Data and AI Driving Smart Cities 3031328272, 9783031328275

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Studies in Big Data 128

Pedro Ponce · Therese Peffer · Juana Isabel Mendez Garduno · Ursula Eicker · Arturo Molina · Troy McDaniel · Edgard D. Musafiri Mimo · Ramanunni Parakkal Menon · Kathryn Kaspar · Sadam Hussain

Data and AI Driving Smart Cities

Studies in Big Data Volume 128

Series Editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland

The series “Studies in Big Data” (SBD) publishes new developments and advances in the various areas of Big Data- quickly and with a high quality. The intent is to cover the theory, research, development, and applications of Big Data, as embedded in the fields of engineering, computer science, physics, economics and life sciences. The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensors or other physical instruments as well as simulations, crowd sourcing, social networks or other internet transactions, such as emails or video click streams and other. The series contains monographs, lecture notes and edited volumes in Big Data spanning the areas of computational intelligence including neural networks, evolutionary computation, soft computing, fuzzy systems, as well as artificial intelligence, data mining, modern statistics and Operations research, as well as self-organizing systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. The books of this series are reviewed in a single blind peer review process. Indexed by SCOPUS, EI Compendex, SCIMAGO and zbMATH. All books published in the series are submitted for consideration in Web of Science.

Pedro Ponce · Therese Peffer · Juana Isabel Mendez Garduno · Ursula Eicker · Arturo Molina · Troy McDaniel · Edgard D. Musafiri Mimo · Ramanunni Parakkal Menon · Kathryn Kaspar · Sadam Hussain

Data and AI Driving Smart Cities

Pedro Ponce Institute of Advanced Materials for Sustainable Manufacturing Tecnologico De Monterrey Monterrey, Mexico

Therese Peffer California Institute for Energy and Environment Center for the Built Environment Berkeley, CA, USA

Juana Isabel Mendez Garduno Institute of Advanced Materials for Sustainable Manufacturing Tecnologcio de Monterrey Monterrey, Mexico

Ursula Eicker Department of Building, Civil and Environmental Engineering Concordia University Montréal, QC, Canada

Arturo Molina Institute of Advanced Materials for Sustainable Manufacturing Tecnologico de Monterrey Monterrey, Mexico

Troy McDaniel The Polytechnic School (TPS) Mesa Arizona, AZ, USA

Edgard D. Musafiri Mimo The Polytechnic School (TPS) Mesa Arizona, AZ, USA

Ramanunni Parakkal Menon Department of Building, Civil and Environmental Engineering Concordia University Montréal, QC, Canada

Kathryn Kaspar Department of Building, Civil and Environmental Engineering Concordia University Montréal, QC, Canada

Sadam Hussain Department of Electrical and Computer Engineering Concordia University Montréal, QC, Canada

ISSN 2197-6503 ISSN 2197-6511 (electronic) Studies in Big Data ISBN 978-3-031-32827-5 ISBN 978-3-031-32828-2 (eBook) https://doi.org/10.1007/978-3-031-32828-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed 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 my dear mother, Margarita, who shows me how to build a better future.—P. Ponce To Gandhi, Isabel, and Victoria who guide me at every step of my life.—J. I. Méndez To my Family Molina—Gutierrez for all their love and support and my wife Silvia, daughter Monse and son Julio.—A. Molina To my lovely wife Sanctifiee Musafiri Mimo, daughter Sanctifiee, and son Edifie.—E. D. Musafiri

Foreword by Aminah Robinson Fayek

In Data and AI Driving Smart Cities, the authors’ experience in their respective fields supports the presentation of topics indispensable to understanding and designing smart cities. Smart cities have the potential to revolutionize our communities and increase everyone’s quality of life. This book provides a much-needed overview of how conventional cities are becoming smart cities and how advanced technologies are accelerating this transformation. It introduces readers to some technical and practical concepts that must be considered in designing and deploying technologies to create smart cities. The opening chapter, The Smart C 3 model—Smart Citizens, Communities and Cities, describes the initiative to create smart cities that transfer specific information among their citizens and systems. The Smart C3 model considers short-term and long-term impacts of technology, from improving public services to creating economic opportunities, and provides a framework for creating a sustainable and resilient future. An introduction to the concepts of citizens living in smart households, communities, and cities is provided in Chaps. 2 through 5. The goal of a smart community is to create a connected, resilient, and sustainable environment that provides citizens with access to and the ability to share resources, data, and opportunities such as development of education and training programs, employment initiatives, and other initiatives to promote social and economic mobility. Smart communities also incorporate green and renewable energy solutions to reduce environmental impact while providing a sustainable and resilient environment. By creating a shared platform for collaboration between citizens, governments, businesses, and service providers, these groups can work together efficiently to develop innovative solutions tailored to the local context. Chapter 6 discusses existing smart communities and cities around the world, and Chap. 7 presents the concept of Demand side Management (DSM), which includes Transactive Energy Strategies (TESs). Smart cities and utilities employ DSM to optimize energy usage through efficient and cost-effective methods while reducing energy demand. TESs, which work as a DSM subsystem, incorporate market-based tools to manage energy use and costs. Cities can use DSM and TESs to reduce energy usage and costs, increase energy efficiency, and meet sustainability goals. In this book, the reader will find a novel approach to designing vii

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and deploying smart cities centered on citizens. This approach can help meet the needs of citizens by using advanced technology and methodologies such as gamification and AI. Additionally, the behavior and personality of citizens are considered in order to tailor dynamic interfaces. Readers of this book can learn about the challenges and opportunities of creating and managing a smart city and the implications for citizens and businesses. Furthermore, they will gain a deeper understanding of the technologies, strategies, and initiatives used to make smart cities more efficient, livable, and sustainable, as well as insight into the future of smart cities. Edmonton, Alberta, Canada March 2023

Dr. Aminah Robinson Fayek

Dr. Aminah Robinson Fayek is the Vice-President (Research and Innovation) at the University of Alberta, Canada, and a tenured Professor with the Hole School of Construction in the Department of Civil and Environmental Engineering. Dr. Robinson Fayek has received recognition for her excellence in teaching, research, innovation, and partnership. She is also a respected member of Alberta’s construction industry and an internationally recognized expert in fuzzy logic and fuzzy hybrid modeling techniques for intelligent decision support in the construction industry.

Foreword by Qipei (Gavin) Mei

I have had the privilege of working closely with Dr. Pedro Ponce, an expert in the field of Industry 4.0 and smart cities. As a researcher in smart and resilient cities myself, I am constantly amazed by the innovative ideas and projects he is spearheading. The insights shared in this book are invaluable for anyone interested in the development of smart cities. The world’s population is growing unprecedentedly, leading to an urgent demand for sustainable and efficient urban environments. Smart cities have emerged as a solution to this challenge, with the help of data-driven methods and AI technologies. This book delves into the role of these technologies in driving smart cities, featuring contributions from leading experts in the field. The book covers a range of topics, including the challenges and opportunities of data-driven urban development, ethical considerations in using AI in smart cities, and the impact of smart cities on the economy, environment, and society. By collecting and analyzing vast amounts of data in real time, smart cities can optimize the use of resources, enhance the quality of life for their citizens, and improve the overall resilience of communities. As we confront global challenges such as climate change, poverty, and pandemics, the potential of data-driven methods and AI technologies to shape the future of smart cities is more important than ever. This book is an essential resource for anyone seeking to understand the intersection of data, AI, and smart cities and the transformative impact they can have on the world. Edmonton, Alberta, Canada March 2023

Qipei (Gavin) Mei

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Preface

This book illustrates the correlation between connected citizens and smart communities and cities. It delves into the fundamental element of smart communities, the concept of a unified smart community and city, and existing examples of smart communities and cities. Moreover, this book considers demand-side management and transactive energy strategies for smart cities. The concept of ‘smart cities’ has been growing in popularity in recent years as cities across the globe strive to use technology to improve their efficiency and sustainability. Smart cities aim to provide citizens with a better quality of life through technology and data to enhance services, reduce energy consumption, and create a more connected urban atmosphere. This book discusses the idea of ‘smart citizens’ and how citizens can be instrumental in forming smart cities. It will demonstrate how households can act as the bedrock for smart communities and the notion of a consolidated smart community and city. Furthermore, this book shows existing illustrations of smart cities and communities and the potential of demand-side management and transactive energy strategies. This book aims to provide a comprehensive overview of smart cities and how connected citizens can be essential to their success. It is intended to provide readers with a better understanding of the opportunities and challenges associated with this new urban model. It is hoped that readers will appreciate the potential of smart cities to transform urban life and the necessity of connected citizens in making this transformation a reality. This book targets readers interested in smart cities, connected citizens, and the development of smart communities. It will be of particular interest to urban planners, engineers, and public policymakers. Additionally, those interested in the prospects of technology to improve urban life and the opportunities and challenges associated with smart cities will find this book beneficial. This book will outline the concept of smart cities and the role of connected citizens in their formation. It will outline the bases for smart communities, current examples of xi

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smart communities and cities, and demand-side management and transactive energy strategies for smart cities. By providing readers with a comprehensive understanding of smart cities and connected citizens, it is intended that this book will assist in informing and guiding the formation of smart cities worldwide. Monterrey, Mexico Berkeley, USA Monterrey, Mexico Montréal, Canada Monterrey, Mexico Mesa Arizona, USA Mesa Arizona, USA Montréal, Canada Montréal, Canada Montréal, Canada March 2023

Pedro Ponce Therese Peffer Juana Isabel Mendez Garduno Ursula Eicker Arturo Molina Troy McDaniel Edgard D. Musafiri Mimo Ramanunni Parakkal Menon Kathryn Kaspar Sadam Hussain

Acknowledgements

The authors would like to acknowledge the Institute of Advanced Materials for Sustainable Manufacturing and Tecnologico de Monterrey’s financial and technical support in producing this work. Also, Arizona State University provides financial and technical support for the development of the content of this book under Grant No. 1828010. In addition, this effort was supported by funding from the Canada Excellence Research Chairs Program and the Tri-Agency Institutional Program Secretariat (Grant CERC-2018-00005), the Natural Sciences and Engineering Research Council of Canada (Discover Grant RGPIN 2020-06804), and the Fonds de recherché du Quebec: Nature et technologies (FRQNT) Doctoral Research Scholarship.

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How to Read This Book and Some Comments

The book presents the Smart C3 model, an innovative concept combining Smart Citizens, Communities, and Cities. It proposes that connected citizens are smart citizens. Also, it describes the smart households which can build more efficient and resilient communities. Thus, smart communities and cities can be integrated to create a unified vision for the future. Real-world examples of smart cities are discussed in Chap. 6, which covers how cities use technology to improve urban life, and Chap. 7 covers Demand Side Management and Transactive Energy Strategies for smart cities, which can be used to increase energy efficiency and reduce energy costs. The Smart C3 model is essential for cities and communities looking to become more sustainable and efficient. By understanding the concept, cities and communities can develop more effective strategies for improving their urban environments. Given below are some specific topics that can be learned in each chapter. To learn more about the Smart C3 model and connected citizens, Chaps. 1 and 2 would be great starting points. These chapters cover the concept of Smart Citizens, Communities, and Cities, and discuss how connected citizens can be considered smart citizens. To learn more about the keystones of smart communities, Chap. 3 would be the most informative. This chapter covers Smart households, which are the keystones of smart communities, and it also discusses how smart households can be used to build more efficient and resilient communities. To learn more about smart communities and cities as a unified concept, Chaps. 4 and 5 are the best choice. These chapters focus on smart communities and cities and how they can be joined to create a unified vision for the future. To learn more about current smart communities and cities, Chap. 6 would be the most informative. This chapter covers real-world examples of smart cities and how they are being used to improve current urban life. Finally, to learn more about Demand Side Management and Transactive Energy Strategies for smart cities, Chap. 7 is the best choice. This chapter covers how smart cities can use these strategies to increase energy efficiency and reduce energy costs.

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Contents

1 The Smart C3 Model—Smart Citizens, Communities and Cities . . . . 1.1 The Needs of Citizens and How to Support Their Needs . . . . . . . . . . 1.1.1 Sustainable Development Goals (SDG) . . . . . . . . . . . . . . . . . . 1.1.2 Social Progress Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.3 World Happiness Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.4 ISO 37120 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 How Smart Citizens Build Smart Communities . . . . . . . . . . . . . . . . . 1.3 Smart Communities—Demands and Needs . . . . . . . . . . . . . . . . . . . . . 1.3.1 Tecnologico de Monterrey, Mexico City Campus as a Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 How Smart Communities Create Smart Cities . . . . . . . . . . . . . . . . . . 1.4.1 Smart Community Case: From Smart Grid to Smart City in Salzburg, Austria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.2 Another Type of Community: Gamified Communities . . . . . 1.4.3 Another Type of Community: Smart Factories . . . . . . . . . . . . 1.5 Smart Cities—Demands and Needs . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1 Digital Twins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.2 The Carbon-Neutral Economy in the Context of Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.3 The Metaverse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Data-Driven Techniques and AI in Smart Cities . . . . . . . . . . . . . . . . . 1.6.1 Data-Driven Techniques and Optimization Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7 Privacy Regulations in Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Connected Citizens are Smart Citizens . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Personality and Behavior for Building a Citizen Classification System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 The Role of Gamification and Serious Games as a Social Connector Between Citizens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 3 4 5 7 8 11 13 16 18 18 20 23 28 32 33 34 34 37 38 43 43 48

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2.3 Learning More About Citizens Through Social Networks . . . . . . . . 2.4 Learning More About Citizens Through Wearables, Virtual Reality, and Augmented Reality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Smart Social Interfaces Using AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Ethics for Social Cyber and Physical Systems . . . . . . . . . . . . . . . . . . 2.6.1 Artificial Intelligence Ethics . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Keystone for Smart Communities—Smart Households . . . . . . . . . . . . . 3.1 Energy, Water, Housing, Security, Environment, Commerce, and Utilities into Smart Households . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Sensing in Smart Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Smart Homes and Data Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Controlling Smart Houses and Buildings . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 BIM and the Control in Smart Houses and Buildings . . . . . . 3.5 Seniors and People with a Disability Living in Smart Houses . . . . . 3.5.1 What is the Place of Seniors and People with a Disability in Smart Cities? . . . . . . . . . . . . . . . . . . . . . . . 3.5.2 How are the Seniors and People with a Disability Learning Process Through Technology? . . . . . . . . . . . . . . . . . 3.5.3 How Accessible are Learning Opportunities for Seniors and People with a Disability in Smart Cities? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.4 What does the Job Market Look Like for Seniors and People with a Disability in the Future? . . . . . . . . . . . . . . 3.5.5 What are Some Potential Work Opportunities for Seniors and People with a Disability Who Should Look at Investing Their Time to be Ready When the Time Comes? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Babies and Children Socializing in Smart Houses . . . . . . . . . . . . . . . 3.6.1 What is the Place of Children in Smart Cities? . . . . . . . . . . . 3.6.2 How is the Children’s Learning Process Through Technology? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.3 How Accessible are Learning Opportunities for Children in Smart Cities? . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.4 What does the Job Market Look Like for Kids in the Future? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.5 What are Some Potential Work Opportunities Children Should Look at Investing Their Time in to be Ready When the Time Comes? . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 What Types of Job Opportunities will AI and Technologies Eradicate? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7.1 What Types of Job Opportunities will be Needed in the Metaverse? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contents

4 Smart Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Empowering Social Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 Universities and Industrial Parks as Smart Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2 Healthcare Buildings as Smart Communities . . . . . . . . . . . . . 4.2 Social, Sustainable, Sensing, and Smart Products . . . . . . . . . . . . . . . . 4.2.1 Smart Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Smart Health Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3 Smart Governance Technology . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Smart Megacities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Data from Citizens, Households, and Communities . . . . . . . . . . . . . . 4.4.1 Personal Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.2 Household Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.3 Community Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.4 Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Reconfigurable Megacities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Global Mitigation of Megacities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 Future Trends in Technologies for Smart Cities . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Smart Communities and Cities as a Unified Concept . . . . . . . . . . . . . . . 5.1 Why Do We Need Smart Communities and Smart Cities Nowadays and in the Future? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Interconnected Public Outdoor and Indoor Environments by Connected Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 The Connected Device and Its Interface for Improving the Quality of Life of Citizens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Interfaces for Education Through Social Robotics . . . . . . . . 5.3.2 Interfaces for Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3 Interfaces for Energy Savings . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Fundamental and Supportive Technologies (5G, IoT, ICT, AI, Renewable Energy, Blockchain) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 5G . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Internet of Things (IoT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.3 Information and Communication Technology (ICT) . . . . . . . 5.4.4 Sensing Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.5 Renewable Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.6 Artificial Intelligence (AI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.7 Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Bridging the Gap Between Smart Communities and Cities . . . . . . . . 5.5.1 Architectural and Urbanism Perspective . . . . . . . . . . . . . . . . . 5.5.2 Engineering Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.3 Information Technology Perspective . . . . . . . . . . . . . . . . . . . .

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5.5.4 5.5.5 5.5.6 5.5.7 References

Manufacturing Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . Public Policy Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Educational Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Social Sciences Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . .....................................................

163 164 164 165 166

6 Current Smart Communities and Cities . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Current Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Some Conventional Indicators of Smart Cities . . . . . . . . . . . . . . . . . . 6.3 Types of Certifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 LEED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 ISO 37120, 37122, 37123 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.3 Passivhaus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.4 BREEAM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Proposed Connected Model in Current Smart City IoT . . . . . . . . . . . 6.4.1 Mexico as a Study Case for Medium-Sized Companies for Deploying Renewable Energy . . . . . . . . . . . . . . . . . . . . . . . 6.4.2 Solar Energy Implementation in Manufacturing Industry Using Multi-criteria Decision-Making Fuzzy TOPSIS and S4 Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.3 Energy Simulations for Understanding Building Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.4 Net Zero Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.5 Sustainable Campus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 The Future of Connected Citizens, Communities, and Cities . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

169 169 171 173 174 175 176 177 177

7 Demand Side Management and Transactive Energy Strategies for Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Stakeholders and Their Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Utility Grid, DSOs, and TSOs . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 End-Users/Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.3 Aggregators and Other Service Providers . . . . . . . . . . . . . . . . 7.3 Existing DSM and Transactional Energy Frameworks . . . . . . . . . . . . 7.3.1 Microgrids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Virtual Power Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.3 Energy Hubs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.4 Transactive Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Enabling DSM Through Smart Grids . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Algorithms and Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.1 Grid Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.2 Aggregator Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.3 End-User and Building Perspective . . . . . . . . . . . . . . . . . . . . .

