions. On the other hand, VIM is responsible for the allocation, management, and release of the virtualized resources provided by the virtualization infrastructure.

Virtualization Techniques Virtualization is the concept of abstracting the underlying physical infrastructure and providing virtualized resources for edge and cloud applications. Edge and cloud

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service providers are the driving force behind the recent advances in virtualization technologies. Traditionally, server virtualization is associated with hypervisor-based virtualization. Recently, container technology has become a promising solution for efficient resource virtualization. Many cloud service providers are now using containerization technologies (Google n.d.; Amazon n.d.). Nevertheless, there are still blurry lines on the differences between traditional hypervisor-based virtualization, system-based containerization, and application-based containerization techniques in the literature (Morabito 2015; Joy 2015). Thus, we provide a concise comparison between those technologies, and we consider kernel-based virtual machines (KVM), LXC, and Docker as references.

Pre-virtualization Prior to virtualization, a physical server is provisioned for a single tenant. That is, each bare metal server is dedicated to run a consumer workload and not shared by others (see Fig. 5a). An advantage of pre-virtualization is the absence of hardware emulation overhead. Although pre-virtualization provides a native computing performance, it does not enable application isolation within the same system. Therefore, it is not easy to deploy instances of the same application on the same machine simultaneously. Hypervisor-Based Virtualization Traditional hypervisor-based virtualization runs at the hardware level and provides independent and host-isolated virtual machines (VM). Each VM runs its own kernel and operating system (OS). Therefore, the hypervisor can create Windows guests on Linux host. However, isolation and host abstraction features come at a cost. Memory, disk, and CPU resources must be specified at runtime to execute VM kernel and OS. Also, hardware emulation is required for I/O operations (see Fig. 5b). In the case of high-density virtualization, VM deployment becomes resource inefficient, especially for small edge and cloud applications. System-Based Containerization Container isolates processes at the OS level and runs on top of the host kernel. There are two types of containers, namely, system container and application container. System containers (also known as machine containers) behave like a standalone

Fig. 5 Virtualization techniques

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Linux system. That is, the system container has its own root access, le system, memory, processes, and networking and can be rebooted independently from the host. While system containers are lightweight due to the absence of guest kernel and hardware emulation, they can only run on Linux host and are bound to the host’s kernel (see Fig. 5c).

Application-Based Containerization An application container (also known as process container) isolates an application from other applications running on top of shared kernel and shared OS. Because of sharing the same kernel and OS, application containers are lighter than system containers. A well-known example of an application container is Docker. The application container only encapsulates the necessary libraries, configurations, and dependencies needed to run the application (see Fig. 5d). Therefore, its resource footprint is significantly lower than VM and system containers. This fact enables the instantiation of lightweight containerized application.

Intelligence for Smart Cities Machine learning and cognitive techniques have made impressive breakthroughs and are being used in many fields such as computer vision, speech recognition, and bioinformatics. Machine learning techniques are especially pertinent in solving many challenges facing smart cities. The advantage of using machine learning techniques is the efficiency with which the complex problems can be solved. Machine learning allows systems to perform with human-like intelligence when it comes to tasks requiring prediction, classification, and decision-making. This section will serve as a primer for machine learning and discusses relevant intelligent applications for smart cities. Machine learning is defined by Arthur Samuel, a pioneer in the field of ML, as “Field of study that gives computers the ability to learn without being explicitly programmed” (Samuel 1969). This definition sufficiently describes what machine learning does. In the modern context, this is about giving the devices (any IoT device) the ability to learn without explicitly programming. A more formal definition is given by Thomas Mitchell: “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E” (Hierons 1999). A simple example which would fit this definition would be voice commands functionality in many smart homes through which the intelligent systems cater to the user’s needs. In this case, the task “T” could be turning on lights or playing a particular audio. The performance “P” refers to how accurately the voice commands are translated to the required action from the user. The experience “E” would be the data collected by the services regarding past performance of the system. Building on this definition, a generic ML system needs the experience “E” to improve performance overtime. This is how “computers learn without being explicitly programmed.”

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Fundamentally, machine learning algorithms can be computed in one of three tiers, namely, IoT device, edge, and cloud. Depending on the computing intensity of the algorithms and the latency requirements of applications, a computing tier or a combination of computing tiers is chosen. ML systems broadly fall into one of the following paradigms: 1. Requirement of human supervision or independent (supervised, unsupervised, semi-supervised, or reinforcement learning). 2. “Learning” online or offline (online learning or batch learning). 3. Infer by detecting patterns or by comparing new data to known data (model-based or instance-based learning). This classification is not exclusive as categories can be combined to specific application requirements. For example, email spam filters can learn from labeled samples on the mail box using a deep neural network model which makes it a supervised online and model-based learning system.

Supervision in Machine Learning Machine learning can be classified based on amount of supervision required by human during training phase. Accordingly, the ML techniques broadly fall into one of the four paradigms as follows.

Supervised Learning The training data is labeled with the desired output. In general, there is an idea of expected output in supervision problems. There is also a definite preexisting relationship between the input and the output in problems involving supervised learning. Spam filter is a typical example of supervised learning where the machine classifies emails based on previously learned examples. Supervision problems can be further classified as regression and classification problems. In regression, the output is over a continuous stream of data. The input dataset is mapped to a continuous function to predict the output. Predicting market prices of real estate or stocks where prices are a continuous dataset are well-known examples. In classification, the predicted output is a discrete value, for example, if the objective of the pricing problem is to determine if the particular stock or real estate would sell above a particular threshold (Ng 2000). Unsupervised Learning The training data is not labeled, and the system learns from the data without an instructor. Typically, there is little to no information on the expected result in unsupervised learning; these can be further classified as clustering and non-clustering problem. Clustering problem: Where from a collection of datasets of different items, the goal is to group the data based on common factors; an example would be a supermarket in a city trying to classify its customers based on brand

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preferences and predicting the demand of goods. Non-clustering problem: The objective is to find structure in a chaotic environment. For example, identifying the voice of the end user to activate the voice command of a service in a chaotic environment like a party, commonly known as the cocktail problem (Ng 2013).

Reinforcement Learning Reinforcement learning (RL) does not require human involvement as the system learns through experience. An agent observes the environment, performs action based on the state of the environment, and then gets a reward or penalty in return. Agent can be any piece of code that is made to use the intelligence to automatically carry out an assigned task (Jȩdrzejowicz 2011). The system develops a policy to gain the most rewards over time. A robot learning to walk, such as AlphaGo, is typical example of reinforcement learning (Kaelbling et al. 1996). Training in Machine Learning Training is an important part of machine learning. Training a machine learning model means learning (determining) good values for all the weights and the bias from labeled examples (raw dataset fed to the system). In supervised learning, a machine learning algorithm builds a model by examining many examples and attempting to find a model that minimizes loss; this process is called empirical risk minimization. And loss is defined as the penalty incurred by the system for a bad prediction (Descending into ML: Training and Loss|Machine Learning Crash Course n.d.). Based on the method of training there are two paradigms: Online Training Machine learning systems that are capable of incremental learning use online training. In such systems, data is fed incrementally, and the systems learn and improve as the data is made available. This type of training is important for systems that need to learn from continuous stream of data and adapt to changes rapidly such as stock market price prediction. Offline Training Machine learning systems that require intensive computation and long training time are suitable for offline training. Such systems are incapable of incremental learning on the go. First, the system is trained on available data and then gets deployed for production. Next, when a new batch of data is available, the system is retrained on overall data again. This is also known as batch learning (Lange et al. 2015).

Generalization in Learning Instance-Based Learning Machine learning system learns from memory of previous examples in the training data and generalizes to new instances. This type of learning system is usually hard coded and does not perform parameter tuning (Daelemans and Van den Bosch 2005).

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Model-Based Learning Reinforcement Learning In contrast to instance-based learning, model-based learning builds a model from the training data and generalizes to new data. In particular, this type of learning system tunes parameters to model a problem as accurately as possible (Fürnkranz et al. 2011).

Federated Learning Federated learning is a more modern approach toward ML pioneered by Google. In this approach the training of the ML algorithm (high-quality centralized/global model) is carried out over multiple devices in the network. The global model is later on updated through an aggregation of data from various devices on the network. Depending on the sensitivity and user privacy, the data transmitted from the device to the centralized model can be restricted (Khan et al. 2019) (Google AI Blog: Federated Learning: Collaborative Machine Learning without Centralized Training Data 2017).

Deep Learning Deep learning is a subclass of machine learning algorithms, where learning happens over multiple layers of abstraction. The techniques discussed in the previous sections are usually applied over multiple hierarchical layers so models become refined (Deng and Yu 2013). This is especially useful to identify complex relationships between data for better results. For example, smart cities resource management can use deep-learning-based prediction techniques to improve allotment of resources (▶ Chap. 4, “Urban Computing: The Technological Framework for Smart Cities” by Bouroche and Dusparic). Further details on deep-learning algorithms in the context of smart cities and healthcare can be found in Bilal et al. (▶ Chap. 5, “Smart Cities Data: Framework, Applications, and Challenges”).

Learning in Different Tiers These ML algorithms can be used in different tiers according to the requirements of specific application. In the implementation of ML algorithms, there are two steps, namely, training and inference. Training is the phase where the system is trained to take the desired action, and inference is the actual response of the ML system for a particular scenario. This process happens over various tiers: device, edge, and cloud depending on the application (see Fig. 6). Usually, for complex application, there is also a deep-learning (DL) framework involved.

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Fig. 6 Inference and training in ML systems

Healthcare When it comes to healthcare systems, a combination of ML and DL techniques are used depending on the system in place. Irrespective of the type of the healthcare system, the training and inference of the model for diagnosis of the patient depends on the time sensitivity. As an example consider the healthcare systems described in section “Healthcare and Telemedicine.” With the existing pre-collected data of the patient’s health condition and with the knowledge of known health conditions, the training of the ML model would be ideally done in the cloud. If the healthcare system detects any health anomalies in the patient’s health, the diagnosis has to be quick; therefore, the inference would be made in the edge node. Thus for healthcare systems, the two tiers used would be cloud for training the model and the edge for inference due to the time sensitivity. RoboNet RoboNet is an open database of robotic experience which can be used to efficiently train robotic interactions for various applications. In the context of smart cities as

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automation becomes more widespread, this may be the direction to efficiently train and deploy robotic ML systems for diverse open world problems. RoboNet is based on a type of offline reinforcement learning (Google AI Blog: An Optimistic Perspective on Offline Reinforcement Learning 2020). The ultimate aim of RoboNet is create an ever-growing open database of robotic experience which hosts millions of video frames from several platforms. This is particularly useful in robotics training due to the limited availability of data set. For training models, RoboNet proposes a Large-Scale Multi-Robot Learning (Dasari et al. 2019) where training is done with the preexisting data of past robotic interactions, and then the model is refined for the desired robotic application. In this case the learning happens in cloud tier, where there is a large repository of data, and the inference is at the edge.

Federated Learning on Multiple Tiers Due to the distributed nature of federated learning, this ML algorithm is most versatile and is applied over multiple tiers – be it device, edge, or cloud depending on the application. This is a prime example of how different layers come into play to completely implement the ML system. Google’s Mobile Vision API and predictive texting of Gboard – the default virtual keyboard in many Android devices – use federated learning. This can be expanded to other applications pertaining to smart cities. In the case of Gboard, the devices download the global model from the cloud, and the training happens in the device. The model learns with the user interaction to the Gboard and improves. According to Google, all training data stays on the device (Google AI Blog: Federated Learning: Collaborative Machine Learning Without Centralized Training Data 2017). A summary update of the improved model from the device is sent as an update to the cloud so the overall universal model can improve. In this case the inference happens at the device, and the training happens both in device and cloud. In another variation of federated learning, the global model is trained in the edge node for IoT devices connected to a small edge network. Inferences and further refinements happen on the IoT devices; this is known as edge-based federated learning. This is in contrast to cloud-based federated learning where the global model resides in the cloud (Khan et al. 2019). The greatest advantage of federated learning lies in the flexibility to choose the tier for training and inference, and depending on the privacy and latency requirements, the data transmitted from the device or edge can be limited to a great extent. Intelligent Drones The application of drones in the context of smart cities as referred in sections “Applicability Use Cases” and Multitier Reference Framework for IoT Data Processing in Smart Cities” is another example of how ML algorithms for computer vision, object detection, is applied over various tiers. Usually the training and the refinement of the model are done in the cloud – which can handle intensive resource computation – and the inference happens right at the edge where the drone is controlled. This is due to strict latency requirements when it comes to drones.

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Data Management and Storage This section covers data management in the realm of heterogeneous IoT sensor data acquisition and storage techniques. Essentially, the main objective is to show that data management can be done in a multitier fashion that covers the IoT device, fog, and cloud tiers. Simultaneously, this section expands the work on IoT data frameworks, such as the in-network sensor query processing systems and vertical pipeline frameworks that collect and aggregate IoT massive sensor data (▶ Chap. 5, “Smart Cities Data: Framework, Applications, and Challenges” by Bilal et al.). The storage of large and heterogeneous data is the backbone of any IoT-based solution applications. For example, the data analytics and machine learning modules will execute algorithms on the acquired data to generate useful knowledge out of it. In the context of IoT, such as smart cities, millions of sensors are deployed across a large geographic area. Therefore, a high volume, high speed, and high variety of Big Data is generated by these sensors/devices. The acquisition and storage of such data becomes a challenge that needs to be addressed. Hence, the following subsections explore the data storage and data acquisition for IoT applications.

Data Acquisition IoT use cases encompass a large area of our daily lives. For instance, smart agriculture, smart meter reading, smart building, healthcare, smart transportation, and smart parking are a few use cases where IoT can play a big role. For the aforementioned use cases, tens of thousands of sensors provide data to the backend systems. Hence, uniformly collecting all data generated from all sensors is a challenge. Current IoT applications adopt the straightforward application-specific connectivity from sensing devices up to the cloud (Lan et al. 2019). Furthermore, authors (Zachariah et al. 2015) demonstrated that the implementation of the gateway is a problem with the Internet of Things. Also, the abstract (Zachariah et al. 2015) gave an analogy to the current IoT implementation problem which is equivalent to a browser for each website. Therefore, there is a need for a framework that supports the heterogeneity of the data in IoT. The remainder of this section presents three different methods that could tackle the heterogenous data acquisition: Bluetooth Low Energy (BLE), Unified Access Platform, and topic-based IoT storage.

Unified Access Platform Authors in Lan et al. (2019) proposed the Ontology-Based Resource Description Model (ORDM) to deal with the heterogeneity nature of IoT sensor data. In philosophy, the ontology term refers to the systematic study of being and the existence of all objects. Work in Lan et al. (2019) proposes a general resource description model based on the ontology theory for IoT sensors/devices. Consequently, their solution is expected to deal with the heterogeneity of the sensing devices in IoT. This solution includes the three tiers of storage: device, edge, and cloud. Supported by edge computing, [ontology]’s approach solves delivering information

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in real-time fashion dilemmas. The access platform job is to get the data from the devices and send them to the cloud. Also, it accepts commands from the cloud services and dispatches it to the sensing/device layer. By placing the access platform at the edge between the sensing layer and the cloud, the data doesn’t have to travel all the way to the cloud. Thus, the real-time application can acquire the data much faster. Furthermore, the access platform is capable of doing primary processing on the data. As a result, the cloud service not only receives raw data but also processed ones. The access platform can unify the heterogenous data before sending it to the cloud.

Bluetooth Low Energy In Zachariah et al. (2015), the authors presented a unified solution to the connectivity of a large variety of IoT sensors by exploiting the Bluetooth Low Energy (BLE) radio. BLE is a low-power wireless communication which is very suitable for resource-constraint devices and sensors. All sensors will use the BLE to connect to the Internet. The Wi-Fi and cellular network Internet service in almost all smartphones are connected to the Internet all the time. Besides, smartphones also have the mobility feature by default. Therefore, the provided solution considers using the smartphone as the gateway. BLE is a point-to-point protocol as one node serves as a master and another node be the slave. In the proposed solution, the smartphone is the master node and the sensors/devices as the slave part of the BLE protocol. The sensor devices (slave) sends periodic advertisement messages to neighboring smartphones (master). As soon as the smartphone receives an ad messages/packet from the BLE slave, it establishes a connection to the device according to the BLE spec. Finally, every application will decide on the information to be pulled from the device and then send it to the cloud/edge layer according to the implementation.

Multitier Storage Firstly, the starting point is the data generation which takes place in the sensing tier. For example, Humidity sensor, temperate sensor, and water-level sensors generate data about the environment characteristics. Secondly, these data are sent to the gateway. Finally, the data is stored in the device tier, the edge, or the cloud depending on the use case in hand.

Device Tier Storing the data in the device tier means storing it locally on the device. If the device resources are not capable enough, then the data is stored on the closest gateway. Storing the data on the device tier comes with the following advantages: 1. Security: One of the main advantages is that the data doesn’t have to travel through the network. Therefore, only the authenticated user is allowed to read the data from the device. Also, eavesdropping of the data in the middle of the device and the cloud is eliminated.

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2. Data analytics is also still possible while data is stored in the device. The federated learning technique (see section “Intelligence for Smart Cities”) allows applications to run machine learning algorithms on the locally stored data on the device. Therefore, data do not have to be stored on the cloud to run ML algorithms on it. 3. Less network overhead.

Fog Tier Mashups For a large-scale IoT application, data sharing is a necessary requirement and a challenge at the same time. One solution is mashups. Mashup oriented model is proposed by the authors in Boulakbech et al. (2017). The main goal of mashups is to allow many different users to access different applications such as smart cities, healthcare systems, and smart homes. It’s a challenge to create one platform that supports such heterogeneous Big Data-based applications and services access. In their work (Boulakbech et al. 2017), the proposed architecture uses a multi-rooted tree-based architecture to provide the scalability issue. Firstly, the solution addresses the storage of big data from such diverse resource-constraint IoT devices. Each level in the tree has a set of IoT Big Service that performs a different type of computing on the data. The layer which is near to the device performs a computation on low-level data and passes to the above layer. In each layer, there are different nodes of IoT Big Service. Such an architecture is very suitable for edge and cloud computing. For example, in the fog layer, the services can only transfer the bits data to a higher-level form of data and send it to the cloud layer for further processing. By placing Big IoT services in the fog tier to do primitive transformation and analysis on the data prices increases the performance of the IoT applications. In summary, mashup applications running based upon multi-rooted tree-based architecture are a solution for the large-scale development of IoT solutions. Data Analytics on Fog-Stored Data If the data is stored in the fog, then how machine learning algorithms are going to run on these data? One solution is to use the federated learning technique (see section “Intelligence for Smart Cities”). The authors in Khan et al. (2019) presented a design to enable the application to run ML algorithms on the edge tier.

Cloud Tier The primary solution of data storage for most IoT applications is the cloud tier. The study of the requirement and the study of the use case in hand should determine which back-end storage to be used for the IoT sensor data storage. In small-scale deployment, RDMS is sufficient. However, in large-scale implementation coupled with heterogeneous data, other solutions should be considered. The authors in Li et al. (2012) proposed the usage of the new paradigm NoSQL database management systems for the storage of IoT sensor data. Fig. 7 shows Relational, NoSql, Timeseries, and In-memory database management systems.

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Fig. 7 Database management systems

Topic-Based IoT Storage The authors presented a model for storing structured and unstructured data in the cloud (Pramukantoro et al. 2018). Structured data is easy to store, query, and search. Images, PDFs, and Word files are examples of unstructured data. How to search for a specific document requires more than relation database management systems (RDMS). The proposed solution is based upon using the NoSQL databases MongoDB along with its GridFS component. GridFS enables the storage of a document with a size larger than 16 MB. A classic three-layer architectural design is adopted in the system. First is the filed layer where the sensors, devices, and actuators reside. Second is the Middleware layer which contains the publishsubscribe broker. Therefore, if an entity needs to receive or send data from/to the Middleware layer, it has to subscribe to a topic at first. The topic holds the data provided by the publishers and the broker will send it to the subscriber. Third is the cloud layer where the data is stored and a WebService resides to interact with the Middleware. During the operation, each sensor publishes the data to a topic in the Middleware layer, which is between the cloud service and the device. The WebService also subscribes to the Middleware broker to receive the data. At the back-end storage, GridFS will store data from the camera sensors (unstructured data), and MongoDB will store other sensors (structured data). This use case shows that a collection of NoSQL features can solve the storage of heterogeneous IoT sensor data.

IoT Data Security in Motion and at Rest Security is paramount and a challenge in the IoT ecosystem across all tiers. The pervasive nature of the interfacing components in the IoT systems makes it hard to meet the security privacy requirements. The IoT technologies enable the connection of massive things that vary in capability to the Internet. Therefore, the communication between the presented tiers and the IoT devices must be reliable and secure. On one hand, data transmission security is a mandatory requirement for IoT systems especially for time-sensitive applications. On the other hand, the data at rest (i.e., historical data), which resides on a specific tier, requires privacy and security

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mechanisms to be in operation. Finally, the rest of this section highlights some security solutions for the safety of healthcare systems. Many healthcare services have moved to the cloud recently. Although this trend has obvious benefits including flexibility, scalability, and expenditure savings, security, privacy, and complexity concerns have slowed the adoption. Patients’ health information such as disease diagnosis, medical records, and physical/mental health states are relatively sensitive (Liang et al. 2012). Misuse or inappropriate disclosure of this information violates patients’ privacy. Health-related information is privacy sensitive, and patients prefer to restrict the data access to third parties such as insurance agencies. Another critical concern is unauthorized health data tampering while stored in untrusted third-party cloud servers or in transmission. In addition, insider and outsider malicious attacks pose a serious threat which may (a) disrupt the effectiveness of healthcare services through denial of service attacks which can render the entire system unreliable, (b) mislead caregivers/doctors with false information through man-in-middle attacks, and (c) manipulate cyber-physical vulnerable medical device to put patients’ lives at risk (e.g., smart pacemaker, smart insulin pumps, ventilators, imaging equipment, and patient monitoring systems). Since 2014, health system security has become a hot topic. The US Department of Homeland Security, the Federal Bureau of Investigation, the Food and Drug Administration, and other international regulators warned and expressed utmost concerns about the security level of medical device ecosystem (Symantec Corporation 2015). In early 2015, a major data breach at Anthem (Anthem Blue Cross Blue Shield: Health Insurance, Medicare & More n.d.), the second largest healthcare provider in the USA, exposed 78 million patient records. Symantec systems traced the attack to a well-funded attack group named Black Vine (Symantec Corporation 2015). Symantec also reported that out of 200 breach incidents in the service sectors which occurred in 2016, 120 incidents are attributed to healthcare in search for intellectual property, identities, insurance information, and medical records. The value of healthcare data is 10 times more than credit card data in the black market (The Washington Post n.d.). Healthcare service providers pay heavy costs due to healthcare breaches. It is estimated that the industry spends $6 billion annually on data breaches and an average of $2.1 million per breach. More than 90% of healthcare providers have reported a data breach, and 40% have reported more than 5 data breaches in the past 2 years (LLC 2015). The impact of healthcare breach on healthcare providers is not only in terms of financial expenditure; cost can also be in terms of disruption of care, potential lawsuits, reputation risks, and patients’ trust. Due to the growth in the number of data breaches incidents and healthcare information increasingly moving to the cyberspace, industry and academia are now paying closer attention toward healthcare information’s security and privacy. In Zhang et al. (2015), for instance, security and privacy issues in mobile healthcare networks were investigated. In addition, they introduced a mobile healthcare network architecture which include cloud-based solutions for data privacy, secure data access, and protection against malicious attacks. All their proposed solutions are from the quality of protection perspective where security schemes can be provided in different levels to ensure suitable trade-off between performance and security

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requirement. Another healthcare network architecture named BSN-Care appears in Gope and Hwang (2016). BSN-Care is and IoT-based healthcare system which utilizes a local processing unit (e.g., smartphone or PDA) to collect sensor data. This data is then sent to an offsite cloud server for analysis, decision-making, and notification. The security consideration in this work is achieved in an anonymous authentication protocol and authenticated encryption. Looking at the industry efforts, security providers are developing solutions specifically to manage and protect healthcare data, on-premises or in the cloud. Symantec claims that their offered security solutions can help prevent data breaches before they happen and manage sensitive data on-premises or in the cloud. Some of Symantec security solutions include: 1. Protect healthcare systems and data from advanced attacks: prevent advanced threats across endpoints, networks, and email through Symantec Advanced Threat Protection, Endpoint Protection, Email Security cloud, and Web Security cloud. 2. Safeguard patient records and sensitive information everywhere: manage and protect data on-premises or in the cloud through Symantec Data Loss Prevention and Encryption solutions. 3. Effective risk management and compliance: comply with regulations such as HIPAA, HITECH, and DEA through Symantec Control Compliance Suite. 4. All-time security monitoring: provide real-time security monitoring and threat intelligence services through Symantec Managed Security Services, DeepSight adversary and technical intelligence services and incident response. 5. Extending security to the medical device ecosystem: manage and track medical devices, and automate medical device risk management. Furthermore, Intel Security along with McAfee are taking part in securing healthcare services infrastructure. Intel states that its Security Connected platform offers an adaptive architecture to help reduce risk and response time and lower overhead and operational costs (McAfee 2013). Similar to Symantec, Intel Security looks at the same goals with unified management platform that can be utilized by products, services, and healthcare providers (McAfee 2015). Among the security services that Intel Security provides are the following: 1. Preventing misuse and data loss by maintaining data integrity and control over sensitive information 2. Blocking phishing and malware through advanced malware engines on gateways to prevent phishing links and malware downloads 3. Encrypting valuable data at rest, in use, and in motion 4. Monitoring access of detailed analysis of interactions with structured data contained in back-end databases Most of the existing healthcare security and privacy solutions are limited to the scope of the considered environment such as indoor or outdoor. In addition, they depend primarily on cloud computing which incurs network transmission delays. As such, it is

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crucial to develop a universal software-defined architectural security solution that also considers edge computing technologies such as fog and mobile edge computing.

Conclusion This chapter introduced a framework for IoT sensor data processing and storage across various computing tiers in the context of smart cities. In the first section, the growth of the data traffic due to the increasing number of connected IoT devices is discussed. This growth cements a relationship between the innovative applications and services and the advances in the field of wireless communication. Then, use cases were introduced to show the applicability and the necessity of a unified and multitier framework to satisfy the stringent requirements. Next, section “Computing Continuum for IoT Data” demonstrated the computing continuum that forms a multitier computing framework which commences at the cloud and terminates at the user IoT devices. To this end, the computing and the latency requirements of the cutting-edge IoT applications can be fulfilled in a suitable tier. For example, time-sensitive applications with relatively low computing intensity requirements can be accommodated in the edge or fog tier near to the IoT devices. In contrast, highly intensive applications that are time-insensitive can be fitted in the cloud tier. Furthermore, the section includes concise description of the prominent virtualization technologies which enables the computing continuum with multi-tenancy, scalability, and on-demand resource provisioning capabilities. Section “Intelligence for Smart Cities” explained the fundamentals of machine learning and cognitive techniques and highlighted their use to specifically address the challenges faced in the smart cities. Equally important, the section introduces smart city intelligent applications and emphasizes that the training and the inference of these intelligent applications can be distributed in different computing tiers. In section “Data Management and Storage,” the IoT data acquisition and management is addressed. With the exponential growth in data volume, new tools and paradigms such as the Unified Access Platform and Bluetooth Low Energy were developed for data acquisition. As for data storage, depending on the requirements such as security, privacy, and volume, the tier of storage varies. Finally, in section “IoT Data Security in Motion and at Rest,” the security and privacy of the IoT sensor data is discussed where the security of the healthcare systems is taken as an example to highlight the importance of strong security for user data.

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Deep Learning for LiDAR-Based Autonomous Vehicles in Smart Cities Vinay Ponnaganti

, Melody Moh

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, and Teng-Sheng Moh

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Deep Learning for Object Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What Is Deep Learning? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What Is Object Detection? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Training a CNN for Object Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inference at the Edge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LiDAR in Autonomous Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensor Types in Autonomous Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LiDAR Fundamentals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LiDAR’s Relevance in Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LiDAR and Deep Learning for Autonomous Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Autonomous Vehicles in the Smart City Ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LiDAR for Pedestrian Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case Study: Creating a Deep Learning Model for LiDAR-Based Inference . . . . . . . . . . . . . . . . . . LiDAR Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Parsing and Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sample Data and Labeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CNN Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dataset Creation and Labeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . System and Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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V. Ponnaganti · T.-S. Moh San Jose State University, San Jose, CA, USA M. Moh (*) Department of Computer Science, San Jose State University, San Jose, CA, USA e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_65

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Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future Research Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Autonomous vehicles and deep learning are an integral part of smart cities. They interact and communicate with their surroundings, requiring high computer vision accuracy to maintain driver and pedestrian safety. Many autonomous vehicles leverage deep learning for detection and utilize a suite of sensors that are specific to their environment or use case. In such deep learning environment, sensor data is used as input to neural networks that make decisions regarding the vehicle’s response or reaction to its environment. These sensors in autonomous vehicles provide details regarding the vehicle’s surroundings and potential obstacles. Many sensor suites are starting to contain light detection and ranging (LiDAR) sensors, as the cost of the technology decreases and becomes more widely available. LiDAR technology uses focused light to detect distance, providing an accurate description of the sensor’s surroundings, such precise account is crucial for autonomous driving in ever-changing smart city environments. This chapter covers different applications of LiDAR technology and the use of the sensor data in deep learning applications for smart cities. A case study is also featured to illustrate a potential implementation, which is followed by discussion of future research directions.

Introduction Smart cities utilize various emerging technologies (▶ Chap. 1, “Smart Cities: Fundamental Concepts,” by James et al.). Among them and most notably, artificial intelligence (AI) is constantly at work, interacting with other AI devices as well as with physical objects and people. The interactions among AI, real-world objects, and people present a significant challenge for developers of smart city technologies. An intelligent voice assistant that helps a resident navigate a smart city, a robot that moves inventory around a warehouse, and an autonomous vehicle that has to brake if a pedestrian runs out in the middle of the road all share the same fundamental needs. They need two essential qualities: (1) high levels of accuracy and (2) the ability to respond to a situation in real time. Deep learning is a subset of machine learning techniques. It has become a popular methodology to develop AI models due to its high accuracy in object recognition, natural language processing, and robotics. When optimized for inference and deployed at the edge on the right hardware, these deep-learning models can achieve the low latency required for smart-city environments. This chapter provides an overview of deep learning for object recognition, with a focus on the potential of AI to harness data from LiDAR sensors in autonomous vehicles for the recognition of pedestrians, cyclists, and other vehicles. Artificial neural networks can detect the presence of people or other cars around an

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autonomous vehicle, helping the vehicle navigate the scene without causing an accident. Note that while regular cameras may also be used for this purpose, this chapter explores the benefits of LiDAR sensor data to address privacy concerns around camera data and its potential uses in smart-city security tools. LiDAR is a sensing method requiring the firing pulses of a laser of light. Since LiDAR data captures information in a 3D point cloud rather than a traditional image, it is inherently anonymous and can provide an effective alternative to camera data, particularly in smart city applications. In such applications, the relevant details are the presence of a human and distance from the smart device – not the individual identity of the person, as is needed for applications using facial recognition; this makes LiDAR the choice technology that preserves privacy. In addition, LiDAR outperforms traditional cameras in low-light and low-visibility conditions, making it a sensor that can be used either in combination with cameras or on its own. As the LiDAR sensors become increasingly affordable, this data source could be used for a diverse range of smart city applications using deep learning. Since the self-driving cars under development today already use LiDAR for training purposes, this chapter dives into the application of deep learning for LiDAR-based object detection in autonomous vehicles.

Deep Learning for Object Detection What Is Deep Learning? A subset of machine learning, deep learning is widely used in the field of computer vision, powering applications such as object detection, facial recognition, and image segmentation. While machine learning encompasses the whole gamut of predictive algorithms, deep learning models, known as artificial neural networks, are inspired by the architecture of the human brain. Just as brains are, in the most simplistic form, composed of neurons and axons, so too are artificial neural networks. However, in deep learning models, neurons are organized in discrete layers. Each layer is connected to previous and following layers with the use of axons, forming a network that passes information from the first layer to the last. Axons are represented by weights, an estimate of how correct its corresponding neuron’s output is. These are tuned during the neural network’s training, or learning, process. Deep learning models are unique from traditional machine learning models because the algorithms learn features of the dataset automatically, without a need for hand-designed features (Alom et al. 2018). With enough training data, artificial neural networks can learn relevant features of a dataset and achieve stateof-the-art accuracy for tasks like object detection, image segmentation, or translation. The desired use case is to train a network using sample data to create a model that can detect or describe information based on an input. These inputs can be images, text, audio, or any number of digital formats that generates an output that can classify objects or detect scene information. Applications of this technique are endless, as they are used in fields from biomedical engineering to autonomous vehicles. But the key element is having enough data – in general, the more data on hand, the better the model can be trained.

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Convolutional Neural Networks As the modeling techniques have matured with time, there exist many architectures and variations that build on this premise. One architecture in particular, a convolutional neural network, is popular in classifying and detecting objects in camera images. Convolutional neural networks (CNNs) build on traditional networks by adding a series of convolution and pooling layers that are usually used before the input layer to the neural network. CNNs are often applied for detecting and classifying objects in images, anything from detecting dogs to brain tumors. The first successful CNN was trained by AI pioneer Yann LeCun and his fellow researchers in 1998 to classify handwritten digits (Alom et al. 2018). Over a decade later, CNNs took off in popularity following a paper by the University of Toronto’s Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton. They applied convolutional neural networks to the popular ImageNet dataset, and were able to classify over a million images with record accuracy levels. Convolutional networks parse 2D and 3D images pixel by pixel, learning the features of an image through a numerical understanding of the pixels that make up the picture – the input layer for the network. Several more layers make up the CNN: matrix operations are performed in the convolution layers, while pooling layers, also known as sub-sampling layers, reduce the size of the output maps, which in turn reduces the amount of memory required and increases the number of layers that can be added to the CNN. Often, convolution layers alternate with pooling layers, with another type of layer, called fully connected layers, in the last couple layers of the neural network. Known as a feedforward neural network, information in CNNs goes from layer to layer to determine the output.

What Is Object Detection? CNNs are often used to process visual data, making them ideal for the task of object detection. Object detection is composed of two subtasks: locating objects and classifying objects in an image (Zhao et al. 2019). An object detection algorithm will draw a bounding box around the object, labeling it with the type or class it has been identified as by the network. Per Zhao et al., there are two types of algorithms used for object detection: region-based CNNs and regression/classification-based models. One type of region-based CNN, known as R-CNN, starts by proposing regions of interest with candidate bounding boxes. The models then look within each proposed region, extracting a feature from each. Lastly, the model uses a classifier model to label each bounding box’s feature based on the categories the model has been trained on. R-CNN takes significant training time and storage memory, inspiring other researchers to create more computationally efficient CNNs for object detection. Rather than the neural network passing over the same image multiple times for region proposal, feature extraction, and classification, researchers developed CNNs that can perform object detection with just a single pass through the input image.

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This not only saves time but also reduces the hardware requirements in terms of GPU memory to efficiently run such models. Known as one-step regression/classification models, these neural networks aim to speed up the training and inference time for object detection, making it easier for real-time object detection – a necessity for low latency edge applications in smart cities. With a single, end-to-end neural network, the YOLO models by Redmon et al. – short for “You Only Look Once” – directly predict both bounding boxes and class labels at the same time, by dividing each input image into a grid and predicting bounding boxes and classes for each cell in the grid (Zhao et al. 2019). Based on the confidence levels of these predictions, a final output is created (See case study in this chapter for an example of YOLOv3 in action.)

Training a CNN for Object Detection When getting ready to train a neural network, developers must choose a dataset that’s large and varied enough to achieve accurate results from the trained model. But finding the right dataset can be tricky – especially in specialized fields like healthcare, where the number of available medical images that show a rare brain disease may be extremely few, or manually labeling a dataset of cancerous tissue slides to train a neural network may be too time-consuming for pathologists. To work around such challenges, it’s essential to understand the different types of learning out there: supervised learning, unsupervised learning, reinforcement learning, and transfer learning. It’s also useful to consider data augmentation, a method of expanding a small dataset through a series of image manipulations. As an example, take the smart city use case of an autonomous vehicle learning to understand objects in its field of view as it navigates through city streets.

Supervised Learning In supervised learning, the neural network trains on a dataset that is labeled with the features it is supposed to learn. For the use case at hand, that might be a dataset of still frames from a video taken as a car drives around downtown San Francisco. Each frame in the dataset might have a labeled bounding box around every car, pedestrian, cyclist, street sign, and bus in the scene. By learning from these labels, the trained model will be able to take an unlabeled still image of a street scene and place its own bounding boxes around the cars and pedestrians on the scene, identifying the elements it has been trained to recognize. The model’s accuracy would be evaluated based on how few errors it makes in the classification task. Unsupervised Learning When labeled datasets are hard to find and too time-consuming to create, developers can simply hand over an unlabeled dataset to a neural network and let it find features on its own, structuring the data based on the patterns it detects. The most common application of an unsupervised learning model is for clustering. For example, if a neural network was fed street view images from a variety of different areas, it might cluster together images of freeways in one category, congested city streets in another,

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and open country roads in a third. Other applications of unsupervised learning are for association tasks or for anomaly detection, which would be like recognizing when a car is facing the wrong way against the flow of traffic. Another option is semisupervised learning, or the use of a partially labeled dataset to augment a larger set of unlabeled data. This method is useful for teaching deep learning models how to detect objects from complex scenes without requiring as much labor to manually label data as would be needed for a fully supervised model.

Reinforcement Learning Reinforcement learning takes a different approach, consisting of an AI agent that takes action in a virtual environment, receiving a reward if the action it takes moves it toward its goal. In autonomous driving, a reinforcement learning model could be used to train an AI to drive in a simulator. The AI agent would be rewarded for staying in its lane, observing safe driving distance from other cars, stopping at red lights, and maintaining the speed limit. Through the reward system, it would learn the rules of the road, and that it should not run into other cars or pedestrians. Transfer Learning Transfer learning can be used to take an existing model that has been created for one task and transfer it to another task in a different domain. For example, a convolutional neural network might be trained to recognize different articles of clothing such as t-shirts, hoodies, jeans, and dresses. But the same CNN could be repurposed to detect stop lights from street view images instead. This is typically done by taking a popular pretrained model that already has high accuracy for one specific task, copying the weights from the convolutional layers, and replacing the fully connected layer(s) at the end of the network, (Shorten and Khoshgoftaar 2019). Through this method, AI developers can simply fine-tune the layers, training the network on a new dataset for the novel task at hand. This is especially useful when the new dataset is small, or when computation power is limited, making it easier to begin with a deep learning model that already has prior training on a similar task than to start from scratch. Data Augmentation Deep learning models trained with too small a dataset run the risk of overfitting, when a neural network performs well on training data but is unable to generalize to other datasets. To get around this problem in situations where only a small dataset is available, data augmentation is often used. Developers can augment their datasets through a series of image manipulations, such as flipping an image on the horizontal or vertical access, doing random crops of images, style transfer, color transformations, and the use of generative adversarial networks (GANs) to create synthetic data that mimics the original training dataset (Shorten and Khoshgoftaar 2019). By doing so, researchers can generate larger datasets overall, and also create more examples of outlier situations, so that the neural network can

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learn to deal with the edge cases accurately. Without it, training datasets can often have a class imbalance, a situation where, as described by Shorten and Khoshgoftaar, the ratio between majority samples and minority samples is skewed. An example in autonomous driving is the need to teach a neural network how to react in rare situations, such as if there’s a skateboarder in the middle of the road or a tricky freeway onramp where the flow of traffic is the opposite of what is usually expected. If the neural network is not trained on enough examples of such a scenario, it may make the wrong assessment when encountering this unusual situation in the real world. But by using data augmentation, a researcher can have, say, 30 examples in the dataset of these edge cases, instead of just one or two – improving the neural network’s ability to understand and interpret the scene in these critical but rare situations.