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Contents

7.6 Implementation and Associated Data Requirements . . . . . . . . . . . . . 7.6.1 IoT and Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6.2 Degree of Autonomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6.3 Blockchains and Market Models . . . . . . . . . . . . . . . . . . . . . . . 7.7 Barriers, Potential Solutions, and Future Lines of Research . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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About the Authors

Pedro Ponce is a control system and automation engineer. He did his master’s and doctoral degree in electrical engineering. He has published over 220 research papers, nine chapters, and 17 books. In addition, he has patents in Mexico and the United States of America. His areas of interest are smart grids, microgrids, smart cities, AI, control systems, robotics, manufacturing, power electronics, digital twins, renewable energy, energy management, electric machines, and optimization. He is a research leader of Enabling Technologies for the Development of Advanced Materials from the Institute of Advanced Materials for Sustainable Manufacturing and a professor in the mechatronics department at Tecnologico de Monterrey in Mexico City campus. Therese Peffer is a project manager and researcher at the California Institute for Energy and Environment (CIEE) within the Center for Information Technology Research in the Interest of Society (CITRIS) at the University of California, Berkeley. Therese manages and conducts research in smart building technologies, building-to-grid, demand response, demand flexibility, and smart grid research projects with the objective of creating comfortable and energy efficient livable spaces. She serves as an Associate Director for CIEE and for the CITRIS Climate initiative and is the co-Chair of the annual Behavior Energy and Climate Change conference. She is currently managing the Energy Commission funded EcoBlock and large commercial decarbonization projects and the Department of Energy-funded Brick

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About the Authors

project. Her previous research includes energy consumption displays, thermostats, consumer behavior, and user interface usability research. Juana Isabel Mendez Garduno is an architect with a Master’s in Energy Management and a laureate Ph.D. in Engineering Sciences from Tecnologico de Monterrey, with a doctoral stay at UC Berkeley, who received the best thesis award at TecScience Summit 2023. Her research focuses on building energy simulations, thermal comfort, tailored dynamic interfaces, personalized gamification strategies, and AI-based decision systems as well as parametric architecture applied to the energy savings field. Isabel has around 30 scientific publications and has presented at national and international conferences. She is currently a postdoctoral researcher at the Institute of Advanced Materials for Sustainable Manufacturing under Dr. Ponce’s leadership. Ursula Eicker is the Canada Excellence Research Chair (CERC) in Smart, Sustainable, and Resilient Communities and Cities and Founder of the Next-Generation Cities Institute at Concordia University in Montréal. Professor Eicker’s research interest focuses on zero emission urban transformation. She works on multiple eco-district projects and is building an urban modeling and data platform to assess urban decarbonization strategies. Her team also designs gamification interfaces and web-based digital twins to engage users. She has published 7 books, 24 book contributions, over 100 Peer-Reviewed Papers, and more than 330 Conference Papers.

About the Authors

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Arturo Molina is the head of the Institute of Advanced Materials and Sustainable Manufacturing. He holds a Ph.D. in Manufacturing Engineering from Loughborough University, a University Doctor degree in Mechanical Engineering from Technical University of Budapest, and M.Sc. and BSc. degrees in Computer Science from Tecnologico de Monterrey. Arturo is a member of various prestigious scientific academies and working groups, such as the National Researchers System of Mexico (SNI-Nivel III). He has authored 14 books, over 120 scientific papers, and holds 3 patents and 7 patent solicitations. Additionally, he has founded 3 technologybased companies, including IECOS, Albiomar, and SMES, which provide innovative solutions for manufacturing enterprise systems. Troy McDaniel is an assistant professor at The Polytechnic School. His research is in haptic perception and haptic interface design for human augmentation, with a focus on assistive and rehabilitative technologies for people with disabilities. He received the 2018 and 2019 Top 5% Teaching Award for faculty at the Ira A. Fulton Schools of Engineering, has authored more than 50 peerreviewed publications, and is currently editing a book on haptics.

Edgard D. Musafiri Mimo is a passionate researcher and a product development systems architect and engineer. His research interests contributing to smart cities and technologies integrate automation, machine learning, and cybersecurity for the smart cities and technologies, with an emphasis on data security and privacy in smart tech applications. He possesses a double major engineering degree from Portland State University in electrical (Power and Energy) engineering and mechanical (Design, HVAC, and FEA) engineering, a master’s degree in engineering management focusing on systems engineering from Embry-Riddle Aeronautical University, and a doctorate degree in systems engineering from Arizona State University.

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About the Authors

Ramanunni Parakkal Menon holds a B.E in Chemical Engineering from BITS-Pilani, Goa, an M.Sc. in Energy from Heriot-Watt University, Edinburgh, and a Ph.D. in Energy from EPFL Lausanne. He has expertise in Energy Management, Optimization and Operations Research, Optimal Control, IoT hardware and software, Smart Buildings, Smart Grids, Machine Learning, and Energy system integration. He has worked on the EU Horizon 2020 project “Sim4Blocks” and is now a Postdoctoral Fellow at CERC in Concordia University, Montreal. His research focuses on Model Predictive Control Strategies for Polygeneration Systems and Microgrids, aiming to create smart, sustainable, and resilient communities and cities. Kathryn Kaspar is a Building Engineering Ph.D. candidate in the Department of Building, Civil, and Environmental Engineering at Concordia University in Montreal, Quebec. She obtained her B.Eng. in Civil Engineering from McGill University in 2015 before managing a post-earthquake reconstruction project in Nepal in 2016. Upon returning to North America, she obtained her M.Sc. in Civil Engineering from the University of New Hampshire while working in energy planning and policy at the City of Cambridge, Massachusetts. Her doctoral research focuses on enhancing both occupant comfort and energy efficiency through building energy management and control at the neighborhood scale. Sadam Hussain received his bachelor’s degree in Electrical Engineering from the University of Engineering and Technology Peshawar, Pakistan, in 2015. He worked as a Packet Switching Core Specialist in ZTE-Pakistan from 2015 to 2018. He received his master’s degree in Electrical and Computer Engineering at Pusan National University Busan, South Korea, in 2020. Currently, He is pursuing his Ph.D. degree in the Electrical and Computer Engineering department at Concordia University, Canada.

Acronyms

3D 4D 4IR 5D 5G ABB ACO ADMM AEC AI AMI ANFIS AR A-R ARIMA ASHRAE BCI BEMS BFI BIM BRE BREEAM CCM CCPA CCTV CDC COVID-19 CPS DER DGNB

Three Dimensional Model Time (BIM terminology) Fourth Industrial Revolution Cost (BIM terminology) 5th Generation Wireless Communication Mobile Network Asea Brown Boveri Colony Optimization Alternating Direction Method of Multipliers Architecture, Engineering, and Construction Artificial Intelligence Advanced Metering Infrastructure Adaptive Neuro-Fuzzy Inference System Augmented Reality Autoregressive Autoregresive Integrated Moving Average American Society of Heating, Refrigerating and Air-Conditioning Engineers Brain–Computer Interface Building and Energy Management System Big Five Inventory Building Information Models Building Research Establishment Building Research Establishment Environmental Assessment Method Tecnologico de Monterrey, Mexico City Campus California Consumer Privacy Act Closed-Circuit Television Centers for Disease Control and Prevention Coronavirus Cyber-Physical System Distributed Energy Resources Deutsche Gesellschaft für Nachhaltiges Bauen xxvii

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DHN DLC DR DSM DSO DSSE ECG EEG EMG ENCEVI ENTSO-E EOG ERCOT ETH FEM FSDT GA GDP GDPR GHG GIS GML GML GWAC GWP HEMS HMI HRV HVAC ICT IEA IEEE IMD IoT ISO LAN LEED LGBTQ+ LMS LTS LV MATLAB MEP MILP MINLP

Acronyms

District Heating Networks Direct Load Control Demand Response Demand Side Management Distribution Grid Operator Distribution System State Estimation Electrocardiogram Electroencephalography Electromyography National Survey on Energy Consumption in Private Homes in Mexico European Network of Transmission System Operators for Electricity Electrooculography Electric Reliability Council of Texas Eidgenössische Technische Hochschule Finite Element Models Fuzzy Signal Detection Theories Genetic Algorithms General Data Protection Regulation European Union: The General Data Protection Regulation Greenhouse Gas Geographic Information System Generalized Maximum Likelihood Geography Markup Language Grid-Wise Architecture Council Gallup World Poll Home Energy Management System Human–Machine Interface Heart Rate Variability Heating, Ventilation, and Air Conditioning Information and Communication Technology International Energy Agency Institute of Electrical and Electronics Engineers Smart City Observatory Internet of Things International Organization for Standardization Local Area Networks Leadership in Energy and Environmental Design Lesbian, Gay, Bisexual, Transgender, Queer, + Least Median of Squares Least Trimmed Squares Low Voltage Matrix Laboratory Mechanical, Electrical, and Plumbing Mixed-Integer Linear Programming Mixed-Integer Non-Linear Programming

Acronyms

MIP ML MR MV NASA NFC NIST NLP NSGA-III O&M OPF P2H P2P PCA PCB PDPA PHQ-9 PIPEDA PMU PSO PSSE PV RASS RC RECS RER RES RFID RL ROBOCOV RTP RWTH S1 S2 S3 S4 SCADA SCPS SDG SE SECTEI SG SGAM SMS SO

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Mixed Integer Programming Machine Learning Mixed Reality Medium Voltage National Aeronautics and Space Administration Near-Field Communication National Institute of Standards and Technology Natural Language Processing Non-Dominated Sorting Genetic Algorithm Operation and Maintenance Optimal Power Flow Power-to-Heat Peer-to-Peer Principal Component Analysis Printed Circuit Board Personal Data Protection Act Patient Health Questionnaire-9 Personal Information Protection and Electronic Documents Act Phasor Measurement Units Particle Swarm Optimization Power System State Estimation Photovoltaic The California Residential Appliance Saturation Study Thermal network models Residential Energy Consumption Survey Renewable Energy Resources Renewable Energy Sources Radio-frequency identification Reinforcement learning Teleoperated robot Real-Time Pricing Rhine-Westphalia Technical Sensing Smart Sustainable Social Supervisory Control and Data Acquisition Social Cyber-Physical Systems Sustainable Development Goals State Etimation Secretary of Education, Science, Technology, and Innovation Serious Games Smart Grid Reference Architecture Model Short Messaging Services Sytem Operator

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SPI TE TEF TOPSIS TOU TRNSYS TSO USA USGBC UX VPP VR WAN WHR WLS WWWW XML

Acronyms

Social Progress Index Transactive Energy Transactive Energy Frameworks Technique for Order of Preference by Similarity to Ideal Solution Time-of-Use Transient System Simulation Tool Transmission Grid Operator United States United States Green Building Council User Experience Virtual Power Plants Virtual Reality Wider Area Network World Happiness Report Weighted Least Squares World Wide Wireless Web Extensible Markup Language

Chapter 1

The Smart C3 Model—Smart Citizens, Communities and Cities

1.1 The Needs of Citizens and How to Support Their Needs The citizens live in a particular city with several needs to fulfill [1]. Each individual has needs that Maslow [2, 3] has pointed out in his five hierarchical levels of needs in which the city can support those needs and enhance the citizens’ Quality of Life (QoL) (See Fig. 1.1). Furthermore, reports such as the Social Progress Index [4] measure how the city helps to support basic human needs; it provides the foundations of well-being and brings opportunities to its citizens. It focuses on human experiences to measure the QoL living in a country. Besides, the World Happiness Report [4] evaluates six key factors to measure the happiness of each country. In that sense, standards, such as ISO 37120, look to improve the citizens’ QoL through nineteen core indicators [5]. Moreover, by 2030, the Sustainable Development Goals (SDGs) aim to address global challenges such as inequality, climate change, and environmental degradation [6]. Since 2012, the Sustainable Development Solutions Network has published the World Happiness Report (WHR) based on how people evaluate their lives. In 2022 [4], they reported that the COVID-19 outbreak brought on the positive side of support and benevolence for the pain and suffering that this pandemic caused. Moreover, Maslow proposed a hierarchy of needs that a satisfied individual must fulfill [2]. He depicted five hierarchical levels: 1. Physiological needs: Biological requirements are fulfilled for humans to survive, such as food, water, sleeping, clothing, shelter, and sex. 2. Safety needs: Protection requirements or a safety-seeking mechanism when individuals’ senses are disturbed. Thus, individuals seek personal security, health, employment, resources, and property. 3. Love and belonging needs: The individuals seek friends, affectionate relations with people, and a sense of connection that motivates behavior.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 P. Ponce et al., Data and AI Driving Smart Cities, Studies in Big Data 128, https://doi.org/10.1007/978-3-031-32828-2_1

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1 The Smart C3 Model—Smart Citizens, Communities and Cities

Fig. 1.1 Maslow’s hierarchy of needs

4. Esteem needs: Individuals looking for a desire for a stable and high evaluation of themselves, for self-respect, self-esteem, and the esteem of others. 5. Self-actualization: Self-fulfillment and personal growth. The individuals do what they are fitted for. According to Maslow’s hierarchy of needs, Shen et al. [7] found that higherincome individuals influence others toward energy-saving behavior. Householders tend to seek high-ordered needs under self-actualization after lower-ordered needs like physiological and security needs are met. As a result, higher-income people prefer investing in and understanding energy-saving appliances’ effects. • The physiological needs are supported by the indicators of Population & Social conditions, Housing, Solid Waste, Energy, Water, Wastewater, and SDG 1 (No poverty), 2 (No hunger), 6 (Cleaned water and sanitation), and 7 (Affordable and clean energy). • The environment and climate change support the safety needs, economy, education, safety, health, and financial indicators, and the SDG 3 (Good health and wellbeing), 4 (Quality education), 5 (Gender equality), 8 (decent work and economic growth), and 13 (climate action). • The three needs are supported by SDG 9 (industry, innovation, and infrastructure), 10 (reduced inequalities), 11 (sustainable cities and communities), 12 (Responsible consumption and production), 14 (life below water), 15 (life on land), 16 (peace, justice, and strong institutions), 17 (Partnerships for the goals). The governance, telecommunication, transportation, urban planning, urban/local agriculture, and food security indicators support the love and belonging needs, and esteem needs. The recreation and sport & culture indicators support self-actualization. • In [3], they suggest using game elements in natural environments or for learning purposes, known as gamification, in Maslow’s hierarchy of needs. The third level

1.1 The Needs of Citizens and How to Support Their Needs

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uses collaboration and competition game elements between individuals. The fourth level considers motivational tools such as badges, points, levels, or leaderboards. The fifth level uses the epic meaning as a motivational tool.

1.1.1 Sustainable Development Goals (SDG) In 2015, the 2030 Agenda for Sustainable Development arose to provide a shared blueprint of seventeen SDGs and thus bring peace and prosperity worldwide. Some of the targets of each SDG include: 1. No Poverty: Reduce at least half the population living in poverty. 2. Zero hunger: Finish all forms of malnutrition and ensure sustainable food production systems that increase productivity and production. 3. Good health and well-being: Decrease the mortality ratio to less than 70 per 100,000 live births. 4. Quality education: Ensure complete accessible, equitable, and quality education to increase the number of the population with relevant skills. 5. Gender equality: End all forms of discrimination, violence, and harmful practices. 6. Clean water and sanitation: Improve the water quality and achieve global, affordable, safe, and equitable access to drinking water. 7. Affordable and clean energy: Guarantee worldwide access to inexpensive or low-cost, safe, reliable, and modern energy services and improve the energy efficiency and the share of renewable energy. 8. Decent work and economic growth: Accomplish higher levels and gradually improve economic productivity through diversification, technological advancement, and innovation. 9. Industry, innovation, and infrastructure: Accrue access to small-scale industrial and other enterprises and promote inclusive and sustainable industrialization by developing quality, reliable, sustainable, and resilient infrastructure. Besides, developing countries encourage household technology development, research, and innovation. 10. Reduced inequalities: Encourage and support social, economic, and political inclusion by ensuring equal opportunity irrespective of age, sex, race, disability, origin, ethnicity, religion, or financial status. 11. Sustainable cities and communities: Guarantee affordable, adequate, and safe housing, transport, and public spaces; and strengthen national and regional urban planning. Besides, provide essential services and upgrade slum communities. 12. Responsible consumption and production: succeed in sustainable management and efficient use of natural resources. 13. Climate action: Include climate change actions into national policies, strategies, and planning by improving education, awareness, and capacitation to strengthen resilience and adaptive response to climate-related hazards.

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Fig. 1.2 The top ten of the overall countries’ total progress towards achieving the 17 SDGs

14. Life below water: Reduce and prevent marine pollution, manage and protect marine and coastal ecosystems. 15. Life on land: Restore degraded land and soil, ensure the mountain ecosystems’ conservation, and combat desertification. 16. Peace, justice, and strong institutions: Diminish all forms of violence, abuse, exploitation, and trafficking and ensure equal access to justice for all. 17. Partnerships for the goals: this goal allies with the finance, technology, capacity building, trade, and systemic issues topics to help achieve the other 16 goals. The SDG developed its report, and the overall score of 165 countries out of 193 United Nations Member countries was 66.8. The first place was from Finland with a score of 85.9, and the last place was from the Central African Republic with a score of 38.3. Figure 1.2 depicts the top 10 countries in the ranking of the SDGs [8].

1.1.2 Social Progress Index The Social Progress Index (SPI) is a measure that provides a holistic and outcomebased measure of a country’s well-being. This Index focuses on human experiences rather than economic performance. The 2022 SPI ranked 169 countries on social progress [9]. Its framework uses three core elements with four components in each core element [10]: • Basic needs – – – –

Nutrition and Basic Medical Care Water and Sanitation Shelter Personal Safety.

1.1 The Needs of Citizens and How to Support Their Needs

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• Foundations of well-being – – – –

Access to Basic Knowledge Access to Information and Communications Health and Wellness Environmental Quality.

• Opportunity – – – –

Personal Rights Personal Freedom and Choice Inclusiveness Access to Advanced Education.

The top ten countries with the highest social progress indexes are displayed in Fig. 1.3. Norway ranks first place. All the top 10 countries are high-income [9]. Besides, in all the countries, the Opportunity core had a lower percentage than the other two core drives due to the elements this core analyzed, such as personal rights in terms of political rights, freedom of expression and religion, access to justice, and property rights for women. The other element was individual freedom and choice, which analyzed the vulnerable employment, early marriage, satisfying demand for contraception, perception of corruption, and young people not in education. The inclusiveness relates to accepting homosexuals, discrimination and violence against minorities, and equality of political power by gender, socioeconomic position, and social group. The last element is access to advance education related to the expected years of tertiary education, women in advanced schooling, quality weighted universities, citable documents, and academic freedom. Worldwide the inclusiveness element was lower in the whole framework [10].

1.1.3 World Happiness Report The WHR measures well-being in 146 countries through three indicators: life evaluations, positive emotions, and negative emotions [4]. Figure 1.4 depicts the top 10 happiest countries. This figure includes seven variables: 1. GDP per capita considers the GDP time series from 2020 to 202. The GDP was adjusted to constant 2017 international dollars. 2. Healthy life expectancy: Uses data from the World Health Organization Global Health Observatory data repository for 200, 2010, 2015, and 2019. 3. Social support: Considers the national average of yes and no responses to the Gallup World Poll (GWP) question:

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1 The Smart C3 Model—Smart Citizens, Communities and Cities

Fig. 1.3 Top 10 countries with the highest indexes of social progress

If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not? 4. Freedom to make life choices: Analyzes the national average responses (satisfied or dissatisfied) to the GWP question: Are you satisfied or dissatisfied with your freedom to choose what you do with your life?

1.1 The Needs of Citizens and How to Support Their Needs

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5. Generosity: The residual of regressing the national average of GWP responses on log GDP per capita to the question: Have you donated money to a charity in the past month? 6. Perceptions of corruption: The average binary answers the questions: Is corruption widespread throughout the government in this country or not? Is corruption widespread within businesses in this country or not?” 7. Dystopia (1.83) + residual: This element allows every country has a positive or at least zero contribution from each of the previous six factors. From 2019 to 2021, the evaluation equaled 1.83 on a 0 to 10 scale.

1.1.4 ISO 37120 The International Organization for Standardization (ISO) deployed ISO 37120: Sustainable cities and communities—Indicators for city services and quality of life [11].

Fig. 1.4 Top 10 of the happiest country from 2019 to 2021

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1 The Smart C3 Model—Smart Citizens, Communities and Cities

The Standard ISO 37122 defines the smart city as: A city that increases the pace at which it provides social, economic, and environmental sustainability outcomes and responds to challenges such as climate change, rapid population growth, and political and economic instability by fundamentally improving how it engages society, applies collaborative leadership methods, works across disciplines and city systems, and uses data information and modern technologies to deliver better services and quality of life to those in the city (residents, businesses, visitors), now and for the foreseeable future, without the unfair disadvantage of others or degradation of the natural environment.

The ISO 37120 standard works as a template for Smart City development because it frames the performance analysis of urban areas, definitions, and the measures required for reporting, comparison, and benchmarking. Table 1.1 depicts the nineteen indicators that ISO 37120 standards consider to monitor city performance progress to improve the quality of life over time, learn from each other, and aid policy development. Furthermore, ISO 37122 (Sustainable cities and communities—Indicators for smart cities) [12] and ISO 37123 (Sustainable cities and communities—Indicators for resilient cities) [13] complement ISO 37120 to specify and establish indicators with definitions and methodologies that measure and considers features that increase the pace at which cities improve their social, economic and environmental sustainability outcomes. There are other ways to define a smart city depending on the structure, function, focus, semiotics, stakeholders, and outcomes. Thus through a modular structure is possible to define a smart city [14]. Some examples are: • Architecture that senses environmental data from citizens for sustainability • Systems to monitor technological information from governments for Quality of Life. Thus, either you can define the smart city as ISO 37120 or provide your definition based on Fig. 1.5. Figure 1.6 depicts how the standards and development goals respond to each level of the Maslow pyramid.

1.2 How Smart Citizens Build Smart Communities IGI [15] defines a smart citizen as: An individual who uses technology to engage in a smart city environment, address local issues and participate in decision-making.

1.2 How Smart Citizens Build Smart Communities Table 1.1 ISO 37120 indicators Indicator Description 1. Economy

3. Energy

5. Finance

7. Health

9. Population and social conditions

11. Safety

13. Sport and culture

15. Transportation

17. Urban planning

19. Water

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Indicator

Description

This indicator calculates the unemployment rate to measure the city’s unutilized labor supply It analyzes the kWh divided by the total population per year

2. Education

Reflects the financial resources available in daily operations and how much money is destined for paying the debts Assesses life expectancy relate to health conditions

6. Governance

Percentage of city population living below the international poverty line The number of police officers per 100,000 population The number of cultural institutions and sporting facilities per 100,00 population Kilometers of high-capacity public transport system per 1000,000 population Measures the amount of green area, natural and semi-natural, parks, and other open spaces (hectares) per 100,000 population Percentage of city population with potable water supply service

10. Recreation

It addresses the educational opportunity among the school-aged population The indicator evaluates the concentration of the fine particulate matter (PM2.5) Reflects the financial resources available in day-to-day operations and how much money is destined for settling debts Measures the percentage of the city population living in inadequate housing Measures the square meters of public indoor recreation space per capita

4. Environment and climate change

8. Housing

12. Solid waste

14. Telecommunication

Percentage of households served with solid waste collection Number of internet connections per 100,000 population

16. Urban/local agriculture and food security

Total urban agricultural area per 100,000 population

18. Wastewater

Percentage of city population served by wastewater collection

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Fig. 1.5 Modular structure to define smart city

Fig. 1.6 Relationship between Maslow’s hierarchy of needs, the three core elements of social progress, and what the city offers through ISO 37120 and the sustainable development goals

Furthermore, citizens represent the linkage, the leading actor, and the sensor to the community, as they actively interact with it [1, 16]. Through their electronic devices, such as phones or tablets, they can provide information about their and the city’s needs; thus, they become participatory sensors [17]. Citizens want to become actors in the city. Thus, they provide feedback to the local authorities or report problems in the city they are facing. Social media has become an essential tool for citizens to become this critical linkage to sensor the city. Besides, they use these digital platforms to communicate with the government, so having access to free Wi-Fi in public spaces is relevant. Citizen participation provides opportunities to become part of the decision-making processes in urban planning. Through participatory sensing, the citizens share their concerns and happiness using smartphones [17]. Then, this smartphone becomes a sensor that communicates and extracts data to monitor citizens’ QoL, environment, transportation, mobility, and energy waste, among others. Moreover, in terms of technology, smart citizens interact in their homes through their devices and their household appliances. These homes then interact with the community and the community with the city.

1.3 Smart Communities—Demands and Needs

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Furthermore, in [18], they analyzed the Mimeo project from Bristol, UK, a medium-sized city. This five-year project came from the Horizon 2020 project. It focused on increasing the quality of life for citizens across Europe through innovative technologies to co-create smart city services with citizens and thus, replicate this success in other cities. Besides, they indicated that there are two models for learning: the banking model, and the creative citizens model. • Banking model: The citizen is usually considered a “service user” where the technology improves the existing infrastructure and systems. This model considers the citizen as a consumer, not a co-creator of services. Thus, the citizens must learn skills that the city leaders and technology deploy to participate in the smart future visions. In other words, technology and innovation are the core approach to the city. Therefore, technology-focused events, such as hackathons, offer opportunities to create new products and services. Thus, citizens are containers waiting to be filled with the technology, knowledge, and skills to understand and live in a smart city. • Creative citizens model: Opposite to the banking model, the citizens are the core approach to the city. Thus, learning enables citizens to understand emerging digital technologies to change the smart city. The citizens are active learners and involve a profound understanding of the current cultural knowledge and experience of the city.

1.3 Smart Communities—Demands and Needs Li et al. [19] define a community as a set of smart homes, amenities, and green areas where residents socially interact with their neighborhoods. These homes are in the same geographic region. They are virtually connected to the same geographic region by a powerline communication technology, wireless communication technology such as Bluetooth or Wi-Fi, phone line communication, and technology that requires dedicated wiring such as Ethernet. Thus, Łucka [20] believes that a community has these characteristics: • Be walkable where people can walk and bike everywhere: A walking distance has a 1,500 to 2,000-foot radius or a quarter to a half-mile (0.5–1 km). • Be organized around public transportation. • Be compact and autonomous, where neighbors can walk or bike everywhere. • Enhance social relationships through interconnected networks of streets and public spaces. • Be safe by noticing strangers and potentially unwelcome them if they plan to commit a crime. • Be inclusive and provide places of work, leisure, and shopping near the community.

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Fig. 1.7 Community’s IEEE standards

However, the concept of a smart community is developing because there is no clear separation between a smart community and a smart city. What is clear is that to transition a community into a smart one, the inhabitants must adopt smart product usage, management methods, and a community philosophy with multinetwork integration. Three stages involve this transition. 1. Initial stage: The community interacts with itself by using smart products and providing management, e-commerce service, and health services. 2. Development stage: A smart platform application reaches the community. This application gathers multiple mobile apps to make the community smarter. 3. Improvement stage: Standards establish a service system for the smart community. This stage continuously improves and develops. Figure 1.7 displays the IEEE Standards suggested enabling intelligent city technologies from the smart community [21]. There are different types of communities: residential, industrial, commercial, mixed-use, and academic, among others. However, in all of these communities, to be smart, there must consider four essential characteristics [17]: • Sustainability: the smart community provides services and the resources needed to fulfill the community’s needs. • Resilience: The smart community accurately reacts when changes come out. The community knows how to adapt and change dynamically. • Empathy-driven proactive intelligence: the community uses artificial intelligence algorithms within smart products or devices to understand and predict the community’s needs. Besides, this type of AI must follow three ethical principles: No harm, justice, and explicability. • Emergent behavior: the behavior continuously adapts and evolves within the community interaction. Hence, this community must profile its inhabitants’ needs through smart products.

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1.3.1 Tecnologico de Monterrey, Mexico City Campus as a Case Study The Tlalpan Innovation Pole project has an inter-institutional, interdisciplinary, and international approach that centers its efforts on the citizens by promoting opportunities for development and well-being [22–24]. The Mexico City government and the Secretary of Education, Science, Technology, and Innovation (SECTEI) lead this project in coordination with the Tecnologico de Monterrey, Mexico City Campus (Tec CCM) [24]. Thus, this project has six essential cores [22, 23]: • Education: Innovation, strengthening of high schools, social service, professional practices, dissemination of science. • Health Sciences: Research focuses on diabetes, hypertension, and obesity. There is a Center for Research on Aging, Physical Activity, and Sports. • Sustainable development: water security, food sustainability (water-energyfood nexus), right to the city, university green challenge. • Security and comprehensive risk management: seismology, resilient university program, citizen security. • Technology and innovation: technological management for open innovation, electromobility, biofuels, design, and development of materials, infrastructure for a smart city, and health technologies. • Science and society: internationalization diplomacy and heritage of science, poverty and social exclusion.

Besides, this project considers a group of higher education institutions to sum their experiences and capacities for transforming the urban and social environment [24]. In that sense, a university is a type of community because it involves socio-cultural aspects and provides services such as education, energy, food, health care, public safety, mobility, transportation, energy, water, or other services that the ISO 37120 measures. Universities must move into a smart and sustainable campus to be more inclusive and supportive of the community, environment, and government. Besides, universities must focus on human experiences to enhance the quality of life [16]. In 2015, Tec CCM launched the Sustainable Campus initiative that involved four significant areas related to the ecological campus management, applied research, climate, and environmental education, and community and business projects. In that sense, smart renewable energy systems positively influence the citizens’ well-being through the urban environments [25]. Thus, these systems are critical in generating clean energy and producing zero emissions as they require the integration of the power grid to ensure stability, protection, and operational restrictions. Thus, Pérez et al. [25] suggest using of decentralized power systems to optimize tasks and stabilize power systems due to their flexibility in integrating renewable energy sources and intelligent control centers in power production and distribution. Besides,

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this power grid, to turn into an intelligent grid, must enhance the operation of the legacy high voltage grid, improve grid-customer interaction through smart metering or real-time pricing; and include new distributed grids such as picogrids, nanogrids, microgrids, and active distribution networks. Furthermore, citizens play a relevant role in the intelligent grid interaction because they need to understand the importance of retrofitting or updating their electric delivery methods and how these actions benefit the environment and their economy. • Decentralized Energy System considers three sizes of grids based on small to medium off-grid or grid-connected systems: the picogrid, nanogrid, and microgrid. These grids use renewable energy sources and can operate independently of grid-supplied power. These grids are easily installed and flexible for connecting to the primary power grid. • Picogrid sizes around 1 kW. Mobile phones, laptops, or sensor networks belong to this category [26]. • Nanogrids sizes below 5 kW, and can be interconnected with each other and with other microgrids. These grids are limited to a single building structure considered a primary load. Distributed generators, electric vehicles, or batteries fall into this category [26]. • Microgrids involve small-scale electricity generation that operates in isolation from the national transmission network. Microgrids are available on campuses, such as universities, hospitals, military establishments, and business parks in urban environments [27].

Figure 1.8 depicts a general proposal of the Tec CCM’s smart grid considering three types of grids: • Microgrid: Interconnected loads from each building. • Nanogrid: A single building capable of being self-sufficient through PV arrays and smart loads. • Picogrid: Each building level comprises lighting, appliances, security, and HVAC systems, among others. Furthermore, a sustainable campus at Tecnologico de Monterrey is proposed that offers services such as smart e-bikes [28], solar chargers for low electricity consumption based on solar energy [29], smart greenhouses fed by solar energy to produce food for consumption within the campus [30], healthcare services such as a teleoperated robot called ROBOCOV (see Fig. 1.9) to reduce the COVID-19 impact; this robot can assist for multiple tasks such as disinfecting, massager, detecting face masks, and so on. Besides, it could be possible to regulate internal transportation using electric buses. Robocov is a reconfigurable platform as it is explained in [31].

1.3 Smart Communities—Demands and Needs

Fig. 1.8 Tecnologico de Monterrey as a sustainable smart campus

Fig. 1.9 ROBOCOV: teleoperated robot

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Fig. 1.10 Tecnologico de Monterrey, Mexico city campus masterplan considering the ISO 37120 indicators

Moreover, in 2017, Mexico City suffered an earthquake leading to the destruction of several buildings at Tec CCM. Thus, the campus began its reconstruction [32] and the new urban design considered the inclusion of urban parks and plazas, the mitigation of annual flooding with a cenote, naturally ventilated buildings with automatic operable windows, and the reduction of carbon footprint. Another consideration includes the implementation of renewable sources such as photovoltaic panels. Besides, Fig. 1.10 shows how ISO 37120 applies to this campus.

1.4 How Smart Communities Create Smart Cities Smart Communities transition into Smart Cities by enhancing the interaction between communities. Esri, a global market leader in geographic information system (GIS) software, location intelligence, and mapping provide a Smart Community Information System by establishing four technology tenets [33]:

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1. Planning and Engineering [34]: Support urban and community design that faces change. This tenet balances the needs of people, the environment, and infrastructure. Furthermore, the system helps the government to forecast the impacts of development, adjust to demographic and lifestyle changes, and measure climate change and economic shifts. This system learns and adapt through real-time data to support urban mobility, resiliency, and sustainability. 2. Operational Efficiency [35]: Balances citizens’ resources and applies them to the best locations that benefit the most people, thus, citizen satisfaction improves. This system gathers information in real-time to feed it into performance dashboards for real impact. Therefore, this system maximizes materials and resources while reducing costs. 3. Data-Driven Performance [36]: Smart devices, the Internet of Things (IoT), and cloud computing feed data based on the location of people, vehicles, entourage, and infrastructure. This system reduces costs, and time to act and supports decision-making and policy decisions to enhance the quality of life in each community. 4. Civic Inclusion [37]: This system help government to connect with the community so citizens understand why it happens and what happens where they live. Furthermore, this system provides civic inclusion by supporting and allowing questions that empower people to speak out about their needs.

Moreover, smart communities and smart cities balance the lifestyle by adopting technologies as the GIS because [34–37]: • Is human-centered • Provides human-centered design. • Senses the community through infrastructure and provides constant feedback and adjustments. • Reduces the impact of stress and enhances the community’s responses. • Supports 3D planning for real-world context (digital twins). • Establishes resilient and sustainable communities by providing models, analysis and benchmarks. • Improves responsiveness through operational dashboards. • Improves decision-making and service delivery based on data and analytics. • Provides relevant product information to the community. • Analyzes data to change the pace of government operations. • Integrates autonomous vehicle information, devices, and sensors in the urban environment. • Uses Artificial Intelligence (AI) and machine learning to understand the communities’ patterns to reduce impacts. • Exposes outliers to improve social equity. • Transforms the relationship between the government and community by empowering collaboration.

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Initiatives such as the Smart Cities Initiative from Austria have launched since 2010 funding projects for developing strategies, solutions, and technologies to allow cities and their residents to transition to an energy-efficient quality of life [38]. Furthermore, they launched the Smart City demo projects to target five areas [39, 41]. • • • •

Sustainable greenhouse gas balance. State of the art technologies with high resource and energy efficiency. Smart and system-oriented solutions to optimize energy systems. Distribution of transport volumes over different type of transport connections to move from motorized transport to soft mobility and public transport. • Social and organizational innovations by focusing on users and help them to become energy aware. Thus, this type of initiatives are excellent examples on how by addressing in each city specific goals as greenhouse reductions or energy efficiency they contribute into the building of the smart city.

1.4.1 Smart Community Case: From Smart Grid to Smart City in Salzburg, Austria Salzburg envisioned for 205 a master plan consisting in moving from smart grid to smart city [41]. This project optimizes the district heating systems in compliance with the city planning and development strategies to promote the reduction of non-renewable energy sources. Furthermore, this project complements the energy planning to become a CO2-neutral settlement by indicating that new housing development and 25% of the building stock must consider a smart grid. The implementation consists in adding photovoltaic solar panels or the use of electric vehicles in the smart grid. This project complements the district heating with solar energy, and the Stadtwerk Lehen building uses a thermal solar facility in central storage. Thus, the solar heat pump distributes through low-temperature microgrids to offices, flats, and retrofitted building blocks. Furthermore, a photovoltaic facility provides electricity in shared spaces. The following step consists in defining which communities require renovation.