Inference at the Edge Once a deep learning model has been fully trained, it has to be deployed to make predictions or interact with data or objects in the real world, in a phenomenon known as inference. To do so, the model must be streamlined and optimized into a more efficient form. When models are trained on large quantities of data, with the neural networks’ weights balanced and finalized, they are extremely computationally intensive (Copeland 2016). To modify a model for inference, it can be pruned of some of the layers within the network that are no longer needed in order to make the correct predictions. Another way to do it is by taking multiple layers of the neural network and combining them into a single layer, compressing the steps for faster inference. Once the neural network itself has been made more lightweight for inference, the next challenge is pairing it with the right hardware. Some neural networks will run in the cloud for inference, with a web browser or mobile application serving as a frontend interface from whence data is sent to the cloud for processing using the deep learning model. This allows for the usage of more heavyweight hardware in cloud servers but adds the latency of transferring data in a round trip from the edge to the cloud and back (CB Insights 2020). Other, more lightweight applications might run on an embedded computer, whether it be the chip on a smartphone, an edge server in a smarty city, or an embedded AI processor on an IoT device that can handle dozens of TOPS, or theoretical operations per second. Optimizing deep learning models for these embedded devices can be achieved using lower-precision arithmetic, or the floating-point or posit number system instead of fixed-point arithmetic to increase the efficiency of memory utilization (Langroudi et al. 2018). Through software and hardware optimizations, deep learning models for object recognition can be efficiently deployed to the edge for real-time smart city applications (Tsai and Moh 2017; Shahriari and Moh 2018), such as traffic control, security, and autonomous vehicles.

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LiDAR in Autonomous Vehicles LiDAR, or Light Detection and Ranging, is a sensing method that requires firing pulses of a focused laser of light. The rate at which the light bounces back is used to determine the distance of surrounding objects, and is typically deployed in sensors that use this data to develop a 3D point cloud of the mapped environment. The LiDAR technique differs from camera sensors because cameras operate much like the human eye, capturing light across the visual spectrum and encountering similar shortcomings to the human eye when it comes to detecting surroundings when the lens is obstructed by water (in the form of rain, snow, or fog) or when light sources are limited (if a vehicle’s headlights fail, for instance, or a security camera is mounted on a dark street corner, it will have little capability to accurately capture useful data from the scene). It is also distinct from radar because LiDAR sensors emit light at a specific wavelength on the infrared spectrum, whereas radar uses radio waves rather than pulsed light. All are used in autonomous vehicle research and development.

Sensor Types in Autonomous Vehicles Autonomous vehicles in development today typically use a combination of different sensors to interpret their surroundings: cameras, radar, ultrasonic sensors, and LiDAR (Campbell et al. 2018). The combination of multiple sensor sources is known as sensor fusion. Individually, each of these sensor types has its own pros and cons and specific properties for helping a self-driving car perceive its surroundings.

Cameras Typically, multiple cameras are mounted around an autonomous car, providing visual data in every direction of the car, including color – an essential capability since traffic lights, lane markings, stop signs, and other features of the road rely on color as a way to indicate the meaning of that particular road feature. Cameras have been around for generations, making them widely available at a low price point and in a portable form factor, making them an easy sensor to use for self-driving cars. However, cameras are limited because since they passively sense light, they are unable to determine how far an object is from the camera without the use of specialized algorithms, which takes time and is not ideal for a real-time self-driving vehicle. As discussed above, cameras are also fallible to environmental conditions including fog, heavy rain, reflective surfaces, or low lighting such as in tunnels or after dark. Radar In the low-visibility situations listed above, where cameras tend to fail or are too inaccurate to be used for an application as critical and dangerous as autonomous driving, other sensors like radar, ultrasonic, and LiDAR can be used as a supplement

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or alternative to traditional cameras. Radar, for example, uses radio waves. A radar sensor will send pulsed waves and measure from where and how fast they bounce back from hitting other objects in the environment (Burke 2019). In this way, radar can determine the distance, angle, and speed of other cars, cyclists, or pedestrians in an environment. Since radar has been used for the detection of planes, submarines, and other vehicles for almost a century, the technology is widely available and cheap, similar to cameras (Campbell et al. 2018). Today’s Advanced Driver Assistance Systems (ADAS) systems often use radar for collision detection and cruise control.

Ultrasonic Modern cars often come with a parking assistant sensor that beeps when the vehicle gets too close to another vehicle, a wall, or other obstacles. Such sensors are based on ultrasonic sensors, which emit sound waves to determine the distance between the sensor and an object (Campbell et al. 2018). Unlike radar, which is typically used for objects 50–100 m away from the sensor, per Campbell et al., ultrasonic sensors are short-range, used with high accuracy in close proximity. These sensors are even cheaper than cameras and LiDAR, but limited in their functionality for autonomous vehicles. LiDAR LiDAR is a powerful tool for autonomous vehicles, because it provides a more thorough picture of the environment, with the instantaneous creation of a 3D point cloud map that is highly detailed, while also being unaffected by the low-light and low-visibility conditions in which traditional cameras often fail (Burke 2019). Still a nascent industry, LiDAR sensors are offered at multiple different price points – from a few hundred dollars to close to $100,000 for a single sensor – with different ranges and resolution levels, which can make them difficult to use when working with limited resources or at a large scale. High-quality LiDAR can be used to map a vehicle’s surroundings at distances of more than 250 m (Campbell et al. 2018), a significantly longer range than either radar or ultrasonic sensors.

LiDAR Fundamentals LiDAR technology relies on the time-of-flight of light that is emitted from the sensor to reflect off an object and return back to the sensor. This also can provide metrics on the reflectivity of the object the light hit. While the fundamental detection technology of the sensor remains the same, there are several variants of the sensor. Three main types of LiDAR sensors are currently in use in the autonomous vehicle industry: scanning, nonscanning, and microelectric mechanical system (MEMS). The paper “TOF Lidar Development in Autonomous Vehicle” by Liu et al. (2018) describes each of these types of LiDAR sensors, along with the pitfalls of each. The most commonly found sensors are currently scanning LiDARs, as they are more readily available and have been in use for a longer time. These can be split into single and multiline sensors, where one is only able to scan data within a plane, and the

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other capable of capturing 3D point clouds, respectively. While single-line sensors may have significantly less spatial information, they are smaller and more costeffective. Nonscanning LiDARs operate similarly to camera sensors. These devices emit a flash of infrared light that is captured by an array detector. Each pixel in the array will represent a distance and requires no moving parts. These sensors are also referred to as solid-state LiDARs and offer 3D data similar to spinning multiline sensors. Solid-state sensors do have a higher resolution, however, they are still under development and are not as readily available. Finally, MEMS LiDAR sensors offer a middle ground between scanning and flash LiDARs. They offer fast mechanical actuators and sensors, however, these are also still under development and are not as readily available.

LiDAR’s Relevance in Industry While the best-known use case for LiDAR is for autonomous vehicle development, the technology has potential use cases across a wide range of industries. Velodyne, a leading manufacturer of LiDAR sensors, retails its high-end products for around $75,000. But new, cheaper solutions are becoming available from multiple sensor companies, some priced at $500 or even lower. Velodyne in January 2020 introduced a shorter-range LiDAR sensor for just $100. Such drops in price point by LiDAR manufacturers could greatly expand the ability of companies to adopt LiDAR in addition to, or as an alternative to, traditional cameras. Automakers also could integrate LiDAR into nonautonomous vehicles for use in backup cameras and ADAS. In the fields of robotics and mapping, LiDAR could be used to more quickly determine time of flight data for autonomous machines that interact with real-world surroundings, in a process known as simultaneous location and mapping, or SLAM (Campbell et al. 2018). LiDAR sensors are also being added to consumer devices, such as the newly released iPad Pro by Apple. A LiDAR sensor is included on the device along with multiple cameras, assumed to be used to enhance the tablet’s augmented reality capabilities for use cases such as virtually placing furniture pieces into a room, which can be modified in real time per the user’s movements. In addition to these use cases, LiDAR also provides the opportunity for de-identified detection of people, which could prove to be a potential alternative to the privacy concerns presented by camera-based systems for smart city and security applications. As the price of high-quality LiDAR sensors comes down to the price of an equally high-quality camera, developers can choose the technology that best suits their application without weighing cost as a major factor in the decision.

LiDAR and Deep Learning for Autonomous Vehicles Deep learning models including classification, scene segmentation, and object detection can be applied to LiDAR sensor data for autonomous driving. In a

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review by Li et al. (2020), the authors explore the application of deep learning models to 3D LiDAR datasets and the challenges of such applications, including the effects of noise in the dataset, incompleteness in the data due to occluded objects or busy landscapes, which lead to gaps in the data when capturing scenes that are changing in real time. LiDAR also collects large amounts of data points compared to standard camera images, requiring significant data preprocessing or heavy computational tools to handle the data workload efficiently for deployment in autonomous vehicles. Despite these challenges, several 3D deep learning models, graph models, viewbased models, and point-cloud-based models have been developed that can parse LiDAR data for AV applications. For the task of object detection from LiDAR data, researchers can either use the 3D point clouds as is or preprocess the data into a regular voxel grid or a 2D grid for processing by the deep learning models.

Autonomous Vehicles in the Smart City Ecosystem Autonomous vehicles are a key element of the smart city vision of smart mobility. Self-driving vehicles can improve the efficiency of public transportation, provide accessible means of private transportation for those who are unable to drive, and reduce traffic congestion through intelligent routing (Olaverri Monreal 2016). In its ideal form, a smart city with exclusively autonomous vehicles could reduce or eliminate traffic accidents caused by human error, saving lives. Car sharing, with the ability to summon a car on demand based on need, would also potentially decrease carbon emissions from private vehicles, per Olaverri Monreal. And rather than prime real estate reserved for parking lots in major cities, self-driving cars could communicate with one another in a swarm fashion and park in faraway suburbs when not needed to transport humans or products.

LiDAR for Pedestrian Detection Smart cities can benefit from a range of image recognition models – whether to measure traffic flow on a road, the number of visitors to a mall or event center, or helping autonomous vehicles detect pedestrians in crosswalks. Since facial recognition AI and models that capture personally identifiable video of passersby can be a privacy concern, particularly in areas of the world like Europe with more stringent privacy legislation, LiDAR-based inference provides a potential medium by which AI could detect the presence of a person while maintaining anonymity, as LiDAR is inherently anonymous (Velodyne 2020). While LiDAR-based smart city AI tools are still largely in the research stage, the use case of pedestrian detection using 2D and 3D LiDAR data has been explored in multiple recent studies. Each of the below takes a different approach to processing LiDAR data, shedding light on the challenges and benefits of working with LiDAR for a smart city application like pedestrian detection.

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In the paper “Pedestrian detection from sparse point-cloud using 3DCNN” by Tatebe et al. (2019), a CNN was used to detect human figures from a 3D voxel representation of point clouds acquired by LiDAR. With a use case of deployment in Advanced Driver-Assistance Systems, known as ADAS, the 3DCNN model relied on a low-budget, low-resolution LiDAR while achieving higher accuracy than detectors trained using other neural network architectures. Deep learning allowed the researchers to differentiate between pedestrians and other confounding features in the environment such as stop signs, trees, or utility poles despite using lowresolution LiDAR data. Models using 3DCNN are rare, and Tatebe et al. show that it is an effective neural network architecture with which to calculate the voxel representation from sparse point-clouds proposed by the authors. To improve model accuracy, the researchers used data augmentation in the training phase and utilized a 3D object detection and tracking algorithm. The trained 3DCNN model classified candidate point-clouds as either showing a pedestrian or not. The authors’ success in harnessing low-resolution LiDAR data opens the possibility for cheaper LiDAR solutions to be used for smart city applications, lowering the barrier to entry for developers of such AI solutions and making these applications more accessible and scalable for real-world deployment. One shortcoming of the low-resolution LiDAR solution proposed by Tatebe et al. is that the network’s accuracy of pedestrian likelihood estimations suffered when applied to distant pedestrians, since the further away the subject is, the lower the point-cloud resolution of the data. So another paper from the same university (Yamamoto et al. 2018) chose instead to evaluate Active Scan LiDAR, which has more control over laser irradiation direction. With this advantage, Yamamoto et al. were able to computationally demonstrate the Active Scan LiDAR’s ability to identify distant pedestrians efficiently with a scanning method developed by the authors. Since Active Scan LiDAR was, at the time of writing, still under development, the researchers had to rely on a dataset that simulated the LiDAR’s function using point clouds from the KITTI dataset, which includes vehicles, pedestrians and more. Despite its reliance on significant manual data preprocessing, and no deep neural network used, this paper demonstrates that pedestrians can be successfully detected from different kinds of LiDAR, even at a distance from the LiDAR sensor. LiDAR data is computationally intensive for convolutional neural networks to run, requiring significant neural network optimization and specialized hardware for the kind of real-time inference that would be ideal or necessary in real-world smart city applications such as crowded public spaces and especially deployed in autonomous vehicles for pedestrian detection. Without rapid inference at the edge, an AI application could not make the split-second decisions required in an uncontrolled deployment environment. For these reasons, 3D LiDAR data and 3DCNNs, while achieving high accuracy, face more challenges for low-latency deployment compared to models that use 2D LiDAR. Additionally, models that use 2D LiDAR can be trained faster, since they take up less GPU memory for neural network training. Chen et al. (2019) relied on 2D LiDAR for their paper “Pedestrian Detection and Tracking Based on 2D LiDAR.” This work operated with just a plane of data rather than a 3D point-cloud, and detected pedestrians’ legs and their directions, making it a

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promising application to detect pedestrians from a level closer to the ground than a LiDAR mounted atop a car or up high like a security camera in a retail store. Rather than an ADAS system, Chen et al. modeled their neural network for the use case of a service robot navigating sidewalks without bumping into pedestrians. Hence, the model used a multiperson tracking algorithm to track pedestrian’s legs – at the “eyelevel” of a typical service robot. The goal of this method was to discriminate the dynamic pedestrians in a scene from the static environment, informing the decisions of a path planning algorithm so that the service robot can navigate more smoothly through the world. Single-line LiDARs, which are less expensive than the multiline LiDARs used in autonomous driving applications, are commonly found in service robots. While these cheaper LiDARs acquire less information from their surroundings, these tools can still be harnessed with noise reduction to detect the legs of pedestrians. Chen et al. used a human leg feature vector to classify legs from LiDAR, and a multiperson Kalman filter tracker is used to track multiple walkers based on consistent speed and distance of the pedestrian’s motion from the mobile sensor. The authors point out that LiDAR is unaffected by changes in light, offering an advantage over camera, or vision-based detection. Since mobile service robots typically operate in an environment that it has already mapped, the system in this paper uses this knowledge of the static environment features to separate the stable environment from the dynamic motion of the pedestrian point clouds. A machine-learning algorithm for feature extraction is used to classify pedestrian legs from the segmented LiDAR data. While this paper showed mixed results, with frequent false detections, it demonstrated the ability of low-cost LiDAR to detect the position and movement of pedestrians for use in service robots. The machine-learning algorithm was able to run in real-time on a 2.6 GHz i5 CPU, a low computational requirement for the task at hand. Another application of 3D LiDAR data is the 1D-CNN pedestrian detection model developed by Kunisada et al. (2018). Created for autonomous driving systems, the authors chose a one-dimensional CNN to process LiDAR waveform data in order to reduce the amount of time required to compute whether a pedestrian is present and in close range of a vehicle. This speed of computation is essential for real-world deployment to prevent accidents where a pedestrian might step out suddenly into the road and an autonomous vehicle or ADAS must quickly process that change in the environment and stop the vehicle’s motion. Kunisada et al. turned the 3D information acquired from LiDAR into 1D waveform data, allowing the 1DCNN model to determine whether a pedestrian is present, using a process similar to semantic segmentation. The researchers achieved around 88% pedestrian detection accuracy. The model uses clustering on a 3D point cloud to identify pedestrians accurately even if other objects are peasant nearby. The clustering approach achieved around 75% accuracy on the 3D plane and 94% on the 2D plane, showing that the 2D plane’s use of only the x and y axes helped better detect multiple pedestrians in a scene. Overall, this method showed a 20% improvement in pedestrian detection over prior methods with faster processing. Since the authors classify individual points as pedestrians, instead of placing a bounding box around the expected region, they achieved high precision in the pedestrian detection task.

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Case Study: Creating a Deep Learning Model for LiDAR-Based Inference In order to conduct research and development on LiDAR-based inference, there are several key components that would need to be addressed: type of sensor and model, data format and parsing, data visualization, labeling, network architecture, training, and evaluation. This case study walks through the process of training a CNN for object detection of vehicles from LiDAR data, using a lightweight model that can run on consumer-grade hardware.

LiDAR Selection Based on the current state-of-the-art solutions presented in (Liu et al. 2018), it was clear that a 3D multiline scanning LiDAR would be ideal. The point cloud data provides accurate time of flight data and what appeared to be a dense enough collection of points to draw inference on. This decision was based on human perception of a LiDAR frame, as well as the availability of the sensors. The hypothesis was that if the LiDAR data provided enough information about a scene to the human eye, a neural network could be trained to do the same. Similar to cameras, a sensor with a high enough resolution would be required to contain enough “pixels” to perceive details. Several sensors were either unavailable or required an initial purchase in order to evaluate the quality. After surveying available sensors and sample data, the Velodyne VLP-16 sensor appeared to fit the requirements. Based on the information provided in the product manual (VLP-16 User Manual), and availability of the sensor, it fit the requirements of the case study. While this sensor was chosen for evaluation purposes, the general concept of leveraging CNNs to detect objects within LiDAR data can be applied to most 3D scanning LiDAR sensors.

Parsing and Visualization While reviewing LiDAR solutions, some time went into visualizing LiDAR data in 3D. While several libraries and open source projects already existed to visualize LiDAR data, either in single frames, or in an accumulated point cloud, there were no solutions at the time that would allow users to label their data with 3D bounding boxes. This led to the initial phase of parsing raw binary points from Velodyne sample data. One sensor from their current offering stood out as readily available and in use in industry. This was the Velodyne VLP-16 sensor, featuring 16 scanning lasers at two-degree increments. The sensor contains a 360° horizontal and 32-degree vertical field of view. The common use cases for this sensor are in robotics and autonomous vehicle platforms to estimate distance and perceive obstacles. Velodyne provides documentation for this sensor on their website and presents directions on how to parse the raw data from their binary format. Again, while

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several solutions exist for point cloud visualization, in order to create an application for inference on LiDAR data, there would need to be a system to label and bound these items for later use in training and testing with a neural network. Since the raw data from a Velodyne VLP-16 sensor can be captured using a network packet capture tool such as WireShark (Wireshark), this tool was used to investigate and begin parsing the raw binary data. Once the data format was well understood, a parser could be written to read raw binary data. In order to come up with data frames to draw inference on, it was important to create a temporal aspect to this data. For the sake of simplicity, a frame was decided to be 360° of horizontal angle by 16 vertical lasers. Since the sensor spins a full rotation at 10 Hz, the frame rate of this visualization model is 10 frames per second. Compared to a camera, this is significantly slower than most solutions out today. With the data parsed in frames, it would then need to be visualized. Leveraging existing frameworks such as OpenFrameworks (OpenFrameworks), a simple program was created to visualize and play through LiDAR frames, as well as add bounding boxes using both mouse and keyboard input. This visualization method was a useful tool to visualize the data, but also left questions of how this data should be output or saved. While this made it easy for users to visualize the data, it was not clear how this would then be flattened into an input layer for a neural network. To solve the flattening problem, there were two primary concerns to address; presenting the data in an efficient structure that does not result in a significant loss in original data, and preserving data concurrency. In order to avoid loss of data, a structure would need to maintain and preserve all the data that is gathered from the LiDAR sensor. The original intent was to use a three-dimensional representation, similar to how a 3D point cloud is visualized. The 3D data structure would similarly have a Cartesian coordinate system that was 200 L
200 W
52H containing a reflectivity value for each point in the LiDAR frame. This reflectivity value is one byte. The dimensions for the x- and y-axes were determined by the specifications of the sensor and the z-axis was determined by using the following right triangle formula. sin ðVertical AngleÞ ¼

Height 2  Max Distance

Height 2  100 meters sin ð15Þ  200 ¼ Height Height ¼ 52 meters sin ð15Þ ¼

ð1Þ

This equation represents the calculation used to find the height dimension of the data structure required to hold the 3D point cloud. This was a simple yet memory-intensive solution, requiring 2.08 megabytes of data. In looking at the point cloud data in Fig. 1, it is clear that there is empty space that does not contain any information. The maximum usage of this data structure is represented by the percentage of the maximum amount of data that can fill the space that is provided by the sensor. Since the horizontal resolution is 360° by a vertical

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Fig. 1 This is a top-down view of a single LiDAR frame captured from a Velodyne VLP-16 sensor visualized using OpenFrameworks

resolution of 16 lasers, with two values corresponding to distance and reflectivity, this data only requires 0.012 megabytes, leaving a maximum utilization of 0.5%. This utilization rate is inefficient and contains primarily empty space within the data structure. Since the data is read serially, one could argue that this could also be represented as a one-dimensional array with two values per entry. This would have a 100% max utilization; however, this would ruin the data concurrency of LiDAR data. Convolutional neural networks rely on data concurrency, when resampling and convolving the image. Neighboring pixels are grouped together and later used when added as input to conventional fully connected layers. In the case of camera images, captured objects will occupy neighboring pixels in the picture. LiDAR data, when read from raw from the sensor, does not present data in a contiguous fashion. The sensor return values are interleaved and not in order, requiring significant refactoring. It would also make it hard to visualize and label the data. To solve both of these issues, a two-dimensional structure was used. In order to preserve concurrency for LiDAR data, a two-dimensional data-structure, similar to a camera image could be leveraged. The polar coordinates received from the sensor can be represented on an XY plane, where the X-axis represents the azimuth angle and the Y-axis represents the vertical angle from the sensor. At each coordinate, there will be two corresponding values representing the distance and reflectivity. This can also be viewed as a two-channel image or three-channel image with an unused value. In this case, the green pixel represents distance and red represents the reflectivity. If there was no value present for that laser firing, the pixel is set to black, or zero for red, green, and blue. This data structure has a maximum utilization rate of 66.6%, as one of the three channels is always unused. Maintaining the unused channel allowed the use of existing CNNs that required a three-channel color image as an input layer.

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Fig. 2 This image shows an example of a LiDAR frame after it has been resampled into an image. This will serve as the raw data before preprocessing

This also importantly created a format that maintained contiguous sensor data and maintained both reflectivity and distance data (Fig. 2). Since a three-channel color image was generated, an image processing library, OpenCV (OpenCV 2015), was used to process and create the frames for ease of development. Each frame was saved as a jpeg image. In later stages, the images were preprocessed to scale and remove pixels, to enhance the visual features of vehicles.

Sample Data and Labeling The process of creating these frames was completed for data that is collected from a Velodyne VLP-16 sensor that was mounted on top of a vehicle. It was a raw packet capture of the sensor data as it was driven down a stretch of road. Several cars passed by the sensor in various states. Some vehicles were parked, driving with, and driving against the direction of the sensor. Upon visual inspection of the saved LiDAR frames, it was hard to distinguish objects to the untrained eye. The most distinguishable object was subjectively a car, however, it was hard to detect unless the frames were played in a series. This was a subjective process as it required human perception to try and understand the changes between frames to distinguish vehicle shapes.

Preprocessing Due to the nature of the LiDAR data, it opened the opportunity to uniquely preprocess the data to better isolate objects in a two-dimensional image. Preprocessing was used to enhance the visual features of vehicles, as well as remove surrounding data that was not relevant. Since one channel represented distance, all points that were detected past a certain range could be selectively removed. For the purposes of this research, all points outside of a 7.5 m range had been removed. This decision was made based on the resolution of vehicles at distances greater than 7.5 m. Vehicles outside of this range were difficult to label based on the shape of the objects. They tended to blend into the background and were unclear to the human eye. Given a higher vertical resolution LiDAR, a larger radius could have been used for labeling (Fig. 3). The ground data also was an item that could be removed from the data as well. Since the LiDAR is mounted on top of a vehicle, the distance to the ground could be

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Fig. 3 This enlarged image shows a vehicle that was just outside the bounds of the preprocessing filter towards the middle-right. The size of the object at this distance, as well as the resolution of the sensor, made it difficult to detect and label with the human eye

Fig. 4 This is an example where the ground data was not entirely filtered and some of the curbs next to the road appeared in the preprocessed image since it was higher

subtracted. This distance stays fairly constant through the run, even though it is subject to change from the vehicle suspension compressing and decompressing, as well as changes in ground levels (Fig. 4). The last contribution to the preprocessing step was the reflectivity filtering. Due to the luster found on most vehicles due to the painted surfaces, a significantly higher reflectivity value was observed. Based on trial and error, a reflectivity value between 24% and 100% was accepted in the preprocessed image. This of course would not work for vehicles finished in a matte or satin finish, where the reflectivity is significantly less than a gloss finish (Fig. 5). While the preprocessing step was far from perfect in isolating vehicles, it was intended to filter the data that was visually hard to perceive. The reasoning was if it was not possible for a human to detect, it would be difficult or impossible for a convolutional neural network as well.

CNN Selection For this case study, an existing CNN was leveraged for detection. The chosen CNN must be able to accept the LiDAR scans as input and output bounding boxes representing a detected object. There currently exist several CNN solutions that

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Fig. 5 This shows a before and after comparison, where the image on the top is the raw image, and the bottom is preprocessed

are used for object detection in camera-based images. These can be repurposed to accept LiDAR scans as input with an output of a 2D bounding box surrounding detected objects. You Only Look Once v3, or YOLOv3, a commonly used CNN was used for its popularity and performance. It is offered in several variants with varying degrees of runtime and performance trade-offs. Overall, however, it offers lower inference time with a minimal decrease in accuracy compared to its competitors, making its results more usable in real-time applications. It also allows devices with lesser resources and lower cost, to yield similar results to larger networks. In testing, YOLOv3–608 performed 70% faster than faster region-based convolutional neural network, or faster RCNN, with only a 1.2% loss in precision (Redmon and Farhadi 2018). Also, given its large audience, the online community and documentation provide useful support when attempting to set up on a variety of hardware and software environments. Of the different variants, YOLOv3–608 was selected as a starting point, however, any could have been chosen. As with many CNNs, the input layer for YOLOv3 must be a three-channel 2D image. The visualization technique covered earlier outputs 2D images that represent depth maps of LiDAR scans. This was intentional, as the input is now saved in a format that can serve as an input layer with no further modifications.

Dataset Creation and Labeling Once the preprocessing method and CNN have been determined, the next step would be to create a dataset of LiDAR frames with labels that can later be used to train the neural network. The frames are created by parsing and turning the frames into twodimensional images as described in previous sections. For demonstration purposes, the raw data was also saved before preprocessing but is not required to train the CNN. Once all the frames are saved, labels for each of the images will need to be created. The types of objects that will be detected will need to be decided. These are called classes. In the context of smart cities, possible classes could include, people, cars, busses, or street signs. Many existing CNNs are complex enough to

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Fig. 6 An example of a label used to represent a bounding box in a training image

successfully detect multiple classes, and in the case of YOLOv3, its pretrained weights that are used for visual images are trained to detect 20 different classes. For the purposes of this case study, cars were the only class, as they were large enough to detect using the human eye when training. With more expensive and higher resolution LiDARs, there would be more detail in each frame that would make smaller objects such as street signs easier to detect and create training data for. Labels for the training data will represent the ground truth of object locations in the image. Different networks may require different label formats, and in the case of YOLOv3, the required format is a commonly used format where each image must be represented with one text file. Each line represents the type of object in the image and ratios representing the bounding box. An example is provided below (Fig. 6). There currently exist several open software applications that provide a user interface to easily create these labels. LabelImg (Tzutalin 2018), a popular dataset labeler can be leveraged for this task as the output label files that are created are in the correct format. Once both the preprocessed frames and labels are generated, they can be used in conjunction to train YOLOv3, or any network with similar input and output requirements. For the purposes of this case study, a dataset of 450 images were created.

Training Training for YOLOv3 is similar to many other CNNs. The dataset needs to be divided into a training and test set with the necessary configuration files updated. The training set is a sequence of images and corresponding labels that will be used only to teach the network. The testing data will then later be used to determine the accuracy of the network on a series of images that it has never seen before. This will determine how well the CNN will perform in the real world, where environments change and will not generate the exact same images that were used to train. For the purposes of this case study, 405 images of the created dataset was used for training and the other 45 was used to validate. With the data split, YOLOv3 was configured to train for 41 epochs with no other modifications. The starting weights were random, as this is a new type of data with a different set of classes.

System and Performance The underlying framework used for this CNN, Darknet, is an open-source C library that utilizes the system’s graphics card for parallel processing. The benefit to Darknet is that it is a lightweight library that can be compiled and run on several different

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Table 1 System specifications used for training and testing

Component Operating system CPU RAM GPU

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Model Ubuntu 16.04 Intel 3820 16 GB DDR3 NVIDIA GTX 980 (4GB)

Fig. 7 This shows a sample inference from the trained CNN where the vehicle to the right of the image was correctly detected

architectures. The system used for this test is a desktop computer with the following specifications (Table 1): The system specifications are not important for training, but rather during inference, as this determines the run-time or frame rate when processing LiDAR data in real time. When using the above setup, each inference took 0.07 s, or a frame rate of 14 frames per second with a mean average precision value of 82%. Values were considered true positives if the overlap of the predicted bounding box overlapped at least 50% of the box provided in the label. This was achieved using the most resource-intensive, yet most accurate variant of the network architecture. Further testing can be conducted to find a different network that can achieve a faster runtime, while reducing loss, depending on the usage (Fig. 7).

Analysis This case study shows just an application of using CNNs with LiDAR data to detect objects. This is the start to one possible solution to using this data in a smart city application; however, the applications are endless. Different CNNs can be leveraged, or custom CNNs of various sizes can be experimented with. The final design and use case is something that will be decided by cost and processing resources.

Conclusion Deep learning, and specifically, convolutional neural networks, are adept at making sense of visual data, a technique that applies itself well to autonomous vehicles. A key technology for smart cities, autonomous vehicles have the potential to decrease vehicle–vehicle and vehicle–pedestrian accidents, while reducing traffic congestion

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and potentially decreasing carbon emissions from driving. Of the many sensors typically installed on autonomous vehicles, LiDAR presents unique benefits from a smart city perspective, as the sensors are capable of creating highly detailed 3D point clouds of a vehicle’s surroundings while preserving anonymity of pedestrians. This is true since LiDAR does not capture colors and texture the way a traditional camera does. Convolutional neural networks for object detection can be used to parse LiDAR data to detect the position of other vehicles, cyclists, and pedestrians to an autonomous vehicle navigating a smart city. The price of LiDAR sensors has been going down, so this method would be used more widely as a real-time inference solution in autonomous vehicles as well as in other smart city technologies, such as robots and security tools in cases where facial recognition is not allowed or not desired. Several different methodologies of preprocessing LiDAR data and harnessing artificial neural networks can be applied to the challenge of pedestrian and vehicle detection from LiDAR data. This chapter’s case study walks through one such approach, which utilizes consumer-grade hardware and the YOLOv3 neural network to detect vehicles from LiDAR data that has been preprocessed from 3D point clouds to 2D images that represent the depth maps of LiDAR scans.

Future Research Directions Our near-term future research focuses on evaluating accuracy and runtime performance on various platforms, including the NVIDIA Jetson Nano, Jetson Xavier, and a desktop environment with an NVIDIA GTX980 graphics card, to provide a comparison of performance of solutions with varying cost, size, and computational resources. The Jetson product family provides users with a small form factor of an embedded computer platform, which is very popular in autonomous vehicles and robotics applications (NVIDIA 2020). The desktop environment will be used as a baseline to provide more contexts to the available computing abilities on these small, lightweight devices. We also plan to validate and measure performance improvements from the preprocessing method to further understand its impact on this detection method. An important future research direction would be building a robust, stable autonomous vehicular system for smart cities using a combination of promising sensing technologies. The sensor resolution used in LiDAR and others are significantly affected by the weather. Reducing the impact of weather to the accuracy performance, better distinction of vehicles or pedestrians, and higher resolution allowing reduced reaction time are all important focuses (Wevolver 2020). To achieve these, a mixture of passive (cameras) and active (sensors such as LiDAR) may be used simultaneously. Furthermore, RADAR (Radio Detection and Ranging) is a different sensor-based technology used to detect objects at a distance, define their speed, and disposition. While radar and lidar may seem to be competing, using them concurrently will be able to overcome the weaknesses presented in each sensor technology.

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Thus, designing effective sensor fusion is significant for providing future vigorous autonomous vehicles for smart cities (Elliott et al. 2019) (Yurtsever et al. 2020). Another vital area for future research is improving the application of machine learning and deep learning methods for autonomous driving in smart cities. While deep learning-based methods, including the ones presented in this chapter, are promising, they, however, have not been widely adopted in the real world. Future works should address the need of labeled data and of explicit safety measures (Yurtsever et al. 2020). In addition, various machine- and deep-learning algorithms have been considered, including CNN used in this chapter, plus recurrent neural networks (RNN), and deep reinforcement learning (DRL). It would be beneficial to consider some combination or hybrid of these methods to increase performance and/ or decrease computation overhead for the applications of autonomous vehicles in smart cities (Elliott et al. 2019).

References Burke, K. (2019). How does a self-driving Car see?: NVIDIA blog. In: The official NVIDIA Blog. https://blogs.nvidia.com/blog/2019/04/15/how-does-a-self-driving-car-see/. Accessed 4 July 2020. Campbell, S., O’mahony, N., Krpalcova, L., et al. (2018). Sensor technology in autonomous vehicles: A review. 2018 29th Irish Signals and Systems Conference (ISSC). https://doi.org/ 10.1109/issc.2018.8585340. CB Insights. (2020). What is edge computing? In: CB Insights Research. https://www.cbinsights. com/research/what-is-edge-computing/. Accessed 4 July 2020. Chen, J., Ye, P., & Sun, Z. (2019). Pedestrian detection and tracking based on 2D Lidar. 2019 6th International Conference on Systems and Informatics (ICSAI), Shanghai, China. 421–426. https://doi.org/10.1109/ICSAI48974.2019.9010202. Copeland, M. (2016). What’s the difference between deep learning training and inference? In: The Official NVIDIA Blog. https://blogs.nvidia.com/blog/2016/08/22/difference-deep-learningtraining-inference-ai/. Accessed 4 July 2020. Elliott, D., Keen, W., & Miao, L. (2019). Recent advances in connected and automated vehicles. Journal of Traffic and Transportation Engineering, 6(2), 109–131. James, P., Astoria, R., Castor, T., Hudspeth, C., Olstinske, D., & Ward, J. (2020). Smart cities: Fundamental concepts. In J. Augusto (Ed.), Handbook of smart cities. Cham: Springer. Kunisada, Y., Yamashita, T., & Fujiyoshi, H. (2018). Pedestrian-detection method based on 1DCNN during LiDAR rotation. In 2018 21st international conference on Intelligent Transportation Systems (ITSC), Maui, HI. 2692–2697. https://doi.org/10.1109/ITSC.2018.8569014. Langroudi, S. H. F., Pandit, T., Kudithipudi, D. (2018). Deep learning inference on embedded devices: Fixed-point vs posit. In 2018 1st workshop on energy efficient machine learning and cognitive computing for embedded applications (EMC2). https://doi.org/10.1109/emc2.2018. 00012. Li, Y., Ma, L., Zhong, Z., Liu, F., Cao, D., Li, J., & Chapman, M. (2020). Deep learning for LiDAR point clouds in autonomous driving: A review. ArXiv. https://arxiv.org/abs/2005.09830. Liu, J., Sun, Q., Fan, Z., Jia, Y. (2018). TOF Lidar development in autonomous vehicle. 2018 IEEE 3rd Optoelectronics Global Conference (OGC). https://doi.org/10.1109/ogc.2018.8529992. Md Alom, Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Mst Nasrin, S., Van Essen, B. C., Awwal, A. A. S., & Asari, V. K. (2018). The history began from AlexNet: A comprehensive survey on deep learning approaches. ArXiv. https://ArXiv.org/abs/1803.01164. NVIDIA. (2020). NVIDIA embedded systems for next-gen autonomous machines. [online]. https:// www.nvidia.com/en-us/autonomous-machines/embedded-systems/. Accessed 7 May 2020.

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Olaverri Monreal, C. (2016). Autonomous vehicles and smart mobility related technologies. Infocommunications Journal, 8, 17–24. OpenCV. (2015). Open source computer vision library. OpenFrameworks. Redmon, J., & Farhadi, A. (2018). YOLOv3: An incremental improvement. In: arXiv.org. https:// arxiv.org/abs/1804.02767. Shahriari, B., & Moh, M. (2018). Intelligent mobile messaging for smart cities. In M. Maheswaran & E. Badidi (Eds.), Handbook of smart cities: Software services and cyber infrastructure (pp. 227–253). Cham: Springer. Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6, 60. https://doi.org/10.1186/s40537-019-0197-0. Tatebe, Y., Deguchi, D., Kawanishi, Y., Ide, I., Murase, H., & Sakai, U. (2019). Pedestrian detection from sparse point-cloud using 3DCNN. In 2018 international workshop on Advanced Image Technology (IWAIT), Chiang Mai. 1–4. https://doi.org/10.1109/IWAIT.2018.8369680. Tsai, C., & Moh, M. (2017). Load balancing in 5G cloud radio access networks supporting IoT communications for smart communities. In 2017 IEEE international symposium on Signal Processing and Information Technology (ISSPIT). https://doi.org/10.1109/isspit.2017.8388652. Tzutalin. (2018). LabelImg. Velodyne. (2020). Lidar technology safeguards privacy in Smart City applications. In: Velodyne Lidar. https://velodynelidar.com/blog/lidar-technology-safeguards-privacy-in-smart-city-appli cations/. Accessed 5 July 2020. VLP-16 User Manual. Velodyne Lidar. Wevolver. (2020). Autonomous vehicle technology report, Wevolver, Feb 2020. Wireshark. Wireshark User’s Guide. Yamamoto, T., Kawanishi, Y., Ide, I., Murase, H., Shinmura, F. and Deguchi, D. (2018). Efficient pedestrian scanning by active scan LIDAR. In 2018 International Workshop on Advanced Image Technology (IWAIT). Chiang Mai. 1–4. https://doi.org/10.1109/IWAIT.2018.8369664. Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020). A survey of autonomous driving: Common practices and emerging technologies. IEEE Access, 8, 58443–58469. Zhao, Z., Zheng, P., Xu, S., & Wu, X. (2019). Object detection with deep learning: A review. IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3212–3232. https://doi.org/10. 1109/TNNLS.2018.2876865.

Part VII Institutions Dimension

Corporate Social Responsibility (CSR): Governments, Institutions, Businesses, and the Public Within a Smart City Context

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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 984 Smart Cities: Promises from Business . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 985 Smart Cities: The Publics Role and Citizenship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 988 Business and CSR Responsibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 992 CSR: A Single Organizational View . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 993 CSR: As Multi-Stakeholder Engagement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 994 Meeting Community and Business Expectations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 996 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1000 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1002

Abstract

Urban populations are growing exponentially. The United Nations estimates that by 2050 more than 72% of the world’s population will be living in cities (UN 2011). Consequentially our cities are transforming socially, economically, and environmentally. Much has been written about the benefits of smart cities, in that they increase productivity, grow social and human capital, and increase new idea development by contributing to knowledge sharing and innovation creation (IBM 2009; Kummitha and Crutzen 2017). However, as this chapter argues, often approaches to smart city design and implementation, driven by politicians, city administrators, and urban planners (Landry 2012; Wood and Landry 2008), somewhat fail to address many of the problems emerging from smart cities, including urban sprawl, poverty, higher rates of unemployment, and growing urban costs and housing affordability issues (Zhang 2016). The chapter argues the role of business in supporting the resolution of these issues is often negated during smart city design (Boyd and Boguslaw 2007). The chapter begins with an exploration of the contributions and promises that businesses can make to our A. D. Roberts (*) School of Business and Law, Central Queensland University, Melbourne, Australia e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_30

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cities and the role(s) they can play in making our cities smarter, more livable, healthy, vibrant, and socially, economically, and environmentally sustainable. By leveraging the corporate social responsibility literature (CSR), the chapter builds an argument for and proposes alternative mechanisms by which those responsible for smart city design, governance, and implementation can meaningfully and productively collaborate and engage with businesses to resolve these emergent socioeconomic problems. Building on this literature, a case is made for a reorientation of the way in which city government(s) and institutions work with both businesses and the public in smart city context(s). The chapter concludes with recommendations as to how those responsible for smart cities may work more cohesively and collaboratively with businesses to more effectively harness and capitalize on their capabilities.

Introduction Urban populations are growing rapidly and expected to grow exponentially, with the UN estimating that more than 50% of the world’s population are currently living in cities, and expected to exceed 72% by 2050 (UN 2011). Consequentially our cities are transforming, not only in terms of people, but also economically, socially, and environmentally. Increasing urbanization can be positive, in that it increases productivity, grows social and human capital, and increases new idea development by contributing to knowledge sharing and innovation creation. However, urbanization also creates problems such as urban sprawl, poverty, higher urban unemployment, growing urban costs, and housing affordability issues. Additionally, lack of investment, weak financial and urban governance contributes to rising inequality and environmental degradation (Zhang 2016). To some degree it has been left to politicians and administrators to identify which problems are to be addressed, and implement mechanisms aimed at strengthening the capacity of urban systems to tackle them (Landry 2012; Wood and Landry 2008). However, this approach would seem to negate and indeed exclude the role that business can play in contributing to and resolving societal problems and issues within our cities (Boyle and Boguslaw 2007). Commentators suggest that cities can best meet these challenges by identifying how best to utilize smart technologies, smart collaboration, a highly educated population, and effective institutions to make cities smarter. It is argued that addressing these issues and resolving these problems can best be achieved by harnessing the power of information and telecommunication technologies (ICT) to make our cities smarter (Kummitha and Crutzen 2017). By creating smart cities, we can make our cities greener, safer, culturally vibrant (Adams 2010; Landry 2006), while simultaneously identifying mechanisms to integrate growing populations from diverse socioeconomic, ethnic, and religious backgrounds (Leach et al. 2019; Meijer and Bolívar 2016). For example, Hollands (2008, p. 307) argues that we can leverage “network infrastructures to improve economic and political efficiency” and enable “socio, cultural and urban development,”, with Datta (2015) arguing that ICT-driven

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cities enable sustainable urban development. Conceptualizing cities as network places and deploying technology improves the quality of urban life, reduces environmental degradation, increases individual and community participation, and addresses many of the challenges associated with urbanization (Bakici et al. 2013). However, this one-dimensional ICT perspective on what makes a city smart leaves little space for the social aspects of sustainable development and for the basic social dimensions of the city (Cugurullo 2013). In reality, cities are complex systems of systems that interweave into “different space-time patterns of nodes and links” (Healey 2007, p. 8) that have an impact on, and are impacted by, competing and often conflicting stakeholders and groups (Cosgrave et al. 2013; Merrilees et al. 2009). This suggests that creating vibrant smart cities requires more than ICT. Rather, identifying urban issues and resolving them through smart city design requires recognizing the important role that stakeholder(s), including businesses, community groups, and citizens play. The degree to which policy makers embrace stakeholders impacts how they contextualize and plan cities and the decisions they make in the urban design process, to improve the economic activity, sustainability, and liveability (Rogers 2017). Further, by involving a wide range of stakeholders, particularly businesses, when planning and designing smart cities, city policy makers and planners can tap into and harness the power of human, social, entrepreneurial, and infrastructure capital to create vibrant and liveable cities (Neirottio et al. 2014). This raises a number of questions: What sort of promises emerge from business involvement in smart city activities? And, what can those who govern our cities do to maximize the benefits emerging from their involvement with the business community? By leveraging corporate social responsibility (CSR) literature, this chapter builds an argument for and proposes alternative mechanisms by which those responsible for policy, urban design, and governance of smart cities can meaningfully and productively collaborate and engage with businesses to resolve the socioeconomic problems in cities. To set the scene we first explore the contributions and promises that businesses make to our cities and identify areas in which they can play a greater role in making our cities smarter, more liveable, healthy, vibrant, and socially, economically, and environmentally sustainable. The chapter then discusses the role of the public and citizenship plays in smart cities. A case is made for an alteration in the way city governments and institutions work with both businesses and the public within a smart city context. The chapter concludes with recommendations as to how those responsible for smart cities may work better with businesses to more effectively harness their capabilities.

Smart Cities: Promises from Business Over the next ten years, at a global level, $100 billion will be invested in technologies to support smart cities (Navigant Research 2011). By 2050, it is estimated that smart cities will account for 2/3 of global GDP (UKTI 2015). It is no surprise that business, including technology vendors and consultancies (e.g., IBM, CISCO, and

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KPMG), see huge potential for active involvement and rapid growth in this sector (Angelidou 2015; Harris 2015). However, scholars and practitioners when framing the smart cities often overemphasize the importance of technology and consequentially fail to recognize that at a strategic level smart city design needs to take into consideration people, governance, mobility, environmental, and living factors (Letaifa 2015). Such a perspective arises from the view that new technology innovations enable the creation of innovative customer-centric service(s) provision (Kuk and Janssen 2011). Much has been written about the promises of technology and the contributions that businesses can and do make in the areas of energy efficiency, reducing carbon emissions (Bakici et al. 2013), public transport, and e-governance aimed at increasing competitiveness and administrative efficiency (Allwinkle and Cruickshank 2011; Caragliu et al. 2011). These scholars argue that by using technology to create new relationships between technology and society, smart cities can make urban infrastructure more efficient and improve the everyday life of citizens (Söderström et al. 2014). In essence this means a smart city is defined by its ability to assemble advanced infrastructure and telecommunication technologies and its capacity to resolve urban and social challenges using engineering (Bell 2011). However, scholars also suggest that this polarized perspective limits those in charge of our cities to identify and resolve societal problems, including urban poverty and growing social exclusion (Cowley et al. 2017). Now city problems are no longer defined as the problems faced by its citizens but become those that the central actors in the process (i.e., technology vendors) identify (Söderström et al. 2014). This creates a problem for governments in that it leaves businesses, such as IBM, in the driver seat of smart city design and development. Scholars have noted that in many cases businesses end up being in command of the authorship, authority, and the profit arising from the decisions and actions taken by governments who are working toward implementing smart cities, and that it creates an unnecessary sense of competition between private companies (Söderström et al. 2014). A case in point is IBM’s smart city program, driven by a recognition of the huge untapped market for urban technologies (Townsend 2013). IBM consistently argues that rising urban populations, aging infrastructures, and shrinking tax revenues in today’s cities demand more than traditional solutions (IBM 2009). Their solutions rest on the provision of expert solutions in the following areas: planning and management, infrastructure, and human services that are divided into a number of sub-areas including public safety, smarter buildings and urban planning, government and agency administration, energy and water, environment and transportation, social programs, health care, and education (IBM 2009). Naturally IBM’s intention is that all of these will be monitored and regulated by IBM’s intelligent operations center. However, several problems emerge from this point of view. Firstly, city administration functions become redefined and are somewhat treated as homogeneous for all cities around the world. Secondly, the model assumes that each solution can be treated as an individual system and that the city is a system of systems. Doing so suggests that IBM can somehow reform cities from the top down by turning gut feeling about what makes and shapes a city into reality through using the language

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and rhetoric of IBM. Thirdly, IBM appears to take for granted that city infrastructures already exist. Yet, there are many cities that either lack infrastructure, or it breaks down and worsens, particularly in the developing south (Cowley et al. 2017). Lastly, IBM’s vision while admirable tends to see cities as one world, when the opposite is the case. Cities are made up of many different worlds that are in many cases incommensurable (e.g., education, business, and safety). Thus, the city becomes somewhat flattened, whereby the disenfranchised, powerless, and nonmainstream groups become disturbances and irritations for the system of systems in IBM’s smart city vision (Söderström et al. 2014). This has led scholars to engage more critically about some of the negative effects of these businesses and their technologies have on our society, including the degree to which they do or do not contribute to social polarization (Hollands 2008). Scholars have recently debated the way in which technology within smart city ecosystems shapes smart citizenry and compels people to become technologically literate (Vanolo 2014), the impacts and implications of concentrating the interconnection and collection of big data in smart cities, and the sense of vulnerability and surveillance this can create for citizens (Kitchin 2014). Advancing technology literacy and big data are not per se bad things, but they do beg questioning. Questions that emerge include the possibility of an unequal playing field for all and the possibility of social exclusion. Yigitcanlar et al. (2018) cite several examples of inequalities resulting from the approach to the design of some smart cities, including Abu Dhabi and their vision for a city that supports sustainable living, and Tianjin’s approach to using eco-technologies to create a smart city that supports environmental sustainability. Abu Dhabi’s smart city design leaves little space for unprivileged groups and is not as sustainable as claimed (Cugurullo 2013). Tianjin’s smart city model fails to recognize the complex web of sociocultural and economic process at play, and their linkage to the lived and environmental characteristics of the city resulting in a lack of recognition of the needs of transient populations and the urban poor (Yigitcanlar 2016). Thus, if the desired outcome of smart cities is smart communities, those who design smart cities and the vendors themselves who have the solutions need to improve access to appropriate technologies, services, and platforms and modify societal and community perceptions and behaviors (Hughes and Spray 2002). This could include giving citizens and community groups the opportunity to participate in the customization and development of locally tailored solutions and the measures by which their relevance is assessed and evaluated. Doing so does more than make solutions culturally and socially relevant to smart city citizens; it also facilitates knowledge creation and sharing, adds to economic and sustainable development, and enables citizens to participate in identifying the factors that make smart cities more productive, liveable, and accessible (Yigitcanlar et al. 2018). Consolidated Land and Rail Australia (CLARA) has adopted these approaches to their approach to smart city design (CLARA 2020). Recognizing that a smart city is a non-static, dynamic, complex, and evolving ecosystem, CLARA is reimagining smart cities from the ground up. In opposition to adopting a techno-centric approach to smart city design, CLARA sees citizens as central to and forming the foundation

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of the design process. Rather than seeing smart city solutions as individual components of systems within systems, they are adopting a more holistic approach to problem and solution identification. By recognizing the relationship that exists between different systems within a smart city ecology and not treating cities as homogeneous, their model allows for the city to evolve and adapt to the changing needs of its citizens. By bringing together technology, government, and society early in the design process they aim to make their smart cities more liveable and economically and environmentally sustainable and vibrant. Societal challenges such as cost of living, affordable housing, transportation, and infrastructure needs were discussed and evaluated at length and a range of viable economic solutions were presented to all stakeholders, during the early planning stages. For CLARA, technology plays a part in smart cities but other factors are equally important, particularly the changes in citizen behavior(s) as a result of the use of smart technology and the need to maximize social inclusion (CLARA 2020). As such CLARA’s approach addresses criticism of the IBM smart city model, particular claims that IBM’s model is too techno-centric and that their solutions fail to accommodate the diverse worlds and perspectives that exist, and are often incommensurable, within our societies and communities (Söderström et al. 2014). As such, CLARA’s smart cities will be less flat, more vibrant, less disenfranchised, and more inclusive of nonmainstream groups. At present CLARA is working with the Australian government on a number of funding models for their smart cities, including value capture and public-private partnerships. To some extent whether their model will or will not be as successful as claimed remains to be seen. Nevertheless, CLARA holds much promise both for Australia and if successful as a model for other countries approaches to smart cities, in that CLARA has the land for smart city development under its legal control, has a commercially and economically viable business model, the funds available to privately fund the cities and infrastructure, and the support of a wide range of stakeholders.

Smart Cities: The Publics Role and Citizenship Resistance and criticism of smart cities is driven by a concern that those who design and construct them often fail to consider the wishes, interests, and needs of those members of society who will live in them, including the practical impacts on their citizens’ daily lives (Rizzo et al. 2013). These concerns are often driven by those scholars who question the need to conceptualize a smart city as network and ICT driven. ICT-driven smart cities certainly increase urban mobility and appear to meet the basic needs of their citizens, though scholars have questioned whether these approaches are sufficient to address the subjective conditions that lead to an increase in health, well-being, and social and community involvement in our cities (Lara et al. 2016). Vanolo (2016) noted that often those who design and build smart cities variously exclude and subjugate the important role citizens play in shaping their city ecosystems. Calls have been made for smart cities to be more “citizen focused” or “people centred” (Saunders and Baeck 2015). What does this mean for smart

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cities? How can those with responsibility for smart city policy making and governance configure smart cities to include a sense of publicness? Scholars have long recognized that smart cities have to “negotiate with the spatiality and geography of place” (Harvey and Harvey 2000, pp.179–180) and that the way in which a smart city is envisioned needs to be translated into and accommodate individual political, economic, and social contexts (Cowley et al. 2017). A smart city is likely to be remodeled, reworked, and reshaped as it becomes embedded in local governance networks and the preexisting strategic concerns of human actors (Prince 2010). Thus, it may be erroneous to construe a smart city as a set of technologies driven and directed by promoters who assume certain deterministic effects on society. Rather, a smart city is contingently shaped by the public, those who live, work, and play in them (Cowley et al. 2017). What do we mean by the public? Within a smart city the notion of “public” is a shift away from a singular whole toward thinking about a heterogeneous public (Young 1989) or the coexistence of multiple publics (Fraser 1990) whose engagement is informed by different perspectives and values, that are somewhat temporal in nature, influenced by “the place-specific articulation of different goals, technologies, material spaces and institutional settings” (Cowley et al. 2017 p. 54). Cowley, Joss, and Dayot’s (2017) analysis of six UK smart cities identified four modalities of publicness: service-user, entrepreneurial, political, and civic. Service user publicness, the most passive and the most common in the six cities, refers to the consumption of urban services, including the enhanced use by citizens of smart technology, such as real-time data for decision making, voluntary feedback, and the provision of channels for accountability. This form of publicness implies a relatively reactive, somewhat compliant form of citizenship behavior within a smart city. The second most common and more active form of citizenship behavior, entrepreneurial, relates to the expectation that the public will be involved in codesigning the services required and creating economic value, common within other smart city governance models (Hollands 2015; Wiig 2015). This form of entrepreneurial activity resonates with putting the citizen at the center of smart city development. However, while service user and entrepreneurial publicness encompass social rights (i.e., access to services) they fail to consider that publicness also includes political (i.e., contributing to decision making) and civil rights (i.e., freedom of speech), seen as equally important (Isin and Ruppert 2015). Findings indicated the lack of public involvement in decision making and deliberation through institutional channels, even though strategy documents repeatedly claim public engagement as vital to successful smart city implementation. Suggesting that a smart city operates on the margins of normal institutional processes may even seek to bypass them and confirms the view of many large technology vendors that the public sector is an obstacle to smart city implementation (Cowley et al. 2017; Shearmur 2016). This sidelining of civic and political publicness creates a degree of bifurcation in smart cities. When service and entrepreneurial publicness becomes the focus of smart cities, other interactive and participatory forms of engagement by the public become one-off events. These events emphasize the manifestation of the virtual/digital spheres in the physical public space and attempt to reclaim the digital sphere from

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the private sector. The former includes the use of digitally created artistic installations in cities and interactive visual displays to unlock social dialogue (Cowley et al. 2017; Watershed 2016). The latter involves the public in the organization of events and digital training with a view to enabling them to make things better together through experimentation, play and fun, to co-create city commons (KWMC 2016). The danger here is that smart cities shift the notion of citizenship “away from civic responsibilities and engagements, to classifying [citizens] as consumers who purchase services from providers” (Powell 2014, pp.15). Viewing the citizen as the consumer leaves businesses seeing quality of service provision as equivalent to the efficiencies their technologies provide and which the datafication emerging from their technologies enables (Cowley et al. 2017; Powell 2014). Doing so means that for those who live in smart cities, citizenship becomes a form of economic exchange in that it is enacted through the collection and sharing of personal data through smart connectivity. The need for smart connectivity being driven by a perception that our cities are in an age of crisis, associated with rapid urbanization, aging populations, climate change, and the twinned pressures of fiscal austerity and interurban competition (Caprotti 2015; White 2016). However, seeing citizens as consumers and citizenship as a form of exchange has its limitations. Traditional perspectives on what it means to be a good citizen such as altruism or meeting broader community and societal responsibilities are forgotten and parallel issues such as “digital inclusion,” transparent governance, open data, and “social sustainability” become sidelined, in favor of smart connectivity goal(s), efficient resource, service, and information provision (Cowley et al. 2017). Notable is the absence in the minds of those who plan for smart cities of entrepreneurship and social innovation, in the broadest sense. While social sustainability is included at a policy level, it is mainly focused on health, e-health, or educating residents to participate in a smart cities digital economy. Such policies place an emphasis on efficiency of service provision and relate to factors such as public safety and problems with service provision reporting. Thus, while a smart city may be smart it also seems to somehow minimize local concerns and agendas that relate to broader societal and economic agendas (Cowley et al. 2017). This begs the question as to the degree to which those who plan smart cities are willing to engage with the public in identifying, targeting, and implementing solutions for social problems such as inequalities and lack of social cohesion in their communities. Perhaps those who plan smart cities need to perceive technology, and the businesses that provide it, as a parallel space to the human, social, and public spheres. This suggests the need for alternative approaches to governance in smart cities. However, the literature on smart city governance, including its conceptualization, its role in modern society, and the actions needed to support smart city transformation, is fragmented and somewhat disjointed (Meijer and Bolívar 2016). Part of the problem appears to be the lack of agreement on the explicit perspectives of what smart city governance looks like and the degree to which those with responsibility for designing and implementing smart city models can or should influence idea generation by investing in smarter collaboration between businesses, communities, and the public, to transform their cities (Osborne 2006; Torfing et al. 2012).

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Views on the degree to which city administrators should or can intervene in the smart city design and implementation ranges from low to high (Meijer and Bolívar 2016). At the low range, governance in a smart city is driven by making the right policy choices and implementing the most effective and efficient solutions (Nam and Pardo 2011) by leveraging the power of technology to collect data about public management from sensors or sensor networks. Under these models, new technologies are used to strengthen the rationality of government decision making by accessing readily available real-time data (Schuurman et al. 2012; Walravens 2012). These forms of governance imply that only governments can develop, promote, and support smart cities, and that they are the ones best placed to identify and prioritize the areas of focus to resolve socioeconomic problems in our cities (Alkandari et al. 2012). This position obviates and excludes any consideration of the role and contribution of both business and the public in identifying and addressing societal challenges. As discussed, failing to consider citizens in smart city design and their implementation means that local concerns are often overlooked in favor of policies targeted at cost reduction and efficiency gains for services and information provision (Cowley et al. 2017). Further, limiting the role played by business to the provision of services and information dissemination minimizes the valuable contribution they have to social innovation, including the identification of and the resources and support they can provide in the implementation of practical and relevant solutions (Carroll and Shabana 2010; Smith 2003). Consequentially, an approach to governance and design of smart cities that excludes contribution from other stakeholders, including businesses and the public, provides a somewhat onesided perspective on societal challenges within our cities and limits a government’s capacity to design truly transformative smart cities (Meijer and Bolívar 2016). Other forms of smart city governance emphasize the need for smarter administration and urban collaboration and decision making. Under the former, a smart state is created that uses sophisticated technologies and restructures the internal processes of government, to better serve smart city citizens by integrating and interconnecting information, data, processes, institutions, and physical infrastructure (Gil-Garcia 2012; Wood 2010). The latter places an emphasis on collaborating across government departments, businesses, and the community to promote economic growth, increase connectivity, identify and address societal problems and issues, and realign the services and operations of smart cities to make them more citizen centric (Batagan 2011). What do these forms of governance mean for smart cities? Firstly, smart city administrators need to reconceptualize engagement from the political to include processes through which business and citizens become more engaged in urban design and the configuration of appropriate solutions to urban and societal problems. This can only be achieved by supporting the enabling conditions for knowledge creation, sharing, and innovation, by leveraging open data and governance to strengthen the collective intelligence of smart cities (Dvir and Pasher 2004). Secondly, those who govern our cities and contribute to the design and development of smart cities must strengthen their understanding of outcomes beyond wealth and economic creation. Only by creating the appropriate circumstances can those who govern smart cities identify what their citizens’ needs are, what problems and issues

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they see, and identify how best to implement and support practical solutions (Wood 2010). As such, smart city designers and administrators must do more to realize the opportunities available from social innovation and entrepreneurship and the benefits emergent from including businesses earlier in the design process (Cowley et al. 2017), including shifting away from one-dimensional viewpoints of smart cities (i.e., as technology driven) (Wood 2010). For smart city designers and administrators this requires a search of socio-techno synergies within urban systems and a recognition that marrying technology with social structures cannot be oversimplified. Smart city design is a complex process of political, policy, governance, and institutional change (Wood 2010). As such, improving health and well-being and addressing societal issues for citizens in smart cities requires altering how we see smart city design from the technical and managerial issues, resting in the domain of city planners, to one that recognizes the interactions between technology, social structure, socio-technical practices, and other stakeholders, including businesses and the citizens who live, work, and play in them, at each stage of a smart city project. Scholars have long argued that such stakeholder engagement can best be achieved by leveraging the willingness and capabilities of businesses to proactively engage in corporate social responsibility (Carroll 2008; Porter and Kramer 2011), including the contribution(s) they make to identifying and resolving societal problems and issues (Crane et al. 2014). However, as the history of CSR indicates, this is not as simple as it may appear. Problems emerge in terms of lack of agreement on how to define CSR, what constitutes CSR, and the best models or frameworks to use.

Business and CSR Responsibility CSR is based on an argument that economic criteria alone cannot guarantee both the success of an organization or its continued existence. Organizations have increasingly recognized the need to include social, moral, and ethical criteria in their decision-making processes (Carroll 2008). One of challenges associated is the lack of a universal definition of CSR. In particular, scholars and organizations have differing views about what CSR encompasses and how best to conceptualize the concept. Cases in point being the following two definitions: (i) The World Business Council for Sustainable Development (WBCSD) defines CSR as “the continuing commitment by business to behave ethically and contribute to economic development while improving the quality of life of the workforce and their families as well as the local community and society at large” (WBCSD 1999), and (ii) The United Nations Research Institute for Policy Development (UNRSD) defines CSR as “specific policies and practices involving codes of conduct, environmental management systems, stakeholder dialogues, community investment and philanthropy, as well as auditing and certification related to social and environmental aspects” (UNRSD 2003). The former with its emphasis on ethics, economic development and social, directs organizations toward conceptualizing the CSR strategies using Carrols model, and the latter directing organizations toward the institutional

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approach (Jhawar and Gupta 2017). Scholars have also questioned whether or not a universal definition is possible or indeed necessary, the ubiquity of the term lending itself to uncertainty in meaning (Sheehy 2015). As such, organizations remain somewhat confused about which model to adopt and how best to implement it to support their organizational objectives and meet societal needs and demands. Nevertheless, CSR is high on the agenda of government, organizations, and institutions with most embracing the triple bottom line (TBL) approach (Elkington 2013; McWilliams et al. 2016). TBL encourages organizations to look at their impacts on people, planet, and profit (Elkington 2013). Not surprisingly, driven by changes at a global, government, and societal level, the notion of CSR has evolved from a single organizational view to one that encompasses a multi-stakeholder engagement perspective, through four stages: corporate philanthropy and volunteerism, social responsibility, stakeholder approaches, institutionalization and extended corporate action (Carroll and Buchholtz 2017; Jhawar and Gupta 2017) and integrated conceptualizations such as creating shared value framework (Porter and Kramer 2011).

CSR: A Single Organizational View Triggered by the labor movement and growth of slum living in the nineteenth century, organizations began providing welfare to a limited degree including making charitable donations to hospitals and bath houses and food coupons, to improve societal health and well-being. Somewhat simultaneously, social groups and communities in society were beginning to recognize philanthropic contributions from business magnates (e.g., John D. Rockefeller), a trend that was strengthened during the 1929 great depression (ESCAP 2011). While not referred to as CSR rationale, arguments based on exchange, distributive, general, and contributive justice were made for organizational leaders to become more involved in societal affairs, beyond generating economic outcomes. To ensure their economic growth and societal relevance, organizations needed to: (i) build trust in their communities to ensure fair exchanges, (ii) ensure a just and fair relationship between government and citizens, (iii) not only accept and adhere to legal and regulatory frameworks but go beyond them to meet their ethical obligations, and (iv) invest in and contribute to the well-being and progress of individuals and society overall (see Fig. 1) (Dempsey 1949; Jhawar and Gupta 2017). Essentially, this meant organizations recognizing that they have the resources to positively contribute to a well-functioning and vibrant society. However, the decisions whether or not to act and the actions taken were somewhat determined by the degree to which the organizations leadership volunteered to become more actively involved in societal affairs (Carroll 1999). CSR as we know it today truly rose in importance post 1950s, becoming increasingly significant from the 1960s onwards (Carroll 2008; Carroll and Shabana 2010). The cold war era and the challenges being faced by society led to a view that governments could no longer do it all, and that major private sector organizations

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Fig. 1 The history of CSR

needed to do more. Pressure was brought to bear on organizations to produce “social goods,” in addition to providing goods and services (Bowen 1953; Wood 2010). However, scholars seemed to be somewhat conflicted about CSR, their notion(s) of CSR ranging from the neoclassical (i.e., it does not have a role in business), enlightened (i.e., it is all about serving an organizations self-interest), responsible (i.e., it is the right thing to do even if it provides no economic benefit to the organization), to the confused (i.e., it is ethical and organizations expect it to pay off) (McGuire 1961; Wood 2010). What scholars took from McGuire’s ideas was that for organizations embracing CSR would mean giving up, not balancing, freedom, efficiency, and meritocracy (McGuire 1977). Yet some organizations saw CSR as a way to generate long-term benefits while also contributing to society (Davis 1960; Fredrick 1960; Wood 2010).

CSR: As Multi-Stakeholder Engagement Driven by a recognition of the growing power and influence of organizations, labor and social movements began to pressure organizations to improve labor standards, address human rights and environmental issues, and become more ethical in the way

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they did business and dealt with corruption (Carroll 1979; Johnson 1971). Within this context, scholars argued that instead of only striving to maximize shareholder value, organizations should be expected to address the needs of diverse stakeholder groups, including employees, suppliers, local communities, and the nations in which they operated, referred to as the stakeholder approach (Carroll 1979). This argument was built on the premise that organizations and indeed wider social institutions (e.g., government and education) did not operate in isolation, rather they were deeply embedded in interrelated and interdependent systems that continually affect each other and that the neoclassical view no longer held truth (Preston and Post 1975; Wood 2010). Building on these aspects, Carroll (1979) argued that instead of measuring the motivational aspects of CSR, organizations should measure their corporate social performance (CSP). For Carroll, measuring of CSP meant evaluating four domains of CSR: economic, legal, ethical, and discretionary, portrayed as a pyramid (see Fig. 1). This has become one of the most cited models in the CSR literature (Wood 2010). These domains are then matrixed to include the social issues that an organization should be concerned with, that is the four philosophical dimensions pertaining to responsiveness (reaction, defense, accommodation and pro-action), which could shrink or grow depending upon the number of societal issues included (Carroll 1979). However, a review of how organizational managers utilized this model indicated that they were developing their CSR responses as though they were managing a rational closed system, not what Carroll intended when the model was developed. Consequentially they adopted a closed view of CSR, failed to take into account the way in which the domains in the model interact with the dimensions, and recognize or consider the sociological complexity of their roles and the effects their actions and decisions had on others (Wood 1991, 2010). During this period civil society organizations (CSOs) and nongovernment organizations (NGOs) began to increasingly pressure organizations to act more ethically and take action to address societal and environmental issues. These pressures ranged from calling out unethical and immoral organizational behavior, to direct attacks on organizations and their leaders. Consequentially political pressure prompted a range of intergovernmental institutions (e.g., WTO, UN, and OECD) to encourage organizations to alter their approaches to CSR. Building on Carroll’s model, using basic systems framework and structural principles, scholars developed an institutional framework model of CSR (see Fig. 1) (Wood 1991, 2010). As Fig. 1 shows, CSR has three structural principles: social responsibility, social responsiveness and the performance outcomes, and impacts of the organization. The principles of social responsibility apply to a particular organization, and the specific duties and responsibilities of managers. Contrary to Carrols model, these do not reflect modes of response. Rather they focus managerial attention toward environmental scanning and stakeholder and issues management (Wood 2010). Critics of this approach argue that while it extends Carrols model to encompass the outcomes and impacts that emerge from the CSR actions and activities undertaken by organizations, it fails to adequately consider that effective CSR needs to result in positive outcomes for people, the environment, and social systems (Wood 1991, 2010).

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As such, scholars have suggested that businesses adopt a multi-stakeholder approach to CSR. Building on stakeholder theory proponents of these approaches to CSR argue that addressing societal issues requires businesses to embrace descriptive, instrumental, and normative views on CSR (Crane et al. 2014). From this standpoint CSR requires managers to at once recognize that they are impacted by and have an impact on the societies in which they operate (Carroll 1979; Carroll and Buchholtz 2017), that sound management requires identifying and taking into account the linkages between groups in society, and that these groups have a legitimate stake in their business (Crane et al. 2014). Porter and Kramer suggest that CSR as a concept be reframed as creative shared value (CSV) (Porter and Kramer 2011). They argue that businesses are trapped in outdated narrow views of value creation and that by focusing on optimizing short-term financial gain they are prospering at the expense of society and the environment. As a consequence, they are caught in a vicious cycle of falling societal trust and increasing government policy making aimed at undermining their competitiveness and reigning in their behavior. Porter and Kramer (2011, p. 64) contend that “learning to create shared value is our best chance to legitimise businesses again” and more effectively contribute to critical societal problems and simultaneously drive profitability. The CSV model has been criticized by scholars for ignoring the earlier work on CSR. As our review of the history shows, as far back as the 1970s scholars were arguing that by engaging in social responsibility and supporting social programs business increase profits (Carrol 1979). Rightly or wrongly, CSV gives the impression that CSR is in some way a bolt-on to other business activities (Crane et al. 2014). A position at variance with CSR literature and research (Carrol 1979; Wood 2010). Research shows that society expects organizations to engage in CSR beyond philanthropic endeavors and that doing so positively benefits businesses (Looser and Wehrmeyer 2016). Lastly, CSV also fails to deal adequately with the trade-offs that occur between economic and social value creation and the negative externalities for stakeholders (Crane et al. 2014).

Meeting Community and Business Expectations The smart city agenda holds much promise for the creation of vibrant communities, economic and social well-being for all, and a more sustainable environment (Cowley et al. 2017; Hollands 2008). However, as discussed, scholars have questioned the way in which businesses and governments have promoted smart cities. Grossi and Pianezzi (2017) suggest that the promotion of smart cities as an urban utopia is really an expression of neoliberal ideology, wherein the interests of business elites take primacy over the needs of citizens, including their community and urban problems. Concerns have been raised regarding the growing role of private corporations in defining and making up smart cities (Harvey 1989; Hollands 2015). As discussed earlier, it is not surprising that scholars have become concerned that business-driven development of smart cities results in a prioritization of business goals over the social and economic needs of communities (Brenner and Theodeore 2002; Hollands

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2008). Hollands (2008) seminal work identifies significance of businesses in driving the smart cities agendas. Specifically, the way in which commentary is focused on ICT as the enabler of network infrastructures that can simultaneously make city infrastructure more efficient and effective, and enable social and economic development. However, smart infrastructure alone does not make the city smart. While ICT is a necessary enabler, it must not be seen as the critical factor in defining and creating a smart city (Hollands 2008). Certainly, those within the knowledge and creative classes, including skilled IT and digital illiterate sections of the community, benefit economically but unskilled, poorer citizens lose out (Peck 2005). A case in point is the transformation of the UK city of Leeds from a manufacturing city to a service-based form. The growth in IT and service-based employment within Leeds led to the creation of upmarket bars and clubs and a gentrification of the city. Sections of the community became excluded from the cityscape leading to growing inequalities of work, housing, and neighborhoods (Hollands 2008). Thus, smart cities are meeting the needs of business including capital accumulation and profit maximization, particularly large technology and telecommunication firms. However, creating smart city ecologies that are one-sided (i.e., where efficiency and profit maximization take primacy) is at variance with the notion that businesses need to do more for the communities and societies within which they operate, and by actively contributing to CSR. Communities expect more from businesses than the provision of efficient and productive technologies and economic growth in their cities. They expect businesses to address societal issues such as rising inequality, poverty, and limited access to services. The history of CSR indicates the growing pressure from communities on businesses to address and support solutions for societal problems and issues, including recognizing the need to take into consideration financial, human, social, and environmental capital when they set strategies and make decisions (Carrol 1979; Crane et al. 2014; Wood 2010). Consequentially the approach taken to smart cities, including their definition and the way they are framed, requires a reorientation from a techno-structural model to one that starts with people and human capital (Hollands 2008). Communities and people, including the way they interact and the degree to which they share and communicate, make up cities. Technology has the potential to be utilized positively to empower, educate, and involve smart city citizens more fully in debates that they see as relevant to their lives, to the communities they live in, and the urban environment they inhabit (Paquet 2001). Doing so requires a shift to take place in the balance of power between businesses, government, communities, and citizens (Amin et al. 2000). Citizens should do more given the opportunity to participate in and influence the decision making that takes place within smart cities. Democratic debate and increased community and citizen involvement in what a smart city needs and how it can best be shaped should be and can be facilitated by technology (Coe et al. 2001; Hollands 2008). In this way issues of social, labor, and educational inequalities can be identified, with potential solutions and their impact being explored within those communities before being implemented. Our review of the history of CSR suggests a somewhat semi-linear progression wherein each model or approach naturally evolves into the other, subject to changes

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in the broader contextual and societal environment within which a business operates. Much could be done from a CSR perspective in smart cities if businesses reorientate their efforts away from top-down approaches to one that allows for the identification of the issues and problems of citizens and communities within our society (Hollands 2008). Recall that cities are complex, non-static, dynamic ecosystems of systems that interact and are interrelated (Söderström et al. 2014). Thus, within the context of a smart city ecosystem, businesses need to adopt approaches to CSR where they continuously sense-make citizens’ attitudes, behaviors, perspectives and opinions, demonstrate an understanding of the issues, problems and challenges, and support initiatives aimed at resolving them. Failing to do so, in a smart city context or not, can lead to a failure to meet the needs of citizens as stakeholders, breakdowns in trust, and may impact the relevance of businesses to their community and society (Carroll 1979; Carroll and Buchholtz 2017; Crane et al. 2014). With this in mind, how best can businesses leverage the approaches to CSR explored earlier, to accommodate the evolving needs of citizens in smart cities? In opposition to the techno-structural view and to minimize concerns that business and private interests take precedence over the needs of citizens, communities, and society (Harvey 1989; Hollands 2015), citizens must be involved early in smart city design. Doing so increases trust in government and business, empowers citizens, and democratizes the process, in that it allows them to determine what a smart city needs to be and how it should be shaped and evolved to meet their needs (Hollands 2008). To do so we need to adopt a multi-stakeholder perspective in combination with CSV (Porter and Kramer 2011) and the descriptive, instrumental, and normative perspectives on CSR (Wood 2010). This approach prioritizes working with multiple stakeholders, including citizens, to identify their needs and potential solutions to create shared value (Porter and Kramer 2011) and balances the processes relating to social responsibility, social responsiveness, and the impacts of performance (Wood 2010) (see Fig. 2). Adopting this approach allows businesses to demonstrate their willingness to reorient their behavior away from one that imposes solutions on citizens concerning the problems that they perceive as relevant (Hollands 2008), to one where those who are impacted by city transformations feel empowered and included in the decision-making processes (Amin et al. 2000; Paquet 2001). As illustrated (see Fig. 2), business involvement in smart cities is simultaneously top-down and bottom-up, across three layers of activity. Each layer generates feedforward and feed-back loops in that the layers are porous with issues, problems and the relevant solutions and suggestions emergent from each layer feeding into the next and vice versa. The relationships between the broader context and the conditions experienced by governments, businesses, communities, and citizens are captured in the outer layer. In essence these are the macroenvironmental factors existing at financial, social, human, and environmental levels that have an impact on and are impacted by the evolving dynamic that occurs within cities. The next or middle layer includes those stakeholders who have a responsibility for planning, design, and implementing the changes needed, in response to the issues and problems they perceive within the outer layer, including local and national governments, not for profits (NFPs), NGOs, and the ICT and infrastructure

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Fig. 2 Smart city design: A top-down/bottom-up model

businesses who will develop and deploy smart city solutions. This layer is where smart technology and infrastructure solutions undergo detailed evaluation against the broader external environment and the emergent inputs from the inner layer. The inner layer illustrates the cycle, dynamic, and iterative processes of interactions taking place between citizens, community groups, businesses, and government to identify the micro (i.e., local) issues, problems, and potential solutions seen from their perspective(s). The inner layer involves four stages operating asynchronously and somewhat iteratively: Stage (1): Business and government collaboration – In this stage businesses and governments collaborate on policy, procedural, and governance challenges that need to be addressed within a smart city, including identifying and developing appropriate programs or activities and action to increase efficiency and productivity in the provision of infrastructure and other services. Stage (2): Multi-stakeholder collaboration – In this stage asynchronous collaboration takes place between government, business, and multiple community groups, to identify and agree the problems that their citizens face within their local context. This process also includes identifying and mutually agreeing upon the linkages and connections between groups and commonalities, in terms of the challenges they face, future potential challenges, and the solutions, from their perspective.