1.4.2 Another Type of Community: Gamified Communities The community of gamified homes feed this type of community to turn it into a gamified community. Hence, Méndez et al. [1] defined this type of community with the following characteristics:

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• This community should not be bounded in the same physical location but with similar characteristics. • The shared characteristics consist in similar household attributes as square meters, household members, region or climatic zone, and type of home (attached, single, multi-family). Personalization is an important aspect to consider in designing interfaces and applications, as it can enhance user experience and engagement. One way to personalize interfaces is by considering users’ personality traits [42–44]. The OCEAN model, also known as the “Big Five” personality traits, is a widely used framework for describing personality [45, 46]. The openness (O) trait refers to a person’s willingness to explore new ideas and experiences, while conscientiousness (C) refers to the degree of responsibility and organization in one’s life. Extraversion (E) is associated with outgoing, sociable, and energetic behavior, while agreeableness (A) is related to cooperative and empathetic behavior. Finally, neuroticism (N) describes a tendency towards negative emotions, such as anxiety and depression [45, 46]. Research has shown that people with different personality traits have different attitudes towards energy conservation and may have different preferences for learning and adopting new technologies. For example, people high in openness may be more receptive to new features and technologies, while those high in conscientiousness may prefer a clear and organized interface. Similarly, those high in agreeableness may value interfaces that facilitate social interaction, while those high in neuroticism may prefer interfaces that reduce stress and anxiety [42–44]. Thus, Fig. 1.11 depicts a gamified platform where Mexicans can interact with the electricity cost of a community of twelve homes. This example shows that depending on the personality trait, different types of gamified buttons were displayed.

Fig. 1.11 Conceptualization of a gamified community

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1.4.3 Another Type of Community: Smart Factories Since the third industrial revolution, industrial processes adopted computational systems that introduced the capability of having automated factories. Such automation features allowed industries to have more consistency, reliability, and faster development times for specific products, shortening the gap between early planning and mass production stages [47]. With the vast number of interconnected devices (Internet of Things, IoT), data analysis strategies can be adopted to create smart, automated systems fueled by advanced machine learning strategies. In other words, Artificial Intelligence (AI) can be coupled with the generated data to introduce a basic understanding of Industry 4.0 and a collaborative experience between machines and operators [48]. Industry 4.0 has five levels: field level, control level, supervisory level, planning level, and management level [48]. It digitizes and integrates the value chain of the lifecycle of product transformation through an integrated supply network of smart suppliers, connected customers, smart factories, production machinery, smart products, and smart materials interacting and communicating with each other in real-time [49]. Furthermore, smart factories use Cyber-Physical Systems (CPS), the IoT, big data analytics, and cloud data to allow human resources, materials, process controllers, and machines to communicate in real-time [50]. Besides, Chen et al. [51] found that developing energy monitoring and management systems generated potential energy savings in factories. Adenuga et al. [52] proposed using sensors, data, analytics, and software during the energy value chain to improve efficiency and business outcomes and lower maintenance costs by using energy cost as a baseline. Industry 4.0 in its smart factories, deploys information and communications technology (ICT)-based solutions throughout the value chain, depending on the IoT, virtualization, cloud manufacturing, collaborative robots, and AI [53]. Specifically, machine vision systems facilitate automated operations through image processing, including automatic inspection, monitoring of processes, and robotic guidance [54]. For Industry 4.0, the machine vision system is an essential component that supports industrial automation technologies in manufacturing processes by providing sensors that can assist the system in obtaining information, allowing it to evolve from an automated machine to a Cyber-Physical System (CPS) as it moves from Industry 3.0 to Industry 4.0 [53]. Nevertheless, this intercommunication is contradictory because Industry 4.0 focuses on either improving processes or adding special features to their operators (banking model), rather than focusing on the operators and their behavior (creative citizen model). It lacks interactions between operators and manufacturing processes in the sense of better understanding the user type to provide activities or elements that enhance sustainability manufacturing in industry 4.0. In that regard, studies reported that individuals have different personality traits, in which gamification provides tailored game elements and activities to engage individuals in activities to achieve specific goals (see previous Sect. 1.4.2). Currently, the industry has contradictory impacts because the more smart machines and devices are deployed, the more energy consumption and higher resources are

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associated with industrial automation [49]. Thus, a challenge for Industry 4.0 is to respond faster to the need to increase sustainable processes by empowering the operators during their daily activities at the factory [55] and providing comfortable and safe spaces. Méndez [56] proposed a four-step methodology for deploying tailored platform products. First, the target user types and market were identified, second, the most representative characteristics of the user were analyzed. Third, personalized strategies were proposed using artificial intelligence techniques. Fourth, a gamified interface was proposed. consisting in proposing connected products or interfaces between the consumer and the product by profiling the householder. Therefore, connected interfaces can promote interactions between operators and manufacturing processes to obtain sustainability of patterns and profiles and to enhance the management of behavior and sustainable attitudes toward industry 4.0. Therefore, it is imperative to fusion operators with the manufacturing process that leads to a further evolution of industry 4.0 is developed. Some operators adopt technologies that assist them in increasing some physical capabilities like exoskeletons for super strength and visual capabilities like virtual reality or augmented reality [57]. Despite the improvements, there is still a lack of techniques that analyze the operator’s behavior and needs throughout manufacturing processes. The lack of supervisory and corrective actions may hinder key outcomes of the manufacturing process, leading to a decrease in part production/information knowledge, and an increase in waste/emissions/energy consumption. Accordingly, Ruppert et al. [58] have mapped the enabling technologies required to achieve the different types of operators described by Romero et al. [57]. Here, we provide examples of how virtual/augmented/mixed reality and wearable technologies can be applied to assist and monitor manufacturing operators. Figure 1.12 depicts the features of operator 4.0.

1.4.3.1

Assisting the Operator 4.0 Using Virtual, Augmented, and Mixed Reality

Digital manufacturing (DM) refers to creating products and defining manufacturing processes supported by computer-based systems. These systems may include functionalities such as simulations, 3D visualization, and data analytics [59]. Virtualization is among digital manufacturing’s potential applications [60]. For example, augmented and mixed reality (AR and MR, respectively) can enrich the manufacturing operator’s real-world work environment with digital content overlaid via mobile or wearable devices (e.g., tablets and AR/MR headsets, respectively). Alternatively, virtual reality (VR) can immerse the operator in a computer-simulated reality replicating a manufacturing environment, allowing the operator to interact with its elements. These scenarios define the essence of the augmented and virtual types of operator 4.0 [57]. As an example of virtualization, Maldonado et al. [61] proposed an optimized path-planning generator that can assist operators in movement planning in manufacturing environments. A path planning generator requires at least two main modules:

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Fig. 1.12 Features of the operator 4.0

visualization and planning. Although both modules are employed as two different tasks, using the virtualization approach improves the complex architecture of a single module because the path planning problem is simulated in virtual reality. Both modules are temporarily considered a single task. Once the virtual simulation is complete, it is possible to determine the generated path using machine learning techniques and translate it back to the real world [62]. Furthermore, the authors reused embedded devices with limited features (namely, smartphones) to implement the path planning generator, which helps reduce the environmental impact of electronic waste [63].

1.4.3.2

Monitoring the Operator 4.0 Using Wearable Devices

Health wearables allow tracking physiological and behavioral signals of manufacturing operators (e.g., heart rate variability (HRV), respiration, galvanic skin response, electrical muscle activity, and body accelerations) [64]. These parameters can be used to estimate health-related parameters such as stress levels, muscular workload, and physical activity levels using data analytics and machine learning methods. The

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information generated can improve workers’ productivity, occupational health, and safety. Thus, this scenario defines the health operator [57]. While the literature on health wearables in the manufacturing industry is somewhat speculative, some studies use them in real-life scenarios to assess mental workload or stress. For instance, in [65–67], they have used health wearables to investigate the associations between mental stress and short and ultra-short HRV features in real-life scenarios. Furthermore, Castaldo et al. have also used health wearables to investigate the associations between mental workload, performance, and HRV features during a repetitive task [68]. Interestingly, their results suggested that HRV features (a mental workload marker) negatively correlate to performance.

1.5 Smart Cities—Demands and Needs Figure 1.13 shows the IEEE Standards grouped in seven areas (green community network, environmental product assessment, smart metering, smart grid, energy efficiency, renewable energy generation, and energy-efficient communications networking) needed for improving the environment through green technology consensus building. The concept of a smart and sustainable city is based on promoting citizens’ quality of life using technology and data. Hence, this type of city must have the following characteristics [54]:

Fig. 1.13 Green technology consensus building considering the IEEE standards

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• • • •

Citizen Centered Design; Optimal technology deployment; Transparency and efficiency; Residents involved, informed, and connected.

Ramaprasad et al. [14] proposed defining the smart city based on a high-level ontology of 25,200 descriptions (see Fig. 1.5): Smart • Structure: Architecture, infrastructure, systems, services, processes, or personnel. • Function (to): Sense, monitor, process, translate, or communicate. • Focus (+): Cultural, economic, demographic, environmental, political, social, technological, or infrastructural. • Semiotics (+): Data, information, or knowledge. City (by/from/to): • Stakeholders: Citizens, professionals, communities, institutions, businesses, or governments. • Outcomes (for): Sustainability, quality of life, equity, livability, or resilience.

Therefore, a couple of examples of a smart city definition are: • Architecture to sense environmental knowledge from citizens for QoL. • Systems to sense technological data from communities for sustainability. KPMG suggests that a smart and sustainable city should prioritize eight key areas centered around the needs of its citizens [69]. These include telecommunications, healthcare services, transportation, security, buildings, education, tourism, and other services. Each area has specific elements that are essential to consider: • Telecommunications requires broadband access, open standards, and privacy and security. • Healthcare services need electronic medical records, telemedicine, and data analysis. • Transportation requires smart traffic routing, smart parking, and infrastructure planning. Security needs access to multiple data, scalability, and compatibility. • Buildings require sensors and devices, smart design and energy management systems. • Education needs accessibility, collaboration, and motivation. Tourism requires advanced technologies, optimized access to destinations and activities, and smart destinations. • Other services require resource consumption optimization, including water management consumption, Smart Grid for energy use, and waste management systems. There exist multiple indexes that assess the smart city status of various cities, such as the Smart City Index by IMD [70] and the Smart Cities Indexes Report [71]

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developed by a research team from the Institute for Manufacturing, University of Cambridge and the Digital Transformation Research Center-Information Systems Intelligence Lab (Yonsei University). In particular, the Smart City Index evaluated 118 cities based on the UN Human Development Index and identified the top five smart cities in 2021 as Singapore, Zurich (Switzerland), Oslo (Norway), Taipei City (Taiwan), and Lausanne (Switzerland). The cities are evaluated based on two main pillars: Structures and Technology [70]. Thus, the structural pillar measures a city’s existing infrastructure, the level of quality in its education, transportation, healthcare, and other public services. The technological pillar measures a city’s technological provisions and services, for instance, its use of digital technologies, data analytics, and smart city applications. The following list shows five topics that these pillars considers [70]: • Health and Safety – Structures: Basic sanitation meets the needs of the poorest areas. Recycling services are satisfactory. Public safety is not a problem. Air pollution is not a problem. Medical services provision is satisfactory. Finding housing with rent equal to 30% or less of a monthly salary is not a problem. – Technologies: Online reporting of city maintenance problems provides a speedy solution. A website or App allows residents to give away unwanted items easily. Free public wifi has improved access to city services. CCTV cameras have made residents feel safer. A website or App allows residents to monitor air pollution effectively. Arranging medical appointments online has improved access. • Mobility – Structures: Traffic congestion is not a problem. Public transport is satisfactory. – Technologies: Car-sharing Apps have reduced congestion. Apps that direct you to an available parking space have reduced journey time. Bicycle hiring has reduced congestion. Online scheduling and ticket sales has made public transport easier to use. The city provides information on traffic congestion through mobile phones. • Opportunities (Work and School) – Structures: Employment finding services are readily available. Most children have access to a good school. Local institutions provide lifelong learning opportunities. Businesses are creating new jobs. – Technologies: Online access to job listings has made it easier to find work. IT skills are taught well in schools. Online services provided by the city have made it easier to start a new business. The current internet speed and reliability meet connectivity needs. • Activities – Structures: Green spaces are satisfactory. Cultural activities (shows, bars, and museums) are satisfactory.

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– Technologies: Online purchasing of tickets to shows and museums has made it easier to attend. • Governance – Structures: Information on local government decisions are easily accessible. Corruption of city officials is not an issue of concern. Residents contribute to the decision-making of local government. Residents provide feedback on local government projects. – Technologies: Online public access to city finances has reduced corruption. Online voting has increased participation. An online platform where residents can propose ideas has improved city life. Processing Identification Documents online has reduced waiting times. The Smart Cities Index Report, presented in 2022, analyzed 31 cities worldwide [71]. These cities were selected due to easy access to raw data, relevant websites, and the availability of experts. This report analyzed tangible civic applications, smart city infrastructure services (smart streetlights and smart grids), smart city projects, and living labs driven by citizens, the public sector, and companies. This report analyzed eight major areas: 1. Service Innovation: Smart cities must encourage sustainable innovation and offer services that provide citizen-centered decision-making, Barcelona, Seoul, London, and Amsterdam were the 2021 leading smart cities in this field 2. Urban Intelligence: This field uses the Fourth Industrial Revolution (4IR) technologies (IoT, AI, VR, AR, digital twin, metaverse, big data) to solve urban problems. Seoul, Barcelona, Amsterdam, and Helsinki led this field. 3. Urban Sustainability: Energy and Environment had become major fields for smart applications. Cities promote carbon neutrality and technological innovation to align with Sustainable Development Goals. Amsterdam, Copenhagen, Helsinki, and Berlin led this field. 4. Urban Openness: This element reflects the city’s competitiveness within smart city development. Thus, a city is open when the government and all sizes of enterprises and young companies working in collaboration. Taipei, Seoul, New York, and Singapore led this area. 5. Infrastructure Integration: This element connects physical urban infrastructure with digital features. For instance, streetlights and buildings with smart applications and data in the cloud. Thus, integrating both aspects on a platform enhances service innovation. Seoul, Incheon, Barcelona, Taipei, Busan, and Dubai led this field. 6. Urban Innovation: This field relates the ease of creating ecosystems where the 4IR technologies, building, and zoning flourish. 7. Collaborative Partnership: These are mutual collaborative relationships that create and make active ecosystems for enhancing smart city services and infrastructures. Seoul, Moscow, Shanghai, and Singapore led these partnerships. 8. Smart City Governance: Organizational structures that bring together stakeholders to solve common problems in the services offering. This report analyzes

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Fig. 1.14 C3 model conceptualization

smart city strategies and policies focused on smart city development. Thus, New York, Amsterdam, Vienna, and Singapore are at the top of this indicator. Thus, Fig. 1.14 shows the conceptualization of the C3 model. The future of smart cities tends to integrate not only the technological aspects of life but also how they

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integrate with the rest of the indicators and how they will help develop them. Therefore, converging into a city with participation from all its inhabitants can reduce stress from traffic and legal problems and reduce crime and suicide rates. At this moment, this may seem a utopic idea. However, with the right planning and partnerships, as long as the public’s help, integrating technology to achieve this ideal is a matter of time.

1.5.1 Digital Twins The evolution of technology platforms has generated several possibilities to improve the quality of life in smart cities. One of the most advanced technologies implemented is a digital twin based on a virtual (digital) model that can run in real-time. The digital twin concept was created in the 1960s by NASA for Apollo 13’s oxygen tank explosion. Thus, they utilized several simulation tools to assess potential malfunctions and augmented a tangible representation of the spacecraft by incorporating digital elements [72]. Thus, the information coming from the digital twin can be evaluated to make decisions about the current and future conditions in the city. Digital twins are models that can represent a system’s partial or complete performance. Moreover, they can be linked with physical variables from the real world. They can integrate signals from different sources and analyze them to create a complete model by data fusion techniques. Some digital twins can include AI algorithms to learn online, so they update all their actions continuously; see Fig. 1.15 to observe the structure of a digital twin and the support technologies.

Fig. 1.15 Support technologies to develop a digital twin

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Fig. 1.16 Digital twin implemented in several sectors in a smart city

Moreover, a digital twin can learn from another digital twin using historical data. Digital twins were defined by NASA [73] to have digital models that could replicate the physical ones. The high-fidelity models developed in digital twins are good enough to be implemented in complex systems. Usually, digital twins are based on mathematical models that can be deployed in embedded systems, so the digital twin can get information from sensors that are monitoring the city by wireless technology. These monitoring signals can be used to detect or predict fault conditions in the city. Digital twins can be implemented in different city sectors; for instance, they can be in the energy sector to implement nano-grids, micro-grids, and smart grids. Thus, renewable energy can be incorporated to provide electrical energy in the city. Besides, the energy response-demand system can be enhanced in the virtual model and deployed in the physical electric grid. Besides, they can be installed in other sectors, such as construction or transportation. Some countries have developed digital twins to forecast or plan the city’s growth. Figure 1.16 shows the implementation of the digital twin in several smart city sectors. The complete model in each sector can be integrated to make decisions about the city. For example, green spaces can be monitored for their acceptability and adoption to be evaluated in the digital twin model and then constructed in the city [74]. Some models have already been created to be used as the fundament elements in a digital twin structure. For instance, 3D models created in the 80s can be used to design a strategy for city planning. In the city of Helsinki, it was created a 3D model first and then enhanced to be used as a tool for making decisions about the

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Fig. 1.17 Digital twin interaction during a disaster in a smart city

city [75]. To deploy better, the digital model’s specific standards are established; for instance, 3D models use the CityGML, a Geography Markup Language (GML), for modeling the transport and storage of geographic information is used the XML [75]. Hence, digital twins for smart cities could have the following characteristics: accurate mapping, real virtual interaction, software definition, and intelligent feedback [76]. To get feedback from the citizens is used an iOS AR mobile app called CitySnap. Thus, the information about the city’s infrastructure is updated using this feedback so the models can be updated and evaluated using the information from citizens [76]. One of the most critical representations models using AI is Singapore, which was cloned entirely into an interactive 3D tour in which an interactive 3D tour is possible. Now the company Vizzio launched the model in the Metaverse for gamification and leisure activities [77]. Since digital twins in smart cities have achieved complex goals, more cities are developing digital twins, such as Renne’s metropole, that develop a 3D model to foolproof urban development that also includes the needs of the citizen. On the other hand, when a disaster affects a community, digital twins can help to organize a better recovery scheme [78]. For example, all the sensors and cameras installed in a city can be used to create an evacuation plan that can run online or predict future conditions to avoid complex situations in the city. Besides, it is possible to use an interactive system that connects the digital twin to rebuild homes or sections of the city that could be affected by the disaster. This response scheme can save citizens from dangerous conditions and reduce economic losses. Figure 1.17 depicts how a digital system based on models can interact during disasters.