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Stage (3): Evaluating and Comparing – In this stage emergent outputs from stages (1) and (2) are evaluated and compared by government against the proposed technology or infrastructure solutions under consideration. Of particular focus here is the degree to which any proposed solution meets the human, social, and environmental needs identified in stage (2) and the governmental challenges identified in stage (1). Positive and negative externalities are also identified and debated, and if need be the iterative process begins again until a pathway is agreed upon that minimizes the negative externalities. Stage (4): Outcome capture – During this stage, almost asynchronous businesses compare the outcomes from stages (1) to (3) against their current and future approaches to social responsibility and social responsiveness, while identifying performance measures and outcomes that are contextually relevant to the communities impacted (Crane et al. 2014). The business also identifies aspects of their current CSR strategy that are already in place to address the challenges faced by the stakeholders, take measures to build upon and extend them, and identify any gaps in their CSR approach arising from stage (2). Thus, the businesses build upon already successful CSR actions and activities. Outcomes, impact measures, and tools to evaluate CSR performance are also put in place in stage (4). By integrating CVS and CSR models, this approach balances the needs of business and government with the expectations of communities. In opposition to current activities by business in smart city ecosystems, business goals are no longer prioritized over the social, economic, cultural, and environmental needs of communities (Brenner and Theodeore 2002; Hollands 2008). The emphasis shifts toward the human and social element of smart city design and development. As such, business and governments are no longer seen by communities as autocratic decision makers who push techno-structural focused solutions on them (Hollands 2008). Further, the ideas emerging from the stages of collaboration between stakeholder groups add value to businesses and smart city designers, in that it encourages open innovation and entrepreneurial activities, an intended by-product of smart cities (Paskaleva 2011). Knowledge and information gleaned by businesses at each stage further provides them with a greater insight into the needs of their customers (i.e., citizens and communities). These insights can be used to facilitate intrapreneurial and entrepreneurial activities and create new business models and sustainable business ecosystems (Visser and Kymal 2015).

Conclusion Shown by the discussions over the last few decades, scholars have become more interested in exploring smart cities as a phenomenon. This work has led to a growing body of literature pertaining to how smart cities should or could be defined and conceived by governments and institutions. Commentators have questioned the notion of smart cities, suggesting that they may be more of a metaphor than a reality (Söderström et al. 2014). In particular, a growing body of evidence suggests that the

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smart city models being explored and built by governments primarily emphasize the interests of private businesses, in particular global technology, technology, and communication firms (Cowley et al. 2017; Hollands 2008). Not surprisingly, scholars have noted that the smart city promises of vibrant communities, healthy lives, and well-being are being met for some but not all in our communities. While smart cities have the potential to make our cities greener, safer, and more culturally vibrant (Landry 2006), critics have questioned the degree to which those involved in the transformation of cities to smart cities accommodate the evolving needs of growing populations from diverse socioeconomic, ethnic, and religious backgrounds (Leach et al. 2019; Meijer and Bolívar 2016). The chapter highlighted that adopting a polarized, techno-structural perspective on smart cities and a vision that smart technology alone can make urban infrastructure more efficient and improve the lives of everyday citizens limits the identification of and resolution of the current problems within our urban environment, including urban poverty and social exclusion (Cowley et al. 2017). Thus, defining a city’s smartness by the degree to which it can assemble new technologies and resolve urban challenges by engineering alone somewhat excludes the central actors within the city (i.e., citizens) (Bell 2011; Söderström et al. 2014). Further, it creates problems for governments, in that large business such as IBM, CISCO, and KPMG remain in the driving seat of the smart city agenda. Thus, their power and influence increase, as they end up being in command of the authorship, authority, and any profits that arise from smart city developments. As a result, businesses define the problems and issues relevant to cities, their communities, and citizens, and direct how they should and will be resolved (Söderström et al. 2014). Further, smart city ecosystems and associated technology and infrastructure shape smart citizenry and demand that people become knowledgeable about technology and digitally literate (Vanolo 2014). Raising the possible, as often occurs, of negative effects for those who are unable to do so through social polarization (Hollands 2008), and a sense of vulnerability and surveillance issues for some citizens (Kitchin 2014). Hughes and Spray (2002) suggest that those who design smart cities and the technology vendors who work with them to do so implement solutions in a way that ensures access for all. A comparison was made between the approach taken by IBM and that of CLARA to smart city design. Contrary to IBM’s top-down approach, CLARA involved community groups and citizens in the early stages of the design process to identify how they saw their city, its challenges and issues, including the human, social, cultural, and environmental. Community groups and citizens were consulted on alternative models prior to the development of detailed city planning. As such, citizens were given the opportunity to participate in the process of customizing and developing locally tailored solutions to their urban problems and issues. This, facilitates knowledge creation and sharing, adds to economic and sustainable development and enables citizens to participate in identifying the factors that make smart cities more productive, liveable and accessible (Yigitcanlar et al. 2018). Of interest is the degree to which businesses and governments top-down approach to smart city design varies with the notion of CSR. CSR literature and what we understand as the best approaches to CSR suggest that businesses need to

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take into account diverse stakeholder needs and demands and implement strategies, including policies, processes, and procedures to address them (Hollands 2008; Porter and Kramer 2011; Wood 2010). In practice this means collaborating with their stakeholders to identify their problems, issues, and challenges and working with them to resolve them. As outlined in our historical review of CSR, CSR is based on the principle that businesses need to address relevant social, economic, human, and environmental issues for their customers to remain relevant to the societies they operate in and to maintain profits and sustainability (Carrol 1979; Carroll and Buchholtz 2017; Woods 2010). CSR history demonstrates that conceptual definitions, models, and frameworks have altered over time, as a consequence of the changing nature of the environment that businesses operate within. Equally, to some degree, businesses are committed to CSR and make genuine attempts toward CSV for the societies and communities within which they operate (Carroll 2008; Wood 2010). Perhaps the issue is not so much that businesses do not see CSR as relevant within the context of smart cities, rather they erroneously see smart technologies, with their ability to improve social and environmental outcomes, as a mechanism to achieve their CSR goals and targets. With these points in mind, the chapter concludes with a conceptual model, combining CSR and CSV approaches, that seeks to ensure the centrality of citizens and communities in a smart city design. By adopting this approach businesses and governments can empower, educate, and involve citizens more fully in debates that they see as relevant to their lives, to the communities they live in, and the urban environment they inhabit (Paquet 2001). This approach, however, requires a fundamental shift in the mindset of businesses that are currently driving authorship and authority over the smart city agenda, and a willingness on their part to reorientate the balance of power in the decision-making processes (Amin et al. 2000). This model holds much promise, in that it allows issues of social, labor, and educational inequalities and inequities to be explored among all stakeholders, with a view to agreeing on and developing locally contextually relevant solutions.

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Social Emergence, Cornerstone of Smart City Governance as a Complex CitizenCentric System

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Claude Rochet and Amine Belemlih

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Smart City: A Collection of Smarties or a System? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . How Smart Were the Cities of the Past? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cities as Far from Equilibrium Adaptive Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What Makes a City Smart? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Why Do We Need Strong Citizen-Based Interactions Within the Urban System? . . . . . . . . . . . Rethinking the Smart City Concept from the Perspective of Citizens’ Bottom-Up Involvement: The Cases of Barcelona (Spain) and Medellin (Colombia) . . . . . . . . . . . . . . . . . . . . The Case of Barcelona: Decidim.Barcelona . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Case of Medellín: “City for Life” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Social Emergence as a Proposed Lens for a Finer Grained Understanding of How Bottom-Up Dynamics Within Smart Cities Initiate and Bring New “Social Orders” . . . . . . . . The Generative Emergence Model, as a Promising Way to Study Processes of Emergence in Socially Smart Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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We consider the smart city not as an addition of “smarties” (technological devices) but as a system capable of evolution all along its lifecycle, described as Urban Lifecycle Management (Rochet and Volle, L’intelligence iconomique, C. Rochet (*) Paris Dauphine PSL University, Paris, France Fondation Robert de Sorbon, Institut Franco Allemand d’Etudes Européennes, Paris, France e-mail: [email protected] A. Belemlih Paris Dauphine PSL University, Paris, France EM Lyon Casablanca Campus, Casablanca, Morocco Transilience Institute for Territory Resilience and Transformation, Casablanca, Morocco e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_29

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les nouveaux modèles d’affaires de la III
revolution industrielle, De Boeck Supérieur, Louvain, 2015) since a city never dies and must be able to reconfigure itself while its internal and external environment changes. The literature on cities as evolving ecosystems (Batty, I/S: J Law Policy Inf Soc 11(1):127–151, 2015) considers this evolutionary process cannot be steered in a top down way, either by a supra rational actor or on a self-regulating basis as claimed by the authors of the first order cybernetics. By integrating all the developments around citizen-centric smart cities, we propose a complex system and social emergence lens to better understand the process of emergence of “bottom-up” dynamics within local communities leading to socially smart cities.

Introduction The recurrent problem appearing in the attempts to define smart cities is the understanding of how a smart city grows and evolves out of a sum of technological devices. Michael Batty’s groundbreaking opus The New Science of Cities (2013) defines the challenge, in the line of thought of Jane Jacobs and Chris Alexander, as comprehending the city “as systems built more like organisms than machines,” that is, a network of flows. Consequently, if we want the city to be smart, we need to monitor the growth of the city and predict its evolution with modeling tools up to the age of the digital economy. We need to analyze the smart cities dynamics through the lens of complex systems architecture, to envisage which competencies, and specifically public ones, may be updated to take on this task of modeling (e.g., Batty 2013; Khatoun and Zeadally 2016). Following Batty and other complex systems scientists, the city aspiring to be smart is to be conceived from the bottom-up and no longer from the top down as it has been the rule until now in the tradition of urban planning, therefore putting emphasis on the role of the ordinary citizen as a key actor. Overall, as will be seen in the next parts of the chapter, smart city discourse is evolving between two antagonistic perspectives, with a growing tendency advocating for the second view, promoting bottom-up and citizen-centric approaches, although the top down, data and technology-driven perspective remains dominant on the ground until now (e.g., Satyam and Calzada 2017 for a review of these trends) (Table 1).

The Smart City: A Collection of Smarties or a System? Mainstream definition of smart cities, adopted by the European Union, relies on Giffinger’s categorization (2010): a city is smart if it gathers “smart” characteristics: smart people, smart governance, smart transportation, smart buildings, smart economy, technology. . . Basing on such criteria, EU accounts up to 240 smart cities in Europe! This approach is meaningless from a systemic point of view: we may have smart people working with cutting-edge technologies in BIM (Building Information

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Table 1 Smart city discourse evolving between two antagonistic perspectives. The authors: Smart city perspective between. . . Techno-centric smart cities (e.g., Calzada 2018), data-driven cities (e.g., Calzada and Cowie 2017), or Datapolis (Pisani 2015), driven through technology vendors (e.g., Greenfield 2013; Morozov and Bria 2018; Rochet 2018), or top down policies (e.g., Batty 2013; Innes and Booher 2000; Rochet 2018) City built like a machine (e.g., Batty 2013), understood as a mechanical system that can be fixed, or steered and controlled through data levers (e.g., Innes and Booher 2000), or conceived as a Cyborg city (e.g., Gandy 2005; Picon 1998)

. . . and Citizen-centric experimental cities and urban laboratories (e.g., Calzada 2018; Karvonen and van Heur 2014), Participolis (Pisani 2015), Smart community (e.g., Gurstein 2014; Mellouli et al. 2014; Meijer 2016), driven bottom-up (e.g., Calzada and Cowie 2017; Morozov and Bria 2018; Peña-López 2019) Smart city as evolving ecosystems (e.g., Batty 2015; Innes and Booher 2000), complex systems composed of interdependent components (e.g., Batty 2013; Khatoun and Zeadally 2016; Holland 1995;), and far from equilibrium systems (e.g., Batty 2012)

Modeling) positive energy buildings (Volk et al. 2014), using trendy solar transportation cars, and overall producing a “stupid system as a whole” (refers to the expression used by Rachel Keeton 2015). Attempting to make sense of the broad range of existing experiences under the umbrella of “Smart City” concept, Albino et al. (2015) examined 22 accounts and cross-referenced these with urban developments which labeled themselves as smart cities. Their conclusion was that a single definition was impossible because of wide variations in the meaning of common terms, and any attempts to include everything inevitably cover too much or too little (Albino et al. 2015). Overall, a growing literature stream has been taking a critical view of the hegemonic smart city discourse (Calzada and Cowie 2017), especially when we look at the increasing number of nuanced critiques of a technology deterministic and hyper-connected understanding of a smart city (Calzada and Cobo 2015). Rather than cities being understood as mechanical systems that can be disassembled into their component parts and fixed, or steered and controlled through data levers, cities are conceived as consisting of multiple, complex, interdependent systems that influence each other in often unpredictable ways (Innes and Booher 2000). A smart city is therefore more than the sum of “smarties” (smart grids, smart buildings, smart computing. . .) in spite of we have no precise and operational definition of what a smart city is (Lizaroiu and Roscia 2012). In the recent literature, the smart city tends to be defined as complex “self-organizing learning systems that can be creative and sustainable” (Innes and Booher 2000, p. 183) that is to say systems where the whole is more than the sum of the parts and has autopoietic properties (Neirotti et al. 2014; Batty 2013). In contrast with a metaphor that the world is like a machine that can be taken apart and fixed, complexity theory suggests that the cities are more like living organisms, growing, evolving, adapting to its environment and facing random events, unanticipated changes or patterns that make top down public policies often fail (Bak 1996; Holland 1995, 1998; Innes and Booher 1999). What makes a system, and most of all

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an ecosystem, is integration. Integration is an emergence, which is a state defined as a process which cannot be described by a fixed model, consisting of invariant distinctions. Hence, emergence must be described by a metamodel, representing the transition from one model to another one by means of a distinction dynamic (Heylighen 1992). The literature on cities as evolving ecosystems (Batty 2015) considers this evolutionary process cannot be steered in top-down way or on a self-regulating basis as claimed by the authors of the first order cybernetics (Heylighen et al. 1991). There are many diverse players who make millions of decisions each day which add up to the evolving form, structure, and character of cities and which collectively shape their economies, their vitality, and their evolution (Innes and Booher 2000). These decisions are “largely beyond the reach of any formal urban policy or plan, much less of any top down regulatory strategy . . . (and) the best planners can do is to help the players in these places to influence the direction of change” (p. 178). Therefore, if we apply the law of requisite variety developed in the stream of complexity theories, we see clearly, as had stated Karl Weick (1995), that in the context of complex social systems, “human thoughts and action must be highly varied to grasp the variations in an ongoing flow of events.” In other words, for such a transition stated above to succeed at the scale of a social system (city, district, etc.), the metamodel and its underlying process must be “as complex as the system they (actors involved) intend to regulate” (Weick 1987). The purpose of this chapter is first to understand the basic tenets of complex adaptive system theory applied to the emerging field of smart city and its participatory, community-centered, and self-regulation dynamics, providing some exemplars with the cities of Barcelona and Medellin, second to explore what kind of “complexity-enabled” process could be adapted to experiment participatory and deliberative democracy principles (Calzada 2018) on a “socially smart” emerging community system. As we choose to focus on the “socially smart cities” (Durose et al. 2019) part of using the lens of “generative emergence” (Chiles et al. 2004; Lichtenstein 2009; Plowman et al. 2007) and its underlying mechanisms, as applied to the transformation of a territory, a smart city or a community, “from the ground up” (Bria 2019).

How Smart Were the Cities of the Past? The seminal book The City in History by Lewis Mumford (1968) tells us cities of the past were self-evolving ecosystems obeying the laws of organic planning. Organic planning, as analyzed by Mumford, has no preconceived objectives. It is a selfadaptive system which reinforces its coherence along time. The resulting pattern has not been foreseen beforehand but is strongly coherent and harmonious. This evolution was made possible by a shared common sense of beauty and life purpose in the city. One of the most salient traits of these towns is that they were free merchant cities ruled by various forms of democracy, drawing from direct democracy – for example, Veliki Novgorod in eleventh-century Russia – to complex mix

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regimes to preserve the equilibrium of powers among the few powerful and the many of citizens – for example, Florence, Venice (Rochet 2018). The sense of the Common good and the sense of harmony as constitutive elements made these cities working as a continuous problem solving and learning system, which reinforced its coherence along time (Ibid.). As Mumford put it, the coherence of these cities was reinforced by the wall that we could call, in the contemporary system language, the perimeter of the system which defines what is inside and outside the system (Mumford 1968). The relationships between the city and its periphery were organized as described at the beginning of the nineteenth century by Von Thünen, by concentric circles (Rosenberg 2020). But what made the success of the medieval town turned out to lead to its downfall. While the wall was fixed, the city evolved over time, increasingly becoming an open evolutionary system, especially with the advent of the “death of distance,” first with more secure roads and with the revolution of transportation by the middle of the nineteenth century. With the appearance of networks of infrastructure technologies and the spread of the telegraph that transformed the government of the city, critical obstacles to the growth of cities were removed making the wall senseless. Today digital technologies amplify this move, providing new tools such as smart phones that became a “digital Swiss army knife” (e.g., see The smartphone is the Swiss Army knife of gadgets (2013)) that allows inhabitants to be active actors in the city life, communicating and coordinating with each other, using and feeding databases (Khatoun and Zeadally 2016).

Cities as Far from Equilibrium Adaptive Systems Growing cities began to be considered a system in the practice of urban planning that appeared formally in the 1950s to solve the problem of transportation between workplaces and housing, under the banner of “social physics,” the utilitarian approach propelled by Stanley Jevons at the end of the nineteenth century (Jevons 1871, 1970) who considered economy ruled by the general laws of mechanics. These key ideas assumed the system was in equilibrium and might be regulated by single feedback loops according to the principles of first order cybernetics. This kind of model relied on spatial interaction for testing, for example, how people might shift from one mode of transportation to another, as decided to solve the congestion in London in 2003 by charging car traffic, and predict the effect on global pollution, the growing density of the city to shorten the traffic between workplace and habitation (Beevers and Carslaw 2005). But in the recent decades, since the 1980s, the paradigm has changed fundamentally. In first order cybernetics, the system is centrally organized, in equilibrium, being able to return to its state of equilibrium after a perturbation – an equilibrium slightly different but not questioning the dominant pattern of the city (Rochet 2018). This kind of system is viewed as centrally organized and structured from the top down, as exemplified by Rio do Janeiro central control system built by IBM (see IBM takes smart cities concept to Rio de Janeiro 2012).

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The development of second order cybernetics in the 1980s moved the structures and behaviors of the city toward a system being organized from the bottom-up. These systems are in dynamic disequilibrium, notwithstanding that disequilibrium is not permanent since the system is undergoing to one state of equilibrium to another (Rochet 2018). Michael Batty has coined the expression “far from equilibrium” to describe this phenomenon (2012), initially studied in thermodynamics (e.g., the work of Prigogine and his colleagues (Prigogine 1955; Nicolis and Prigogine 1989)), and later, in complex social systems (Meyer et al. 2005). In this theoretical framework, organizing far-from-equilibrium is what leads to “. . . emergence and ongoing, perpetual novelty” (Meyer et al. 2005, p. 450b), explaining the origin of systemic state change. These systems are adaptive (Arthur 1997) meaning that equilibrium is renewed from within through unanticipated innovations reacting unanticipated events. This is an endogenous evolutionary process, compared to the exogenous command and control process of the first order cybernetics. Here we find this kind of “architecture without architects” as described by Mumford (1968), in the case of the Middle-age city, with cities growing organically from the bottom-up. Christopher Alexander, in his seminal book on system architecture of cities, A Timeless Way of Building (1980) has given an iconic definition of organic growth, putting that “quality in buildings and towns cannot be made, but only generated, indirectly, by the ordinary actions of the people, just as flower cannot be made but only generated from the seed.” This supposes some sort of genetic code, like in biology, that made the system self-regulating. In that case, asserts Alexander, this code is “replaced by people conscientiousness of the larger scale patterns, which provides the rules of growth. If people have agreements about these larger scale patterns, then they can use their knowledge of the patterns, and the degree to which these patterns have been attained, or not, to guide the growth and the assembly of the smaller patterns. Slowly, under the impact of this guidance, the sequence of small-scale transformations will, of its own accord, create the larger patterns, piece by piece: without any individual person necessarily knowing how or where these larger patterns will be in the finished town” (Alexander 1979). To sum it up, the more the city as a system is confronted to as well endogenous as exogenous changes, the more it accumulates this “people consciousness” that allows new patterns to emerge. The smartness of the city consists of this continuous learning process that relies on interactions between basic cells and actors of the city. If the lessons of the middle-age city as an archetype of organic development that produced the smart city of that time, its failure was it was conceived as a closed system locked in behind the wall. In the nineteenth century, intents to reinvent such self-contained cities were made by utopians such as Ebenezer Howard (1898, 1985) in reaction to the unhealthy sprawling of industrial revolution cities. He thought of the smart city as an ideal city conceived from scratch as a mix of country and city. His insight was to conceive the city as an interaction between a city with jobs and opportunity, but with pollution, and the countryside with fresh air and cheap land, but with fewer opportunities, each one acting as magnets attracting and repelling people. He invented a third magnet,

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The Garden City, which combined the most attractive elements of both city and countryside (Howard 1898, 1985). Garden city was the Songdo of its day (Townsend 2013) that galvanized architects, engineers, and social planners in search of a rational and comprehensive approach to building city. Howard’s approach was excoriated by Jane Jacobs in her Death and Life of Great American Cities (1992, 1985) for not giving room to real life: “He conceived of good planning as a series of static acts; in each case the plan must anticipate all the needed. . . He was uninterested in the aspects of the city that could not be abstracted to serve his utopia.” As Dennis Hardy (1991) put it, Howard’s garden cities were a quasi-utopia of a perfect city in an imperfect world (while communist and fascist utopias have dreamed of the city as a perfect city in a perfect world). Unable to evolve, the garden city dream, not relying on a global systemic architecture, has degenerated in the banal reality of suburban sprawl. The same risk exists today with digital technologies, which could revive the ideal city dream, under the impulse of the big technology vendors who have an interest in a top-down and deterministic approach that reduce smart cities to the adoption of their intelligent technology (e.g., Calzada 2018; Calzada and Cowie 2017; Rochet 2018).

What Makes a City Smart? In their analysis of present smart cities initiative, Neirotti et al. (2014) notice that there is no practice that encompasses all the domains, hard and soft, of the cities. The most covered domains are hard ones: transportation and mobility, natural resources, and energy. Government is the domain in which the cities report the lowest number of initiatives. More, in the present smart cities research program, there is an inverse correlation between investment in hard and soft domains, smart government being still the poor relative in smart cities initiatives, while cities that have invested in hard domains are not necessarily more livable cities. In fact, two models emerge from Neirotti and colleagues’ survey: one focused on technology (with a strong impetus for technology vendors) and another focused on soft aspects (e.g., related to welfare and social inclusion policies – such as assistance of disabled citizens, culture and education), the hard model being dominant (Neirotti et al. 2014). The problem is there are no vendors for soft domains apart from the citizens themselves whereas systemic integration relies on soft domains, mainly taking into account the context and valuing social capital. These approaches are dead ends, as analyzed by Adam Greenfield in his pamphlet Against the Smart City (2013). Promoted by vendors of technology, the ideology of the smart city is a techno-centric approach that relies on top down methodology that has produced the nonhabitable cities of Songdo, Masdar, Plan IT valley. . . The pamphleteer and digital philosopher Evgueny Morozov has excoriated this mood in his To Save the World Click Here (2013) as “solutionism,” a term that Morozov draws from urban planning and architecture studies, referring to situations when someone (i) invents a problem, (ii) misrepresents this fiction as a genuine and urgent

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dilemma, and (iii) advocates using technology to fix it. Morozov has further argued that the economics of these giant technology vendors’ data extraction are creating a world in which they build addictive services to gather citizens’ data to develop artificial intelligence (AI) and machine learning solutions for the very addiction problem they created (Morozov and Bria 2018). The metaphor of the “Cyborg City” has often been used (Gandy 2005) to describe the dominant type of Smart City initiatives “that seeks to optimize (the city’s) operations through the capacity and speed of algorithms and artificial intelligence (Computing City) to handle the huge amounts of Big Data constantly collected by a network of sensors in the physical city and online” (UNESCO and Netexplo 2019, p. 349). Coined by Antoine Picon (1998), the term appeared a year later as the title of a movie, where Michael Burke, director of Cyborg City (Burke 1998) “describes how beneath the ‘glass and concrete’ of the future city there will be a ‘humming mass of technology’ acting as a central nervous system, ‘constantly monitoring and controlling both its own functions and those of its citizens’” (Burke 1998, cited by Gandy 2005, p. 29). Netexplo study (2018) also observed an alternative model of “Community City” that could be defined by its social innovations as a priority response to the real life needs of citizens. Smart cities as “smart communities” (Granier and Hiroko 2016), or Community Cities as opposed to a Cyborg City (UNESCO and Netexplo 2019), are a type of Smart Inclusive Cities that aims to improve urban sociology through relations, mutual assistance, and the collective identity of the population with all its components, including Inclusive City aspects (UNESCO and Netexplo 2019). What some authors term “Smart community” (e.g., Granier and Kudo 2016; Gurstein 2014; Mellouli et al. 2014; Meijer 2016) corresponds, to smart cities where public participation is considered as an end in itself, with technologies being used for a “distributed intelligence,” allowing to steer the diversity of actors more effectively and eventually leading to more integrated services and better policies (Meijer 2016). In the same vein, Francis Pisani (2015), considers that the challenge for the future our cities is to overcome the discord between two extremes, between “Datapolis,” the city completely managed using data collected by the technological infrastructure, and “Participolis,” the city in which citizens participate in the design and management of the space in which they live. He recognizes though that “Participolis remains more an aspiration than a reality, a multitude of active points throughout the world which are struggling to connect with each other” (p. 193). Considering the city evolution as a fluid, ongoing and “always in the making” product (Guy 2009) of a collective and recursive learning process that resonates with ideas of participatory, deliberative or direct democracy (Karvonen and van Heur 2014), we might look at the smart city as a smart “autopoietic social system” (Luhmann 1986). A social system considered to be autopoietic is a “network of events which produces itself . . . the reproduction of events by events” (Luhmann 1986, p. 175). Following Luhmann’s definition, the smart city as an autopoietic system is never made once and for all but is continuously co-constituted by its actants, humans, and

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nonhumans (Dainow 2017) and therefore is able to continuously reframe itself to adapt to an ever changing environment, along the urban life cycle (Rochet 2018). Autopoiesis is a property of living dissipative systems: strong entropy and correlative capabilities to reproduce itself permanently thanks to its internal interactions (Maturana 1981; Varela et al. 1981). This property makes the system able to face with the rapid changes of the environment: “This generalized view of autopoiesis considers systems as self-producing not in terms of their physical components, but in terms of its organization, which can be measured in terms of information and complexity. In other words, we can describe autopoietic systems as those producing more of their own complexity than the one produced by their environment” (Gershenson 2015). As a result, urban system scales from local actions and interactions that lead to global patterns which can only be predicted from the bottom-up (Miller and Page 2007). In this new view of the city being the result of emergent patterns, we need to focus on the role of citizens and direct or deliberative democracy at play in a “city in the making” (Guy 2009).

Why Do We Need Strong Citizen-Based Interactions Within the Urban System? In urban planning expert-based, hierarchically organized policy making and governance have led in the postwar years to an erosion of process and output legitimacy due to the increased complexity of societies and their institutional fabric (Anttiroiko 2016). Wagenaar (2007) argues that participatory and deliberative models of governance are effective in harnessing complexity because they increase interaction within systems and thereby both enhance and utilize their diversity and creativity. What emanates from this is a collective intelligence, be it aggregation of opinions or the wisdom of crowds (Surowiecki 2005), that translates into a participatory culture that supports, guides, and controls such development (Foth et al. 2011). The social side of such intelligence assumes that a heterogeneous group of people is generally able to provide smarter solutions than an individual expert, that is, diversity trumps expertise (Howe 2009; Surowiecki 2005). This connects the smart city discourse to inclusive, open, and user-driven innovations as critical elements of smart urban development (Anttiroiko 2015; Antikainen et al. 2010). Some cities and regions in Europe and globally are already being self-organized by following what is called “city-as-a platform” (Anttiroiko 2016), or “urban laboratories” (Karvonen and van Heur 2014), through the emergence of new tacit communities and relational spaces (Peña-López 2019), and more broadly through transformative alliances among the public sector, private sector, academia, and civic society allowing to democratize the smart city concept (Calzada and Cowie 2017) but also to experiment across institutional boundaries in search of the urban commons (Oström 2010). By squaring the circle of the Stakeholders involved in the development of communities in cities or regions, which has been coined as “Helix Strategies”

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(Deakin 2014; Etzkowitz and Leydesdorff 2000; Satyam and Calzada 2017), what is at stake is allowing territorial communities to find their own way through the assembling of stakeholders’ present interests and future visions (Calzada and Cowie 2017). A good illustration of the above perspective can be found in the rebuilding of Christchurch by its inhabitants. After the city of Christchurch (NZ) has been destroyed by an earthquake in 2011, the government of NZ proposed to rebuild the city based on a traditional top-down approach. The answer of Lianne Dalziel, the newly elected mayor, was to rely on citizens’ intelligence initiatives insisting on the fact that a resilient city able to withstand a shock as an earthquake needed to be built bottom-up mobilizing empirical mundane knowledge and creating the conditions to appropriate scientific knowledge (see https://www.rnz.co.nz/news/national/401725/ lianne-dalziel-reiterates-commitment-to-rebuilding-christchurch “Lianne Dalziel reiterates commitment to rebuilding Christchurch”). The second reason to plead for bottom-up approaches is the economy. To generalize, in the rapidly changing world, there is a need to smarten up economic renewal, which involves urban-regional functions that are based on the reflexivity of the urban actors involved, those who can learn, repair, and redesign their smart-city sub-systems in a wider regional context (Anttiroiko 2016). The smoothness and success of such a systemic whole and level of synergies depends to a large extent on the collective intelligence generated by the collaborating actors (Anttiroiko 2015; Herrschel 2013; Innes and Booher 2002). An economic structure based on synergies on economic activities is the condition to wealth creation which reinforces itself through interaction of a political power based on the Common Good (Reinert 2008; Rochet 2012). In the case of FFF (Failed, Fragile and Failing states), Kattel et al. (2009) note that “State failure and fragility are often preceded, or at least accompanied, by failure and fragility of cities.” When a city sprawls out of control, it produces negative externalities without positive synergies. “The missing link in the economics is related to the lack of increasing returns based on “coopetitive” diffusion of means in a predictable and conducive environment. (. . .) productive governance often enforces the development sustainable productive structures based usually on a participatory system. The more the participatory system is closed to democracy and shared economic growth with special focus on health, education and communication infrastructure building, more quickly the divergence between countries narrow down” (Kattel et al. 2009). The third reason is the technological intensity of smart cities. To generalize, such factors as connectedness, sharing, and interdependence have given impetus to the rise of the platform economy (Evans and Gawer 2016), and analogously there is a gradual transition towards platform cities (Anttiroiko 2016), which facilitate interaction, exchange, and transactions through physical and virtual platforms or real-virtual hybrids. Emerging interconnectedness and multilayeredness have a connection to both the increased dynamism of economic and social processes as well as increased flexibility in territorial governance (Somerville 2011). Such developments also have a potential to increase local choices, ad hoc social formations,

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virtual nomadism, and individualism, as platforms can reduce gatekeeper functions and facilitate self-expression and interactive processes, allowing dynamic and context-sensitive aggregation of individuals’ interests. This creates a natural connection with such tendencies as the democratization of innovation and the participatory turn in public governance. The power of these technical systems requires strong political control to be both fully efficient and not becoming the level of a totalitarian system (Simondon 1958). The current debate about the suitability of a participatory and representative democracy is timely in the evolution of smart city citizenship and inevitably reflects on “citizens’ awareness of (big) data and (big) data’s techno-political and psychopolitical implications” (Calzada 2018, p. 47). These implications include transparency as “no longer just a desirable virtue in politics but an imperative tactic if the aim is to stay clear of disrepute” (Castells 2018, p. 101). The city of Barcelona can be seen as a good illustration and laboratory for the implementation of a transformative, democratic innovation project, starting from real citizens’ needs, and leveraging digital technology to devolve more power to citizens and residents (Bria 2019; Morozov and Bria 2018). Critiquing the “solutionist, technocratic smart city agenda previously promoted,” which was “top-down and technology first,” Francesca Bria, the Chief Technology and Innovation Officer of the Barcelona City Council, advocates for a political and pragmatic approach (Bria 2019, p. 86). The key questions that must be posed, she adds, are “why technology is actually needed, what kind of urban problems we should solve, who manages them, who owns what, and, most importantly, how we govern technology to implement policies.” The key issue, she says, is that “in order to change the existing smart city model, technology must be aligned with the city’s politics, and not the other way around” (Bria 2019, p. 86). Indeed, the ethos behind putting the citizen at the center is the ethos of the Information Age as described by Himanen (2001), which reflects the distributed way that collective production has been working since the digital revolution (Raymond 1999). This new ethos is what leads the transformation of social production (Benkler 2006), in the political arena, with more horizontal and democratic approach to decision-making, or, digitally speaking, to a “wiki mode of government” (Noveck 2009).

Rethinking the Smart City Concept from the Perspective of Citizens’ Bottom-Up Involvement: The Cases of Barcelona (Spain) and Medellin (Colombia) In response to the techno-centric smart city, the citizen-centric, or experimental, city is emerging through several experiments that demonstrate nuanced democratic and co-operative service provision models for cities (Anastasiu 2019; Goldsmith and Keliman 2017; Gupta 2014). We provide here two exemplars of community and citizen-driven approach to smart city in two different geographies associated to very different local contexts: Barcelona and Medellin. Those two cases have been

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thoroughly documented as being a success along a significant period of time, both for Barcelona (e.g., Calzada 2018; Peña-López 2019) and Medellin (e.g., Corburn et al. 2019; Dávila 2013).

The Case of Barcelona: Decidim.Barcelona Barcelona is currently undergoing a citizens’ democratic revolution from below, promoting networks of rebel cities which innovate public policy and challenge the status quo (Morozov and Bria 2018). The Barcelona strategy consists of engaging the city’s ecosystem through a series of co-creation workshops where they can provide solid inputs to the City’s strategy, evolving from a top-down to a bottomup process, promoting the collective intelligence of citizens, and involving all players (p. 28). In February 2016, Barcelona began initiated a participatory democracy project, decidim.barcelona (“Barcelona, we decide,” in Catalan), to enable participatory strategic planning for the municipality from the ground up (Bria 2019), which was clearly a “reversal of city management habits (making), the transition from top down technological priorities to citizen defined priorities” (Francesca Bria in UNESCO and Netexplo 2019, p. 209). Decidim.barcelona has been used as a supporting tool to draft the strategic plan of the city for 2016–2019, with the ambition that the platform becomes the axis of all decision making of the city, where the citizen will have a personal profile through which they can propose, engage with, and monitor all the activities, topics, etc., that they might be interested in Peña-López (2019). Barcelona Smart City Approach: A Citizens’ Democratic Revolution from Below

Decidim.barcelona has 27,000 registered users presenting over 11,700 proposals, with 11 participatory processes running in parallel. One of the best use cases regarding participation in Barcelona has been the participatory urban planning process. Here, the city involves neighborhood groups and citizens in the planning process through offline citizens’ assemblies and the online platform decidim. Together with its citizens, the city drafted an ambitious mobility plan to curb excessive air pollution, lower noise levels, and reduce traffic by 21%. The plan is based around the idea of superilles (superblocks) – mini-neighborhoods around which traffic will flow, and in which spaces will be repurposed into green space for citizens, freeing up 60% of streets currently used by cars (Morozov and Bria 2018, p. 51).

The abundance of open documentation (Ajuntament de Barcelona 2015, 2016) available demonstrates that decidim.barcelona has increased the amount of information in the hands of citizens, created momentum around key issues, and has led to an increase in citizen participation.

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This improved participatory culture has had a positive impact on democratic processes, especially in creating legitimacy around decision making, which can be summarized in four key points (Peña-López 2019): • Deliberation becomes the new democracy standard. • Openness as the prerequisite for deliberation. • Accountability and legislative footprint as an important by-product to achieve legitimacy. • Participation leads to more pluralism and stronger social capital, which fosters deliberation, thus closing the (virtuous) circle of deliberative democracy (Dryzek 2010; Elstub 2018). What we are witnessing with Barcelona is the ongoing transition between (techno-centric) smart cities and (citizen-centric) experimental cities (Calzada 2018). Not only the participatory process of early 2016 has been widely put into practice, but it’s been technically designed and integrated into the core of policy making in sustainable and replicable ways, with a widespread adoption of the model across other Spanish cities and also by supra-municipal entities (Peña-López 2019).

The Case of Medellín: “City for Life” Much has been said and written about the transformation of Medellín, Colombia, over the past 20 or more years (e.g., Fukuyama and Colby 2011; Kimmelman 2012; Vulliamy 2013; Brodzinsky 2014; Martin 2014). Once the most violent city in the world, famous for its drug lord Pablo Escobar (Warnock-Smith 2016), Medellín was recognized in 2013 as the most innovative city in the world by the Wall Street Journal and the Urban Land Institute and received the Lee Kuan Yew World City Prize in 2016. In the 2000s, when many Latin American cities were struggling with growing levels of urban violence and inequality, Medellín was celebrated as an impressive case of urban transformation and a model of successful public initiatives that reduced not only gun violence but also poverty, segregation, and inequality (The Economist 2014). “City for Life” was the slogan of the Medellín municipal government from 2011 to 2015 and the title used for the City when it hosted the 2014 World Urban Forum (UN Habitat 2014). In a direct continuation with the abovementioned “socially smart” city (e.g., Durose et al. 2019), Medellín’s strategy of progress in its “smart-community-city” transformation roadmap was clearly citizen-centric actions, targeting the human community of residents, to improve both their material conditions of life and also the mixing of populations, to improve their self-image and that of the city, and further an incentive to collaborative participation (UNESCO and Netexplo 2019). In Medellín, the focus is on “citizen-centric . . . and collective psychology, overcoming the feeling of exclusion, rebuilding solidarity among the population in ‘a city that belongs to us all, for us all’ . . . This is how Medellin defined work for recovery of the sick ecosystem of a ‘Community City’” (p. 150).