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IMPORTANT NOTE An important differentiation between digital twins and finite element models (FEM) is that, although both of them represent physical systems in a digital format, they differ in the following [79]: • Scope: Digital twins represent entire physical systems, including their behavior and performance over time, whereas FEM focuses on specific components or subsystems and their mechanical behavior under different loads and conditions. • Data Sources: Digital twins use data from sources, for instance, simulations or sensors to create an understandable physical system. On the contrary, FEM usually has a limited set of inputs, like material properties and boundary conditions that are analyzed at a specific point or period of time. • Complexity: FEM has a lower level of detail in comparison to digital twins. Furthermore, digital twins employ machine learning techniques to deploy more detailed models of the physical system with a higher accuracy due to the faster response, rather than the FEM. • Time Dimension: Digital twins are real-time models that provide a dynamic view of the modeled system. On the other hand, FEM usually uses static models that represent a snapshot of the system’s behavior under specific conditions. BIM models can be a starting point for creating a digital twin. By adding sensors and other data sources to a BIM model, it is possible to create a more comprehensive and detailed representation of the physical asset that can be used for real-time monitoring, analysis, and optimization [80]. Another relevant distinction between digital twins and building information models (BIM) is that, although both of them use digital technologies to build models of physical assets, they differ in how BIM employs 3D modeling software to depict a digital form of the building project considering its structure, mechanical, electrical and plumbing systems, with technical datasheets, list of material, a budget list, and scheduling information [81]. On the contrary, digital twins are dynamic, real-time models that simulate and optimize the performance of the building [82]. Hence, a BIM model provides deep detail of the project construction; whereas, digital twins provide that detailed information in real-time. Thus, any changes made in the physical building are reflected in real-time in the digital twin model; however, the BIM model needs to be rebuilt to consider the changes done at the site. Therefore, digital twins have an automatic response and the BIM model requires an individual to modify the model.

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Digital twins, conventional 3D models, and SCADA ((Supervisory Control and Data Acquisition) computer systems have some elements in common. They are all used to improve the efficiency, safety, and reliability of industrial processes and systems. Besides, these tools use digital technologies to represent and manipulate physical assets and systems. They use sensors, data collection, and analysis to provide insights into the performance of the physical systems they represent. They are also all used to facilitate communication and collaboration between different teams and stakeholders involved in the design, operation, and maintenance of industrial systems. Nevertheless, they differ in their scope and functionality. Digital twins are dynamic, real-time models used to simulate and optimize the performance of physical assets. They use data from sensors and other sources to reflect the current state of the physical system. They can be applied to track behavior, forecast performance, and find areas for development [83]. On the other hand, traditional 3D models are static images primarily utilized for design, visualization, and communication. They do not provide real-time information about the behavior or performance of the physical system they represent. SCADA computer systems are used to monitor and control industrial processes and systems. They use sensors and other devices to collect data about the system and use that data to make decisions and issue commands to control the system’s behavior. While they provide real-time monitoring and control capabilities, they do not provide a detailed virtual representation of the physical system like digital twins do [84]. Therefore, each tool has unique strengths and capabilities that make it useful for applications and scenarios.

1.5.2 The Carbon-Neutral Economy in the Context of Smart Cities In a carbon-neutral economy, carbon emissions are minimized. Any remaining emissions are offset by measures that remove carbon dioxide from the atmosphere, such as reforestation or carbon capture and storage [85]. This can help to reduce the impact of climate change by stabilizing the concentration of greenhouse gases in the atmosphere. Achieving a carbon-neutral economy requires significant changes in energy production and consumption, as well as resources and managing waste. This includes transitioning to renewable energy sources, improving energy efficiency, adopting sustainable production practices, and promoting low-carbon transportation options [86]. Hence, a carbon-neutral economy is an important step toward mitigating the effects of climate change and ensuring a sustainable future for generations to come. Smart cities use technology and data-driven solutions to manage resources efficiently, reduce energy consumption, and minimize carbon emissions [87]. Therefore, a carbon-neutral economy in smart cities must include the following:

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• Renewable energy sources: Smart cities can generate clean energy from renewable sources such as solar, wind, and geothermal energy. This reduces reliance on fossil fuels and helps to reduce carbon emissions [25]. • Energy-efficient buildings: Smart cities can use innovative construction techniques, energy-efficient materials, and technologies to reduce energy consumption in buildings. For instance, through green roofs, smart lighting, and connected thermostats [42, 43]. • Electric vehicles: Smart cities can promote them to reduce carbon emissions and air pollution. This can be achieved through incentives such as charging stations and subsidies [88]. • Urban agriculture: Smart cities can promote urban agriculture by setting up community gardens and rooftop farms, which reduces the carbon footprint of food production and transport [30]. • Smart grids: Smart grids are intelligent electricity networks that use data to optimize energy supply and demand, reducing energy waste and minimizing carbon emissions [89, 90]. • Waste management: Smart cities can use advanced waste management technologies such as recycling, composting, and waste-to-energy to reduce landfill waste and methane emissions [91]. Thus, by lowering greenhouse gas emissions and encouraging sustainable living, a carbon-neutral economy in smart cities can help to lessen the effects of climate change. Cities may be able to use technology and data-driven solutions to become more energy-efficient, cut back on waste, and lessen their carbon footprint.

1.5.3 The Metaverse According to McKinsey, [92] the metaverse is: The emerging 3-D-enabled digital space that uses virtual reality, augmented reality, and other advanced internet and semiconductor technology to allow people to have lifelike personal and business experiences online.

Moreover, the metaverse represents a progression from the existing internet that merges with digital technology to unite and broaden the application and scope of artificial intelligence, virtual reality, augmented reality, and spatial computing, among other technologies. The metaverse has three basic features: 1. a sense of immersion 2. real-time interactivity 3. user agency

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The additional features that make it a full vision of the metaverse are: • use cases beyond gaming • simultaneously interacting with other people • platforms and devices that work seamlessly with each other. At present, the metaverse consists of four main categories. The first category comprises content and experiences generated by users, creators, and developers, designed to enhance the metaverse experience. The second category consists of platforms that facilitate access to and discovery of content, as well as those aimed at creators of 3D experiences. The third category encompasses infrastructure and hardware that enable people to interact through their devices. The fourth and final category comprises enablers that focus on ensuring security, privacy, and governance, and provide access to the metaverse economy through payment and monetization options.

1.6 Data-Driven Techniques and AI in Smart Cities Data-driven techniques and AI are playing an increasingly important role in developing smart cities. These technologies gather data from sensors, cameras, and other sources to make informed decisions and provide insights into city operations [93]. One of the most common applications of data-driven techniques and AI in smart cities is managing urban infrastructure. For example, sensors can be used to monitor the condition of roads, bridges, and other structures to detect problems before they become major issues. AI algorithms can analyze the data collected from these sensors to identify patterns and predict future problems, allowing city officials to take action before they occur [94]. Another area where data-driven techniques and AI are being used in smart cities is public safety. Cameras and other sensors can monitor public spaces for potential threats, and AI algorithms can analyze the data to detect abnormal behavior or identify individuals who may be involved in criminal activity [95]. In addition to infrastructure and public safety, data-driven techniques and AI are being used in other areas of smart cities, such as transportation, energy management, and waste management. For example, AI algorithms can optimize traffic flow, reduce energy consumption, and improve waste collection and recycling [43, 95]. Thus, data-driven techniques and AI in smart cities enhance the QoL for citizens, enhance sustainability, and increase efficiency in urban services.

1.6.1 Data-Driven Techniques and Optimization Methodologies Data-driven techniques are methods used to analyze large amounts of data and extract useful insights or patterns. These insights can be used to make better decisions,

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solve problems, or identify opportunities. There are numerous data-driven techniques available to analyze and understand data. Some of the most common techniques are regression analysis, classification, clustering, association rules, time series analysis, deep learning, sentiment analysis, principal component analysis (PCA), decision trees, natural language processing (NLP), random forests, support vector machines (SVMs), and neural networks [96]. • Regression analysis finds a relationship between two or more variables. For example, it might be used to see if there is a correlation between a person’s age and income. • Classification groups data into different categories based on their features. An example of a classification data-driven technique in the context of smart cities could be using machine learning algorithms to classify traffic data into different categories, such as heavy traffic, medium traffic, and light traffic. • Clustering gathers similar data points based on their characteristics. For example, it might group customers based on their buying habits. • Association rules are used to find patterns or relationships between different items in a dataset. For instance, it can analyze data from smart meters to identify energy consumption patterns in different parts of the city. By using association rules, the system can identify if there are any relationships between energy consumption and other variables, such as time of day, weather conditions, or the type of buildings. For example, the system may identify that on hot summer days, there is higher energy consumption in residential areas with single-family homes, which may indicate that air conditioning systems are being used more frequently. • Time Series analysis is used to analyze data that changes over time. For example, it might be used to see if there is a pattern in stock prices over time. • Deep Learning builds complex models to learn from data and perform tasks such as image recognition and natural language processing. • Sentiment Analysis reviews text data, such as social media posts or customer reviews, to establish the emotion behind the text. Sentiment analysis can be used to monitor brand reputation or to understand customer feedback. • Principal Component Analysis (PCA) is used to reduce the dimensionality of a dataset by identifying the most important variables or features. PCA can be used to simplify complex datasets and improve the performance of machine learning models. • Decision trees build a model that predicts an outcome based on input variables. Decision trees are often used in classification problems and can be used to identify the most important factors that affect an outcome. • Natural Language Processing (NLP) is used to analyze and understand human language. NLP can be used for tasks such as text classification, sentiment analysis, and language translation.

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• Random Forests are a machine learning algorithm that builds multiple decision trees and combines their predictions to improve accuracy. Random forests are often used in classification and regression problems. • Support Vector Machines (SVMs) are machine learning algorithms that can be used for classification or regression. SVMs find the best boundary between classes of data to improve accuracy. • Artificial Neural Networks are machine learning algorithms inspired by the structure of the human brain. Neural networks can be used for various tasks, including image recognition, speech recognition, and natural language processing. Thus, data-driven techniques are essential for analyzing and understanding large amounts of data. Numerous techniques are available, and the choice of technique depends on the type of data being analyzed and the specific problem that needs to be solved. Understanding the strengths and weaknesses of each technique is crucial to select the right technique for the problem at hand. Moreover, optimization methodologies are data-driven techniques that aim to find the best solution or decision from a set of possible options, given constraints and objectives. Here are some examples of data-driven techniques through optimization methodologies [96–98]: • Linear programming optimizes a linear objective function subject to linear constraints. • Nonlinear programming optimizes an objective function that is nonlinear in its variables. • Mixed-integer programming optimizes a function subject to constraints where some of the variables are restricted to be integers. Mixed-integer programming solves problems such as production scheduling or facility location. • Genetic algorithms are optimization algorithms inspired by natural selection. It solves complex optimization problems where the solution space is large and the search for the optimal solution is difficult. For example, the best orientation of a building depending on the incident radiation and location. • Simulated annealing is a stochastic optimization algorithm that simulates the physical process of annealing in metals. It solves combinatorial optimization problems, such as the traveling salesman problem. • Gradient descent minimizes a cost or loss function by iteratively adjusting the parameters of a model. Gradient descent is often used in machine learning algorithms, such as linear and logistic regression. • Convex optimization optimizes a convex objective function subject to convex constraints. Convex optimization is a powerful tool for solving many optimization problems, including signal processing, machine learning, and control theory.

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1.7 Privacy Regulations in Smart Cities The regulation of smart cities and privacy information differ markedly depending on the country, region, or state in question, as unique legal and cultural contexts influence each. Variances in cultural values impact the regulation of smart cities and privacy information [99, 100]. For instance, some Asian countries may prioritize government surveillance and control, which can shape their approach to regulating smart city technologies. Given the significant quantity of data, including personal information like biometric data, health data, and location data generated by smart cities, it is crucial to set forth unambiguous guidelines and criteria for gathering, storing, and utilizing this data. In this regard, several regulations and public policies from various world regions are relevant to smart cities and privacy information: • European Union: The General Data Protection Regulation (GDPR) is a comprehensive law for data protection that encompasses all European Union member states. It lays out guidelines for the collection, processing, and storage of personal data while granting individuals certain rights to manage their data [101] • United States: A framework has been created by the National Institute of Standards and Technology (NIST) to enhance the cybersecurity of crucial infrastructure, such as smart city systems [102]. Similarly, the California Consumer Privacy Act (CCPA) outlines regulations on managing personal data and confers specific rights to individuals [103]. • Canada: The regulations established by the Personal Information Protection and Electronic Documents Act (PIPEDA) dictate the procedures for the acquisition, utilization, and sharing of personal data by private sector entities. Furthermore, the Office of the Privacy Commissioner of Canada has created guidelines for smart cities that highlight the significance of transparency, accountability, and user control [104]. • Singapore: The Personal Data Protection Act (PDPA) is a law for data protection that oversees the collection, utilization, and sharing of personal data in Singapore. Moreover, the Smart Nation initiative is a government initiative that strives to enhance the living standards of citizens by employing technology and data, all while ensuring the security and privacy of personal information [105]. • China: The Cybersecurity Law of the People’s Republic of China (CSL) lays down regulations for safeguarding network security and personal information [106]. These are just a few examples of the many regulations and public policies being developed worldwide to regulate the implementation of smart cities and protect the privacy of citizens’ information.

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70. IMD Smart City Observatory Web Page. IMD Business School (2022). https://www.imd.org/ smart-city-observatory/home/ 71. DTTM, Lab, I., Engage, I.: Smart Cities Index Report (2022). https://smartcitiesindex.org/ intro01 72. Allen, B.: Digital twins and living models at NASA (2021). https://ntrs.nasa.gov/citations/ 20210023699. NTRS Author Affiliations: Langley Research Center NTRS Meeting Information: Digital Twin Summit; 2021-11-03 to 2021-11-04; undefined NTRS Document ID: 20210023699 NTRS Research Center: Langley Research Center (LaRC) 73. Glaessgen, E., Stargel, D.: The digital twin paradigm for future NASA and US Air Force vehicles. In: 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA, p. 1818 (2012) 74. White, G., Zink, A., Codecá, L., Clarke, S.: A digital twin smart city for citizen feedback. Cities 110, 103064 (2021) 75. Ruohomäki, T., Airaksinen, E., Huuska, P., Kesäniemi, O., Martikka, M., Suomisto, J.: Smart city platform enabling digital twin. In: 2018 International Conference On Intelligent Systems (IS), pp. 155–161 (2018) 76. Deren, L., Wenbo, Y., Zhenfeng, S.: Smart city based on digital twins. Comput. Urban Sci. 1, 1–11 (2021) 77. VIZZIO.AI Modelling the World in High Resolution 3D. Backed by Smart AI (2022). https:// www.vizzio.ai/ 78. Asia, T.: Here’s how the world uses digital twins to solidify smart city development (2020). https://techwireasia.com/amp/2020/02/heres-how-the-world-uses-digital-twins-tosolidify-smart-city-development/ 79. Wright, L., Davidson, S.: How to tell the difference between a model and a digital twin. Adv. Model. Simul. Eng. Sci. 7, 1–13 (2020) 80. Nour El-Din, M., Pereira, P., Poças Martins, J., Ramos, N.: Digital twins for construction assets using BIM standard specifications. Buildings 12, 2155 (2022) 81. Race, S.: BIM demystified. Routledge (2019) 82. Ponce, P., Mata, O., Castellanos, S., Molina, A., McDaniel, T., Mendez, J.: The energy 4.0 concept and its relationship with the S3 framework. In: Smart Multimedia: Third International Conference, ICSM 2022, Marseille, France, August 25–27, 2022, Revised Selected Papers, pp. 215–227 (2022) 83. Attaran, M., Celik, B.: Digital twin: benefits, use cases, challenges, and opportunities. Decis. Anal. J. p. 100165 (2023) 84. Flamini, A., Loggia, R., Massaccesi, A., Moscatiello, C., Martirano, L.: BIM and SCADA integration: the dynamic digital twin. In: 2022 IEEE/IAS 58th Industrial And Commercial Power Systems Technical Conference (I&CPS), pp. 1–7 (2022) 85. Parliament, E.: What is carbon neutrality and how can it be achieved by 2050? | News | European Parliament (2019). https://www.europarl.europa.eu/news/en/headlines/society/ 20190926STO62270/what-is-carbon-neutrality-and-how-can-it-be-achieved-by-2050 86. Chen, L., Msigwa, G., Yang, M., Osman, A., Fawzy, S., Rooney, D., Yap, P.: Strategies to achieve a carbon neutral society: a review. Environ. Chem. Lett. 20, 2277–2310 (2022) 87. Ma, Z., Wu, F.: Smart city, digitalization and CO2 emissions: evidence from 353 cities in China. Sustainability 15, 225 (2022) 88. Medina, A., Ponce, P., Ramırez-Mendoza, R.: Automotive embedded image classification systems. In: 2022 International Symposium On Electromobility (ISEM), pp. 1–7 (2022) 89. Ponce, P., Molina, A., Mata, O., Ibarra, L., MacCleery, B.: Power System Fundamentals. CRC Press (2017) 90. Ibarra, L., Rosales, A., Ponce, P., Molina, A., Ayyanar, R.: Overview of real-time simulation as a supporting effort to smart-grid attainment. Energies 10, 817 (2017) 91. Chavarrıa-Barrientos, D., Camarinha-Matos, L., Molina, A.: Achieving the sensing, smart and sustainable “everything”. In: Collaboration In A Data-Rich World: 18th IFIP WG 5.5 Working Conference On Virtual Enterprises, PRO-VE 2017, Vicenza, Italy, September 18-20, 2017, Proceedings 18, pp. 575–588 (2017)