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In the wake of social urbanism (Dávila 2013) and the PUI projects (Integrated Urban projects), the Medellín government began actively engaging residents through “imagination workshops,” co-facilitated by the Urban Development Agency (or EDU), which is a municipal agency that helps design, manage, and implement strategic neighborhood upgrading projects (Corburn et al. 2019). EDU planners focused on developing strong community-based relationships by dedicating hundreds of hours to participatory planning activities and negotiating small truces between the municipality, local leaders, and youth gangs (Sotomayor and Daniere 2017). Medellín is also one of the largest cities in the world engaged in participatory budgeting (Uran 2009). With paramilitary groups controlling most local institutions and a state with limited credibility, participatory budgeting became one way to reengage citizens and build trust by allowing communities to discuss their priorities and vote on how municipal resources ought to be allocated in their neighborhood (Hajdarowicz 2018). Participatory budgeting can also be a way for citizens to understand how government can work for, not against, their interests: the allocation of the participatory funds for community driven projects is managed by a group of popularly elected neighborhood planning representatives called Juntas Administradoras Locales (JALs) (Guerrero 2011). In the research conducted by Jason Corburn and colleagues (2019), it’s been highlighted that Medellín’s transformation has focused on both processes (i.e., who is included and how are decisions made) as well as products (i.e., the plan, programs and built form) of healthy city planning and development. The authors found seven interrelated factors that together have contributed to the successful transformation of this city from a place characterized as greatly unequal and violent, to one of increasing prosperity for all and a City for Life, including: (i) Governance continuity and transparency – successive leaders implementing long-range plans and strategies; (ii) Planning a “city for life” – centering the social determinants of health in redevelopment; (iii) Adaptation and innovation – adjusting programs as you learn what is and is not effective; (iv) Sustained civic engagement – committing to on-going civil society involvement in generating and implementing solutions; (v) Integrated projects that include public-private partnerships – working across sectors and spatially integrating services that promote wellbeing (Corburn et al. 2019, p. 1).

Metrocables, a “Jewel in Medellín’s Social Urbanism Crown”

Like all “social urbanism” projects, the Metrocables (aerial cable-cars) were implemented with close involvement and participation of local communities. The Metrocables improved access to what were previously considered dangerous areas and opened them up to local, national, and international tourism, a significant means to “make poor neighborhoods visible” to the rest of the city. (continued)

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This type of understanding developed by the city’s inhabitants, those that live in the communes and those using the Metrocables, contributes to major processes of community organization, participation, and local management. This project and others related to the “City for life” transformation have been aided by the practice of participatory budgeting and is articulated to wider consultations leading to the joint formulation and implementation of local development plans by residents and local authorities (Dávila 2013; Dávila and Brand 2013).

The bottom line in these two exemplars of citizen-centric, bottom-up, and socially focused vision of smart cities is that such cities are “always in the making, on the move and fluid” (Guy 2009), championing process over product (Karvonen and van Heur 2014), with a large variety of direct and/or collaborative democracy practices often hybrid, combining online and offline interaction, with the emergence of new organizational models involving citizens and collective intelligence in the policymaking process (Morozov and Bria 2018; Satyam and Calzada 2017; Saunders and Mulgan 2017). Such dynamics cannot be steered and controlled through data levers or top-down policies, and they constitute social emergences, involving complex, interdependent systems that influence each other in often unpredictable ways (Innes and Booher 2000). This complexity, and the fact that there are still few cities or territories that call on citizens to design collective solutions to their most pressing problems (Unesco and Netexplo 2019), leads some authors to the following question: “Is the bottom-up innovation perspective simply wishful thinking?” (Satyam and Calzada 2017).

Social Emergence as a Proposed Lens for a Finer Grained Understanding of How Bottom-Up Dynamics Within Smart Cities Initiate and Bring New “Social Orders” As we have seen previously, existing experimentations of smart human cities as an alternative paradigm to the dominant technocentric conception of smart cities are the result of complex human interactions, often leading to unexpected and emergent social phenomena. In what follows, we propose to further analyze what actually initiates the emergence of new order creation within social communities. Complexity sciences provide a structure for gaining insight into the phenomenon of emergence, a means for exploring what emergence is and how it occurs. As defined by Lichtenstein (2014), who has conducted a very comprehensive review of the notion of emergence in social complexity sciences, “emergence is the creation of order, the formation of new properties and structures in complex systems. . . when emergence happens, something new and unexpected arises, with outcomes that

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cannot be predicted even from knowing everything about the parts of the system . . .emergence is present at every level of reality” (Lichtenstein 2014, pp. 1–2). Emergence is at the heart of complexity science with more and more scholars seeking to understand patterns of collective social behavior (e.g., Chiles et al. 2004; Epstein and Axtell 1996; Goldstein et al. 2010; Lichtenstein 2009, 2014; Macy 1991; Schelling 1978; Sawyer 2005). Most studies of complexity examine the “bottom-up” emergence of agents into higher order entities that is the creation of order solely through local interactions with no external influence or top-down control. (e.g., Lichtenstein 2014 in his broad literature review). One might ask the following question: “do emergent (social) systems always act ‘of their own accord,’spontaneously?” (Lichtenstein 2014, p. 12). This question is at the core of the very notion of smart city governance when considering the place of citizens in the very conception and functioning of their city (Unesco and Netexplo 2019). The Netexplo Observatory has spotted a “trend toward rebalancing the urban progress approach between top-down dirigisme and bottom-up openness. . . based on mobilization of citizen-actors?” (Unesco and Netexplo 2019, p. 216), recognizing that there is still a great deal to be done in our understanding of such emerging processes. Recent studies on emergence in complexity science have been exploring this issue of spontaneity by investigating how agency can catalyze social innovations and emergents in ways that are both spontaneous and planned, emergent, and constructive (Goldstein et al. 2010; Lichtenstein 2009, 2014). The Case of Branson

One of the best studies on the emergence of real social urban systems is the dissertation research of Todd Chiles (Chiles et al. 2004), who pursued a 100year study of emergence in Branson (Missouri), now one of the most visited tourist areas in the United States. Using dissipative structures theory as a framework, their analysis explored the dynamics underlying the “punctuated emergences” that transformed the region from a small town to a thriving organizational collective. In the context of social emergence, their study showed how emergences in the region generated more capacity for growth, leading to further emergences over time in a long series of positive feedback processes. The findings reflect organizing and emergence at multiple levels, including individual entrepreneurs, key families, organizations and businesses, community-wide associations, and regional and national effects, showing how emergence was generative at each level, leading to greater capacity in future eras. Chiles and colleagues (2004), for instance, described how the emergence of the Branson Mall was usefully kept in check by a strong set of common cultural values, long-standing pro-business policies, and a coordination of marketing efforts, through the actions of collective organizations in the area. (continued)

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Collective action played a significant role in the emergences at Branson, primarily through a number of influential organizational collectives around the region. By the 1920s, two such collectives were already helping draw tourists to the Ozarks by promoting the “Land of A Million Smiles.” Increasing tourism was one of the critical resources that fueled Branson’s astounding growth. Moreover, Branson’s first theater started as a collective organization, back in 1955. In more recent years, the Ozark Marketing Council has attracted increasing waves of tourists to the area, “channeling and accelerating” the resources necessary for emergence. Even more broadly, the collective organizing of entrepreneurs, financers, and community leaders created a context for the ongoing transformations of the region (Chiles et al. 2004). These findings, combined with those demonstrating the importance of individual agency, the pivotal role of specific organizations, and the interplay of the broader social system, suggest that a rich theory of organizational evolution must adopt a multilevel approach: focusing from the individual and organization, through the organizational form and population, to the organizational community and social system (Chiles et al. 2004, p. 515). By explaining how microprocesses generate macro-order, complexity theory is therefore ideally suited to such a multilevel approach, providing scholars with a fuller understanding of the dynamics of change that allows for emergence and surprise (Tsoukas and Chia 2002, p. 568).

The Generative Emergence Model, as a Promising Way to Study Processes of Emergence in Socially Smart Communities Complexity and emergence scholars have identified three catalysts of emergent order: far-from-equilibrium dynamics (e.g., Meyer et al. 2005), adaptive tension (e.g., McKelvey 2004) and opportunity tension (e.g., Lichtenstein 2009) that overall converge and complement each other (Lichtenstein and Plowman 2009). When grounded in social and environmental sustainability, a dynamic creation approach, driven by cycles of opportunity tension (Lichtenstein 2009), can explain the process of emergence and development of human societies (Carniero 1970, 1987), of cities (Dyke 1988), and of expanding order in society (Adams 1988; Coren 1998). Lichtenstein and colleagues’ work on generative emergence (Lichtenstein 2009, 2014; Lichtenstein and Plowman 2009) led to the identification of five key processes that lead to emergence and two outcomes of the process (emergence or dissolution). This has led to the conceptualization of an emergence cycle around five key processes or phases, as shown in the following idealized diagram of one cycle of emergence, that are a direct analogy to the microprocesses of order creation in dissipative structures (Fig. 1).

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Fig. 1 One cycle of emergence – idealized diagram of five-phase emergence sequence (Lichtenstein 2014, p. 326)

These phases have long been identified by management scholars studying organizational emergence and transformation (Lichtenstein 2014): four- or five-phase models are found in Smith (1986), Smith and Gemmill (1991), Browning et al. (1995), Chiles et al. (2004), and Plowman et al. (2007). What the studies conducted by Lichtenstein and colleagues show is that these five phases operate sequentially, as a cycle of emergence (Lichtenstein 2009, 2014; Lichtenstein and Plowman 2009). The entire process within a cycle can be summarized as follows (Lichtenstein 2014, p. 325): (i) A cycle of generative emergence is initiated by an opportunity tension that generates disequilibrium organizing, which gives rise to stress and experiments; (ii) if these qualities continue to increase, the system will reinforce and amplify experiments and other energy, toward a critical event – the system reaches a tipping point; (iii) on the other side of this threshold, new order will emerge, through a recombination of components. In virtually every successful case, this emergent order results in a more adaptive system; (iv) if the emergence is successful, the system will produce stabilizing feedbacks that institutionalize the changes into a sustainable dynamic state; (v) this whole process eventually leads to dissolution or stabilization of a new (emergent) order. To illustrate this processual view of social emergence, we provide an example of bottom-up emerging development of a rural village in Morocco, Tizi N’Oucheg (“Tizi”) in which one of the authors has been associated as a researcher (Amine Belemlih). Although it may seem far from the classical exemplars of smart cities development, this case is very enlightening for the availability of its longitudinal

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data over a period of 10 years, which made possible the documentation of its process of social emergence “along the way.” (The research has been conducted with the collaboration with the chairman and founder of the “Open village” association that has played the role of facilitator and catalyst over a 10-year period, see www.openvillage.org) The “Tizi” case is at the counterpoint to the dominant smart cities approach and discourse, focusing almost exclusively on urban and techno-centered initiatives, as seen previously. But it is in line with recent and growing number of studies on smart-city-regions perspectives (e.g., Calzada 2017), smart rurality, and lagging regions (e.g., Oliva 2019). What is mostly considered here, that has been the foci of many cited studies, is the human-citizen-centered and bottom-up dynamics of collective initiatives (e.g., Calzada and Cowie 2017; Corburn et al. 2019; Durose et al. 2019; Karvonen and van Heur 2014; Peña-López 2019). The Tizi village has been through an impressive transformation “from the ground up,” starting 10 years ago as one of many examples of socially depriving community, suffering an accumulation of socio-economic handicaps (severe poverty, rural exodus to name a few). The first phase of “Disequilibrium organizing” has started with the creation of a “Jam’ia” (local association) of young and proactive villagers, seeking to “do something about the poverty and education flaws,” alongside the traditional popular assembly that we find in most Berber villages, the “Jama’a” (the “assembly,” in Arabic). While they started a visioning process, with the diagnostic of “all that the village needs to succeed in the next 10 years,” and a first purpose to “make do with what we have,” which eventually led to an objective of economic empowerment. This initiative has brought a lot of resistance within the Jama’a: while the elders continued to tell the association – Jam’ia – members “you’ll never succeeded” both circles agreed on one wicked issue: 100% of the pupils in primary school failing to move up to high school. One important fact to mention here is the previous negative experience with a Luxembourgian association that tried to impulse the culture of quinoa “in a top down manner without involving the villagers,” only dealing with local authorities (which led to a strong push back from the villagers, left aside the process). In sequence 2 (“Stress and experiments”), several experimental initiatives have gradually led to a significant increase of the “entrepreneurial and action-oriented energy” of an increasing number of villagers, eventually building trust which further enhanced collective action in a recursive way. For instance, a dialog between one of the village sympathizers, KB (KB is one of the co-founders of the Open Village association, that has been created to document and spread the self-organized approach to development adopted by the Tizi village), has led to the set-up of a local production of blackberry jam, which managed to value the presence in large quantities of blackberry that is endemic to the Tizi territory (surrounded by the Atlas mountains, close to Marrakech). In parallel, a working group performed a diagnostic of the educational situation, in conjunction education experts and led to a series of small steps, like building a small house for the local teacher, designing a preschool education program to teach Arabic to the pupils as a communication language (the whole village is primarily Amazigh-Berber speaking) which has managed to address

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the primary cause of failure in primary school (the official program is designed only for Arabic speakers as a mother tongue). These experiments, among others, have yielded over time encouraging results, which led to further reinforce the “opportunity tension” related to emerging opportunities perceived, leading to new initiatives, etc. In sequence 3 (“Amplification to a critical threshold/event”), the virtuous cycle of actions/positive results/new actions or extension of actions led to some initiatives that had a profound impact on the village’s self-perception and level of confidence, all of which went on reinforcing each other further on. One first threshold has been reached when 100% of the primary pupils succeeded making the transition to secondary school, with the best grades in the region. Another threshold has been crossed when the villagers succeeded in designing and building by themselves their own water system, including water storage, purification, and supply systems providing drinking water to each house. In sequence 4 (“New order through recombination”), the Tizi village managed to disseminate their approach to extended circles villages within their surrounding territories, first by building a road proving access to the national road, with the support of four neighbor villages. The encouraging and rather unprecedented actions performed by themselves led more and more funders and experts to proactively propose their support to the village, leading to a redefined set of resources available, not exclusively limited to the local resources at hand. In the same line, a 100 villages that heard of the Tizi experience formed a federation of villages and started studying with the help of Tizi ways to roll out adapted version of the self-organized development process, joined by several public and research institutions seeking to understand “how Tizi made it.” In sequence 5 (“Stabilizing feedback”), the new governance and collective action scheme reached a plateau, with all of the key development objectives drafted in the “vision statement” largely over-achieved before their due dates. Furthermore, the jama’a and the jam’ia reached a firm foundation of trust which contributes to amplify and accelerate collective action and amplify the momentum. We must draw the attention on the many limits of this case study given its nonstandard features (focusing on a rural community) when compared to the smart city perspective, although our intention here was to illustrate how community bottom-up dynamics unfold over time as it is the case in socially smart cities. One caveat is related to the need to better understand the nature and behaviors of the exogenous factors that helped “catalyze collective action” (Lichtenstein 2014, p. 399), apart from top down supervision that was not at play in this case. Another caveat is related to the fact that it is still to be proven that the generative emergence model is applicable to large social change, beyond the scale of organizations or small collectives, or in the case of “macro-emergence” (Lichtenstein 2014). Although previous empirical research studies in the case of cities and large communities (e.g., Chiles et al. 2004) are suggestive that the five-phase model may be applicable at larger scales (Lichtenstein 2014), we believe there is an opportunity to further conduct processual and empirical based studies on the emergence bottom-up dynamics of socially smart cities in the future.

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Conclusion In this chapter, we have analyzed the smart cities dynamics through the lens of complex systems architecture, stating that the smartness of a city consists of this continuous learning process that relies on interactions between basic cells and actors of the city. In this new view of the city as the result of emergent patterns, we have focused on the role of citizens, proposing an original perspective of the dynamics underpinning bottom-up initiatives. To further explore this perspective, we have proposed to use the generative emergence model as a lens to get to finer grained understanding of the citizencentered and self-organized dynamics, at play in smart cities, regions, and rurality, which remain scarce and unusual situations that are expected to expand in the future.

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Sunil Choenni, Niels Netten, Mortaza S. Bargh, and Susan van den Braak

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Big Data: Views and Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Existing Views on Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Issues when Using Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Discerning Definition of BD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inductive Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . In Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Challenges of Using Big Data in Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Quality Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evolving Semantics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . System Realities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Statistical Truths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Towards a Framework for Responsible Use of Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Achieving Transparency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dealing with Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Illustrative Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion and Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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S. Choenni (*) · N. Netten · M. S. Bargh Research and Documentation Centre, Ministry of Justice and Security, The Hague, The Netherlands Research Center Creating 010, Rotterdam University of Applied Sciences, Rotterdam, The Netherlands e-mail: [email protected]; [email protected] S. van den Braak Research and Documentation Centre, Ministry of Justice and Security, The Hague, The Netherlands e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_82

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Abstract

A promising vehicle for a smart government is the exploitation of the large amount of heterogeneous data that becomes available increasingly. The field of Big Data (BD) responds to this promise by leveraging the potentials of such data. In this paper, a systematic approach is adopted to argue why the use of BD, particularly for policymaking within smart government, is challenging. Wrongly interpreting BD-based models may impact individuals adversely and affect society negatively. Therefore, it is important to take care of a proper interpretation of BD models and their appropriate practical application. To this end, it is advocated adopting a glass box approach. The major building blocks of this glass box approach correspond to the data as the input for analyses, the algorithms to analyze the input data, and the models as the output of the analyses. Depending on the application at hand, each of these building blocks should be made transparent to an appropriate extent. The overall transparency (i.e., the outcome of the transparencies of these building blocks) should aim at making the output models understandable for policymakers so that they can assess the consequences of those models adequately when used for policy development. Further, policymakers should adopt well-thought-out strategies for using uncertain BD models responsibly. A framework is suggested to systematically specify (and address) the issues of achieving the glass box objectives and discuss the strategies needed for dealing with uncertain BD models.

Introduction Nowadays, a large volume of data of various types are being generated, collected, analyzed, and distributed at a fast pace. This proliferation of data has various reasons. First, the environment is equipped with many connected devices such as cameras, smart phones, sensors, and smart household appliances. Second, individuals use social networks intensively. Third, organizations digitalize their services and processes. Altogether, large volumes of heterogenous data are being developed in an increasing rate, a phenomenon being coined as Big Data (BD). There is a growing demand to exploit the opportunities of BD and develop (new) applications and services that make daily lives more comfortable, create added value for businesses, provide insight into societal phenomena, contribute to democratic governance and institutions, and guide policymaking. These applications and services are crucial for shaping a smart city and, in its generic form, a smart government. In a sense, a smart government utilizes BD applications to interconnect and integrate information generated by digital infrastructures with physical infrastructures to better serve communities and citizens. In ▶ Chap. 30, “Towards Autonomous Knowledge Creation from Big Data in Smart Cities” by Nowaczyk et al., a number of BD applications can be found as well as a more extensive discussion about the relationship between BD and smart cities.

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Realizing BD applications, one the one hand, entails a number of technical issues and challenges like establishing efficient mechanisms for integration, storage, and retrieval of large volumes of data and processing different types of data in almost real time. On the other hand, often the usage areas of BD do not fully coincide with the purposes for which the original data was collected. This entails a number of the so-called soft challenges for using BD in practice. Relying on data gathered from various sources and for diverse purposes, for example, can result in violations of fundamental human rights such as privacy, liberty, autonomy, and dignity (Bargh and Choenni 2013; Kalidien et al. 2010; Prins et al. 2012). First, linking data can reveal privacy-sensitive information about individuals (Bargh and Choenni 2013; van der Braak et al. 2012; ▶ Chap. 32, “Data Protection and Smart Cities” by Vojković and Katulić). Second, data analytics based on BD can lead to wrong classification of individuals, which can adversely impact their liberty, autonomy, and income. Third, even labelling individuals correctly can be harmful and illegal when, for example, individuals become subject to unjustified or unjust discrimination. This contribution is devoted to systematically studying the soft challenges of BD applications, particularly in the context of policymaking (see, e.g., Anisetti et al. 2017, 2018). Such a systematic way of specifying and highlighting the soft challenges of BD is not covered in works promoting the use of BD (like (Anisetti et al. 2017, 2018) that propose a BD platform for making public health polices based on BD. An early work that critically scrutinizes the assumptions made in BD analytics and the biases of BD is done by Boyd and Crawford (2012). Characterizing BD as a rising phenomenon with many sociotechnical issues, the authors discuss a number of these issues, namely, the claim of objectivity and accuracy being misleading, bigger data not necessarily being better data, and BD leading to violation of privacy – refer to, for example, ▶ Chap. 32, “Data Protection and Smart Cities” by Vojković and Katulić for the privacy issues and the data protection challenges that arise from the Internet of Things-based BD in the context of smart cities, BD losing its meaning if taken out of context, being available not necessarily being ethical, and BD creating new digital divides. Similar issues were also raised in the context of data analytics in general (see, e.g., Kalidien et al. 2010). While a lot of effort is devoted to tackle the technical challenges, only little effort is taken to identify and tackle the soft challenges. Particularly, the research on how to interpret and apply the outcomes of BD is in its infancy. Tackling the soft challenges of BD (i.e., preventing misinterpretations, privacy breaches, and ethical violations in BD applications, while exploiting the outcomes of BD) is not straightforward from various perspective, including legal, ethical, and data quality perspectives. Therefore, there is a strong need for guidelines, best practices, and frameworks on how to address the soft challenges of BD. To systematically study the soft challenges of BD, particularly in the context of policymaking, the meaning of BD according to some scientifically well-established principles that underlie BD applications is discussed first. Based on that, a framework is provided specifying how to perceive, interpret, and apply the outcomes of BD in practice. Such a specification is particularly important for policymaking purposes so that the soft challenges of BD can be elucidated and made clearly

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observable and comprehensible for decision makers and policymakers. The proposed framework distinguishes three major elements of BD applications: (1) data, (2) model extraction, and (3) model. The first element is the data used in the BD application, which basically relates to the data preparation activities. The second element encompasses the algorithms used to analyze the data in order to derive models from it. Finally, the third element is the outcome of the analysis, i.e., a model that is subsequently interpreted and applied to a specific context in practice. With respect to the first element, it is observed that data sets are often incomplete and skewed. In BD applications, these data sets are mainly used for other purposes than the one they are originally collected for. As a consequence, often these data sets miss crucial attributes or attribute values. Furthermore, it is not always clear how to define and measure the data quality (Gibbs et al. 2005). With respect to the second element, it is argued that the algorithms to analyze the data sets and to derive the models are based on induction, and therefore, the obtained models are not truth preserving in the sense of being certain. Furthermore, a model is always a limited representation of the real world. With respect to the third element, this insight, then, raises the question of to what extent the induced models represent the real world and, therefore, whether and how they are sufficiently grounded to base new policies on. The soft challenges raised have existed long before BD, as they are also common for the fields of data mining, Knowledge Discovery in Databases (KDD), and even statistics. However, the added value of this paper is a sound underpinning for why the raised challenges are still important in the context of BD and why they become more difficult in this context. A sound link between these issues and BD is missing in literature, and the paper aims at establishing this link systematically. This contribution shows that the soft challenges stemming from BD and BD-based models may have a significant impact on individuals, groups, and the society. Therefore, the outcomes of BD analytics should be applied with caution in practice. It is argued that if the models are wrongly interpreted and applied, they may lead to decisions that may appear fine at first sight but are far from being correct, just, and fair. This is illustrated by means of some real-life examples. Since the interpretation of the models is far from unambiguous, also a framework is provided in the form of guidelines and strategies to support the use of uncertain interpretations of BD models in practice. It is advocated to adopt a glass box approach, which makes the obtained BD models understandable for policymakers so that they can assess the consequences of those models within their application domain. This work builds on the results of the studies presented in Choenni et al. (2018a, b). The remainder of this paper is organized as follows. In section “Big Data: Views and Usage,” an overview of different views on BD is provided. In section “A Discerning Definition of BD”, BD is considered from the perspective of some well-established principles, and provides a formal definition of BD to accentuate the fundamental problems of applying BD. The practical implications of and the limitations of BD are then discussed in section “Challenges of Using Big Data in Practice.” A framework for interpreting and applying BD outcomes in a responsible manner is the topic of section “Towards a Framework for Responsible Use of Big

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Data.” Finally, section “Conclusion and Future Research” concludes the chapter and describes the future work for evaluating the framework.

Big Data: Views and Usage In this section, two well-known definitions of BD are presented, and it is argued that there is a need for a more insightful definition (which will be provided in section “A Discerning Definition of BD”). Subsequently, the relevance and importance of this study by providing a number of issues that may arise if BD is applied in the field of policymaking are discussed.

Existing Views on Big Data In literature, BD is characterized by various aspects and properties. These aspects range from technology-oriented to usage-oriented. The technology-oriented aspects pertain to volume, velocity, and variety, aim at capturing the inability of traditional systems to manage, store, and analyze increasingly large volumes and complex sets of structured and unstructured data within a reasonable time frame (Kim et al. 2014; ▶ Chap. 30, “Towards Autonomous Knowledge Creation from Big Data in Smart Cities” by Nowaczyk et al.). The usage-oriented definitions, such as value and visualization, try to capture the properties that BD delivers to users, businesses, and society. The value property refers to the fact that a BD application should create added (business) value and the visualization property helps creating that value. A discussion of the so-called Vs of BD can be found in among others (▶ Chap. 30, “Towards Autonomous Knowledge Creation from Big Data in Smart Cities” by Nowaczyk et al.). Looking at BD from these perspectives (e.g., the Vs of BD) does, however, not provide insight into the way in which BD operates and might be even misleading about the potentials and limitations of BD. Being oblivious to these potentials and limitations can cause irreversible and undesirable impacts on both individuals and society, especially when BD is used in the context of policymaking. Therefore, section “A Discerning Definition of BD” is devoted on the elaboration of a formal definition of BD that structurally brings the potential soft challenges (i.e., issues and implications) of BD to the forefront. This definition relies on the fact that BD can be regarded as a unique collection of some traditional concepts coming from different fields in computer science and the related fields (Netten et al. 2016), which results in many novel applications.

Issues when Using Big Data BD can deliver misleading insights and may lead to unjustified outcomes if these insights and outcomes are adopted blindly in decision-making and policymaking

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Observations

Observing

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Fig. 1 An illustration of the relation between (big) data analytics and the real world

processes. Let us take a close look at how data is collected and exploited in a typical (big) data analytics setting. As shown in Fig. 1, first some data is collected by observing the world. These data, which represent the observations as shown in Fig. 1, is stored in, for instance, databases. Subsequently, for some exploitation purposes, the data is analyzed. This analysis results in a model of the world as shown in Fig. 1, which is carefully interpreted and used for, for instance, improving processes and applying appropriate interventions to a social setting. If necessary, simulations are carried out to gain insight into the impact of the devised improvements or interventions. Once the impacts are assessed as valuable, they are implemented in the real world, where they, in turn, contribute to new observations. When applying BD for policymaking, for example, this process is sometimes flawed and improper. This may have far-reaching implications for how the society is being constructed (for instance when invalid results based on skewed samples are used to underpin policy decisions). To illustrate such flaws, consider the following example. Assume that the number of crimes committed is distributed uniformly among six areas in a community (see the left histogram in Fig. 2). Also assume that the police does not have the capacity to enforce the law in all of these areas equally. Therefore, based on their gut feeling and/or based on some tips (e.g., the inhabitants in area 2 complain more often about crime), they choose to put most of their efforts to tackle the crime in area 2 and a small part of their efforts in area 5. In this scenario, it is likely that the police will find suspects and crimes in these two areas. These suspects and crimes will be registered in the databases of the police. For reasons of simplicity, let us assume that in reality 100 crimes are committed in each of the six areas (see the left histogram in Fig. 2). Further, assume that the efforts and focus of the police in areas 2 and 5 have resulted into the prosecution of 70 and 40 of the crimes (out of 100 crimes committed in total), respectively. Since only those crimes reported or detected are registered in the police database, this database does not provide a complete view of all crimes committed in the different areas. Now, the analysis of the police databases according to the depicted findings shown in the right histogram in Fig. 2 could lead to conclusions such as the crime rate in area 2 is high and, therefore, it is an unsafe area. Policymakers may use these

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Fig. 2 An illustration of the result of a flawed BD process

conclusions to underpin their decisions that more police efforts and resources are required to fight the crime in area 2, while area 2 is in reality equally safe as all the other areas. Thus, the application of BD may lead to misleading outcomes that are untrue. In this example, in reality there was a uniform distribution of crime in the areas, while the BD process yielded a skewed distribution. In this example, the problem lies in the fact that the collected data is not a representative sample of all crime committed in these areas. Although the example above is strongly simplified, it illustrates how a flawed BD process, for instance, due to unrepresentative sampling, leads to misleading outcomes. In practice, it is hard to overcome this problem because determining the extent of data representativeness is a hard problem for the phenomena in the real world. More importantly, data representativeness is less relevant in BD settings, because BD often entails collecting and integrating all possible data that might be useful or relevant, whereas the original purpose for collecting those data is not important. Reusing already existing data, BD offers many new insights, opportunities, and room for innovations, while, at the same time, reuse of existing data exposes a wide range of vulnerabilities that must be mitigated appropriately. Note that these issues are not exclusive to BD. They may arise in any data-driven reasoning. What makes them special in the case of BD, especially for policymaking, is that (a) the data is collected for a different purpose than the one for which it is analyzed, (b) beforehand it is unclear whether the data represents an phenomenon adequately, and (c) as a result of these, the data often has low quality or lacks crucial information for the purpose at hand. The next section is devoted on the elaboration of these issues by taking a more systematic look at BD processes and thereby describing some fundamental BD problems.

A Discerning Definition of BD At a high level, a BD analytics system can be characterized by three main functional building blocks, namely, (1) data collection, (2) model extraction, and (3) model interpretation as shown in Fig. 3. Inspired by this simple functional model, BD is defined as the induction of models from large and various types of data sets. Note

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Social network data

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… Register data

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Fig. 3 An illustration of the main building blocks of a BD analytics system and the scope of BD definition adopted in this contribution

that the system view shown in Fig. 3 also applies to traditional data analytics. In the context of BD analytics, however, the data fed to the functional building blocks of the system is of larger volumes, with higher rates, and of heterogeneous types. In this section, the building blocks shown in Fig. 3 are used to explain the BD definition further. Specifically, in the following subsections the here presented BD definition is analyzed with respect to the three key elements surrounding the model extraction component as indicated in Fig. 3, namely, (a) data (or databases) as the input of, (b) models as the output of, and (c) induction reasoning as the logic within the model extraction block. To this end, also the issues associated with these elements will be touched upon.

Databases Three cornerstone concepts (in the design) of databases are (1) universe of discourse (relevant during data collection), (2) abstraction (relevant during data collection), and (3) closed world assumption (relevant when analyzing data) (Date 1990). First, the universe of discourse encompasses the set of the objects for which the data is collected. The relationships with other objects that are not of interest are ignored. Second, abstraction entails the simplification of a real-world object or phenomenon. Via abstraction it is defined how specific an object in the universe of discourse is described. Applying the principles of universe of discourse and abstraction on a real-world phenomenon leads to a database model which provides a limited view or representation of this phenomenon. Third, databases are based on the closedworld assumption. This assumption implies that all data stored in the system are true and complete (Date 1990). As will be argued in section “Challenges of Using Big Data in Practice,” this closed-world assumption is merely a theoretical notion, since it is hardly ever achieved in information systems. It is concluded that, on the one hand, the concepts of universe of discourse and abstraction introduce incompleteness regarding real-world phenomena, while, on the other hand, the concept of closed world assumption relies on a naive notion of completeness. This observation is particularly relevant in using BD for policymaking, because, as mentioned in section “Big Data: Views and Usage,”

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BD in this context is more about the secondary use of data. When applied to secondary use of data, these concepts impact the reliability of the BD outcomes adversely.

Inductive Reasoning Model extraction through inductive reasoning is used to retrieve information from the data, for instance, to predict unforeseen outcomes based on some observed events. Inductive reasoning has been the subject of philosophical debate for years. Inductive reasoning assumes that when repeating patterns are observed, these patterns will always exist and repeat. It works as follows: some concrete examples are observed in which there is some truth, and subsequently, a universal rule is induced from these examples. Hume argued that this is, in fact, a logically incorrect way of reasoning and, as such, an inductive inference is not correct necessarily (Hume 2003). Nothing guarantees that those patterns that have been seen today do necessarily exist tomorrow. For example, when ice is heated, it is expected that it melts. It is possible to imagine that from tomorrow on, this will no longer be the case. This is what Hume denoted as the induction problem. If nothing guarantees that these relationships will hold in the future, knowledge from experience cannot be really gained. It seems that there is still no solution to the induction problem that can guarantee the correctness of the outcomes. However, in the twentieth century, a movement called Bayesianism proposed an insightful and pragmatic alternative. The novel idea was that belief and knowledge do not necessarily have to be either correct or incorrect. Rather, they can come in degrees that conform to certain constraints related to the axioms of probability theory (Grimmett and Stirzaker 2001). Bayesianism is in close correspondence to the human intuition. If it is observed that something happens a million times, it is highly likely, but not certain, that it will happen again. The inferences from induction can therefore gain an epistemic status somewhere between the two extremes of right and wrong. This status depends on the quality of the evidence and the quality of the reasoning with the evidence and can be adjusted in the future when new evidence comes to light. This approach to inductive inferences is the only justification for reasoning with BD. Consequently, all outcomes derived in this manner cannot be perceived as knowledge, but merely as probable conclusions (i.e., uncertain knowledge). While this justification does provide a reason for using inductive reasoning, it is a weakness of the way in which models are generated from the data.

Models BD creates value by inducing models from the data stored in databases. A model is a meaningful representation of the real world, for instance, in the form of a profile or business rule. In general, such models are useful for two reasons mainly: either as a

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way of understanding the environment (i.e., being insightful) or as an attempt to predict the environment. These reasons are both contradicting and reinforcing. Models that make good predictions do not necessarily have to be insightful (i.e., being contradictory), but insightful models can result in better predictions (i.e., being reinforcing). Each time the real world changes, the model needs to be updated. Essentially, a model continuously needs feedback to adjust its structure and parameters in order to remain an accurate representation of the world as closely as possible. As models are merely reflections of the real world, they may miss some (relevant) parts of the real world. As a model might miss crucial information, the model may be a skewed representation of the world. In other words, observing occurred real-world phenomena via BD can be insufficient to create good models. This is because a) an observation can be a proxy of a phenomenon a and/or b) there might be too few data points (i.e., samples) to capture a phenomenon adequately. Note that sometimes observing a few samples from some phenomena, especially in exact scientific fields, is not an issue to reconstruct an accurate model of those phenomena (van den Braak et al. 2013). For example, according to the Nyquist–Shannon sampling theorem in signal processing, if there are at least 2 W samples of a signal per second, where W represents the bandwidth of the signal, the original signal can be reconstructed completely (for a brief history see section “Nyquist-Shannon sampling” of Marks II 1991). In other words, induction based on 2 W samples per second can lead to a complete model of the original signal. In general, the better a model is able to capture the real world, i.e., the more expressive a model is, the more complex a model is to interpret. This is a relevant problem in the context of policymaking, because when the resulting models are hard to interpret, it is also hard to think of appropriate actions (e.g., interventions or policy decisions) based on them.

In Summary The interaction of the three building blocks involved in BD leads to models (see Fig. 3) that are incomplete and/or uncertain. On the one hand, the incompleteness is caused by the fact that the data in databases is incomplete. On the other hand, the incompleteness is reinforced by the fact that the derived models are unable to capture a complete complex reality. While the application of induction has some justification, the models obtained through it are not truth preserving. As argued in Subsection “Inductive Reasoning,” a probability measure is attached to the models, which implies that there is also uncertainty with regard to the actions that are based on these models. The fact that the models obtained by BD represent a part of the real world at a certain abstraction level with some degree of uncertainty complicates the interpretation of the models and the assessment of their value in practice. Another complicating factor is the fact that the real world changes over time. Models that are well-understood and are assessed as useful today may become hard to understand and less useful in a changing context. Actually, the models obtained from BD ask for continuous updates and learning. However, learning new models is useless if the old models dictate the scope and space of the observations (i.e.,

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impacting the data collected which, in turn, impacts the learning of new models), such that the changes in the real world cannot be observed anymore. Note that the issues discussed in this section may also hold for many data-driven reasonings, like statistics and data mining (Choenni et al. 2005). However, here a theoretical underpinning why these issues (a) will remain fundamental in BD settings, (b) are more complex in BD settings, and (c) have become more crucial for successful BD applications is discussed. In the next section, the practical implications of the limitations outlined so far are discussed.

Challenges of Using Big Data in Practice Applying BD in practice, especially in the context of policymaking, comes with a number of challenges and implications. It may result in unjustified implications if insufficient attention is paid to addressing these challenges. Consequently, irresponsible use of BD may inflict harm to individuals and society as a whole. This section is devoted to four issues that complicate the use of BD in practice. The last three of these issues have, to our best knowledge, hardly received attention in the literature. In line with the main building blocks of BD shown in Fig. 3, these issues are (1) and (2) data quality issues and evolving semantics of data issues (both related to the “data collection” block in Fig. 3), (3) system realities (related to the “model extraction” block in Fig. 3), and (4) statistical truths (related to the “model interpretation” block in Fig. 3).

Data Quality Issues Often sources of BD introduce noises (or errors) and are far from complete. Possible reasons for errors are faulty measurement tools (e.g., sensors), inadequate batch processes, or human mistakes. Additionally, NULL, redundant, and inconsistent values often occur in data sets. This is particularly bothersome when the data comes from different sources and the data needs to be integrated before being analyzed. In the worst case, the integration of data entails an exponential complexity (Choenni et al. 1993). Issues with data quality (e.g., missing data) may result in imperfect and incomplete data sets. Such deficiencies inherently exist in collected data sets, particularly in BD settings. It is also foreseeable that not all data that is required according to a given model of the real world is collected (or given access to). For example, some relevant data pertaining to a certain period, region, or any other object of interest might be missed. As a result, for most BD sets the closed world assumption does not hold. This means that BD algorithms based on induction do not necessarily yield probabilistically valid results for these sets. Data quality issues occur because, in practice, BD is not collected with a specific purpose in mind. Often, BD is collected for multiple purposes, and some of these

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purposes might not be clear beforehand. This means that the data that is relevant for the data analysis is not always collected or does not have the required quality. Before applying BD analytics to such skewed data sets, the quality of the data should be taken cared of, and errors should be cleaned as much as possible (Sheth and Larson 1990; Winkler 1995; Choenni et al. 2010). For example, choices should be made to replace NULL values. When data from different sources is used for data integration, redundant data should be removed and inconsistencies within data should be resolved. These issues and how to deal with these issues are extensively discussed in the literature. Data cleaning may be a tough, tedious, and error-prone process, especially when it concerns data stored in legacy systems. Often, the data in such systems is documented poorly, and the semantics of the data have to be guessed. In some cases, the semantics of the data may even have changed, as explained in the following.

Evolving Semantics Given the modeling principles explained in section “A Discerning Definition of BD,” an additional problem (with legacy systems) is that the data quality deteriorates due to the changes in the environment. Many environments are subject to changes, and it is not always clear how these changes should be processed in the systems. Usually in changing environments, the data was correct at the time that it was inserted into the database, and therefore the data quality was acceptable at that time. Later on, due to environmental changes, the data quality may be judged as poor because some values do not pertain to a valid entity in the new world. Thus, on the one hand, changes in the real world lead to an old database model that does not fit the new reality. However, on the other hand, changing the model of the database according to environmental changes leads to a database model that does not fit the old reality. Evolving semantics may even cause unjustified trend reversals. This occurs when certain values suddenly do not occur in a database anymore and are replaced with different values from there on. As a result, a database attribute may have a different set of possible values before and after the change. Because of semantic changes, the database model does not completely match (the changed) reality anymore. Therefore, the closed-world assumption does not hold for many legacy databases. This has to be taken into account when analyzing a data set in which this occurs. To prevent data quality deterioration and unjustified trend reversals, procedures should be defined to handle these changes by, for example, keeping track of them. However, in practice, it is not always easy to record the history of data over time. Often, maintaining data provenance (Bertino et al. 2014), i.e., making documentation and achieving reproducibility, is hard. This is because it is difficult to create reversible relations in such situations. In other words, having reversible relations between old and new values means that given the old values the new values can be determined uniquely and vice versa (i.e., given the new values, old values can be determined uniquely).

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System Realities BD models can be considered as a system reality. Since a system reality is, as explained above, often based on uncertain and incomplete data, this system reality may deviate from the actual reality. If this is the case, the BD models do not make sense, are hard to interpret, and are unusable in practice. Unrealistic system realities are caused not only by imperfect data (e.g., not enough samples are observed) but also by imperfect inductive reasoning algorithms. The former is particularly the case with legacy data, because these data are based on observations in the past. When using legacy data, the resulting system reality may be true for the real world of the past, but may not always correspond to the real world of today. Thus, the gap between the system reality and the reality of today may become unacceptable, while it is acceptable for the reality of the past. Therefore, a factor that should be considered when interpreting system realities is to which reality in time it actually pertains. An estimate should be made to what extent data collected in the past is still representative today. Such an estimate is especially important for the data pertaining to social phenomena, humanities, justice, and security, since in these fields the environmental conditions and circumstances are subject to changes.

Statistical Truths In practice, statistical truths that hold for groups of objects (like profiles) are derived from system realities as they are captured in the data held in information systems. A statistical truth implies that there is a distribution function for the possible outcomes, but no conclusion can be drawn for a single object. Because of this, even if there is a perfect data set, the semantics of the data is clear, and a proper algorithm to analyze the data set is applied, the outcome of the algorithm may still be difficult to interpret. When using BD in practice, the main questions regarding statistical truths are (1) how should these truths be interpreted and (2) how should they be applied to an individual object? It remains a challenge to find a proper interpretation for the probability value and to apply this value in practice, especially in the context of social phenomena and for policymaking purposes. Even if a statistical outcome attaches a probability value to a single object, this probability value cannot be used to predict the behavior or state of that object in the future. As an example, suppose that a driver profile assigns a probability of 0.8 to a person, called Mr. Jones, that he will be involved in a car accident and therefore is a risky driver. From a frequentist probability approach, the so-called frequentist inference, a possible interpretation is that if Mr. Jones performs an infinite number of drives, he will be involved in an accident in 80% of the drives (and will not be involved in an accident in 20% of the drives). This can be true if, for example, it is assumed that Mr. Jones will never learn from his mistakes and he does not remember any of his previous drives. Therefore, if a single drive of Mr. Jones is

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considered, one still cannot predict whether this will result in an accident. An alternative interpretation might be that many clones of Mr. Jones are available and all these clones will go for a drive and then 80% of the clones will be involved in an accident. This difficulty of properly interpreting a statistical truth in the context of social phenomena (e.g., for policymaking purposes) can be explained also by the fact that there is no ergodic random process (Walters 1982) behind social behaviors due to, e.g., people learning from their mistakes and personality characters. Non-ergodicity means that the statistical truth is often obtained and thus holds across individuals (i.e., a group) in a given time and it often does not hold on an individual along a period of time (e.g., from now on). In summary, choosing the frequentist approach to interpret the notion of probability on a specific individual and along the time dimension leads to a contrived interpretation. Therefore, this is not very useful in practice. Furthermore, using frequentist inference in this way is far from practical since performing an infinite number of such experiments is infeasible and the assumption that individuals will not learn from practice is false. As an alternative, consider the subjective notion of probability. According to the subjective approach, a probability is a quantified judgment of an expert entity (like a doctor) that an event will occur. If new information becomes available, this probability, referred to as prior probability, is updated to a posterior probability. In practice, the prior probability may be constructed based on the frequentist approach or based on less tangible concepts such as intuition or (implicit) experiences. Thus, it is not precisely clear what the interpretation of such a subjective probability is. To illustrate this, consider the statement “Mr. Jones has a probability of 80% to be involved in a car accident.” Following the subjective probability approach, this statement implies that an entity, e.g., an expert or an algorithm, quantifies the judgment that Mr. Jones will be involved in a car accident with a 0.8 probability value. From the perspective of this entity, an interpretation of the 80% might entail that the entity has studied many drivers and observed that drivers who are similar to Mr. Jones are often involved in car accidents. Subsequently, the entity combines this statistical fact with its own past experiences and opinions regarding Mr. Jones and concludes that it is likely that Mr. Jones will be involved in a car accident with an 80% chance. When new information arises, e.g., Mr. Jones stops driving cars, this prior probability can be updated to a posterior probability value. Given the prior probabilities, there are sound theoretical grounds for determining the posterior probabilities by using rules based on, for example, the Bayes or the Dempster-Shafer theory (Grimmett and Stirzaker 2001; Shafer 1976; Choenni et al. 2006). It is, however, unclear how the prior probabilities are obtained, and in practice, these prior probabilities may be biased. From the perspective of Mr. Jones, a prior probability of 80% that he will be involved in a car incident is difficult to interpret. For a non-statistician, it is hard to determine what this means in practice as many questions may be raised in practice, such as Is the chance of being involved in a car accident 80% for every drive or does the chance of being in a car accident increase (for one’s next drive) when the one had

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a lot of safe drives? Additionally, for a proper interpretation, besides the outcome, Mr. Jones needs to know who or what generated the probability. If the probability generating entity is a rogue authority, then the interpretation of the probability of 80% will be valued differently than if the authority is reliable and has a good reputation. It should be clear that in the approach of subjective probability, the interpretation of a probability depends on whether you are a receiver, i.e., Mr. Jones in the above-mentioned examples, or the probability generating entity. For the receiver, not only the outcome, i.e., the probability that an event occurs, but also information about the probability generating entity are relevant for a proper interpretation. In case of BD applications, the probability generating entities are not human experts but (data mining or statistical) algorithms, and the receivers are the domain experts or the users who want to exploit the outcomes of the algorithms. For laymen, it is hard to understand the working of such algorithms, and therefore, it is almost impossible to verify their validity. Furthermore, it is shown that the notion of (prior) probability is hard to grasp and may contain biases. This shows that interpreting BD models as statistical truths is far from trivial. Therefore, in order to fully grasp the opportunities of BD, laymen such as policymakers need practical strategies on how to overcome the challenge of interpreting such statistical truths. Additionally, they need guidelines to respond to the other challenges mentioned in this section, such as problems associated with data quality. In the following section, a framework comprising two solution directions is provided.

Towards a Framework for Responsible Use of Big Data According to Gartner, half of the violations of business ethics will be caused by the improper use of BD analytics by 2018 (Herschel et al. 2006). Using BD results, i.e., interpreting BD-based models and creating valid actions appropriately, requires developing a framework consisting of some relevant guidelines and best practices. In this section, two solution directions for responsible use of BD results ae outlined namely (1) making BD applications transparent for policymakers (see Subsection “Achieving Transparency”) and and (2) dealing with the uncertainty inherent in BD appropriately (see Subsection “Dealing with Uncertainty”). Both solution directions consist of several alternatives, as illustrated in Fig. 4. The components of the framework depicted in Fig. 4 will be described in the Subsections “Achieving Transparency” and “Dealing with Uncertainty.” In Subsection “Illustrative Examples”, two illustrative examples of handling uncertainty in BD models are described.

Achieving Transparency In relation to transparency, two extreme approaches may be adopted for exploiting BD. In the first approach, referred to as the black box, the implementation of the underlying concepts and the relationships between these concepts are unclear and

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Data transparency Making BD system transparent Algorithm transparency

Application dependent mix of data and algorithm transparency

A framework for responsible BD usage Hypothesis supporting strategy Dealing with BD uncertainty Hypothesis weakening strategy

Application dependent mix of supporting and weakening the hypothesis strategies

Fig. 4 An illustration of the scope of the proposed framework for responsible use of BD

considered as a black box. Black boxes are systems that hide their internal logic to the user (Guidotti et al. 2018). According to the black box approach, data sets and some constraints are fed to the black box, and only the outcomes of the black box are observed. In case the outcomes are not satisfactory, some of the constraints and perhaps the data are altered and again fed to the black box. This process may be repeated until the outcomes are satisfactory. In the second approach, referred to as the open box, the implementation of the concepts and their relationships are fully documented so that one can track down how the outcomes are obtained exactly. In case the outcomes obtained are not satisfactory, it is possible to find out which concepts and relationships contribute to this dissatisfaction. These concepts and relationships can be adapted accordingly. It is proposed, however, to take a view on BD analytics which lies in between these two extreme views. The here proposed view, referred to as the glass box, does not require to know precisely all the ins and outs of the implementation of the used concepts and their relationships. Instead, the crucial and relevant concepts and their relationships between them are explained in a meaningfully transparent way. The goal of a glass box approach is to facilitate a proper interpretation of BD models in a way that policymakers are able to assess the consequences of the actions based on those models in practice. Transparency of the BD process is an important means to realize this goal. Two crucial questions in the context of a glass box approach are (1) what concepts and relationships should be made transparent and (2) what level of transparency is desired?

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Unfortunately, there are no straightforward answers to these questions as they depend on the BD application, i.e., the usage scenario, at hand. Two extreme situations can be distinguished, as illustrated in the right upper side of Fig. 4, namely, (1) transparency of algorithms and (2) transparency of data. In the first situation, the focus is on the transparency of the algorithms, while the transparency of the other components can fully be neglected. As an example of a situation in which this is recommended, consider a neural network-based application that is trained for recognizing murder weapons (i.e., the possible output classes are weapons). So, if a picture of a human being is offered to this application, then the application classifies the picture as a weapon, e.g., a gun. As the neural network is trained to classify everything as weapons, it may be very good in recognizing weapons but not in recognizing the faces of humans. Therefore, it does not matter what type of pictures you offer to the neural network; it will always recognize all of them as a weapon. Thus, in order to prevent disappointments in classifying the pictures of human beings, it is better to be transparent about the algorithm. Transparency with regard to the other components of BD will not contribute in preventing such disappointments. In the second situation, the focus is on the transparency of the data, and the transparency of the other components is neglected fully. As an example of how this works, consider an application that predicts what type of women has high chances to win a beauty contest and, for instance, become Miss World. Not many people will be surprised by the prediction of this application that a tall light-skinned woman with blue eyes will be the next Miss World. In this case, transparency with regard to the data is crucial for a proper interpretation and use of the outcome. If most of the previously elected Miss Worlds can be characterized by these features, and these features are reflected in the data, then the prediction is understandable. If for some reasons the prediction is undesirable, then the data should be adapted. Since – to our best knowledge – for prediction purposes all BD algorithms focus on exploiting the features of those women that have been elected Miss World in the past, transparency with regard to the algorithms will not help to prevent such undesired outcomes. Probably, most applications might not benefit from one of these two extreme situations, but require a combination of both, that is, a varying degree of data and algorithm transparency. Therefore, defining a glass box should be tailored to the application at hand. This application dependency is illustrated in Fig. 4 as a mix of the two types of transparency (i.e., data transparency and algorithm transparency). In the following, a number of concepts and techniques that may serve complementary for creating transparency are discussed.

Dealing with Uncertainty Given the fact that the interpretations of BD models are far from trivial, and deal with uncertainty, it is proposed to consider these models as a central body of evidence. From this body of evidence, a hypothesis for an individual case can be derived.

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Social network data Sensory data

… Register data

Large & heterogenous data

Data Collection

Model Extraction

Data (new observation)

Model

Hypothesis Creation

Hypothesis Validation Model Interpretation

Action Fig. 5 An illustration of the proposed strategy to deal with BD uncertainty

Then, other evidence that may support or weaken this hypothesis has to be searched. This process is illustrated in Fig. 5. As an example, suppose that a BD application is fed with a large amount of data of those who were involved in car accidents. After analyzing the data, the tool produces the following profile: “young men living in zip code 1234 have a higher than average probability to cause car accidents.” Additionally, consider a young man, named Mr. Green, who lives in zip code 1234. To exploit the knowledge that is captured in the profile, the following hypothesis for Mr. Green is formulated: “Mr. Green will cause car accidents.” To evaluate this hypothesis, two strategies may be used, as illustrated in the right lower side of Fig. 4, namely, (1) a hypothesis supporting strategy and (2) a hypothesis weakening strategy. In the supporting strategy, evidence that supports the hypothesis is collected, e.g., Mr. Green caused car accidents in the past. Note that this evidence should not be based on, or derived from, the data that is already used in the BD application. If enough supporting evidence has been collected, the hypothesis can be accepted. A disadvantage of this strategy of collecting supporting evidence is that it may strengthen confirmation biases and lead to a self-fulfilling prophecy, i.e., a false hypothesis might appear true due to this bias. In the weakening strategy, evidence that weakens the hypothesis “Mr. Green will cause car accidents” is collected. If a set of evidence is found that gives rise to rejecting the hypothesis, the hypothesis should be rejected. Unlike in the previous

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strategy, the same data set from which the hypothesis is derived may be used to search for weakening evidence. Other data sets can also be used to search for evidence to weaken the hypothesis. A disadvantage of this strategy is that it may lead to a self-denying prophecy, i.e., a true hypothesis might appear false due to bias. In addition, for the average policymaker, it may also be too time-consuming and not in their interest to disprove something they would rather assume to be “true.” Which strategy to use for which application depends on the nature of the application and the impact of possible false positives and false negatives. A false positive refers to an accepted hypothesis while it is false, and a false negative refers to a rejected hypothesis while it is true. The supporting strategy tends to reduce the false negatives and to increase the false positives, while the reverse is true for the weakening strategy. The weakening strategy is applied in contemporary judicial courts. The public prosecutor makes a statement, which is based on police investigations of a suspect. Subsequently, in court the lawyer of the suspect aims to disprove this statement by presenting counter proofs. Such a strategy is chosen to convict someone only if he is indeed guilty, i.e., to avoid false positives. In sensitive applications that have a large impact on someone’s life, the weakening strategy is recommended. The supporting strategy, on the other hand, focuses on strengthening a hypothesis and avoiding false negatives. In some application areas that are related to public security (like searching for terrorists), the supporting strategy might be useful. Given the uncertain nature of BD outcomes, it can be concluded that there often will be both false positives and false negatives. Therefore, independent of which strategy is chosen to implement BD outcomes in practice, it makes sense to make an estimate of the impact of false positives and false negatives and a procedure to anticipate on them. It is foreseen that, in practice, depending on the application at hand, a mix of the two strategies can be chosen, as illustrated in Fig. 4. There are also procedural measures (which are not data science related) that can be used to implement the strategies. For example, Crawford and Schultz (2014) propose using procedures to mitigate predictive privacy harms of BD on individuals’ life, liberty, and property. The proposed process requires that before applying any BD-based action that deprives an individual of a liberty or property right, the individual must be given a notice, an opportunity for a hearing, and an impartial adjudicator and judicial review. Next, some examples of the strategies applied by different organizations to handle uncertainty in their BD models are described.

Illustrative Examples The police are exploiting the increasing amount of data available in order to predict criminal behavior or criminal hotspots. Example applications of such predictive policing systems are, for example, PredPol (2018) and the Crime Anticipation System (CAS) (Lonkhuyzen 2017; Willems and Doeleman 2014). PredPol is used in the United States at a local level, whereas CAS is used in the Netherlands at a

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national level. Both applications are able to identify areas in a neighborhood where serious crimes are more likely to occur during a particular period. This approach is called predictive mapping. The main advantage of predictive mapping is that police capacity can be allocated more efficiently (e.g., to those neighborhoods with an increased risk of crime). However, such predictive applications provide results that may inhibit errors. Sometimes, people or areas wrongly receive a high-risk score, as already explained in the example in Fig. 2. To reduce the number of errors, the police combines the BD results with other information to become more certain. However, since there is not always enough time available to analyze thoroughly (because the police have to decide quickly in order to prevent serious crimes such as terrorism and/or reduce the number of victims), mistakes can be made. In some contexts (e.g., preventing terrorism attacks), errors are accepted: in order to save lives, false negatives (i.e., missing a terrorist attack) must be avoided at all costs, while false positives (i.e., arresting an innocent suspect) are considered less important. In the medical domain, the use of medical applications on mobile phones (i.e., medical apps) is becoming increasingly popular. The Skin-Vision app (SkinVision App 2018), for example, helps individuals to check their skin for signs of skin cancer and get instant results on their phones. The app uses an algorithm that evaluates the uploaded picture and automatically returns a risk score based on the probability that a birthmark is cancer. The app contains a follow-up option in case of a high score, so that the user can contact specialized dermatologists affiliated with the app. These experts can determine if there actually is a possibility of skin cancer or if it was a false positive. In this case, it may be better to be on the safe side and send more people to a dermatologist, so that skin cancer is detected in an early phase (thus decreasing false negatives). This increases the patients’ changes of being cured. Therefore, for the patients in questions, false positives (i.e., a false sense of being sick) are better than false negatives (i.e., a false sense of being healthy). This should be incorporated in the given risk score. The dermatologist then has the task to verify the app’s judgement. Therefore, it is important for the user of the app to seek medical attention after a high-risk score. In general, the strategy to deal with uncertainty in BD models depends largely on the context of the application. Relevant considerations are the consequences of making mistakes and the time available to use additional data to verify the results.

Conclusion and Future Research Businesses, organizations, and institutions have a strong desire and a practical need to exploit the opportunities that BD offers for improving their processes or for launching new services. As discussed systematically in this paper, exploiting these opportunities is far from trivial in practice, particularly in the domain of policymaking. Therefore, well-thought-out strategies are required for using BD applications. However, these BD applications are crucial for shaping a smart government as they interconnect and integrate data, processes, institutions, and physical infrastructures to improve governance and services.

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It is argued that there are many contributing factors that complicate the interpretation of BD models. Some of these complicating factors have received extensive attention in the literature, such as several dimensions of data quality, while the other are hardly discussed. This paper has discussed three of these factors: (1) evolving semantics, (2) system realities, and (3) statistical truths. Furthermore, a discussion is devoted to how these factors complicate the interpretation of BD models due to their inherent uncertainty. It has been illustrated that an inadequate interpretation of uncertain BD models may lead to unjustifiable and unjust consequences for individuals and society. Nevertheless, uncertain BD outcomes are not entirely useless as long as they are interpreted and applied with care and as a starting point for further inquiry. A framework to deal with these issues carefully is proposed. This framework consists of two parts for a responsible use of BD models, namely, (1) making BD applications transparent for policymakers and (2) dealing with the uncertainty inherent in BD models appropriately. For the former, the notion of a glass box to support the interpretation of BD models is proposed. For the latter, two strategies to exploit BD models in practice are proposed. Both strategies consider the results of these models as a central body of evidence. One strategy tends to accept the outcomes by searching for evidence that support the central body of evidence, while the other strategy tends to reject the central body of evidence by means of counterevidence. Depending on the application at hand, one of the strategies or a mix may be chosen. Currently, a paper that evaluates a number of the existing applications (SkinVision App 2018; PredPol 2018; Lonkhuyzen 2017; Willems and Doeleman 2014; Franse 2018; Netten et al. 2018) that have implemented (parts of) the building blocks of the here proposed framework is in preparation. In this work, the elaboration of the notion of transparency (Guidotti et al. 2018; Lacave and Diez 2004; GDPR 2016; Doshi-Velez and Kim 2017) will be the main topic. As future work, specific guidelines will be developed, and best practices for designing and realizing glass boxes will be identified. Further, a search for guidelines to tailor the proposed approaches and strategies to different types of domains and application areas will be continued. This will answer questions such as how many and what kinds of evidence are required to accept or reject the central body of evidence and how should different pieces of evidence be weighed? Furthermore, it is planned to experiment with these strategies and evaluate them in real-life cases.

References Anisetti, M., Bellandi, V., Cremonini, M., Damiani, E., & Maggesi, J. (2017). Big data platform for public health policies. In 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/ CBDCom/IOP/SCI) (pp. 1–6). Piscataway: IEEE. Anisetti, M., Ardagna, C., Bellandi, V., Cremonini, M., Frati, F., & Damiani, E. (2018). Privacyaware big data analytics as a service for public health policies in smart cities. Sustainable Cities and Society, 39, 68–77.

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Bargh M. S., & Choenni, R. (2013). On preserving privacy whilst integrating data in connected information systems. In Proceedings of 1st international conference on cloud security management (ICCSM), October 17–18, Seattle, USA. Bertino, E., Ghinita, G., Kantarcioglu, M., Nguyen, D., Park, J., Sandhu, R., Sultana, S., Thuraisingham, B., & Xu, s. (2014). A roadmap for privacy-enhanced secure data provenance. Journal of Intelligent Information Systems, 43(3), 481–501. Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662– 679. Braak, S. W., van den Choenni, S., Meijer, R., & Zuiderwijk, A. (2012). Trusted third parties for secure and privacy-preserving data integration and sharing in the public sector. In Proceedings of the 13th annual international conference on digital government research (dg.o). ACM, pp. 135–144, June. Braak, S., van den Choenni, S., & Verwer, S. (2013). Combining and analyzing judicial databases. In Discrimination and privacy in the information society (pp. 191–206). Berlin, Heidelberg: Springer. Choenni, S., Blanken, H., & Chang, T. (1993). Index selection in relational databases. In Proceedings of the 5th international conference on computing and information (ICCI’93), pp. 491– 496. IEEE, May. Choenni, S., Bakker, R., Blok, H.E., & de Laat, R. (2005). Supporting technologies for knowledge management. In Knowledge management and management learning, pp. 89-112, Springer, Boston. Choenni, S., Blok, H. E., & Leertouwer, E. (2006, April). Handling uncertainty and ignorance in databases: a rule to combine dependent data. In Proceedings of international conference on database systems for advanced applications (DASFAA) (pp. 310–327). Springer: Berlin, Heidelberg. Choenni, S., van Dijk, J., & Leeuw, F. (2010). Preserving privacy whilst integrating data: Applied to criminal justice. Information Polity, 15(1, 2), 125–138. Choenni, S., Netten, N., Bargh, M. S., & Choenni, R. (2018a). On the usability of big (social) data. In Proceedings of the 11th IEEE international conference on social computing and networking (SocialCom), 11–13 December, Melbourne Australia. Choenni, S., Netten, N., Bargh, M. S., & Choenni, R. (2018b). Challenges of big data from a philosophical perspective. In Proceedings of international conference on multidisciplinary research (MyRes), 22–23 June, Mauritius. Crawford, K., & Schultz, J. (2014). Big data and due process – Toward a framework to redress predictive privacy harms. Boston College Law Review (BCL Rev), 55(93) http:// lawdigitalcommons.bc.edu/bclr/vol55/iss1/4. Date, C. J. (1990). Relational database writings. 1985–1989 (Vol. 1). Reading: Addison-Wesley. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608. Franse, A. (2018). Je moet weten wat de foutmarge in een model is (translation: You need to know the margin of error in a model). Big data JenV, Ministerie van Justitie en Veiligheid, mini symposium data kwaliteit, Vol. 1, Nr. 2, pp. 5–6. GDPR. (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data and repealing Directive 95/46/EC. GDPR: General Data Protection Regulation. Gibbs, M. R., Shanks, G., & Lederman, R. (2005). Data quality, database fragmentation and information privacy. Surveillance and Society, 3(1), 45–58. Grimmett, G., & Stirzaker, D. (2001). Probability and random processes. Oxford: Oxford University Press. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys (CSUR), 51(5), 1–42.

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Herschel, G., Linden, A., & Duncan, A.D. (2006). Seven best practices for your big data analytics projects. Gartner technical report, published 30 June 2015 – ID G00278185, April. Hume, D. (2003). A treatise of human nature. Courier Corporation, ISBN 10: 0-486-43250-5. Kalidien, S., Choenni, S., & Meijer, R. (2010). Crime statistics online: potentials and challenges. In Proceedings of the 11th annual international digital government research conference on public administration (dg.o), pp. 131–137, May 2. Kim, G., Trimi, S., & Chung, J. (2014). Big-data applications in the government sector. Communications of the ACM, 57(3), 78–85. Lacave, C., & Diez, F. J. (2004). A review of explanation methods for heuristic expert systems. The Knowledge Engineering Review, 19(2), 133–146. Lonkhuyzen, L. (2017). To predict crime, it is possible, NRC, May. https://www.nrc.nl/nieuws/ 2017/05/16/misdaad-voorspellen-het-kan-echt-9100898-a1558837. Accessed 1 Nov 2018. Marks, R. J. I., II. (1991). Introduction to Shannon sampling and interpolation theory. New York: Springer. Netten, N., Braak, S., van den Choenni, S., & van Someren, M. (2016). A big data approach to support information distribution in crisis response. In Proceedings of the 9th international conference on theory and practice of electronic governance (pp. 266–275). New York: ACM. Netten, N., Bargh, M. S., & Choenni, S. (2018). Exploiting data analytics for social services: On searching for profiles of unlawful use of social benefits. In Proceedings of the 11th international conference on theory and practice of electronic governance (ICEGOV’18), Galway, Ireland, April. PredPol. (2018). PredPol 2018. http://www.predpol.com/. Accessed 1 Nov 2018. Prins, J. E. J., Broeders, D., & Griffioen, H. M. (2012). iGovernment: a new perspective on the future of government digitization. Computer Law and Security Review, 28(3), 273–282. Shafer, G. (1976). A mathematical theory of evidence. Princeton: Princeton university press. Sheth, A. P., & Larson, J. A. (1990). Federated database systems for managing distributed, heterogeneous, and autonomous databases. ACM Computer Survey, 22(3), 183–236. SkinVision App. (2018). SkinVision, https://www.skinvision.com. Accessed 12 July 2020. Walters, P. (1982). An introduction to ergodic theory. New York: Springer. Willems, D., & Doeleman, R. (2014). Predictive policing: wens of werkelijkheid. Tijdschrift voor Politie, 76, 4. Winkler, W. E. (1995). Matching and record linkage. In B. G. Cox, D. A. Binder, & B. N. Chinnappa (Eds.), Business survey methods (pp. 355–384). New York: Wiley.

Part VIII Smart Cities Infrastructure Ecosystem

Feeding a Smart City

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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Brief History of Urbanization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Agricultural Revolutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Agriculture 1.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Agriculture 2.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Agriculture 3.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Agriculture 4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Food and Employment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Food Regulation, Fraud, and Deception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Food and Religion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Judaism: Kosher . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Christianity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Muslim: Halal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hinduism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sikhism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Buddhism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quakers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amish . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Subsidies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Monoculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Retail Practice, Shelf Life, Dates, and Food Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shelf Life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Retail Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Processing of Food . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Preserving Food . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1063 1065 1066 1066 1066 1067 1068 1068 1072 1074 1074 1074 1074 1075 1076 1076 1076 1077 1077 1078 1078 1079 1080 1081 1081

J. Lodge (*) City Farm Systems Ltd, Slough, UK e-mail: [email protected] © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_52

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Meat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Extending the Shelf Life of Meat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sheep/Lamb/Mutton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cow/Beef . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pig: Pork, Ham, and Sausages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Blood and Offal (Internal Organs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alternatives: Bison, Camel, Deer (Venison), Goats, and Kangaroo . . . . . . . . . . . . . . . . . . . . . . . Poultry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . More Than Food . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fish . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Milk and Dairy Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Milk Consumption Around the World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nutritional Value of Milk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dairy Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Initial Milk Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Beyond Pasteurization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Eggs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Egg Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Egg Preservation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Plant Based Foods: Varieties and Genetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Legumes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Brassicas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Genetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Seeds, Grains, Nuts, and Bread . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Seeds as Food . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Culinary Nuts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bread . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Animal Feed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Grass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Grain as Animal Feed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Food Security, Continuity, and Transparency of Supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Endangered Crops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vitamins, Allergies, Intolerances, and Deficiencies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vitamins and Dietary Supplements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Plastics, Carbon, and the Future of Local Food . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The New Local . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1082 1082 1083 1083 1084 1084 1085 1085 1086 1087 1089 1089 1089 1090 1090 1090 1091 1091 1091 1092 1093 1093 1095 1096 1096 1098 1098 1098 1099 1099 1100 1100 1101 1101 1103 1103 1104 1106

Abstract

This chapter looks at what it takes to feed those living in a smart city. As cities increase in size and attract people from around the world, the need for food changes. Countries evolved with different viewpoints, cultures, and religions. With city populations becoming increasingly mixed, these differences create problems that are not ways obvious. Agricultural and industrial revolutions have a codependency and steadily separated large populations from their source of food which had always been ultra-local and circular. With supply chains increasingly linear waste has also risen – not only food but in increased packaging and agricultural production no longer used for other purposes. Where once all agricultural output was valued some is no longer used. Sheep farming started not for food but wool. Large

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industries built up to clean, spin, and weave wool but have been displaced with more predictable synthetic fibers. Significant proportions of plastics used in food packaging and textiles end up polluting our environment with unintended harm to ecosystems and some food sources. Along with these changes, specialism has encouraged monoculture, and large food industries have changed food supplies beyond recognition. The latest revolutions have been largely about data increasing the understanding and ability to revive the circular approach. There is still, however, a need for increased circularity of data, water, and nutrients to improve efficiencies and reduce waste.

Introduction Modern food supply is immensely complex. Urbanization has disconnected populations from their food sources. Further reading is advised, and nothing here should be taken as a guide for personal dietary needs. Usually the term Smart City is used to showcase the need for fast, always on connectivity, access to the web, and what that means for city populations personally and in controlling complex public services. One key benefit of improved connectivity reduces the need for city dwelling – the ability to work from home but retaining many benefits of working alongside colleagues in an office. This chapter was planned before the Covid-19 pandemic. The virus has put the spotlight on many difficulties. Some already existed, but those pursuing “business as usual” hadn’t felt the need for change until recent events forced the situation with some expected to be permanent. One issue increasingly clear according to Louise Stephens (2017): “It’s no secret that our high sugar, high refined carbohydrate and seed oil infused modern food is killing us.” There are many studies and reports about food and diets making claims across the spectrum from promoting diets of only meat through to strict veganism. Unfortunately too many rely on incomplete reports to justify personal prejudice and make choices without understanding the bigger picture. There are moves to rebuild the link between people and primary food production. One of the most important UK farming conferences takes place in Oxford University in January with a rolling board of directors. One current director, Barbara Bray MBE (2020) is a leading light in the nutritional world which shows a growing concern about nutritional qualities of agricultural production. Much recent thinking promotes a need to reduce or avoid highly refined foods and to eat a more varied diet complete with “dietary fiber.” For many this has become a mantra, and yet some studies show fiber is not needed in a balanced diet as it is not digested. Dr. Zoe Harcombe (2010) suggests the need to return to healthy balanced diets: If we have been eating food in the form that nature intended for 24 hours, agriculture (large scale access to carbohydrates) developed four minutes ago and sugar consumption has

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increased twenty fold in the last five seconds. I wonder which food is more likely to be responsible for obesity, diabetes, or indeed any modern disease. . ..

Artificial intelligence (AI) and machine learning (ML) are promoted as enabling data to improve processes. The author has applied this thinking to offer an economically and environmentally sustainable alternative to vertical farming (VF) and what this means for whole dietary needs of a city. While vertical farms address a narrow range of leafy herbs and salads, the reluctance to collaborate and take a holistic approach means they increase financial and environmental costs while precluding significant supply chain improvements offered by an alternative business model. A key issue is the affordability of food for lower-paid workers who frequently rely on unhealthy options and charity. A Smart City must help feed whole populations and create work for all ages and abilities. Food banks, end-of-day redistribution, and other free food programs are solutions for problems that can be avoided. While it is admirable to avoid excess being wasted, there is a greater need to avoid the cash and environmental costs of growing, packing, and delivering food that data can show is unnecessary. Food suppliers need to go beyond the simple principles of Lean 6Sigma used in many industries to optimize supply and include reference to demand timelines. For many items, this is a nuisance forcing customers to wait. While a customer can be forced to wait 3 months for a car assembled just along the road (even when final details are not specified until nearing completion), people cannot wait 3 months for their daily food. Processors talk of fast-moving consumer goods (FMCG) referring to products like chocolate bars and tinned beans yet makes more sense for short shelf life items. Evening newspapers are a good example. Planned in the morning and printed and distributed by lunchtime; they are obsolete a few hours later. The same applies to takeaway food such as burgers and pizzas. These have a useful life of, at most, an hour or so after cooking to order or assembled from ingredients with a longer shelf life. Processing without adding preservatives reduces shelf life. The need to reach Net Zero shows fresh produce speed and carbon footprint needs to be measured from Farm to Fork or, better still, include what happens in the field. Current thinking talks of Industry 4.0 and Agriculture 4.0 revolutions. Each revolution had consequences first for towns and then large city populations extending the divide between people and their food source. The first three revolutions of both types created, and then exaggerated, the linearity of create, use, and dispose. A large part of Industry 4.0 is about digitization with some using data to look at resource efficiency and rebuilding a circular approach. Starting with a focus on recycling, this increased rapidly with organizations such as the Ellen MacArthur Foundation (2020) promoting “cradle-to-cradle” circularity. While this is very successful for metals, it is all but impossible for food once digested. There are however aspects of the circular approach that should be applied. The UK plans an Interdisciplinary Circularity of Food Centre (iCFC) supported by universities and businesses of all sizes.

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The author explored the use of data in urban agriculture in a previous book (Ambient Intelligence and Smart Environments 2019). This highlighted the economics and application of data for growing perishable produce within a city. Having learnt more and looking at wider dietary needs, this chapter highlights how complex supply chains have become and how recent lessons can improve Smart City nutrition. One point often ignored is that increased consumer choice and the disconnect with the source of food have not improved diets. Agricultural intensification has reduced nutritional qualities, and the health of populations is declining. The link between agriculture and nutrition needs rebuilding. Urbanization has pressured farmers to increase food production often aided by chemical treatments and plastics. But a third of current food production and related resources (packaging and transport) is wasted. Where products like confectionery and ice cream were a treat for special occasions they have become an everyday occurrence. Diabetes costs health-care systems considerable sums with too many failing to manage their condition adequately causing poor circulation and even blindness. The belief and trust people put in health-care systems is such they rely on insulin (and other drugs) yet many have reversed type 2 diabetes with a healthier diet and exercise.

A Brief History of Urbanization The opening ▶ Chap. 1, “Smart Cities: Fundamental Concepts” notes some question whether agriculture enabled urbanization or was forced by it. Looking back numbering assumes industrial and agricultural revolutions had similar timings. Many have written about the revolutions, and there is a clear codependency. Dr. Peter Dewey (2008) argues the first industrial revolution enabled an agricultural revolution. Most importantly productivity gains reduced the need for agricultural labor enabling urbanization. Prof Mark Overton says key players “. . . in a few years, transformed English agriculture from a peasant subsistence economy to a thriving capitalist agricultural system, capable of feeding the teeming millions in the new industrial cities” (Overton 1996). He also writes about agricultural productivity controlling population growth arguing part of this was enabled by changing crops and “From the 16th century onwards, an essentially organic agriculture was gradually replaced by a farming system that depended on energy-intensive inputs.” Most obviously recent change has been the increasing size of tractors and the rise of the agrochemical industry. For each revolution, the biggest changes for agriculture were found in arable and cropping rather than livestock. However significant amounts of crops are used as feedstock to intensify housed livestock production. This created a decline in soil health and a backlash from environmentalists campaigning for vegetarian or vegan

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diets. A key threat rising around the world is the average age of farmers nearing retirement. This is worsened by the difficulty attracting new entrants when the majority no longer have a family connection with agriculture. The timing of agricultural revolutions varied across the world – including within some countries. Some regions are yet to adopt all the technologies of Agriculture 3. Many countries depend on imports with differing levels of automation. This is not simply one developing country helping to feed a developed city. In some cases, highly developed industrialized farming is feeding staple crops to developing countries. Other aspects include differing standards for animal welfare, use of drug and chemical treatments, and subsidies or tariffs. These all effect nutrition, finances, and politics. This has led to the creation of accreditation bodies such as Fairtrade, the UK’s Red Tractor, and several differing Organic Status bodies.

Agricultural Revolutions Agriculture 1.0 The first revolutions added mechanization to basic tools. It makes sense to think of this as a step on from a plough pulled by cattle or horses. One important aspect was the development of bearings and new metal implements, mechanical links, and the transfer of motive power needed for longer periods of continual work. Previously food supply chains had been very local. Farms were not only places of primary food production. They were the main point of food processing and distribution. Many farms had some form of butchery and dairy processing.