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92. McKinsey What is the metaverse and where will it lead next? | McKinsey (2022). https:// www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-the-metaverse#/ 93. Grimaldi, D., Carrasco-Farré, C.: Implementing Data-Driven Strategies in Smart Cities: A Roadmap for Urban Transformation. Elsevier (2021) 94. Ranyal, E., Sadhu, A., Jain, K.: Road condition monitoring using smart sensing and artificial intelligence: a review. Sensors 22, 3044 (2022) 95. Ingle, P., Kim, Y.: Real-time abnormal object detection for video surveillance in smart cities. Sensors 22, 3862 (2022) 96. Bourdeau, M., Zhai, X., Nefzaoui, E., Guo, X., Chatellier, P.: Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities And Society. 48, 101533 (2019) 97. Wang, J., Ding, R., Cao, F., Li, J., Dong, H., Shi, T., Xing, L., Liu, J.: Comparison of stateof-the-art machine learning algorithms and data-driven optimization methods for mitigating nitrogen crossover in PEM fuel cells. Chem. Eng. J. 442, 136064 (2022) 98. Solomatine, D., See, L., Abrahart, R.: Data-driven modelling: concepts, approaches and experiences. In: Practical Hydroinformatics: Computational Intelligence And Technological Developments In Water Applications, pp. 17–30 (2008) 99. Razmjoo, A., Østergaard, P., Denai, M., Nezhad, M., Mirjalili, S.: Effective policies to overcome barriers in the development of smart cities. Energy Res. Soc. Sci. 79, 102175 (2021) 100. Joshi, S., Saxena, S., Godbole, T., et al. An integrated framework: developing smart cities. Proc. Comp. Sci. 93, 902–909 (2016) 101. Commission, E.: Data protection in the EU. (2021). https://commission.europa.eu/law/lawtopic/data-protection/data-protection-eu_en 102. NIST National Institute of Standards and Technology (2023). https://www.nist.gov/, Last Modified: 2023-03-09T09:31-05:00 103. California, S.: California Consumer Privacy Act (CCPA) (2018). https://oag.ca.gov/privacy/ ccpa 104. Canada, O.: The Personal Information Protection and Electronic Documents Act (PIPEDA) (2021). https://www.priv.gc.ca/en/privacy-topics/privacy-laws-in-canada/the-personalinformation-protection-and-electronic-documents-act-pipeda/, Last Modified: 2021-12-08 105. PDPC PDPC | PDPA Overview. , https://www.pdpc.gov.sg/Overview-of-PDPA/TheLegislation/Personal-Data-Protection-Act 106. CSL Translation: Cybersecurity Law of the People’s Republic of China (Effective June 1, 2017) (2017). https://digichina.stanford.edu/work/translation-cybersecurity-law-of-thepeoples-republic-of-china-effective-june-1-2017/

Chapter 2

Connected Citizens are Smart Citizens

2.1 Personality and Behavior for Building a Citizen Classification System The previous chapter remarked two learning models for improving the quality of life of citizens [1]. This chapter focuses on the creative citizens model. This model focuses its efforts in teaching the citizen how to understand new and emerging technologies. Furthermore, it is essential to identify the behavior and usability problems when dealing with emerging technologies. Nielsen [2, 3] proposed ten usability heuristics for the evaluation in applications. Muller et al. [4] added three more usability heuristics to complement that evaluation. Furthermore, this evaluation has been used in the evaluation of electronic shopping, for interfaces for household products [5, 6], or for the evaluation of user experience [7]. Hence, Fig. 2.1 depicts the thirteen usability problems when using new technologies or devices. 1. Match between system and the real world: The system does not use real-world convention language. 2. User control and freedom: The system controls the interface actions, thus, the users feel that there is no option to choose between options or redo things. 3. Consistency and standards: The interface does not follow platform conventions. Therefore, the system must use a structure based on individuals’ mental thinking, rather than the company’s internal thinking. 4. Error prevention: The system should display messages confirming the individuals’ actions. For instance, make sure that the individual wanted to delete a picture. 5. Aesthetic and minimalist design: Dialogues contain irrelevant or rarely needed information. The system should avoid dialogues that compete with relevant information.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 P. Ponce et al., Data and AI Driving Smart Cities, Studies in Big Data 128, https://doi.org/10.1007/978-3-031-32828-2_2

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Fig. 2.1 Thirteen usability problems

6. Pleasurable and respectful interaction with the user: The design is unpleasing and nonfunctional. The interface does not reflect the user’s professional role, personal identity, or intention. The design does not balance artistic and functional values. 7. Skills: The interface tries to replace the user’s capabilities, background knowledge, and expertise. The system does not give support, extend, supplement, or enhance the user’s skills. 8. Help and computer: Information is complicated to search and is not focused on the user’s task and is extensive. 9. Privacy: The system does not protect personal or private information. 10. Help users recognize, diagnose, and recover from errors: Messages are coded in a way that users cannot recognize, diagnose, and recover from errors. 11. Recognition rather than recall: The objects, actions, and options are not visible and thus, the users’ memory is not activated. 12. Flexibility and efficiency of use: Lack of custom actions. Access and operation are limited to average users. 13. Visibility of the status: The interface neither informs the status nor gives appropriate feedback.

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The usability of a product depends on the user interface. Thus, if the interface does not solve at least some of these thirteen usability problems, it may be inoperable.

Hence, users must be aware that they are adopting socially connected products so they can exploit their benefits. On the other side, the connected products must interpret individuals’ lifestyles and requirements. These devices need to be low-cost and quick to obtain. Their operation must be easy to use with inherent privacy and security elements based on the consumer’s skills to avoid any possible failure [5]. Figure 2.2 shows an example of a connected thermostat case. This socially connected product considers the user as the main core and the linkage between the connected device and the Human-Machine Interfaces (HMI). In this case, the communication with the other products shows information of specific about the overall performance of the other connected products to create an energy consumption profile.

Fig. 2.2 Connected thermostat example for the adoption of socially connected products

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Thus, this type of product has four system modes: 1. 2. 3. 4.

heating or cooling mode fan mode automatic mode tailored mode based on the user type. In addition, the energy consumption control has four features:

1. 2. 3. 4.

energy saving feature thermal comfort feature energy consumption forecast feature gamified interface feature as a mean of motivating individuals through game elements applied in real-context to save energy.

Appealing ludic HMIs can engage end-users to in interacting with platforms. Therefore, it is imperative to understand better the individuals’ patterns [8]. In Sect. 1.4.2 it was briefly described about personality traits. Thus, personality traits offer the possibility to profile and understand better the type of user is through personality traits [9]. In [9, 10], they extensively described the “Big Five” Factors. These factors describe five personality traits depending on the perception and attitudes of the individual [8, 11–14]: • The openness personality trait (O) is open to learning new things and appreciates of divergent thinking that offers new ideas. • The conscientiousness personality trait has clear purpose in life and is rational. • The extraversion personality trait adores social interaction and new activities. This personality is optimistic, assertive, and loves social interactions that allow diverse activities. • The agreeableness personality traits is altruistic and cooperative. This personality always answer yes to any petition before thinking twice because of his/her sympathetic and empathetic attitude. • The neuroticism personality trait is opposite to the agreeableness personality. This personality is impulsive, bad-tempered, and has negative emotions. This personality always answer no to any petition before thinking twice due to his/her attitude. Rammstedt and John [15] proposed and validated a 10 question Big Five Inventory (BFI-10) rather than the usual 44 questions of the Big Five Inventory (BFI-44). The BFI-10 is a 10-question survey that profiles the personality traits in less than five minutes through a Likert response scale: (1) Very inaccurate; (2) Moderately inaccurate; (3) Neither accurate nor accurate; (4) Moderately accurate; (5) Very accurate. Furthermore, there are online datasets with the personality traits answers around the world [16, 17]. Additionally, in [8, 11, 18–20] they strongly suggest to use gamification and serious game strategies to engage consumers in activities to achieve specific goals, for instance, energy savings.

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Gamification is different than gaming, gamification uses game technology and elements in real-context environments to achieve goals by learning new concepts, whereas gaming has the only purpose of fun and is not focused on encouraging the users to achieve real-context goals [21].

Gamification enhances a platform with affordances for gameful experiences to support the user’s overall value creation [21]. Furthermore, gamification uses game elements in real context to achieve specific goals, for instance, energy savings or improve mathematical skills [8, 20]. Bartle [22] described four types of gamers (killer, achiever, explorer, and socializer). Marczewski proposed six types of gamified users and the game design elements associated with each user type. Whereas Tondello et al. [23] linked those gamified users (disruptor, free spirit, achiever, socializer, philanthropist, achiever, player) and associated them with the personality traits. Therefore, Tondello et al. [23, 24] related the socializer and philanthropist players with the openness, conscientiousness, extraversion, and agreeableness personality traits. The free spirit or explorer player relate with the openness, extraversion, and agreeableness traits. The achiever and player type associate with the conscientiousness trait, whereas the disruptor or killer type with the neuroticism trait. Hence, the personality traits represent the main core in which each trait is related to a gamified or serious game user, energy end-user segment, and target group. In addition, the gamified users have the following motivations: • Purpose motivates philanthropists because of their altruistic nature. • Relatedness motivates the socializers by creating social connections • Autonomy motivates free spirits through their freedom to express themselves and act without external control • Competence motivates achievers because they want to progress through task completion. • Extrinsic rewards motivate the players through any available activity to gain any type of reward. • Change motivates the disruptor because they prefer to force positive or negative changes. by changing the system directly or through others. Hence, Fig. 2.3 depicts the existent relationship between the personality traits with the type of gamified user and serious game user. This figure shows an example of game elements associated to each type of gamified user. Furthermore, a link between the type of consumer and gamification is required. A solution is through decision systems based on Artificial Intelligence (AI) [8]. Using current technologies in artificial intelligence as an adaptive neuro-fuzzy inference system (ANFIS), fuzzy logic, or neural network decision systems provides outcomes regarding the type of gamification elements required for deploying tailored humanmachine interface (HMI). The relevance of the adaption of these AI decision systems is that they emulate humans making decisions so goals can be achieved. Non-typical users are not con-

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Fig. 2.3 Relationship between personality traits and the type of gamified and serious game user

sidered when the products are designed and deployed. Therefore, it is necessary to understand how the consumer behaves or thinks to propose tailored product platforms for all types of users, typical and non-typical.

2.2 The Role of Gamification and Serious Games as a Social Connector Between Citizens Gamification is a human-focused design process that uses game thinking and game mechanics to engage individuals and provide solutions to real-world or productive activities. The human-focused design optimizes human motivation rather than function efficiency [20]. Chou [25] researched the most common elements and what motivated the consumers to engage in activities and proposed the Octalysis Framework, which is illustrated in Table 2.1. The framework is divided into eight core drives; extrinsic, intrinsic, positive, and negative motivation, which motivates and engages users to continue using a game. Each core has the following significance [25]: • Core 1 Epic meaning and calling: Users believe that they are doing something greater than themselves and were chosen to take action. • Core 2 Development and accomplishment: Internal drive for succeeding, progressing, developing skills, achieving mastery, and so on. • Core 3 Empowerment of creativity and feedback: Users become engaged in a creative process when they try different combinations to achieve goals. They also need to see the results of their creativity, receive feedback, and adjust their creativity. • Core 4 Ownership and possession: The desire core, in which users are motivated because they believe and feel that they own or are in control of something.

2.2 The Role of Gamification and Serious Games as a Social … Table 2.1 Octalysis game mechanics and their types of motivation Core name Game mechanics Int Ext Epic meaning and calling

Development and accomplishment

Empowerment of creativity and feedback

Ownership and possession

Social influence and relatedness

Narrative, elitism, humanity hero, higher meaning Beginners lunch, free x lunch, destiny’s child, cocreator Points, badges, fixed action rewards, leaderboard, progress bar, quest lists, win prize, high five, crowing, level-up symphony, aura effect, step-by-step tutorial, boss fight Milestone unlock, x evergreen mechanics, general’s carrot, real-time control, chain combos, instant feedback, boosters, blank fulls, voluntary autonomy, choice perception Virtual goods, build from scratch, collection set, avatar, earned lunch, learning curve, protection, recruitment, monitoring Social x invite/friending,social treasure/gifting, seesaw bump, group quest, touting, bragging, water cooler, thank-you-economy, mentorship, social prod

x

49

Pos

Neg

x

x

x

x

x

x

x

x

x

x

(continued)

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Table 2.1 (continued) Core name Game mechanics Scarcity and impatience

Unpredictability and curiosity

Loss and avoidance

Appointment dynamics, fixed intervals, dangling, prize pacing, options pacing, patient feedback, countdown, throttles, moats, torture breaks Glowing choice, mini quests, visual storytelling, easter eggs, random rewards, obvious wonder, rolling rewards, oracle effect Sunk cost tragedy, progress loss, evanescence opportunity Status quo, scarlet letter, visual grave, weep tune

Int

Ext

Pos

x

x

Neg x

x

x

x

x

x

• Core 5 Social influence and relatedness: This core has social elements that motivate people, including mentorship, social acceptance, social feedback, companionship, competition, and even envy. • Core 6 Scarcity and impatience: Users want something just because it is extremely rare, exclusive or immediately unattainable. • Core 7 Unpredictability and curiosity: People constantly become engaged because they do not know what is going to happen later; this is the core behind gambling addictions. • Core 8 Loss and avoidance: Users try to prevent something negative from happening. They feel the urgency to act immediately and that they may otherwise forever lose the chance to act. In contrast, the four types of motivations are as follows [25]: • Extrinsic motivation: People are motivated because they want something they cannot obtain, and obtaining it implies external recognition or even economic rewards. • Intrinsic motivation: The activity is rewarding on its own without a specific target to achieve. • Positive motivation:The activity is engaging because it lets the user feel successful, happy, and powerful. • Negative motivation: The activity is engaging because the user is constantly in fear of losing something.

2.2 The Role of Gamification and Serious Games as a Social …

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Fig. 2.4 Relationship between personality traits and the core drives of the octalysis framework

In [18, 19], they related the personality traits with the type of gamified and serious game user. Thus, Fig. 2.4 shows the relationship between the OCEAN model with the Octalysis framework. On the other hand, Serious Games (SG) are games designed for primarily nonentertainment purposes with a balance between entertainment and education to engage consumers in learning new concepts. SGs have a goal-oriented nature with specific rules or a feedback system, competitive comparative elements, and element challenging activities, choices, and fantasy elements. SGs are effective because [20]: 1. The individual receives feedback from the other participants and immerses in the gaming experience. 2. They are ludic and the individuals learn easily that information. 3. They allow further research in the behavioral aspect. Furthermore, any SGs have some of the following characteristics [20]: • Theme: The scope of the game, such as climate change, water management, or energy. • Player’s role: The identity of the character that a player assumes in the game. • Game objective: The outcome that the individual must achieve to complete the game. • Number of players: The number of players.

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• Participants: The type of players, such as professionals, students, stakeholders, or any other. • Type of game: board, card, digital, online, or hybrid game). • Graphics: 2D or 3D game. • Availability: Free or paid game.

Serious games and gamification differentiation: • Serious games are explicitly and carefully oriented for ludic educational purposes. Example: Duolingo. • On the contrary, gamification uses game elements in real contexts to improve the user experience and user engagement. Example: Starbucks Card.

2.3 Learning More About Citizens Through Social Networks Social creation designs new products or improves them through an open, dynamic, and collaborative value-creation process that engages internal and external stakeholders. Besides, social creation reduces time and costs, while increasing precision in results because the consumers willingly exchange information regarding the product attributes, requirements, and satisfaction [26]. Interface design is one of the most challenging steps of the social creation process. It deals with the human behavioral aspect such as language, interaction capabilities, and culture. In other words, it deals with different personality traits. Researching social media’s role in digitizing services and products becomes relevant because they serve as a key interaction node between machines and humans. Hence, a way to learn about citizens is through YouTube or Facebook. On one hand, YouTube has been one of the most popular social networks in recent years due to its scope. Besides video clips, movies, or songs, it is used for educational purposes. The primary users of this content are people between the ages of 18 and 34 who are content creators or consumers. On the other hand, one out of five page views in the United States belongs to Facebook. Although 83 million fake profiles exist, these profiles can also be considered potential customers. Furthermore, data mining with Facebook helps to build personas to design, for instance, products to fulfill the consumer’s needs and expectations. These personas refer to a marketing

2.3 Learning More About Citizens Through Social Networks

53

technique in which the response to a specific product depends on the social group or even personality traits. The basic definition of a persona is a fictitious individual with characteristics that can represent a group of consumers [26].

The social mining methodology uses the following steps [26]: • Finding main keywords based on initial product expectations and consumer needs. • Mining users’ activities and personalities to build descriptors, such as status, number of likes, visits, tags, number of friends, and TV shows, among others. • Mining Demographic features. • Combining data from mining activities and personality with demographic characteristics. • Building a persona model to design a product. Personas, being artificial creations, are primarily shaped by data. However, when information is absent, the model must incorporate certain assumptions. Given that obtaining information indirectly through online repositories contrasts with traditional principles of informed consent and confidentiality, researchers must take into account privacy settings, as well as the potential risks and benefits for both individuals and society that their research may generate. For highly specialized devices such as thermostats, the scope of potential uses limits the impacts that technology can have. However, this also means that the individual and collective effects of this innovation can be identified. From an ethical perspective, using public Facebook profiles for thermostat design can be justified on two grounds. First, simplifying the skills and knowledge required to operate such devices ensures more democratic access to technology and promotes collective wellbeing. Second, when technology becomes more financially accessible over time, individuals and societies can use economic resources more efficiently. To create personas, data from Facebook is used, as shown in the flow diagram in Fig. 2.5. If there is limited data available for creating a persona, it can be challenging to develop a persona that accurately represents the problem at hand. Conversely, if there is an excessive amount of data, processing it can be time-consuming. Thus, the time required to define a persona depends on the amount of available data. The first step in creating personas is to define the target market segments and their characteristics, which establishes the relationship between general data and information from Facebook. Next, the obtained attributes are used to create a basic persona model, which is then completed by collecting information from Facebook profiles. This complete persona model is used to create the product. Additionally, Fig. 2.6 shows the steps involved in proposing a product prototype based on personas, scenarios, and usability heuristics. Furthermore, Ponce et al. [26] have proposed a flow diagram that outlines the steps involved in creating personas, scenarios, and usability heuristics.

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Fig. 2.5 Flow diagram for using Facebook to improve the persona model

Fig. 2.6 Flow diagram of personas, scenarios, and usability heuristics

1. Create personas: Analyze personas’ goals, and activity scenarios. 2. Define usability problems: Heuristic evaluation, cognitive walkthroughs, feature inspection, and standards inspection are different steps of usability inspection that help to identify usability problems. 3. Define scenarios: Outline scenarios of immersed market segments by using personas and usability problems.

2.4 Learning More About Citizens Through Wearables, Virtual Reality …

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4. Define usability heuristics: Involve the participation of specialists to analyze all interactive elements based on usability design principles. 5. Simplicity interactivity: The heuristics design basic elements to avoid usability issues. 6. Mock-up elements: A mock-up interface of a low-cost device or product can be deployed.

2.4 Learning More About Citizens Through Wearables, Virtual Reality, and Augmented Reality Wearables allow tracking physiological and behavioral signals of manufacturing operators (e.g., heart rate variability (HRV), respiration, galvanic skin response, electrical muscle activity, and body accelerations) [27]. These parameters can be used to estimate health-related parameters such as stress levels, muscular workload, and physical activity levels using data analytics and machine learning methods. The information generated can improve individuals’ productivity, occupational health and safety. Castaldo et al. have used wearables to investigate the associations between mental stress and short and ultra-short HRV features in real-life scenarios [28–30]. Furthermore, they have also used wearables to investigate the associations between mental workload, performance, and HRV features during a repetitive task [31]. Their results suggested that HRV features (a mental workload marker) negatively correlate to performance. Wearable technology, virtual reality (VR), and augmented reality (AR) are all emerging technologies that have the potential to provide new ways of learning about citizens and their behavior. Wearable technology, such as fitness trackers and smartwatches, can collect data on a person’s physical activity, heart rate, and sleep patterns, among other things. This data can be used to better understand a person’s health and well-being, and can help inform decisions related to health care, urban planning, and infrastructure development. VR and AR, on the other hand, can provide immersive experiences that can help people understand complex issues in a more tangible way. For example, architects and urban planners can use VR and AR to create virtual models of buildings and neighborhoods to help people better understand how new developments will impact the surrounding environment. Additionally, these technologies can be used to create educational experiences that help people learn about the natural world or historical events in a more engaging way. However, it’s important to consider the ethical implications of collecting and using data from wearables and other technologies. There are concerns about privacy and the potential for misuse of data. Additionally, there are concerns about accessibility,

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as not everyone may have access to these technologies or may be able to use them effectively. It’s important to approach the use of these technologies in a responsible and ethical way and to consider how they can be used to benefit society as a whole.

2.5 Smart Social Interfaces Using AI Social products have been suggested for the design process and development of social products on various platforms. One such platform is for smart home applications, where social products fulfill the occupants’ comfort demands [11, 32, 33, 35, 36], recreation, well-being, and safety [37], and health monitoring [47, 48]. Another approach is to use socially connected platforms to profile consumers based on their personality traits, gamification type, and energy usage [11, 19, 20]. Therefore, it is essential to understand the behavior and thinking of users to propose tailored products for both typical and non-typical users. In this context, tailored products based on gamification and serious game structures within AI decision systems are suggested during product design and deployment. To deploy tailored user-oriented platforms that bridge the gap between the platform’s information, the consumer’s expectations [8], and the HMI, four steps are required. First, the platform’s goal must be determined while considering the Quality of Life as this platform is user-focused. Second, user characteristics, associated gamified elements, and platform usage patterns must be learned. Third, statistical data analysis must be performed to obtain insights and patterns from the previous steps while considering national or global regulations. Fourth, an AI decision system must be employed to consider the strategies based on the goal and statistical data outcome. Finally, a tailored dynamic gamified platform should be proposed. After six months, as suggested by the theory of planned behavior [20], if the goal is not achieved, the platform can be adjusted in the first step and feedback can be provided as needed. Figure 2.7 illustrates these platform steps. As mentioned in Sect. 1.4.3, Méndez [8] proposed a four-step methodology for deploying customized gamified platforms in [8]. In addition, various sectors can benefit from this proposed platform, including education [38], energy efficiency [20, 34, 39–43], thermal comfort [11, 32, 33, 35, 36], rapid prototyping [44, 45], healthcare [46–49], smart communities/cities [18, 19, 37, 50–54], and reducing CO2 emissions during manufacturing. Figure 2.8 illustrates Mendez’s methodology [8]. These steps, which involve determining the platform’s objective, learning user characteristics and platform usage patterns, performing statistical data analysis, and employing an AI decision system to consider the goal and data outcome. Finally, the theory of planned behavior recommends evaluating the behavior change six months after the implementation. Thus, a tailored dynamic gamified platform is proposed and evaluated within six months [20].

2.5 Smart Social Interfaces Using AI

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Fig. 2.7 Platform steps

Fig. 2.8 Methodology

1. Step 1: Learn user and product type. • This particular step involves the collection of information from various sources such as databases, surveys, and literature reviews, which is then utilized to profile the user. By analyzing the collected data, one can identify the type of energy end-user, personality traits, and whether they prefer gamified or serious games [11, 19, 20]. Additionally, this step involves the analysis of household appliances through surveys or simulations. For instance, surveys such as RECS, RASS, ENCEVI, or similar are used to analyze the energy impact of connected thermostats through energy simulations [11, 35, 36].

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2. Step 2: Learn building, process, or service usage pattern • In this step, the platform’s product usage pattern, process, or service is analyzed through simulations and statistical analysis to determine the internal and external variables that affect the product behavior associated with the users’ characteristics. For example, in an energy household application, descriptive statistics are utilized to obtain information about outdoor and indoor temperature, thermostat setpoints, occupant behavior, and adaptive thermal comfort [11, 32, 33, 35, 36]. 3. Step 3: Design and deploy an Artificial Intelligence algorithm • This step proposes an AI-based decision system and applies three types of AI, depending on the study’s objective and the available information. – Fuzzy logic: the research team gathers information from literature reviews, expert consensus, or databases to obtain descriptive statistics for implementing rules and membership functions [18, 20, 42, 45, 46, 48]. Furthermore, if the dataset contains representative data, artificial neural networks are preferred. – Artificial Neural Network: During this step, information is extracted from datasets to classify output variables based on input values. The research team utilizes feed-forward or forward propagation network topology to predict energy consumption, cost, thermal comfort, and game elements based on location, date, and personality traits [11, 44, 54]. When there is not enough information available in the dataset and it is better to implement fuzzy inference rules, researchers prefer to use adaptive neuro-fuzzy inference systems (ANFIS). – ANFIS: The research team extracts information from datasets to create fuzzy inference rules. For example, this decision system is utilized to create automatic fuzzy rules for classifying seniors’ emotions based on their facial expressions [47] or suggesting game elements based on energy consumption and thermostat setpoint [32, 43]. The research team could employ the Neuro-Fuzzy Designer toolbox from MATLAB, which has the limitation of allowing only one output. 4. Step 4: Propose tailored dynamic gamified platforms. • During this step, the research team launches a dynamic interface platform that operates as a tutored platform by providing gamified strategies based on goals, motivations, game elements, or core drives [11, 18, 19]. The theory of planned behavior recommends evaluating the behavior change six months after the implementation, which allows for a reasonable amount of time for behavior change to occur. This evaluation helps to determine whether the implementation has been successful in changing behavior or not [20]. • Researchers can build these dynamic gamified platforms into software such as LabVIEW or SIMULINK.

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Furthermore, Fig. 2.9 exemplifies this methodology through Concord’s household case study. Another application of this methodology was used for deploying a tailored platform based on personality traits and electricity bills in Mexico [19] (See Fig. 2.10). Furthermore, the research methodology is not limited to product platforms such as connected thermostats or connected interfaces. The applied methodology for engaging students in solving mathematical problems for educational purposes is illustrated in [38] (Figs. 2.11 and 2.12).

2.6 Ethics for Social Cyber and Physical Systems Cyber-Physical Systems (CPS) refer to integrated systems that combine physical and cyber elements, including computing, communication, and control technologies, to monitor and control physical processes in real-time [55]. These systems can process, manage, and transmit large amounts of data by integrating cloud storage and computational power with embedded devices in physical systems [56]. CPSs use machine learning algorithms to interact with humans and make decisions autonomously, making it possible to develop fully automated and intelligent systems [57]. CPSs have significant advantages in sensing, communicating, and processing information from both local and global sources [58]. They can also represent realworld conditions in virtualized environments, enabling them to process and acquire knowledge. CPSs can be implemented in smart cities as fundamental elements that collect, process, and manage information, and transmit actions to be taken in the city. This integration of CPSs in smart cities can facilitate better management of traffic, energy, water, and waste, leading to more efficient and sustainable urban living+ [51]. Therefore, CPSs are intelligent and autonomous systems that can provide a more comprehensive understanding of the physical world, leading to the selection of the most appropriate actions to be taken in real-time [59]. Some of the actions that CPSs can perform include monitoring, controlling, and optimizing physical processes, improving energy efficiency, and enhancing security and safety [60]. Some actions that the CPS can perform are: • • • • •

Detecting events with sensors Affecting a physical process with actuators Communicating with other CPS Evaluating data Acting as human-machine interfaces.

Bagheri and Lee [61] proposed a five-level architecture for Cyber-Physical Systems (CPS), which is depicted in Fig. 2.13. The CPS serves as the interconnection between the five levels. Level 2 of the CPS architecture can be regarded as the cognition level, which acquires knowledge and makes decisions. It is crucial to incorporate

Fig. 2.9 Case study validation methodology

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Fig. 2.10 Methodology employed for the tailored platform in the Mexican context

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Fig. 2.11 Methodology employed for solving mathematical problems

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Fig. 2.12 Methodology employed for solving mathematical problems

moral and ethical aspects as social characteristics into the Artificial Intelligence (AI) system to ensure responsible decision-making [52]. Social characteristics enable effective communication between citizens and devices and among different products to enhance the quality of information collected and decisions made. These social features primarily aim to improve the communication between citizens and products and are not part of the technical functioning of products. For example, the social characteristics of a smart TV include its ability to understand and communicate with the user through voice or keyboard, but adjusting the color and channel is not considered a social characteristic [62]. However, incorporating social features in the TV’s artificial intelligence, such as detecting depression or other illnesses, could improve citizens’ quality of life [63]. Smart cities can be seen as large-scale Cyber-Physical Systems (CPS), with various roles such as Cyber Computing Infrastructure, Smart Living and Governance, Public Safety Systems, Smart Transportation Systems, and Smart Street Systems [64]. For instance, a transportation system for self-driving cars can use an AI method called Adaptive Neuro-Fuzzy Inference System (ANFIS) to optimize highway traffic control. Still, such technical solutions alone cannot address social factors like driver behavior or conflicts [65]. To address this, researchers have proposed integrating a social component into CPS, creating Cyber-Physical Social Systems or Social Cyber-Physical Systems (SCPS) in a virtual world. However, implementing SCPS requires privacy protection since sensitive data about citizens could have undesired impacts if made public.

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Fig. 2.13 CPS attributes and functions presented in [10]

Additionally, biased AI programs could lead to unethical behavior, making it crucial to integrate ethical considerations and moral values in smart city decision systems [52]. SCPS aims to meet people’s social interaction demands and react ethically to the physical world [66]. Social devices are designed to understand and interact with citizens, addressing their technical and social needs. To achieve this, a trade-off between social and technological requirements must be fulfilled. AI systems must comply with applicable laws, ethical principles, and values and be technically and socially robust throughout their lifecycle. Collecting contextual data and managing it through knowledge-based machine learning algorithms could enhance CPS’s ability to sense specific situations and provide user-friendly human-machine interfaces [67, 68]. The deployment of laws to delimit the actions of AI systems can constrain their behavior and limit the solutions they can provide to smart city needs. However, when a solution is found, it is more likely to be accepted and implemented. To integrate social capability into cyber-physical systems (CPS), special sensors must be added to detect social activities and human behavior. Using AI to analyze the information collected from citizens, correlations between stimuli and outputs that fulfill citizens’

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requirements can be identified. The SCPS can be defined as a set of social systems that combines the virtual and physical worlds into social environments, integrating the information from a conventional CPS into the system. The priority of the SCPS is to improve communication with humans drastically and understand their needs and expectations to provide social and technical solutions accepted by citizens and cities [51, 52]. The concept of a social interface refers to the sensing, modeling, managing, and interoperating of social factors [50]. To optimize the solution, social and technical requirements must be aligned. Additionally, smart city solutions must consider the needs of disadvantaged groups, such as the elderly. Méndez et al. proposed using Alexa and cameras to track older people and check their daily status and mood to improve their quality of life [45, 47] or in other cases for pre-diagnosis depression [48]. They also suggested using social interfaces with game elements to guide them in their physical activity and avoid social isolation. Smart community can be modeled as cooperative blocks, where information flows dynamically from citizens to local and global governments [19]. Each block, or home, can be defined as a multi-sensor since it can sense several conditions regarding safety, health care, and other public conditions. This approach allows local and global solutions to be sought based on the information received from each cooperative block. However, the search for solutions is limited by moral and ethical restrictions or biased AI algorithms that might have been implemented, which can prevent social issues from being resolved. To prevent learning algorithms from acquiring unethical biases, citizens must have ethical and moral competencies to evaluate the AI systems that make decisions. Evaluating ethical and moral issues is a complex task that citizens and governments must perform under the best conditions. Cultural factors also have to be considered, as they affect how AI systems make decisions. Additionally, continuous evaluations of the deployed AI algorithms must be conducted [69].

2.6.1 Artificial Intelligence Ethics The responsibility for artificial intelligence (AI) lies with humans, who create it for specific purposes. Therefore, AI ethics aims to address the impact of technological transformation on individuals’ lives. Education is crucial in creating awareness of AI’s potential and shaping societal development based on diversity and inclusivity. Several European countries have proposed human-centric AI guidelines that include ethical principles such as the no-harm principle, justice principle, and explicability principle to ensure that AI algorithms avoid discrimination, protect vulnerable groups, and are understandable by non-experts [51, 52]. Many European countries have proposed a new set of ethics guidelines for a human-centric AI approach. These guidelines consider the following ethical principles [70]: • No harm principle: AI algorithms must avoid discrimination, manipulation, and negative profiling and protect vulnerable groups such as children and immigrants.

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• Justice principle: Developers and implementers of AI must ensure that individuals and minority groups maintain freedom from biases against them. • Explicability principle: AI systems must be auditable and understandable by nonexperts. Expanding engineering education curricula to include the humanities and social sciences is essential to ensure responsible AI design and development, leading to a more diverse student population. Furthermore, promoting inclusion, diversity, and access to AI is crucial for ongoing diversity in AI development teams and professionals. This interdisciplinary approach enables engineers to understand AI’s societal impacts and ethics’ role in decision-making [51, 52]. The House of Lords Select Committee on Artificial Intelligence 2018 published suggestions on how the United Kingdom can take advantage of the opportunities that IA offers and their potential risks. These suggestions are also applicable worldwide [71]. The committee calls for the creation of a national AI strategy, an AI code of ethics, and the establishment of a new AI council to oversee its implementation. It also recommends measures to address concerns around the impact of AI on jobs and the need for upskilling and reskilling of the workforce. This Committee suggested five principles for a cross-sector AI Code [71]: 1. Artificial intelligence should be developed for the common good and benefit of humanity. 2. Artificial intelligence should operate on principles of intelligibility and fairness. 3. Artificial intelligence should not be used to diminish the data rights or privacy of individuals, families, or communities. 4. All citizens should have the right to be educated to enable them to flourish mentally, emotionally, and economically alongside artificial intelligence. 5. The autonomous power to hurt, destroy or deceive human beings should never be vested in artificial intelligence.

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Chapter 3

Keystone for Smart Communities—Smart Households

3.1 Energy, Water, Housing, Security, Environment, Commerce, and Utilities into Smart Households The concept of smart homes has gained significant attention as an innovative way to integrate various technological systems into a single household to optimize energy, water, housing, security, environment, commerce, and utilities management [1, 2]. Internet of Things (IoT) technology connects appliances, devices, and systems in a household, enabling them to communicate with each other and external networks for more efficient and effective management of resources and services [3, 4]. This allows for more efficient and effective management of resources and services. Smart homes can incorporate features like connected thermostats and energy monitoring systems for energy management, smart irrigation systems for water management, and smart locks, security cameras, and motion sensors for enhanced security [2, 5]. Smart homes can also provide enhanced security through smart locks, security cameras, and motion sensors that alert homeowners to potential intrusions or hazards [6]. For environment management, smart homes can use sensors to monitor air quality and humidity levels, allowing for more efficient use of heating and cooling systems and ensuring a healthier living environment. Additionally, smart homes can incorporate features like solar panels and energy storage systems to reduce reliance on the grid [2]. Commerce management can include smart shopping lists that automate grocery deliveries, and utility management can involve smart appliances that optimize energy use during off-peak hours and reduce overall consumption [7]. Thus, integrating energy, water, housing, security, environment, commerce, and utilities into smart households can lead to increased efficiency, reduced costs, and improved quality of life. The structure of a smart household must consider two perspectives, the local point of view and the global point of view [1]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 P. Ponce et al., Data and AI Driving Smart Cities, Studies in Big Data 128, https://doi.org/10.1007/978-3-031-32828-2_3

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Fig. 3.1 Smart household structure divided by type of utility and needs to be satisfied by the smart community

The local point of view considers household utilities as water, gas, and electricity. Thus, these utilities support and enrich the householders’ QoL. Furthermore, there are needs that the smart community environment should satisfy, such as safety, transportation, healthcare, and sustainability, among others. Figure 3.1 displays the smart household structure divided by the utility type and needs to be satisfied by the smart community. Thus, smart households provide information about consumption patterns, and those consumption needs to be satisfied by the community. The community can provide weather sensing or air quality, so the smart home adapts. For instance, the thermostat setpoint can adapt depending on the outdoor temperature (weather). Through an application, the household or householder can access the public system and review routes to take the better option to arrive at the destination. Following up Chap. 1, Sect. 1.3. A community must fulfill four keystones [1]: • The smart community must provide services and resources to the smart households so that needs are satisfied without losing the sustainability aspect. • The smart community must react when changes arise and act with resilience. Thus, the community knows how to adapt depending on the household’s needs and therefore dynamically change.

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Fig. 3.2 Applicable IEEE standards in a smart home

• The smart community uses artificial intelligence algorithms within the devices to profile and forecast community needs. This can be done by analyzing household consumption patterns with empathy-driven proactive intelligence. • Finally, as householders or individuals interact within the community, different behaviors interact, and the community must adapt and evolve based on the community interaction. Thus, through households or urban activity, the smart community adapts based on that emergent behavior.