Agriculture 2.0 The key here was transport. The horse and cart were replaced first by barges and then trains. The adoption of farm mechanization increased rapidly with steam engines. These offered huge productivity – not only for ploughing and soil preparation but reducing the time between harvest and storage. This marked the start of removing hedgerows to give larger fields with the consequential reduction in wildlife habitat and reduced protection from soil erosion. Trains reduced delivery times and increased distance from farm to consumer. Along with this was the multiplication of communication speeds. Railways offered a safe route for wiring offering electronic communication over hundreds of miles. Market gardeners could harvest crops for next day delivery to a distant city. This disconnected farmers from the point of sale and gave rise to the “middleman.” There had been commodity traders for many years. Now they

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could trade perishable goods, and the number of links between producer and consumer increased. Prof. Overton states introducing nitrogen-fixing crops such as peas and beans into agricultural rotations gave a fundamental increase in productivity. Scientists learned that nitrogen was the driver and created the agrochemical industry: just as a sustainable agriculture had been achieved, the development of chemical fertilisers and other external inputs undermined this sustainability. An essentially organic agriculture was gradually replaced by a farming system that depended on energy-intensive inputs dependent on the exploitation of fossil fuels. (Overton 1996)

Agriculture 3.0 The third revolution brought fundamental change breaking the link between grower and consumer both arable and livestock. For many countries, Industry 3.0 started in the early twentieth century and accelerated rapidly after WW2. The war caused many changes to food supplies and consumption. With supply chains heavily disrupted and a shortage of labor, many countries entered a period of rationing and changed cooking methods and diets. While gas had been around for a while, the need for adults to be at war or farming accelerated the move away from solid fuel ranges. In the UK, this resulted in a restricted diet with much of it being overcooked. To this day, many overcook vegetables and pour much of the nutrition away with the water. WW2 accelerated the use of small tractors and a reduction in the physical effort needed. In the UK, there was a policy of moving dairy processing off the farm. Agriculture 3 kickstarted the widespread use of agrochemicals and industrialization. The British Broadcasting Corporation (BBC) addressed this change by creating the longest running radio “soap opera” The Archers. This was aimed at farmers as a diary to publicize “best practice” and when to apply fertilizer. This was also a move toward agronomy advisors rather than handing knowledge down through generations. Increasingly produce was taken by truck to collective processors. This revolution was not so much agriculture but the industrialization of food. Prof. Overton suggests this as the point agriculture lost track of carbon. Despite the widely recognized need to return to Net Zero carbon, the footprint of food is invariably measured at the farm gate rather than for the whole supply chain or Farm to Fork. This incomplete measure is seen by many as justification to promote plant-based diets. In some developing countries, earlier revolutions are still underway. In large parts of rural China and other countries, 50-year-old machines or ploughs pulled by oxen are still used. Linear supply chains were now the norm. These are referred to as the ‘make, deliver, use, and dispose’ model or ‘grow, pack, deliver, and consume’ for food.

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Along with monocultural practices increasing chemically intervention gave shortterm yield increases but hid the dangers of long-term decline in soil health. Widespread storage of food at low temperatures was not yet common. It was not rare to see European homes without a fridge in the 1970s. The ability to store food in the home changed shopping habits. Whereas once dry and “ambient” groceries were bought in larger quantities less frequently perishable items were usually bought for consumption on the day of need. For many, the fridge reduced this to once a week.

Agriculture 4.0 Increasingly specialized farm machinery is problematic. A combine harvester can cost over £500,000 but is only active 18 days a year (Lennon 2017). Imagine an accountant elsewhere being asked to sanction the acquisition of capital assets only used for 5% of a year. Worse still, unlike many simple implements, they cannot be simply parked up over winter and require careful preparation for storage and the next season. The precursor to Agriculture 4.0 was digitalization and computers. The reduction in cost of digital storage allowed fundamental change. Previously computers simply increased the speed of manual databases. Reduced memory costs now allowed time and date recording multiplying the value of data. Understanding primary food production data shows the extent of potential savings. Reducing waste in the perishable sector depends on sharing data to match supply and demand but is often blocked by retailers. The Internet of Things (IoT) and blockchain are key drivers offering increased traceability for consumers increasingly concerned about where their food has come from. Data can reconnect consumers with primary food production and reduce the number of food scandals.

Food and Employment Much has been written about the reducing number of agriculture workers. This is a simplistic statement as, alongside productivity gains, there has been a significant change in where processing occurs. Where once most farms had dairies, the processing and labor employed have moved to industrial areas. Max Roser (2013) writes about number of people employed in agriculture see Fig. 1: As countries develop, the share of the population working in agriculture is declining. While more than two-thirds of the population in poor countries work in agriculture, less than 5% of the population does in rich countries. It is predominantly the huge productivity increase that makes this reduction in labor possible.

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Fig. 1 Our World in Data – 1300–2012

Fig. 2 UK Government figures, 2017

In contrast there were few restaurants and no “fast food” outlets until recently. Many millions are now employed in food service – see Fig. 2. The UK’s DEFRA (2017) states: The food sector in GB employed 3.5 million people in Q1 2018 (3.9 million if agriculture and fishing are included along with self-employed farmers), a 1.0% increase on a year earlier. It covered 12% of GB employment in Q1 2018 (13% if agriculture and fishing are included along with self-employed farmers). Non-residential catering accounted for 50% of the postfarm gate food chain in Q1 2018.

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Fig. 3 Labor shortages. (Tweet from Prof Chris Elliot, 27/04/2020)

While mass urbanization reduced rural labor, the need for migrant seasonal labor has risen. Arable harvests are mostly mechanized, but this is not the case for continually harvested crops. Many developed countries rely on migrant labor although sourcing it has become increasingly difficult. Many come from relatively poor areas with high unemployment. As home prospects improve, there is less incentive to travel for work. Countries including the UK, Germany, and the Netherlands struggle to find seasonal labor. This is partly due to minimum pay rates. Workers now go home part way through a season when they reach a tax threshold. Significant amounts of late season strawberries have been left unharvested recently. Planned legislation about the UK leaving the EU (Brexit) was about to make this a bigger problem just as the pandemic hit – see Fig. 3. With emergency restrictions on travel and many being paid to stay at home, some have suggested they should replace migrant labor. Talking to a farmer recently, he said while this sounded good, it fails for several seasons: • Migrant labor needs housing. Many farms have facilities and expect workers to give up some of their pay to cover this. Local workers already have home and won’t want to pay twice leading to cost rises. • Casual agricultural labor is not yet covered by Health & Safety regulation to the extent urban workforces expect. • Those that travel for work are more dedicated and determined to succeed. Frequently it’s reported that local people agree to start on a farm and, finding it far harder than expected, don’t stay.

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Food Regulation, Fraud, and Deception There have been cases of food deception throughout history. More recently complex supply chains and larger sums involved have increased fraud. Known deceptions have varied from hiding the country of origin through to lower cost substitution. Prof. Chris Elliot (see Fig. 4) of the Food Safety Department at Queen’s University Belfast is a leading player in fighting food fraud. He is a director of The Institute of Global Food Security and led the UK’s recent Independent Review into the horsemeat scandal. Recent scandals include: • Claimed sales of Manuka honey regularly total several times the maximum possible. Amy Goodrich states “consumers. . .are paying higher prices for Manuka labelled honey while they are getting normal, cheaper honey instead” (Goodrich 2016). • The passing off of horsemeat as higher value beef. This was described by Sarah Taylor: “when meat from horses entered the supply chain as beef and ended up being sold in many products in the UK. Initially identified in Ireland, the scandal

Fig. 4 Professor Chris Elliot. (New Food Magazine, Tweet introducing Prof Chris Elliot, 13/05/ 2020)

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stretched across Europe and beyond. The horsemeat scandal infiltrated numerous supply chains and lead to millions of products being withdrawn. Additionally, there was a huge loss of consumer confidence in some of the biggest brands in the UK market” (Taylor 2019). • The use of melamine to bulk out baby formula milk. Echo Huang used the headline: “Ten years after China’s infant milk tragedy, parents still won’t trust their babies to local formula” (Echo Huang 2018). The EU allows animals raised in one country to be classified as a product of another country when butchered there. This can apply even where animal welfare regulation or permitted processes differ – see Fig 5. These scandals were hidden by a lack of traceability. IoT and blockchain are both relevant tools in the fight against fraud. They may not be needed in less complex chains. For distributed growing as developed by the author builds a single dataset from “Seed to Sale™.” This is not achieved in vertical farms, and, despite rising in popularity, they rarely, if ever, prove economically viable or capable of full traceability. The poor economics of vertical farming was highlighted by an independent reviewer of the author’s contribution to a book in 2019 (Ambient Intelligence and Smart Environments 2019) and by Robert Harding in January 23, 2020 (Harding 2020).

Fig. 5 Differing food regulations. (Tweet from Prof Chris Elliot, 13/05/2020)

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Food and Religion Many religions impose restrictions on food. Some object to the eating of individual species, require specific slaughter methods or are believed to object to meat consumption. In the Friend’s Journal Shaun Chavis (2010) wrote: “Many of the world’s faiths and religious organizations make faith-based eating easy for their believers: Don’t eat pork. Don’t eat any animal flesh at all. Eat fish on Fridays. Avoid garlic. Skip coffee. Abstain from alcohol. Wine and bread are meaningful. Fast between dawn and sunset on these days. Don’t eat leavened foods on those days. No bacon cheeseburgers ever.” What is clear is most religions expect respect to be given to any animal consumed. For foods such as fish, there are obvious signs that some restrictions are due to safety.

Judaism: Kosher Jews talk of kosher food as safe or suitable to be eaten and usually blessed by a Rabbi. They expect specific methods of slaughter and that meat and dairy products should be kept and eaten separately. Strict Jews will have two sets of cooking pots and, even in small kitchens, two sinks. The UK’s kosher certification website (The Badatz Igud Rabbonim 2020) states: “the Kosher Supervisor and his team. . . remove forbidden fats and veins. . .it is soaked in a bath in room temperature water for a half hour. To draw out the blood, the soaked meat is then placed on special salting tables where it is salted with coarse salt on both sides for one hour.” Only the meat and eggs from geese, ducks, chickens, and turkeys are permitted. Fish must be scaled fish, and shellfish are banned.

Christianity Most Christians don’t restrict diets other than to avoid excess in the 40 days of Lent before Easter. This is the origin of Pancake Day when the last of indulgent ingredients were consumed before Lent. Many in the Roman Catholic church avoid meat on Fridays and often eat fish as an alternative (Catholics and Culture 2020).

Muslim: Halal Muslims follow Islamic laws and eat according to to halal standards. The Learn Religions website (Huda 2020) states:

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“Islamic dietary law distinguishes between food and drink that are allowed (halal) and those that are prohibited (haram).” In particular they forbid: • “the carcass of an already-dead animal (one that was not slaughtered by the proper method). • Blood. • The flesh of swine (pork). • Intoxicating drinks. For observant Muslims, this even includes sauces or food-preparation liquids that might include alcohol, such as soy sauce. • The meat of an animal that has been sacrificed to idols. • The meat of an animal that died from electrocution, strangulation or blunt force.”

These requirements create problems where Islam is not the norm. For many countries, Halal meat does not need to be avoided, but the method of slaughter is illegal. The traditional method is to slit an animal’s throat to drain the blood (and life) from the animal while being blessed. Muslims are like Jews when it comes to fish and ban shellfish. The ninth month of the Islamic lunar calendar is Ramadan a month of fasting, reflection, and prayer. Dates vary – in 2020, Ramadan began in April. Unlike the Chinese year, the Islamic year does not have a leap month. This means little in countries near the equator, but in extreme latitudes, such as the UK, winter daylight can be less than 7 h, while in summer, they can be 17 h. For Muslims fasting during daylight, this is a significant difference. Children and expectant mothers are not required to fast, but others go without food, and many refuse all fluids during the day. Just as some of the ritual slaughter of animals is not always aligned with recent legislation, it is inconceivable health and safety legislators would encourage 17 h without food or drink.

Hinduism Hinduism is considered one of the oldest religions. Writing for the India Facts website Shatavadhani Ganesh (2015) state: “there are more vegetarians in India than the rest of the world combined. There is a widespread notion that such a high level of vegetarianism is due to Hinduism. While it is true that many Hindus are vegetarians, it is incorrect to say that Hinduism forbids meat-eating.” The article goes on to reference aspects of eating meat including that a cow “doesn’t deserve to be killed. . . only barren cows were killed.” This last part shows that while many Hindus don’t eat beef, they honor the cow for milk and shouldn’t let an infertile cow’s meat be wasted. Of particular relevance to today: If we truly want sustainability of the planet and all the living beings in it, then we have to look at our intake not just from the point of view of food. Just as a start, think about how our food is produced, processed, and shipped. If we learn more about food procurement, then we can make more informed choices of what foods to avoid and how we can help sustainability in the large sense.

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Whatever positive ecological effects one might have by being vegetarian might be cancelled out by a bad choice in what kind of foods we pick (heavily processed food, genetically modified food, etc.) Similarly, the negative effects of meat-eating can be tempered by making better choices in how the meat is procured.

Sikhism Some Sikhs are similar to Hindus fasting at times such as the full moon or other festivals. While many are vegetarian, it is not that simple. The Ethnicity Online website (Ethnicity Online 2020) states: One area of prohibition that is still often debated is the injunction to refrain from eating some meats (kutha). Translations of the Guru Granth Sahib seem simply to forbid the eating of meat that has been ritually slaughtered or prepared for another religion – such as kosher or halal meat. This certainly ties in with the Gurus’ aim to remove unnecessary ritual from their lives, and in fact is one of the four founding taboos in Sikhism (smoking tobacco is another one). . . .For some Sikhs, this prohibition extends to cover all meat and meat products, no matter how the animal is slaughtered, and may even include eggs, fish, milk and other dairy products too. For others, the meaning is much more limited and all meat apart from halal, kosher, beef and pork products may be eaten.

Buddhism The Buddhist approach to food is very similar to Hinduism and Sikhism. The Reference.com (Buddhism 2020) website states: The modern sects of Buddhism have different rules regarding diet. While most practice nonviolence, many consume meat. Chinese and Vietnamese sects consume meat, fish and eggs. However, these same sects reject the Five Pungent Spices, which include garlic and onion. Tibetan Buddhists will not consume fish, avoid fowl but may consume red meat. The belief is that the animals from which red meat comes are large and can provide for many people with their sacrifice.

Quakers Puritanical Christians broke away as the Religious Society of Friends. Shaun Chavis writes “applying Quaker testimonies to food and food choices, I’ve found, is not easy.” It seems that there are no rigidly applied rules but a requirement to keep to simple, local foods and avoid extravagance. There are many articles about the Quaker Oats Company and references to it starting in America as a way of encouraging humans to eat food intended for horses. Eating oats has however gained recognition as an effective way of reducing cholesterol.

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Amish The Amish are a puritanical branch of the Baptist Church living in communities who shun modern technology. This includes relying on horse drawn implements and vehicles. This gets blurred when carts and carriages use LED lights. Very similar to Quakers, the Amish have little restriction on diet other than to keep recipes simple and containing local ingredients. Further reading spotlights John Harvey Kellogg. The Orange Bean Indiana website (2019) talks about how he and his brother Will almost single handedly created the breakfast cereal market, first with a crushed mixture of cereals and grains he dubbed “granola” and then with “cornflakes.”

Subsidies Both world wars worsened food supplies, and much of the developed world had periods of food rationing. After WW2, effort was put into securing food supplies. Europe saw the creation of the common market (later becoming the European Union or EU) and the Common Agricultural Policy (CAP) (2020) which started a growing trend for subsidy and, for a while, substantial surplus. In the mid to late 1970s, there was much talk of “butter mountains” and “wine lakes” as subsidized agriculture created food regardless of need and governments funded long-term storage. Farms in many countries range from those struggling to survive despite subsidy through to huge areas of land under single ownership using contract labour making short-term profits but with no regard for soil health. Subsidy creates heated political debate throughout Europe. As agriculture became more industrialized, large farms could afford bigger machinery, while small farms couldn’t compete unless they had a niche specialism. Successful farms are often those that diversified and/or moved back to on farm processing. Long-term subsidy has been counterproductive. Rather than encouraging the uptake of new technologies, it has reinforced bad practice and, unlike most industries, keeps inefficient farms operating. In the UK, subsidy has become mostly about keeping food prices artificially low and achieves little more than manipulating a farmer’s cash flow. Large retailers can afford far more accountants than farmers, and subsidy simply ends up on their bottom line. It can be said that subsidies are like international trade tariffs and distort the market. While there is much to be said in environmental terms for developed countries subsidizing higher labor content local agriculture it does little to help improve the effectiveness of farming anywhere and, in many respects, hides the value of food that needs better recognition. Before joining the common market, the UK created the Milk Marketing Board (MMB). The need to increase production saw farms encouraged to focus on quantity while receiving a guaranteed price. When the MMB was broken up and privatized regional processers were often the only buyer and forced prices down. For many years, school children had been given milk every school day to improve nutrition. Once stopped the resulting overproduction was managed down by enforced quotas.

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Larger farms then sought to reduce costs and saw what some refer to as a race to the bottom in the “white water” industry. Subsidy also encouraged a move away from farming practices most suitable for local farmland to those that attracted the biggest subsidy. In some cases, this has accelerated soil health deterioration alleviated in the short term by increased chemical intervention. In many areas, soil health has reached a critical point in terms of water retention and trace nutrients – often those not needed for healthy plant growth but essential for those consuming what is harvested. This becomes a vicious circle with heavy rains no longer retained in fields and recently applied chemicals washed off into lakes and oceans causing significant harm.

Monoculture There is a similarity between intensive farming and urban populations both being a form of intensive monoculture. In the New Testament’s Gospel according to Saint Matthew, it states that “Man shall not live by bread alone.” This was not intended simply to reflect restricted diets but many aspects of lifestyle. Despite this historic advice, artificial restrictions occur frequently. Many issues complained about in intensive livestock farming can be found in city populations. High densities, poor food, an over reliance on drug intervention, narrowly focused targets, a lack of nature, and artificial lighting all spring to mind. Historic crop rotation practices have been abandoned. Whereas once fields were left fallow to recover or included a year of livestock grazing in rotation large arable farms never see a farmed animal. Many have argued for or against recent EU legislation requiring a minimum rotation of three crop varieties. Even this low number is only possible with chemical intervention. There are several forecasts of agricultural soils only being able to support commercial harvests for a few more decades. The need to improve soil health is rising up the agenda. Soil health professionals call for regenerative farming with many seeing the presence of livestock as an essential part of improving soil health. Specialisms such as orchards and asparagus growing can take several seasons to reach a commercial harvest. Asparagus plants deplete soil, and most growers don’t take them beyond 10 years. It is common practice for potato and asparagus growers to rent land to each other and move between areas every 10 years. This is especially an issue for crops susceptible to disease build up in soil or for fruiting trees that cannot achieve a commercial harvest when grown in the same soil as a previous tree.

Retail Practice, Shelf Life, Dates, and Food Processing The complexity of feeding a city’s people created large distribution operations. Local markets were replaced with larger city markets and then by retailers creating distribution depots and dedicated transport infrastructure. Some markets remain, but even smaller independent shops have formed or subscribe to cooperative

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distributors. Controlling these large operations is complicated. While large fish and meat markets operated on a daily basis, the new distribution methods take longer. When market stock remains unsold, prices drop to clear stock in the knowledge that more is expected the next day. Stock control becomes critical as supply chains lengthen. Change is not always an improvement with many cities already passing peak traffic speed. Many roads are so congested modern vehicles are no faster than a horse and cart.

Shelf Life Shelf life is the length of time food is thought to remain safe to eat after harvest or last processing. Timing is critical for products like fish and poultry that become dangerous to eat, but other products can be stored almost indefinitely. Cheeses and ham started as a means of keeping food without refrigeration and can, like a fine wine, improve with age. Parmesan cheese and Parma ham from Italy are good examples. When it comes to fruit and vegetables, fitness to eat is usually obvious. Increasing the distance food travels means many crops are harvested to survive the journey rather than for eating quality. Many items such as melons can reach a sell by date before they are ripe. While some fruit ripen off the plant, others such as strawberries don’t. Inevitably errors occur in production and keeping older stock at the front of a shelf. Fig. 6 shows both. The bottle without a use by date was put in a bin despite it being practically impossible to have been filled before the earliest date shown on others.

Fig. 6 Date errors. (Photo by author)

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Retail Practice Retailers talk of stock rotation, and the need to ensure earlier dates are at the front of the shelf and brought before later dated items. Whereas these were seen as the end point for food safety, some now use a “best before date” to indicate the food is safe to eat but may not be as good. The UK’s Waste and Resources Action Programme (WRAP) campaigns and promotes Circular Economy & Resource Efficiency around these issues – see Fig. 7. Many younger people are wasting good food, while older people remember being much more careful when food was in short supply. One recent event springs to mind when looking for a particular bakery product in a UK retailer. There was a gap on the shelf, while further along a group of managers huddled around the expected items. They mentioned “date expired” and that they couldn’t be sold. AI can avoid this with point of sale (POS) software holding the information needed. With every transaction recording, time and date an alarm should be triggered. If 100 items arrive on a Monday with a need to sell by Friday a Thursday report showing 50 items remain suggests little chance of selling them without a price reduction and the need to reduce future deliveries. As shelf life is not always for safety it has become a point of increased waste. For a remotely managed chain of stores there is little a local manager can do beyond ensuring his team presents an attractive store to customers. Before automated ordering, a store would ask for what they expected to sell along with imposed promotional items. Head office tasks local stores primarily with sales targets with sell by dates used to drive sales regardless of need. If a consumer buys more than needed, the store makes greater profits.

Fig. 7 The Waste and Resources Action Programme . (www.wrap.org.uk)

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Retailers claim they give consumers what they want but complex systems stop this. New store openings use an average of their nearest stores, but competitors doing the same distort this. No longer are customers loyal to one retailer with many going to one chain for some goods and another chain for other preferences. The more retailers rely on rigid stock profiling, the more it becomes a vicious circle – nobody buys products at a store if they are unavailable. Proper analysis can determine if greater numbers should be stocked but doesn’t change the number of SKUs. It is common practice to sell multipacks (with additional packaging) and offer “two for one” prices. Fruit and vegetables were offered loose and now packed in plastic bags to encourage customers to buy more than needed. Retailers rarely address changing demographics such as single-person households. A family may want a pack of tomatoes all at the same state of readiness but a single-person household will not. Many fresh items don’t suit a rigid sell by date. One item may appear as good as others but have an imperceptible trace of mildew that spoils a whole pack regardless of date. Many items are picked at a particular size rather than state of readiness. Melons, stone fruit, and avocado pears can remain unripe and lack flavor days after an arbitrary date.

Processing of Food Food has become increasingly industrialized. A recent craze is for “smoothies” where fresh leaves, vegetables, or fruit are blended to be like a milkshake and consumed as a drink. Proponents claim this is an ideal way of making an easily consumed nutritious drink. However, the processing cheapens calories for a digestive tract evolved to process food. Subcontracting the processing of food changes this with energy and enzymes once no longer balanced. People are consuming what they are told is the same number of calories but no longer need as many to power digestion. The amount of time for enzymes to work on food can be reduced to the extent that much needed nutrients are not absorbed in expected quantities starting a vicious circle. As the body asks for what it needs, more is consumed, and artificially released sugars lead to diabetes and obesity.

Preserving Food Humans sought ways to store food through lean seasons. When food was produced locally, there was a range of techniques used for storage. Sue Shephard writes about these (Shephard 2000). Where once storage depended on natural processes such as salting, drying, and freezing, it evolved into industrialized processes such as bottling and canning. This has been taken further with a heavy reliance on plastics and the addition of chemical preservatives. Preserving food prevents adverse bacterial growth or oxidation. Simply excluding oxygen helps, but it is not that simple. An apple lives and taking oxygen levels below

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1–2% kills it. Knowing this storage can be extended to almost a year with the drop in eating quality slowed significantly. Storing root crops such as potatoes below 5 °C sees carbohydrates (complex sugars) reduce to simple sugar. Much of the flavor of tomatoes comes from an enzyme unable to survive below 5 °C. Cooking destroys bacteria and mildews making food safe to eat. It also destroys any natural ability to survive storage meaning further bacteria must be excluded. Modern plastic packaging often includes vacuum or gas exchange to exclude oxygen and pathogens. This is a dilemma of modern life with the desperate need to avoid plastics but finding they are needed for modern-day living. Using chemicals to prevent degradation has unintended consequences. Further down it can be seen agricultural chemicals can survive digestion and remain active in waste streams. Drugs and traces of disease can be tracked in sewage. It has proved possible to predict accurately the numbers infected with Covid-19 in a community before individuals can be tested. If chemicals and organisms can survive our digestive systems what happens to preservatives? A pharmatutor.org article (Surekha et al. 2010) discusses how some preservatives prevent food being digested. So many are consuming more calories (often sugar) than needed as their bodies demand individual nutrients consumers believe they are providing. Early urbanization enabled workers to find better paid regular work. Much of this was manual labor in docks and factories. Just as athletes consume vast amounts of food to fuel their bodies, laborers need fuel. The need was such that foods rich in sugars gave the additional calories with sufficient nutrients. As cities progressed to service economies and the much wider use of heating the need for calories reduced. There is a divergence between those that count calories and those that track nutrition. With some driven by religion, incomplete science, or personal prejudice, it has never been more important to educate and reduce reliance on health-care systems and drugs to overcome poor nutrition.

Meat The English language has a divide between an animal and meat. It’s not cow, pig, deer, or sheep that is consumed but beef, pork, venison, and mutton. Oddly there is no renaming of the meat from young sheep (lamb) or of poultry birds. A further disconnect comes when meat is preserved.

Extending the Shelf Life of Meat Different parts of an animal have naturally different timings between slaughter and safe consumption. This is shown in hunting both by human and other animals. Hunters usually gut an animal at the point of capture. The intestinal tract and other organs are removed as these are not safe for human consumption or go off quickly. Hunters often cook and eat the liver before taking home what they want. This highly nutritious organ has a short safe keeping time and gone out of fashion in richer

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nations. With large animals providing more food than a small community needed, they found ways to preserve meat for later consumption. In cooler regions, it is common practice to “hang” a whole carcase in a cool place sometimes for weeks. This started with large carcases allowing blood to drain and allowing flavor to increase in intensity and longer protein chains to break down tenderizing the meat. Many don’t realize beef can be 30 days from slaughter when it reaches a kitchen. The greater the time, the more money is tied up in the supply chain with some retailers now marketing 14 or 21-day-aged steak at premium prices. Contemporary consumers have been conditioned to expect animals to be younger when slaughtered. Part of this arises from the UK’s Mad Cow Disease (BSE) (2020) – which could be part of why some promote human consumption of insects rather than processing them for animal feed which is banned in some countries.

Sheep/Lamb/Mutton Sheep could be thought of as nature’s lawn mower. Many parklands were managed with sheep. Grazing all young plants down prevents taller plants getting established with grass one of the survivors. Sheep can fend for themselves much of the year and are often farmed on hilly land not suited to cultivation. Some environmentalists complain this changes landscapes. However grazing was an essential part of creating fertile soils we depend on and part of regenerative farming. Having originally been bred for wool sheep need shearing in late spring. The increasing weight of fleeces that kept them warm through winter can prevent them standing after a fall. With wool going out of fashion, the slaughter of the previous year’s lambs is timed to avoid the expense of shearing. With most lambing taking place in late winter or early spring, farmers send the previous season’s lambs to market before the nest season making local lamb a seasonal meat popular at Easter. The changing date of Easter and changeable weather creates uncertainty of supply and demand with feast or famine reflected in prices and impacting farm income. If wool was valued, older sheep would be eaten as mutton over a longer season, and less lamb would be transported around the planet. One UK supermarket chain controls the supply of lamb from many farms yet only takes 22% of a carcase as prime cuts. Another 20% goes to other food suppliers with the remaining 58% not consumed by humans.

Cow/Beef Many countries consume large amounts of beef which is closely allied to the dairy industry and usually coming from castrated bullocks. Beef cattle are not as efficient as other animals converting grass to meat. The UK’s BBC had a series of programs about where leading chefs would choose to eat. Nisha Katona chose a steak restaurant in Northern Spain (Nisha Katona 2020) so popular it dictates what it serves and how it is cooked:

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They “source their meat from old dairy cows, who after a life of providing milk, are fattened to create a steak rich in marbling and intense in flavour. Steaks weighing between 1 and 1.5 kilograms are slow cooked on the charcoal grill which takes pride of place in the centre of the restaurant. Just as the menu only offers one steak, diners aren’t offered the chance to choose how they want their steak cooked – all steaks here are served rare, slow cooked for 10 minutes on each side so that all the fat inside renders creating intense flavours and melt-inthe-mouth tenderness.”

Many abattoirs penalize farmers for animals outside an optimal size. A more datadriven approach would manage herds more effectively. Large retailers talk of many farms supplying a few animals at a time making supply chain management complex and an ideal case for blockchain methods.

Pig: Pork, Ham, and Sausages Globally pigs are the animal most farmed for meat. Breeding is focused on high reproduction and length of the back where most of the meat comes from. A sow will have her first litter at about a year old. Pregnant for about 4 months, they usually give birth to 8–12 piglets and can have 2–2.5 litters each year. This makes pigs a high-density animal with sows spending a large part of their life pregnant or suckling their young. Traditionally the difference in size between mother and piglets meant many died before being weaned. Animal activists campaign about the conditions, sows are kept in unaware careful husbandry, and caging reduces these losses significantly and shouldn’t be seen as cruel. Processed meat is most commonly from a pig. Ham is a preserved meat and usually a whole haunch (rear thigh). Bacon comes from the back and ribs. This was once a matter of drying (often accelerated by smoking) and aging meat, but industrialized methods aim to reduce the level of “work in progress” and use chemicals to achieve a similar result. While legislation is steadily being introduced to list these added chemicals, many are simply called preservatives. As mentioned earlier, some religions frown on pork, and so similarly processed meat from turkey or even chicken may also be referred to as ham and bacon.

Blood and Offal (Internal Organs) The amount of animals slaughtered for meat generates a considerable amount of blood. Pig blood and, occasionally, cattle blood may be mixed with fat, spices, and salt to create “back pudding.” Considering the nutritional value, this should be much more common. A good black pudding is delicious. Collectively the edible internal organs are referred to as offal. Historically, much like in nature, the upper classes were given prime cuts and the rest consumedlower classes – even though these are often richer in nutrients. Many top chefs are now leading a return to using offal.

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The classic Scottish dish haggis was traditionally a mix of sheep heart, liver, and lungs, with onion, oatmeal, suet (animal fat), spices, and salt, mixed with stock, and encased like a large sausage. As urban populations have become more sensitive and picky about their food, many have abandoned traditional nutrient-rich foods that gave greater efficiency for food production as a whole.

Alternatives: Bison, Camel, Deer (Venison), Goats, and Kangaroo In general, the level of beef production matches the scale of milk production. Whereas a bull mating with cows was essential artificial insemination (AI) is now routine. There are ways to nudge the gender balance with timing of insemination but, until embryo gender selection becomes routine, this will not change significantly. Several breeds of animal are more efficient converters of grass to meat. Bison were once found in huge numbers across America. They are a breed that needs very little husbandry and can be left to their own devices for long periods. Deer are similar across the UK with a large number roaming free. In Scotland, there are regular reports of numbers being reduced and referred to as culling rather than harvesting a valuable source of food. Goats, sheep, and deer can all digest vegetation others cannot get close to digesting. With much of the UK, Ireland, and the Netherlands best unsuited to intensive cultivation, it would be foolish not to consume meat. This was highlighted by Wageningen University and Research (WUR); see Fig. 8. In Australia, UK, and America, there are many farmers paying to fence off land to prevent roaming animals damaging crops grown specifically for animal feed. One wonders if it wouldn’t be better to avoiding the expense and harvest the wild animals that need little management.

Poultry The birds most used for meat are chickens, turkeys, and ducks. Like cattle breeding has diverged to optimize egg laying or meat production. Industrialization of chicken production has seen considerable change. Until the 1960s, UK roasting a chicken was a special occasion. Increased demand has required faster growing birds. While many claim they are bred for nutrition, protein levels have halved over a few decades. Some birds reach the desired slaughter weight in 30 days in densely stocked sheds, but broken bones are not uncommon. It is no surprise these end up at fried chicken restaurants. Slower grown birds will often cost twice as much. Turkeys are a larger bird with producers hoping for them to be eaten more frequently than Thanksgiving or Christmas. They are often grown so quickly they lack the flavor of other birds. Ducks are a bird that may be used for eggs or meat. The feathers and down can also be used which leads to uses other than food. A few years ago, a representative of Silver Hill Farm in Ireland (2016) talked at conference about the efficiency of their ducks. When slaughtered, the duck feathers

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Fig. 8 Circular agrofood system. (Wageningen University and Research, The Netherlands, https:// www.wur.nl)

are used for stuffing pillows, and the down (fine hairs) used for duvets. Local hotels proudly state how their high-quality bedding comes from a local farm. Other parts of the duck such as the feet are exported to China where they are considered a delicacy and deliver essential trace nutrients.

More Than Food Along with the disconnect between primary food production and city populations, there is a disconnect between agriculture for food and other goods. Many farmers and butchers used to talk about making use of an animal from nose to tail. Historically sheep created fortunes for the North of England – not from the meat but their fleeces. Wool is a high-quality natural fiber. Many believed heavy woollen clothes were hopeless when compared to modern synthetic fibers. Recent recreations of clothing used by early polar explorers proved them to be far more effective than expected. This suggests a need to return to natural fibers wherever possible. Much has been said about the amount of microscopic plastic particles now being found in oceans with one source being synthetic fibers coming off clothing during washing and drying. The Textile School website (2019) states that

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Wool is possibly the oldest fiber known to humans. It was one of the first fibers to be spun into yarn and woven into the fabric. Of the major apparel fibres, wool is the most reusable and recyclable fibre on the planet. The eco-credentials of wool are enhanced by its long service life and suitability to be recycled to new textiles for clothing, resilient upholstery or products that call on its natural resistance to fire and temperature extremes. Aside from premium next-to-skin apparel, wool can be used in industrial applications such as thermal and acoustic insulation or in pads to soak up oil spills. At the disposal stage, natural fibres such as wool reduce the impact of the textile industry on pollution and landfill build-up. In warm, moist conditions such as in soil, wool biodegrades rapidly through the action of fungi and bacteria to essential elements (i.e. Nitrogen and Sulphur) for the growth of organisms as part of natural carbon and nutrient cycles.

Recent sheep breeding has focused on meat production. Wool demand has dropped so low fleece prices rarely cover shearing costs. Another key use of animals was for leather and fur. Both used to reduce the cost of meat by increasing the value of the whole animal. Just as synthetic fibers have been promoted in favor of healthier and sustainable natural fibers, they have displaced fur and leather. Some campaign against all livestock farming while promoting environmentally harmful fashion that makes all food production less sustainable. In addition to textiles, there has been an extension of woodland management into more recognized agricultural practices such as growing trees for Christmas. Alternative uses for agricultural land are increasing rapidly with crops grown for energy. This is mostly crops grown for biomass boilers but for a while was oilseed rape grown for processing directly into biodiesel, when it would make far more sense to process used oil at the end of potato crisp or chip production lines. The latest craze is to take farm land out of food production and cover them with solar PV panels. A few years back, the author asked a farming union’s renewable energy specialist why laborers were cutting weeds back when mounting the panels a bit higher would allow sheep to graze the land. It is now considered best practice to replace a fossil fuelled overhead with a secondary income with PV panels causing a very slight reduction in grass growth.

Fish Aquatic life consumed as food separates into shell fish and ordinary fish originating from sea or fresh water. As noted earlier, some religions reject the consumption of shellfish. This could be due to the difficulty in assuring freshness and safety especially in warmer climes or the dislike of the most common practice of cooking from live. While seawater shellfish can be transported packed in ice live fish need to be kept in oxygenated water. Perhaps related to these difficulties, shellfish trigger one of the most common allergies.

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Fish from cooler waters are, in general, slower growing. A recent move in urban farming is “aquaponics” where fish and plants are grown in combination. There is nothing new about this with Chinese paddy fields growing rice and fish 8000 years ago and some watercress farms combining with fresh water trout. These examples work well but not in a city facility. Most aquaponic growers combine warm water tilapia with salad leaves. Water is pumped through a bed where bacteria process fish waste into nutrients for the plants. The plants consume the nutrients and excess water is returned to the fish. Practice shows this works well for small non-stressed educational facilities but does not scale commercially (see Fig. 9). Tilapia are happy in close proximity but are not always clean and will attract similar comments as the intensive stocking of poultry or livestock. Some suggest the best use of the fish is for feed for salmon farms. This adds to the evidence that aquaponics is not a sensible option for city locations. As a rule leafy salads make a sensible secondary income for a fish farmer but fish are an expensive way to restrict varieties for a salad grower.

Fig. 9 Aquaponics schematic

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Milk and Dairy Products There are large numbers abusing the word milk to describe heavily diluted emulsions of ground grains or nuts. Milk is the nutritious fluid intended for new or recently born offspring. The difference between the two could hardly be greater with real milk addressing the entire dietary need of new born mammals and alternatives being highly diluted and incomplete. An important part is the colostrum that comes before milk. This is more than food containing growth hormones, antibodies, and nutrients to give a healthy start to life. Along with health-care systems and the desire to be thought of as more than animals, many mothers choose not to or are unable to provide enough milk. This means the essential components of colostrum must come separately or remain missing which could explain many health deficiencies. Milk has been recognized as an important part of diets across all cultures. The UK’s Agricultural and Horticultural Development Board (AHDB) (2020) gives an interesting history of milk.

Milk Consumption Around the World The consumption of milk varies around the world. Tropical areas with high temperatures and little refrigeration consume less than people in colder regions. Many populations lose the ability to digest milk during weaning. This is due to decreased production of the enzyme lactase required for digestion of the milk sugar lactose. Therefore, in many areas of the world, consumption of milk ends with weaning. The persistence of lactase production and activity beyond weaning is seen predominantly in Northern European populations. Here dairy farming has been commonplace for up to 10,000 years.

Nutritional Value of Milk Milk is a highly nutritious food, providing us with a whole range of essential nutrients. An article in the New Food Magazine (2016) talks of the British Journal of Nutrition (2011) and that milk can be a better sports recovery drink than many high-priced energy, sports, and performance drinks. Increased diversity requires dietary change. Increasingly areas that had no tradition of consuming milk now see demand. Many Chinese people are now drinking yoghurt- based drinks which are often used to deliver favorable bacteria.

Milk provides: • Vitamin A • Vitamin B12 • Calcium

needed for: Eyesight Red blood cells Strong bones and teeth

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Energy Muscle function to release energy Nerve function Growth and repair Healthy skin Immune system

Dairy Products Milk was one of the first foods to be processed to produce cream, butter, cheese, and now yoghurt and other related products. While these can be produced before pasteurization, most processing is performed after this process.

Initial Milk Processing Many countries demand pasteurization. In 1864 Louis Pasteur found that heating milk to 62.8 °C for 30 min killed the majority of pathogens – especially those that caused tuberculosis. Milkfacts.info (2020) state that: “Common milk borne illnesses during that time (pre-pasteurisation) were typhoid fever, scarlet fever, septic sore throat, diptheria, and diarrheal diseases. These illnesses were virtually eliminated with the commercial implementation of pasteurization, in combination with improved management practices on dairy farms. In 1938, milk products were the source of 25% of all food and waterborne illnesses that were traced to sources, but now they account for far less than 1% of all food and waterborne illnesses.” In some places, dairy farmers who test their herds regularly sell unpasteurized milk direct to consumers. There are many who prefer the flavor. Some suggest pasteurization causes problems for those who claim to be dairy intolerant. The heating process destroys naturally occurring enzymes such as lactase and galactose, and it can be this that prevents those who have no lactase digesting milk.

Beyond Pasteurization Recently two processes have been added to pasteurization. First came homogenization to prevent or delay the fat content settling out. This is achieved by breaking down the fat which takes longer to reform. The downside is that tiny particles are absorbed into our bloodstreams too easily. The latest process is a momentary high temperature that kills more pathogens allowing a significantly longer shelf life without a noticeable change in flavor.

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Eggs Eggs are the way many birds, reptiles, and fish reproduce. Within a skin or shell, they need immediate incubation and hold all that is needed for offspring to develop and emerge from the outer egg. Bird eggs develop into chicks that need feeding by their parents, while fish lay in vast numbers so some survive early predators. A bird egg therefore contains everything required for a healthy chick and offers many mammals a near perfect diet. Many vegans eat some eggs which provide essential nutrients their elected diet cannot otherwise provide. There are very few places where chicken eggs are not available. Chickens lay eggs regardless of season or the presence of a male bird. Just like intensive meat production, egg layers are often housed in high densities. Once free to roam and lay at a leisurely rate, many were consumed as meat once past their best. Commercial egg producers (like dairy cows) push to extremes, and once past their useful life worthless birds end up as pet food. Many large-scale producers house their birds inside permanently and use artificial lighting. Some enclosed farms time their lighting to reduce apparent daylength and achieve 8–9 eggs per week. Unsurprisingly, just like humans, birds should experience natural sunlight. Recent experience shows adding ultraviolet (UV) light reduces stress levels and fighting.

Egg Regulation When a bird lays an egg, the last action adds a bio-secure coating protecting porous shells from contamination. In the UK, top grade eggs must retain this coating. The USA requires eggs to be washed before selling meaning they are no longer protected and need refrigeration. With increasingly mixed populations, there is confusion about how Italians are happy to keep eggs at room temperature for days and others insist on keeping them in a fridge. Maybe this is why cooking of eggs varies so much. In America, it is rare for a fried egg to be offered with a liquid yolk, whereas in the UK, it was rare not to. These differences mean many foods are stored at lower temperatures than needed.

Egg Preservation While most eggs are sold uncooked, some are preserved or cooked. In China, it is common to bake quail eggs in salt. Another practice is for roadside food sellers to have large pans of eggs cooked slowly in hot water with tea and can be delicious. In the UK, there is demand for “Scotch eggs.” Here the egg is boiled, shelled, and coated with sausage meat and then coated in breadcrumbs before deep frying. It has become a point of honor for a chef to serve a hot Scotch egg with a liquid yolk – which can transform something usually served as part of a cold meal into a culinary delight.

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Plant Based Foods: Varieties and Genetics Many farmers have become increasingly specialized and depend, not only on bigger on farm machinery but larger deliveries. For broadacre (usually arable), farming crops harvested in one go make sense, but for other crops, it can be inflexible. Conservationists talk about the need to maintain or rebuild biodiversity. Nature evolved with many diverse species and varieties with a codependency either to enable reproduction (in plants) or to protect from pest and disease. Despite this most food comes from a narrow range of species with an increasing reliance on chemical inputs as discussed by Prof Chris Elliot – see Fig. 10. The restricted varieties used as food are obvious when you look at plant families such as allium (from leeks to onions), melon (squashes, pumpkins, marrows), and brassicas. A key point is the way crops are harvested. Where once food was ultra-local, mass urbanization has changed the business model. When first researching urban farming, the author met many specialists. One early meeting was with Dr. Jeremy Wiltshire (2014) whose PhD thesis was about rye grass. Often used for lawns in formal gardens, it can be seen a gardener wants a neat green carpet of grass without tall seed stalks but depend on reliable seed for the same plant.

Fig. 10 The narrow range of food families. (Tweet from Prof Chris Elliot, 27/12/2019)

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Dr. Wiltshire said “looking down you need to see green” for several good reasons. While few crops grow substantially over winter leaving a field brown (without a crop) leads to soil erosion, reduced water retention and released carbon. Sowing a cover crop or “green manure” can improve soil health and/or offer animal feed. Another meeting was with economics Professor Marian Rizov (n.d.) who comes from a family of farmers. He suggested reducing the minimum efficient scale (MES) – the point at which economies of scale kick in and increases profit.

Legumes Legumes are the family of plants that give us peas, beans, and peanuts. The distinguishing factor is the formation of seeds within a relatively flat pod that usually splits along two edges. Some varieties such as mangetout, sugar snap peas, French beans, and “Runner beans” are eaten complete with pod before the seeds ripen. As the seed nears ripeness, the pods get tougher and less easy to eat. This is the point at which most peas are harvested. In the UK, whole fields of peas are harvested rapidly. Good-quality garden peas are one of the most commonly frozen vegetables. Processors boast about harvest to frozen within 2 h. Obviously freezing a vegetable extends shelf life enormously, and, providing temperature is managed successfully, distribution is relatively simple. The first part of processing is separating peas from pods. Most pea varieties are bred for all pods to be harvest ready at the same time. Usually arriving as airfreight mangetout are flat pods with individual peas just forming, while sugar snap peas are swollen pods with juvenile peas and can have excellent eating qualities that are imported via airfreight. Higher-value crops are usually bred for continual harvest throughout a season. Why wait for seed to ripen and harder to digest when the whole pod can be consumed? This is where seasonal labor or robotics are needed. If the economics work continually harvested crops will be fresher and have better nutritional and flavor qualities. Most bean varieties are left to ripen and dry on the plant with harvest timed shortly before the pods split. Many are processed to remove the seed skin as, like many skins these become hard and impossible to digest as the cellulose content increases. Many split into halves at this point such as lentils and peanuts. Dried beans can be stored for long periods. To make them edible, many beans need soaking and long periods of cooking. Kidney beans used in chili con carne require intense periods of boiling to make toxins found on the outer skin safe.

Brassicas Looking at brassicas not many expect to see a link between common foods although it is easy to see the progression on to rapeseed oil not included in Fig. 11. An important point is how plants have been bred to exaggerate certain qualities and ignore others. Cauliflowers are transported with a few leaves left to protect the

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Fig. 11 The Brassica plant family. (Dr Simon Stott, https://scienceofparkinsons.com/2017/09/30/ broccoli/)

flower. Most people cooking a cauliflower discard the leaves – yet they are, essentially, cabbage leaves, etc. and many will buy both. Searching for something suitable, Fig. 11 was found on Dr. Simon Stott’s Science of Parkinson’s website (2017) where he describes: “Cruciferous vegetables are vegetables of the Brassicaceae family (also called Cruciferae). They are a family of flowering plants commonly known as the mustards, the crucifers, or simply the cabbage family. They include cauliflower, cabbage,

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garden cress, bok choy, broccoli, brussels sprouts and similar green leaf vegetables.” Looking at the range the scale of threat to some foods can be seen. Many have heard of the cabbage white butterfly – but how many realize the range of foods they threaten? Some cultivars are grown for leaves (watercress to cabbage), some for their roots or tubers (wasabi, radishes, and turnips), and some for flowers or seeds (broccoli and mustard). Brussel sprouts are side shoot growth where there have been recent changes. Major retailers claim they sell what the consumer wants. Increasingly they demand producers deliver consistent size and color. For Brussel sprouts, this has made plant breeders lean toward long tough stemmed varieties – often in preference to flavor qualities. It is standard practice to remove growing tips late in the season to encourage upper sprouts to swell. Just like “tender stem broccoli” and other young parts of a plant, these top shoots need very little cooking and are packed with flavor. Unfortunately, while they are well worth eating, they are difficult to collect and usually wasted. Arguably this is where robotics could make a significant difference.

Genetics For centuries selective breeding of animals and plants improved yield, flavor, or disease resistance. Genetic modification (GM) can accelerate this process. Where this displaces many seasons of trial growing, there are few problems. The process becomes controversial when adding genes from a completely different variety and creates headlines about “Frankenstein food.” Many campaigners complain about corporate profiteering by building in infertility or the dependence on certain chemical treatments. In reality, there is nothing new here. After all what is a Jaffa Orange, a seedless grape or a Cavendish banana? GM techniques have moved on from the early days. Modern technologies are so advanced researchers talk of gene editing with precise outcomes. Commonly referred to as CRISPR this involves a bacterial process which precisely cuts DNA strands and can be used on both plant and animal genes. The CRISPR-Cas9 approach has generated great excitement in the scientific community because it is faster, cheaper, more accurate, and more efficient than other existing editing methods (CRISPR-Cas9 2020). Another form of modification is common in a huge number of fruit crops. The majority of commercial apple trees across the world are varieties grafted onto the M9 root stock. This was bred by East Malling Research (2020) in the UK. . .who didn’t protect the right to use it and struggled to fund further research. Intriguingly there are many examples of two or more apple varieties grafted onto the same root stock. Whilst the roots deliver the same nutrients and water and the grafted branches experience the same weather conditions fruit remains true to the original variety. Other crops that rely on grafting include grapes and most citrus fruit. Unlike animal grafting or organ transplants these plants don’t need continual drug treatments to avoid rejection.

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Seeds, Grains, Nuts, and Bread Plants form seeds to reproduce much like eggs. The significant difference is the ability to survive long periods of hibernation or storage. Seeds form after pollination and usually have a high rate of success although many flowers are formed without regard to the chances of success. Seeds have often evolved to require transporting away from the parent plant before germination. Seeds range from a very small size, sometimes with lightweight parts enabling transport by wind such as dandelions and sycamore through to nuts with hard woody shells.

Seeds as Food Like eggs seeds contain DNA material of the parent plant(s) and sufficient key components to establish a new plant. This suggests they hold a wide variety of plant nutrients. Without water which makes up a substantial part of any plant nutrient density is high. However simply being present does not ensure the ability of a digestive system to absorb them. Many seeds evolved to avoid digestion. Some seeds grow in fruit designed to attract birds so they can be spread around and germinate in new locations. Humans do not digest tomato seeds. It always used to be said the best tomato plants were found naturally germinated around sewage treatment works. It is widely recognized that members of the capsicum family (from chili to sweet peppers) deliberately produce seed with capsaicin a chemical poisonous to animals. It is this that gives the “heat” of a chili measured on the Scoville Scale (2019); see Fig. 12. Birds do not see these chemicals as poison so are an intended method of seed distribution. There is a difference between eating whole seeds and those that have been milled or germinated to break or remove outer shells. There may still be difficulties in digesting the seed kernel. Monica Corrado discusses these points (Corrado 2020): • Phytic acid blocks the absorption of minerals in your small intestine, and grains are particularly high in this anti-nutrient. This is a major problem! Humans need minerals right down to the cellular level. Think also our hearts, our bones. . .. • Might the overconsumption of grains (containing enzyme inhibitors) that were not prepared well be one of the reasons for the rise in pancreatic disease and cancer? • The human body needs food to be in the simplest form in order to absorb and use it. The word “complex” means that there is work to be done by our digestive tract. Though it works and works, these proteins cannot be broken down. The worst offender? Notorious gluten. Gluten is a complex protein that the body cannot break down. . .and it’s even more complex than ever, after 50 years of hybridizing for increased gluten content!

The benefit of soaking and cooking or germination unlocking nutrients suggests the need to question the benefit of many “health foods.” Many food manufacturing

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Fig. 12 The Scoville scale. (www.ChiliPepperMadness.com)

companies are promoting cereal bars and foods containing Omega 3 seed mixes claiming they are full of essential nutrients but never mention they may be in a form that cannot be digested.

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Culinary Nuts As a culinary generalization seeds with hard, woody shells are referred to as nuts. Botanically many are not nuts but drupes – the single seed that forms within a fleshy fruit that does not open to release the seed when ripe. These range from peaches and plums through to almonds, walnuts, and coconuts. Another misnomer is peanuts which are a legume. Some trees produce nuts that are poisonous to humans but can be consumed by other animals. Acorns (Oak) and chestnuts attract squirrels who bury them to store over winter. Many are not found again and will germinate some distance from the parent tree. Acorns are poisonous to horses but attract great attention from pigs. Pigs fed on acorns are used for hams in Spain and command premium prices.

Bread Bread is a baked item based on flour milled from grain. Many cultures eat bread ranging from flat breads through to baked dough using yeast as a raising agent. Working a dough develops gluten retaining carbon dioxide expelled by yeast in bubbles that become permanent when baked. The early 1960’s saw the Chorleywood Process invented. This industrialized method creates and bakes dough in a much shorter time frame. Dr. Robert Verkerk states these “soft, squishy loaves sitting so temptingly on supermarket shelves have very little to do with real bread” (Verkerk 2014). He goes on to give the history and highlights the negative aspects of using industrial chemicals in place of the historic, natural products used in food for many generations. A key comment in the article is a comment by an anonymous insider: “It’s just not possible to make something digestible from grains in 20 minutes. If you want to make bread from grains you have to ferment it. If you just do chemical leavening instead of natural fermentation, it’s just not digestible.” This raises an interesting point repeated in several areas including livestock farming and animal feeds. Just as there are digestive gains to be found by germinating/sprouting grain, it would seem likely this should apply to the human diet.

Animal Feed Most farmed animals are vegetarian with many having more than one stomach to aid the digestion of plant cells – many of which we humans will never get close to digesting. However livestock farming depends on many crops from the grass family for grain or leaf matter.

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Grass The botanical family of grass is described by the Royal Horticultural Society (RHS)’s John Ardle as “from the family Poaceae, are one of the most successful plant groups on earth, ranging from diminutive, fine-leaved fescues (Festuca), only a few centimeters tall, to mighty bamboos. Mankind depends on grasses: wheat, barley, rice, oats and sweetcorn are all grasses” (Ardle 2007). The UK, Ireland, and the Netherlands have large areas best suited to pasture. Significant rainfall and cold winters make it impractical to maintain significant numbers of livestock out on pasture all year. Historically some areas of grass were cut, dried, and stored as hay to feed cattle through winter. A friend who harvests hay for horse owners spoke about chemical treatments for grass. Some selective herbicides survive haymaking and digestion. Horse droppings, once highly valued as a manure, now fail to give the expected result. Other areas of agriculture show this is a wider issue. The excessive use of agrochemicals has resulted in a rapid drop in soil health and worsening moisture retention. Regenerative agriculture now talks about an essential path of rebuilding biological matter in soils and improved moisture retention. The need for this is highlighted by nutrient runoff reaching watercourses and the increasing need to irrigate farmland. Plastics allow farmers to produce an alternative to hay. Plastics allow a costeffective way to exclude oxygen for making silage in preference to hay. Mostly produced from grass cut in spring before summer maturity, allowed to dry slightly, and then sealed to reduce the oxygen content. Naturally occurring bacteria multiply and pickle the grass preserving the nutrient content. This turns the grass acidic, and anaerobic digestion slows as the pH level drops. If the pH level doesn’t reduce enough other bacteria take over and spoil the crop at which point ammonia and methane are produced. This is the point where “renewable gas” (biomethane) is produced.

Grain as Animal Feed The intensification of livestock farming has been driven by the use of grain. Oats have fed horses for centuries – often crushed and mixed with water as a “porridge.” Most feed grains are crushed to ease digestion. Poultry feed for intensive operations use barley and need to add enzymes to aid digestion. Germinating the barley avoids this need. The amount consumed drops as digestion of “sprouted barley” is more complete. The problem is commonly used feed delivery equipment can handle dry grain but not sprouted barley, and so overall costs rise. Sprouted barley is also used as horse feed – but again the labor input and costs rise significantly. This brings to mind bean sprouts which are common in stir fries. If animals find it easier to digest sprouted grain does the same not apply to humans? This suggests the need for cities to have facilities to automate germination and delivery of sprouted grain. Vertical farms growing “micro-herbs” could be on the right track – but not with their excessive cost levels and lack of delivery systems. Their process needs significant cost reduction rather than being reserved for garnish in expensive restaurants.

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Food Security, Continuity, and Transparency of Supply The bigger the city, the more critical food supply becomes. Many countries have cities with more than ten million. Most cities are taking action to reduce traffic congestion with obvious implications for distribution. Beijing bans trucks during the working day. Most warehousing and distribution centers are found in lower-cost areas, but the ability to deliver around a city is increasingly difficult. Cities are often so congested average traffic speed is no longer faster than a horse and cart. Whereas populations were effectively controlled by agricultural productivity, many countries are now heavily dependent on food imports. So while regions like Northern Ireland and Yunnan Province in China are net exporters, many others are net importers. For perishable items like fruit and vegetables, transport is so critical; the delivered value is as much based on fuel prices as on weather. There is a correlation between value of individual varieties and the quantity that can be loaded on a truck.

Endangered Crops Some crops are under severe threat. Across the globe, there is heavy dependence on a single banana cultivar. The Cavendish Banana makes up 45% of all bananas consumed and 95% of banana exports. It is under threat from “Tropical Race 4 (TR4) – a strain of the fungus Fusarium oxysporum cubense that lives in the soil, is impervious to pesticides, and kills banana plants by choking them of water and nutrients” (Reynolds 2018) (see Fig. 13).

Fig. 13 Banana disease. (Photo by Jeff Daniels, https://www.wired.co.uk/article/cavendishbanana-extinction-gene-editing)

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Vitamins, Allergies, Intolerances, and Deficiencies One of the consequences of cities becoming ever more diverse is differing risk. Cultural differences are hard to change and have consequences for health and diet. The level of allergies and intolerances (including many not based on science) has never been greater and yet almost unheard of in some areas. Clive Dixon held a Michelin Star in his own right and then was Head Chef at Heston Blumenthal’s pub (Dixon 2008). He now works as a consultant with one client in a boutique hotel in Southern France. Over 2 years, they have had one request for vegetarian dishes and one from an allergy sufferer. Elsewhere the number of deaths or serious illnesses reaches alarming levels with large numbers of food service company management time devoted to avoiding dangers to their customers and public image. While some with allergies carrying emergency drugs, there are many who have minor reactions to foods. Development of wheat varieties often focuses on gluten. This is a key parameter for bread dough and increased significantly over the last 50 years. Rather than an allergy or intolerance coeliac disease is an autoimmune disease that may develop in people where others in the family also suffer. This disease prevents the absorption of other nutrients and harms overall diet. Others avoid gluten in their diet because it makes them feel bloated or have tummy pain. Other common allergic reactions can be caused by shellfish or peanuts.

Vitamins and Dietary Supplements Vitamins are organic molecules essential as micronutrients to aid the proper functioning of a host’s metabolism. They are often referred to by name or letters, and there is a large industry marketing vitamin supplements although there is plenty of evidence they may not be absorbed adequately or can be taken to excess. For most people, a healthy balanced diet has no need for supplementation. Vitamins fall into two categories; those soluble in water leave the body in urine and need constant replacement, while others are soluble in fats and can be retained although they can build up and become toxic in high concentrations. While some are formulated in the body, others need to consumed in whole through foods (see Fig. 14). Vitamin D or, rather the lack of sun, hit the headlines after high numbers of migrants arrived in the UK in the 1970s. Arriving from warmer regions where it was customary to wear long flowing robes, to keep out of the sun, and little need for dietary vitamin D, there were many who suffered from low levels. The extreme effects of long-term vitamin D deficiency include problems retaining calcium, can result in rickets and, now we learn, can reduce the ability to fight disease. There is much talk about equality of opportunity and experiences. While not yet reviewed, an SSRN study shows a hard to refute link between vitamin D and the ability to fight disease (see Fig. 15). It soon becomes obvious dietary change promoted by the World Health Organization (WHO) (2019) and Prof. Walter Willet et al. in the Eat-Lancet Report (Willett 2019) can cause harmful unintended consequences.

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Fig. 14 The comprehensive review on fat-soluble vitamins. (https://www.semanticscholar.org/ paper/The-Comprehensive-Review-on-Fat-Soluble-Vitamins-Ravisankar-Reddy/5ddfa828e93 4cbfafea9a6f300874a7ba34cc803)

Fig. 15 Vitamin D SSRN. (https://papers.ssrn.com/sol3/papers.cfm?abstract_id¼3571484, Graphic from Mailonline.co.uk)

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Most animals including humans rely on a range of mutually beneficial bacteria in their digestive tracts. There is a lot more to understand about how restricted diets and processed foods impact on them. It is not uncommon for holiday makers to suffer when trying local dishes. Some well-traveled people talk of the benefits of eating local live yoghurt to ease digestion and local honey to relieve hay fever.

Plastics, Carbon, and the Future of Local Food There are many harmful environmental aspects of feeding Smart City populations. As the importance and need to achieve Net Zero carbon and to address the United Nations’ 17 Sustainable Development Goals (SDGs) (2015) rise up the agenda, many are looking at individual disciplines, aiming for fairer societies and circular economies – see Fig. 16. What many miss is the impact gains for one goal may have on others. Nowhere is this more important than for our food supply. Delivery from fast food outlets and takeaways is no different to that of online purchases. Many vans on the road only carry a few boxes and make up an increasing amount of city traffic. This cannot continue, and they are likely to be taxed out of existence when recognized as a large part of unwanted city traffic.

The New Local Many cities depend on short lead times. The more expensive a city, the more critical this becomes. London is said only to hold sufficient food for 1.5 days and New York

Fig. 16 UN’s Sustainable Development Goals

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2.5 days. For a long time, economic thinking has focused on “economies of scale” that have increased the minimum efficiency scale (MES). It can be seen at a top level that there are several clashes and points of opposing need. Where once food was produced within a very short distance of consumption, the globalization of industry has not only extended the distance between production and consumption but in some cases created confusion and opportunity for fraud. Tesco, the large UK food retailer, recently spoke of 60% of bagged salads being wasted. They spoke of one of their biggest suppliers extending their greenhouse and transporting mass harvested salad leaves 300 miles to a processing factory for washing, packing and onward distribution. Many of the packs would then be transported back to outlets within a few miles of the grower. In other sectors, it is noted that Scottish farmed salmon is often transported to the far east for low cost processing and much then transported back to the west. So food that many think of as locally produced may have traveled significant distances. As the world becomes increasingly urbanized, too many have lost sight of primary food production. This disconnect plays a part in creating the drive to vegetarian and vegan diets. This is a major issue for long-term health as misinformation and dubious industrialized practices swing the pendulum further from a balanced environment.

Conclusion Mass urbanization has resulted in complex food supply chains with recent events proving how fragile and inflexible they are. The disconnect between people and agriculture has never been greater. While some are trying to re-educate people, not all know enough to achieve what the planet needs. Recently the planet breathed a sigh of relief as countries locked down due to the Covid-19 pandemic and there are clear signs the reduction in energy use has improved air quality. One has to question the principles of modern food production. What is the point of on farm intensification when there is little regard for nutritional quality or whether produce will be consumed? Breaking supply chains into separate contractual operations and profit centers loses sight of the bigger picture which shows environmental efficiency must be addressed. Some large corporations are tackling “sweat shop” labor in their supply chains, but there are too many examples of modern slavery, child labor, and deprivation throughout societies. Many of these practices are common in rural farms where they are hardest to police. There are some organizations looking at this but often with a narrow focus. There is a saying that “charity begins at home” yet, while the Fairtrade Foundation (2020) highlights the poverty of coffee, chocolate, and other remote farm workers, they don’t address the very similar issues faced by low-paid workers in cities or rural areas of developed nations where they are frequently employed in food service or delivery. For every supply chain link costs are added and profits taken. Vertically integrated supply chains show transparency of supply is much simpler, and ethical long-term profits are easier to achieve. Covid-19 has accelerated change with many companies allowing working from home and suggesting they no longer have a need for large office buildings. Smaller

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business outlets, especially those such as coffee and sandwich outlets, are facing a substantial reduction in turnover with many expected to close. Food retailers who developed large edge of town stores are seeing a reduction in footfall and basket size. The move to online shopping calls put these facilities in doubt. The early stages of Covid-19 showed food supply chains to be very fragile. Panic buying left empty shelves with many products not available. Retailers could do much more to prevent this. Chickens didn’t suddenly stop laying eggs. Populations were not suddenly eating far more food. Why do POS systems not flag up panic buying and excessive purchases? For once there would have been far less anger if the computer had said no. Apart from later shortages of fresh produce caused by short-term labor shortages, there were no real food shortages. While retailers had empty shelves food remained unsold in wholesalers’ warehouses as bars and restaurants were told to remain shut. Food was close by but in the wrong size packets. There have been moves to encourage customers to fill their own packets from instore dispensers. This is much like a hardware store mixing paint colors. There is far lower level of total stock and much less chance of not offering consumer choice. The problem is food safety. Current use of plastics seal containers very effectively. The need to avoid plastics needs a different approach. An animal carcase can be delivered to a retailer without the need for packaging, and shelf life is not shortened until final butchery. It’s the final retail presentation that causes problems (Fig. 17). Big landlords now seek multiple uses for their expensive buildings with some such as Segro plc who were previously wary of anything other than warehousing

Fig. 17 The scale of recent difficulties. (Flaws in Food Systems, Tweet from Prof Chris Elliot quoting a foodtank.com article, 15/05/2020)

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now looking at multistorey units with domestic dwelling floors above office or hotel space which are themselves above retail or warehouse floors. Optimization suggests workers should extend the practice of having online deliveries at work to include food. The obvious next step is for work placed food outlets to be extended and for workers to take ingredients or whole meals home from their place of work. Some will be living above their place of work. Blocks of flats and residential streets are likely to have spaces reserved for co-working space. Together with more appropriate urban farming solutions (including on the roof of hotels and residential flats), food costs can be reduced while increasing value. Connectivity and a smarter use of data offer many ways to improve the way a city feeds its people.

References Agricultural and Horticultural Development Board. (2020). https://ahdb.org.uk/ Ambient Intelligence and Smart Environments. (2019). Chapter 1. ISBN print 978-1-61499-982-9. Ardle, J. (2007). https://www.rhs.org.uk/plants/articles/misc/grass-sedge-rush Barbara Bray MBE. (2020). Director Oxford Farming Conference 2020–22. https://www.ofc.org. uk/council British Journal of Nutrition. (2011). https://www.nutritionsociety.org/publications/nutritionsociety-journals/british-journal-of-nutrition BSE. (2020). https://www.fda.gov/animal-veterinary/animal-health-literacy/all-about-bse-madcow-disease Buddhism. (2020). https://www.reference.com/world-view/buddhists-eat-24d57d27ad91abc3 Catholics and Culture. (2020). https://www.catholicsandcultures.org/educator-resources/foodchristianity Chavis, S. (2010). https://www.friendsjournal.org/applying-quaker-thought-food/ Common Agricultural Policy. (2020). https://ukandeu.ac.uk/fact-figures/what-is-the-commonagricultural-policy/ Corrado, M. (2020). https://www.thehealthyhomeeconomist.com/reasons-why-grains-are-hard-todigest/ CRISPR-Cas9. (2020). https://ghr.nlm.nih.gov/primer/genomicresearch/genomeediting DEFRA, UK. (2017). https://www.gov.uk/government/publications/food-statistics-pocketbook2017/food-statistics-in-your-pocket-2017-food-chain Dewey, P. (2008). Iron harvests of the field. ISBN: 978-1-85936-160-3. Dixon, C. (2008). Head Chef, The Hinds Head (Fat Duck Group) 2008–10. https://www.linkedin. com/in/clive-dixon-0231a911/ East Malling Research. (2020). https://www.emr.ac.uk/ Echo Huang. (2018). https://qz.com/author/echohuang92/ Ellen MacArthur Foundation. (2020). https://www.ellenmacarthurfoundation.org/ Ethnicity Online. (2020). http://www.ethnicityonline.net/sikh_diet.htm Fairtrade Foundation. (2020). https://www.fairtrade.net/ Goodrich, A.(2016). https://naturalnewsblogs.com/author/amygoodrich/ Harcombe, Z. The obesity epidemic. 1 Oct 2010. ISBN-10: 1907797009. Harding, R. (2020). The Financial Times. https://www.ft.com/stream/1f87e6a9-f548-4c3b-bc8857ae030c2e7d Huda. (2020, February 11). Halal and Haram: The Islamic dietary laws. Learn Religions. https://www.learnreligions.com/islamic-dietary-law-2004234 Lennon, S. (2017, October). CNH 2017 IAgrE conference. Rothamsted Reasearch. Milk Facts. (2020). http://www.milkfacts.info/Milk%2520Processing/Heat%2520Treatments% 2520and%2520Pasteurization.htm

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New Food Magazine. (2016, October). https://www.newfoodmagazine.com/news/27292/milkultimate-sports-drink/ Nisha Katona. (2020). https://www.bbc.co.uk/programmes/articles/4P2X6BySs0F4vCX511xXp3H/ five-stand-out-food-experiences-from-remarkable-places-to-eat Orange Bean. (2019, August). https://orangebeanindiana.com/2019/08/20/enemas-galore-probingkelloggs-battle-creek-sanitarium/ Overton, M. (1996). Agricultural revolution in England. Cambridge University Press. ISBN: 9780521568593. Reynolds, M. (2018). https://www.wired.co.uk/article/cavendish-banana-extinction-gene-editing Rizov, M. (n.d.). Lincoln University, UK. http://www.businessdictionary.com/definition/minimumefficient-scale.html Roser, M. (2013). Employment in agriculture. https://ourworldindata.org/employment-inagriculture Scoville Scale. (2019, June). https://www.chilipeppermadness.com/frequently-asked-questions/thescoville-scale/ Shatavadhani Ganesh. (2015, August). India facts. http://indiafacts.org/the-hindu-view-on-foodand-drink/ Shephard, S. (2000). Pickled, potted and panned. ISBN: 0 7472 2334 3. Silver Hill Farm. (2016). http://www.silverhillfarm.ie/ Simon Stott. (2017, September). https://scienceofparkinsons.com/tag/gst/ Stephen, Louise. Eating ourselves sick. 31 Jan 2017. ISBN: 9781743549889. Surekha, V., et al. (2010). https://www.pharmatutor.org/articles/review-on-determination-preserva tives-food-stuffs-different-analytical-methods Sustainable Development Goals. (adopted 2015). https://sdgs.un.org/goals Taylor, S. (2019, June). https://www.highspeedtraining.co.uk/hub/author/sarah/ The Badatz Igud Rabbonim. (2020). https://koshercertification.org.uk/?gclid¼Cj0KCQjwyJn5BR DrARIsADZ9ykHNkob_qCCKeWFxTXnGqBRc0LsvApbeeygwH3m3SnmWslHcUWCar rYaArxpEALw_wcB The Textile School. (2019, May). https://www.textileschool.com/162/wool-fiber-basics-character istics-properties/ Verkerk, R. (2014, April). https://www.anhinternational.org/2014/04/23/the-chorleywood-processand-the-rise-of-real-bread/ Willett, W. Eat-Lancet. 16 Jan (2019). https://www.thelancet.com/journals/lancet/article/PIIS01406736(18)31788-4/fulltext Wiltshire, J. (2014). https://ee.ricardo.com/experts/water-and-environment/jeremy-wiltshire World Health Organisation. (2019). https://www.who.int/

IoT and Blockchain-Based Smart Agri-food Supply Chains

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Lehan Hou, Ruizhi Liao, and Qiqi Luo

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Architecture of Smart Agri-food Supply Chains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The IoT Architecture and Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Key Components of Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key Data Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Practical Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Challenges of Blockchain-Based Smart Agri-food Supply Chains . . . . . . . . . . . . . . . . . . . . . . . . . . . Legal Provisions Lag Behind Blockchain’s Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Cost of Devices and Maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Security Concerns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Storage, Throughput, and Velocity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

With the development of the Internet of Things (IoT), connected devices are rapidly penetrating into every aspect of our daily life. Thus, IoT is considered as a key building block for the agri-food supply chain due to its pervasiveness, interoperability, and scalability. In this chapter, we first review IoT and L. Hou School of Data Science, The Chinese University of Hong Kong, Shenzhen, China R. Liao (*) School of Humanities and Social Science, The Chinese University of Hong Kong, Shenzhen, China Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology, Shenzhen, China e-mail: [email protected] Q. Luo School of Management and Economics, The Chinese University of Hong Kong, Shenzhen, China © Springer Nature Switzerland AG 2021 J. C. Augusto (ed.), Handbook of Smart Cities, https://doi.org/10.1007/978-3-030-69698-6_91

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blockchain-based agri-food supply chains and then identify the key building blocks for this type of system. We put the emphasis on core components of IoT and blockchain, as the former lays the foundation for data collection and transmissions, while the latter paves the way for the data transparency and integrity, and more importantly, it lowers the financing cost for all participants of the supply chain.

Introduction Agricultural food (agri-food) supply chain consists of multiple echelons that are usually geographically disjointed entities to serve consumers. The traditional agrifood supply chain can be simplified to the following work flows: (1) wholesale agents purchase crops from end farmers, (2) one or several intermediate factories process raw materials, (3) logistics companies transport and distribute products to retailers, and (4) food products reach end consumers. In real life, the primary products will often go through many more steps before reaching out to the end consumers, resulting in higher food safety risks, logistic overheads, and financing costs. In addition, even though there are strict laws and regulations on food safety, we still saw food safety incidents or violations from time to time. For example, Sudan red dye (Kwok and Yau 2006), contaminated baby formula (Gossner et al. 2009), and “recycled” rotting meat (Ma 2013). Another significant problem lying in the traditional agri-food supply chain is the prolonged payment cycle, e.g., a wholesaler pays farmers on the day when crops were well received, while secondary wholesalers or retailers will usually pay the wholesaler later on a monthly basis. In other words, the first-tier wholesalers have to reserve cash flows of worth at least 1-month turnover. Otherwise, they will need to seek short-term loans or will face a liquidity squeeze. However, it is costly for small business to get short-term liquidity loans, as offering loans to small business means high default risks for financial entities. As a result, financial entities will usually request a very strict loan guarantee and a high interest rate when wholesalers borrow from them. Therefore, the supply chain finance has entered the stage of intellectualization (Zhu et al. 2019b). In a multi-object collaborating IoT system, the cost of trust is high among different entities. In this regard, blockchain can be a very good solution for improving traceability and transparency, as the decentralization and disintermediation characteristics of blockchain can reduce the trust cost through various consensus procedures. More importantly, transaction records on blockchain can be collateral vouchers for the supply chain finance, reducing the financing cost for small business. Thus, in order to build a smart food supply chain that can track the whole life span of agri-food products, accelerate both food and cash flows, and build trusts among all participating entities of the supply chain, coupling IoT and blockchain technologies are imperative.

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The smart agri-food supply chain has the following characteristics: 1. The product traceability platform provides consumers with comprehensive information on product flow and product traceability services. 2. Transactions of all the entities in the supply chain are stored in the blockchain to achieve complete traceability of products’ whole life cycles. 3. The consistency of data improves the efficiency of the industrial supply chain and reduces the cost of mutual trust.

Literature Review In the early stage of smart agriculture development, information technology was of sporadic involvement in facilitating agricultural supply chains. The main technologies were sensing technology and control technology to achieve effective reduction of energy consumption and sustainable development. Barcodes, quick response (QR) codes, or radio frequency identification (RFID) was employed to carry product or transportation information. Consumers could inquire about relevant product information or verify its authenticity by calling a number or looking for details on a website (Yang et al. 2009). In recent years, IoT has been one of the main focuses of reshaping the agricultural supply chain. The author in (Chen 2015) designed an agricultural IoT architecture that operates in 780 MHz, which is compatible with the 6LoWPAN standard (IPv6 over low-power wireless personal area networks). In terms of the transportation process, the author in (Sun 2016) designed a model based on near-field communication (NFC) and reinforced its security by encryption algorithms of Data Encryption Standard (DES) and RSA (Rivest-Shamir-Adleman). The author in (Tian 2016) demonstrated a temperature control supply chain based on RFID and blockchain to accommodate the uprising need for cold logistic chains with transparent, neutral, and secure information for all stakeholders of the product. Due to the opaque nature of supply chains, food safety is highly dependent on regulations and government/media monitoring. According to the white paper of Provenance (Jessi et al. 2015), 30% of consumers in the UK are concerned about issues regarding the origin of products, which inspired the authors of the white paper to study the feasibility of applying blockchain to secure traceability and transparency in the supply chain. Abeyratne and Monfared in (2016) took carton manufacturing to illustrate how blockchain can empower the global supply chain network. Blockchain prevents incorrect, unclear, or forged storage, deducing the default risk for the financial institution. Once a transaction is validated, it is included in a block and cannot be deleted or modified (Hofmann et al. 2018). The biggest advantage of blockchain-enabled supply chains is that it can provide validated credit records for accounts receivable, facilitating the pledge of receipts financing (Du et al. 2020). Furthermore, Choi in (2020) developed an analytic model to compare supply chains with and without the support of blockchain. The author suggests that utilizing

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blockchain is a mean-risk dominating policy that brings a higher expected profit and lower risk. The authors in (Gaetani et al. 2017) discussed data storage problems in practical application. It is challenging to transfer rich dimension metadata of product in the supply chain in terms of data security and privacy, e.g., suppliers’ trade secrets and buyer-supplier relations (Feng et al. 2019). In this regard, mixing services, ring signature, and non-interactive zero-knowledge proof are used in blockchain-zeroknowledge to protect users’ anonymity (Knirsch et al. 2018). The cloud-based database can be utilized to store the information. Zhu et al. in (Zhu et al. 2019a) designed a controllable blockchain data management (CBDM) model using a bilinear pairing technique processing data in a cloud environment.

The Architecture of Smart Agri-food Supply Chains In this section, we will introduce the key components of a smart agri-food supply chain, including the underlying infrastructure (e.g., IoT architecture, communications, and blockchain), key data flows (e.g., product flow, information flow, and financial flow), and practical applications.

The IoT Architecture and Communications IoT is an information network that connects things to the Internet through communication technologies to realize intelligent identification and management. In broad terms, IoT has three core functionalities: sensing, information transmitting, and information processing; thus, the corresponding layer for each component is the perception layer, the network layer, and the application layer, respectively. We will briefly go through each layer and then sample one of the most prominent protocols in each layer. The perception layer: it is also known as the physical layer, which includes sensors, QR codes, tags, and RFID to collect data from the environment or contain data from “things.” The network layer: it transmits data from sensing devices to the data processor, which can be a neighboring node (edge), a cluster head (fog), or a computing center (cloud). Various communication protocols are adopted in this layer according to the purpose of the IoT or the transmission range. The communication options include RFID, ZigBee, 6LoWPAN, and Bluetooth for the short range (0–10s m); 802.11 a/b/ n/p/ac/af/ah for the short to medium range (10s–100 m); and 2G/3G/4G/5G, longrange wide area network (LoRaWAN), Sigfox, NB-IoT, and LTE-M for the long range (kilometers). The key features of the most representative communication protocols are compared and summarized in Table 1. The application layer: it is responsible for delivering specific IoT-based services to users, such as commodity status information and position tracking. Message

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Table 1 Comparisons of representative communication protocols Protocol RFID

Based on –

Range