3.2 Sensing in Smart Households A Smart Household monitors and manages household devices through an Internet connection [8]. These socially connected devices include thermostats, cameras, voice assistants, lights, doorbells, and household appliances; they provide householders with information about their behavior. Figure 3.2 shows the applicable IEEE Standards in a Smart Home [9]. Moreover, there are five groups of smart homes based on technology services:

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Hence, the smart home communicates the collected data with the users and service providers to enhance their QoL and improve the ability to manage these socially connected devices [8].

Moreover, there are five groups of smart homes based on technology services: • The surveillance home processes data to notify householders about possible natural disasters or security concerns [10]. Some common types of sensors that might be used include: – Motion sensors: These sensors are typically used to detect movement in a specific area and can trigger an alarm or record when motion is detected. – Door and window sensors: These sensors detect when a door or window is opened and can trigger an alarm or record if the sensor is tripped. – Glass break sensors: These sensors detect the sound of breaking glass and can trigger an alarm or record if the sensor is activated. – Smoke and fire sensors: These sensors can detect smoke or fire in the home and can trigger an alarm or notification to the homeowner or authorities. – CO sensors: These sensors can detect the presence of carbon monoxide in the home and trigger an alarm or notification to the homeowner or authorities. – Temperature sensors: These sensors can be used to monitor the temperature in the home and can trigger an alarm or notification if the temperature rises above or falls below a certain threshold. • The assistive home help householders depending on their daily routine [10]. This home type provides care for senior householders, children, or healthcare purposes [3, 11, 12]. Some of the most common sensors include the motion, door and window, temperature, and the following – Light sensors: These sensors detect changes in ambient light levels and adjust lighting accordingly. – Voice assistants: These devices are integrated with various sensors and can control home appliances, answer questions, and provide other assistance through voice commands. – Pressure sensors: These sensors detect when a user is sitting or standing and can be integrated with automated seating and mobility systems. – Fall detectors: These sensors detect when a user has fallen and can be integrated with an alert or notification system to notify caregivers or emergency services. • The detection and multimedia information home gathers videos and photos of householders to enhance their QoL [10], for example, by analyzing their thermal comfort [13, 14]. Some of the most common sensors and devices include the motion, door, and window sensors, and the following: – Security cameras: These devices can capture video footage of the home’s interior and exterior, which can be used for security and monitoring purposes.

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– Audio sensors: These sensors can capture audio data from specific areas of the home, which can be used for security or monitoring purposes. – Microphones: These devices can be used to capture audio data throughout the home, which can be used for various purposes, such as voice recognition and control. – Image sensors: These sensors can capture high-resolution images of the home’s interior and exterior, which can be used for security or monitoring purposes. – Smart speakers: These devices can be used to control other devices in the home, as well as to play music, provide information, and perform other functions. – Smart displays: These devices can display multimedia information such as news, weather, and other data, as well as provide visual feedback for smart home devices. • The ecological awareness home promotes sustainable practices [10] by monitoring and controlling energy consumption [2, 13, 15]. The specific sensors and devices used in an ecological awareness home will depend on the goals and preferences of the homeowner, as well as the level of automation and functionality desired. Here are some common types of sensors and devices that might be used in this type of home: – Connected thermostats: These devices can monitor temperature and humidity levels in the home and adjust the heating and cooling systems accordingly to reduce energy consumption. – Light sensors: These sensors can detect changes in ambient light levels and adjust lighting accordingly to reduce energy consumption. – Solar panels: These devices can capture solar energy and convert it into electricity, powering the home’s electrical systems. – Water sensors: These sensors can detect leaks or changes in water usage and provide alerts or notifications to the homeowner to promote water conservation. – Air quality sensors: These sensors can detect levels of pollutants or other harmful substances in the air, and provide alerts or notifications to the homeowner, to promote healthy living. – Rainwater harvesting systems: These systems can capture and store rainwater for later use, reducing reliance on municipal water supplies. – Smart irrigation systems: These systems can monitor soil moisture levels and weather patterns and adjust irrigation to reduce water usage and promote healthy plant growth. • The gamified home uses socially connected products to provide personalized interfaces based on game elements to teach householders to achieve specific goals such as energy reduction [1, 11, 12, 15–18]. Besides, some of the common types of sensors and devices that appear in this category are: – Motion sensors: These sensors can detect movement in specific areas of the home and trigger interactive elements in the game, such as movement-based challenges or rewards.

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– Touch sensors: These sensors can be integrated into various objects in the home, such as furniture or appliances, to trigger game elements when touched. – Voice assistants: These devices can control game elements through voice commands, such as choosing a game or controlling game characters. – Light sensors: These sensors can trigger game elements based on changes in ambient light levels, such as a game that can only be played in low-light conditions. – Wearable sensors: These sensors can be worn on the body and used to track movement and other biometric data, which can be used to control game elements or provide feedback to the player. – Augmented reality devices: These devices can overlay game elements onto the real world, such as virtual objects that can be interacted with using physical gestures or movements. – Interactive screens: These screens can be used to display game elements, such as puzzles or challenges, and can be controlled using touch or other sensors. Smart home services can be added to homes by transitioning from a traditional home to a smart home. Thus, smart homes can potentially improve the QoL for many householders. The electrical grid has been the base for economic growth and modern society [2]. Sensing and control technologies allow market applications and potential development and research due to the widespread use of monitoring, instrumentation, and sensing technologies. Besides, sensing the household monitors essential variables outside and inside it to improve the household operation [19, 20]. Gradual changes and improvements become the community sustainable and connected (smart). Furthermore, the arising of cutting-edge electrical devices improves individuals’ lives more safely and comfortably. Thus, technology allows automation, interconnectivity, and high performance due to the Internet of Things (IoT) and Artificial Intelligence (AI) techniques, like machine learning and big data [21]. Furthermore, household sensing technologies can be classified into traditional HVAC sensors that monitor temperature from the outdoors, the chilled water temperature or the supply air temperature, and the humidity, flow, pressure, and gas flow for boilers. Thus, these sensors maintain the controlled variable at a setpoint. Other types of sensors are the occupancy sensors grouped into motion sensors such as the infrared, ultrasonic, and microwave sensors or through methods such as vision-based that uses computing, sensing, and computer vision technologies. The emerging sensors use the next generation of IoT-based building energy management sensors for advanced control methodologies for energy management or to enable mobile controlling. Besides, these sensors, if placed optimally, require less infrastructure. Virtual sensors are used for low-cost approaches and to estimate internal loads or radiant temperatures. Furthermore, environmental monitoring sensors measure temperature, pressure, daylight, air quality, illuminance, and CO2 for building energy applications and demand response. Figure 3.3 depicts a general list of common sensors at smart homes [22]. Hence, these technologies produce the amount of information that can be processed to profile the householders’ QoL. Thus, the data comes from the smart meter-

3.3 Smart Homes and Data Fusion

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Fig. 3.3 Sensor types by category

ing infrastructure (gas, water, electricity), mobile phones, household appliances, and social networks. Thus, data must be consistent and clear, which is where data fusion applies, as it comes from different sources [23].

3.3 Smart Homes and Data Fusion Combining data from multiple sensors through data fusion can be beneficial in generating more reliable inferences than relying on a single modality alone. This is especially true when a reference framework is employed to translate attribute or property values into quantitative measurements consistently and predictably. A multi-sensor data fusion framework offers advantages in information processing, integration, communication, and compensation. This makes it a desirable approach for handling data from multiple sources. The concept of multi-sensor data fusion is rooted in the fundamental ability of animals and humans to combine redundant and complementary information from different sources to increase the likelihood of survival. The primary objectives of this concept are [3]: • Compensation: diagnose, calibrate, and adapt to environmental variations. • Information processing: attention, event recognition, and decision-making.

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• Communication: standard inference protocol for conveying perceptions and interpretations of sensor data to the external world. • Integration: seamlessly coupled sensing, processing, and actuator subsystems. • Decision-making: analyze and predict from sensed data to inform decisions. Although data fusion is not a new concept, technological advancements in sensors, artificial intelligence, digital systems, information processing techniques, and embedded systems can result in higher performance and more robust data fusion systems. These improvements can be utilized in various applications, such as home automation systems. For instance, data fusion can optimize energy and water consumption based on real-time information about resource availability and usage patterns. Data fusion can improve safety and security by enabling real-time monitoring and detection of potential threats such as intruders, fires, or gas leaks [24, 25]. Smart homes, coupled with data fusion technology, have the potential to revolutionize the way people live and interact with their homes, offering improved comfort, efficiency, and security. This has been highlighted by previous research on the application of data fusion in smart homes [26].

3.4 Controlling Smart Houses and Buildings Smart houses and buildings are becoming increasingly popular due to their ability to enhance comfort, convenience, energy efficiency, and security [27]. These smart environments are equipped with various Internet of Things (IoT) devices, such as sensors, actuators, and controllers, that enable them to monitor and control different aspects of the indoor environment, such as lighting, temperature, air quality, and appliances. The control of smart houses and buildings can be achieved through different means, including voice commands, smartphone apps, and centralized control systems. One of the main benefits of smart houses and buildings is their ability to optimize energy consumption by adjusting different devices based on occupancy, weather conditions, and time of day [28]. For example, smart thermostats can learn occupants’ preferences and adjust the temperature accordingly, while smart lighting can be programmed to turn off when no one is in the room. This optimization can lead to significant energy savings and reduce the carbon footprint of buildings. However, controlling smart houses and buildings also raises privacy and security concerns. As these environments are connected to the internet, they can be vulnerable to cyberattacks and unauthorized access [29]. Hackers can access sensitive information about the occupants, such as their daily routines, preferences, and personal data. Therefore, it is crucial to implement appropriate security measures, such as encryption, authentication, and access control, to protect the privacy and security of smart houses and buildings [30]. Moreover, the control of smart houses and buildings also raises ethical concerns related to the potential misuse of the data collected by IoT devices. For example,

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data about occupants’ activities and preferences could be used for targeted advertising or profiling, infringing upon their privacy and autonomy [31]. Therefore, it is essential to implement ethical guidelines and standards to ensure that the use of data is transparent, fair, and respectful of the occupants’ rights and values. Besides, the 1888.4 protocol, also known as the IEEE Standard for Green Smart Home and Residential Quarter Control Network, is a set of technical standards that define how networks in households can be designed, deployed, and managed to enable green and smarter functions. The protocol is intended to support the development of intelligent, efficient, and sustainable homes and residential quarters by providing a framework for the interoperability of various devices, sensors, and appliances. The protocol was developed by the IEEE Standards Association and was first published in 2015. It is available for download from the IEEE Xplore Digital Library [32]. According to the protocol, the key focus areas for achieving green and smart homes include energy management, environmental monitoring, and home automation. The protocol specifies the requirements for the communication protocols, data models, and interfaces that are necessary to enable seamless integration and communication between different devices and services. For example, the protocol defines a common data model for energy data, allowing different energy management systems to exchange data in a standardized way. One of the primary goals of the protocol is to promote energy efficiency and sustainability by optimizing energy consumption in households. This is achieved through advanced control algorithms and intelligent energy management systems that can monitor, analyze, and adjust energy use based on different factors such as occupancy, weather conditions, and time of day. The protocol also supports the integration of renewable energy sources, such as solar panels and wind turbines, into households. By providing a standardized interface for these devices, the protocol enables them to be seamlessly integrated into the overall energy management system, allowing households to maximize their use of renewable energy sources and reduce their reliance on the grid. Another key area of focus for the protocol is home automation, which refers to using smart devices and systems to automate various tasks in the household, such as controlling lighting, temperature, and security systems. The protocol provides a standardized framework for integrating different home automation systems, allowing users to control and monitor their homes from a single interface, such as a smartphone app.

3.4.1 BIM and the Control in Smart Houses and Buildings Autodesk defines Building Information Modeling (BIM) as “the foundation of digital transformation in the architecture, engineering, and construction (AEC) industry” [33]. Besides, BIM is the holistic process of generating and controlling information for a constructed asset. BIM relies on an intelligent model supported by a cloud-based platform, facilitating the integration of structured data from various

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fields to create a digital representation of an asset throughout its life cycle. This process encompasses planning, design, construction, and operations of the building. BIM can play a crucial role in the control of smart houses and buildings, as it allows for the integration of various systems and components into a single model. This enables the design team to simulate the performance of the building and its systems, including lighting, HVAC, and security before construction begins. It also provides ongoing monitoring and control of these systems, allowing for real-time adjustments and optimization. In [34], the authors discuss the application of Building Information Modeling (BIM) and Cyber-Physical Systems (CPS) in disaster prevention. The authors propose a BIM-based CPS framework that integrates various sensors, data sources, and models to improve the resilience of buildings and cities in the face of disasters such as earthquakes, floods, and fires. Other benefits include real-time monitoring and control of building systems, early warning systems, and improved decision-making capabilities. Moreover, the safety of buildings during earthquakes can be assessed by integrating structural models with real-time sensor data. BIM can be used to optimize the control of HVAC systems in smart buildings. In [35], they explain that the traditional design process for HVAC systems is timeconsuming and prone to errors due to the complex nature of the system and the need to consider multiple factors such as comfort, energy efficiency, and indoor air quality. Thus, a BIM-based automated design can be a useful tool for designing HVAC systems in office buildings, saving time and reducing errors in the design process, leading to more efficient and sustainable buildings. Hence, Fig. 3.4 shows the lifecycle management of BIM and consists of the following items [36]: 1. Programming - project initiation: During this step, the project team is set up, the roles and responsibilities of each team member are defined, and the workflow is established. 2. Conceptual Design: The project team provides the conceptual design of the building through sketches. 3. Detailed Design: the team develops deep detail of the building design, including technical drawings, specifications, and 3d models. 4. Analysis: The team performs structural, energy, lighting, acoustic, HVAC, and environmental analysis to identify potential problems and provide insights into optimizing the design for better performance. 5. Documentation: The team creates the documents for bidding and construction purposes. 6. Fabrication: Involves the production of the building components. It involves the production of building components off-site or in a factory. The production process includes computer-controlled machinery to ensure accuracy and consistency, quality control measures to ensure the components meet required standards, and logistics planning to ensure timely delivery to the construction site.

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Fig. 3.4 BIM lifecycle management

7. Construction 4D/5D: incorporates time (4D) and cost (5D) dimensions. The 4D aspect adds the time dimension to the 3D building model and simulates the construction process to identify potential delays or problems before they happen. 8. Construction Logistics: The team plans and coordinates the transportation of materials, equipment, and personnel to and from the building site. Close collaboration between all stakeholders involved in the construction process, such as contractors, suppliers, transportation providers, and security providers, is essential for successful construction logistics planning and coordination. When done effectively, this can lead to cost reduction, delay minimization, and improved safety on the construction site. 9. Operation and Maintenance: After the construction and the occupancy of the building, the final phase of the Building Information Modeling (BIM) lifecycle, known as the operation and maintenance (O&M) stage, begins. The primary goal of this stage is to ensure that the building operates efficiently and effectively while also meeting the requirements of its occupants. 10. Renovation: This stage focuses on improving the existing building to meet changing needs or upgrading outdated features. This stage considers upgrading mechanical, electrical, and plumbing (MEP) systems, retrofitting the building for energy efficiency or reconfiguring indoor spaces to improve functionality.

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11. Demolition: This stage happens when the building reaches the end of its lifecycle, with no cost-effective option to maintain or renovate it. BIM considers the demolition process by removing hazardous materials and keeping reusable materials. Furthermore, this step can be followed by constructing a new building on the same site and starting the BIM lifecycle again. The renovation and demolition stages are two separate stages of the Building Information Modeling (BIM) lifecycle after the O&M stage. Successful completion of both stages requires close collaboration between various stakeholders, including architects, engineers, contractors, and other involved parties, to ensure that the work is performed safely and efficiently. BIM can be vital in streamlining the process and facilitating accurate and timely communication among all stakeholders, minimizing the potential for mistakes or delays [36].

3.5 Seniors and People with a Disability Living in Smart Houses The World Health Organization indicates that most people experience disability due to aging and chronic health conditions [37]. Over 1 billion people, about 15% of the global population, are associated with some form of disability, and only a few countries consider adequate services for this population. Smart houses provide a range of benefits for seniors and people with a disability, including improved safety, increased independence, and enhanced quality of life [38]. For instance, sensors can be used to monitor for falls or other emergencies and alert caregivers or emergency responders as needed. Smart homes can also provide reminders for medication, appointments, and other important tasks and help residents manage chronic health conditions [39]. A very common impairment is related to mobility problems. Different causes can compromise a person’s ability to manipulate objects: sudden accidents, cerebral vascular problems, or limb amputations; also, other mobility impairments advance gradually as a consequence of multiple sclerosis or Alzheimer’s disease; besides, mobility can be affected by extremities pain because of Osteoarthritis. People with disabilities experience several barriers to developing everyday activities. Thus, they depend strongly on relatives and caregivers [3, 39]. This strong dependence negatively affects their quality of life, as they are impeded from being productive or having access to health services, education, and work opportunities. For instance, a typical computer is operated by a keyboard or a mouse; however, these interfaces are useless for a person without upper extremities. Therefore, there are developments of assistive technology for helping people with disabilities to control electronic machines and gadgets around the house or working environment. This technology enables users to operate devices by alternative methods like touch, voice activation, body movements, and gesture recognition, among other interfaces [3]. This assistive technology has been proven to be very useful and to

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bring a degree of independence for the individual, as presented by [40]. Studies have shown positive aspects of using alternative interfaces, as users with disabilities or the elderly improve their quality of life, autonomy, and security [41]. However, alternative interfaces should be designed to fit the user’s abilities without causing pain or discomfort [11]. Also, the interfaces may represent minimum physical effort and a simple learning procedure to test and use the technology; on the contrary, this will not be accepted and abandoned. A smart household, from the automation and environmental control systems perspectives, is grouped into four main areas: 1. Leisure: Television, video game consoles, music, and smart devices. 2. Comfort: Blinds, lighting, air conditioning, chair or bed position, home appliances. 3. Security: CCTV, secure locks, call alarms. 4. Communication: Hands-free telephones, computers, e-mail, video conferences. There are proposals for alternative computer access, which allows individuals with disabilities or the elders to interact with a computer by non-conventional interfaces: head movements detection, facial expressions recognition, eye tracking and gaze estimation, voice commands recognition, body postures detection, platforms for detecting biosignals like electrooculography (EOG), electromyography (EMG) and electroencephalography (EEG) [11]. Moreover, for users suffering from other severe disorders, there have been proposed brain-computer interfaces (BCI) for accessing computers by detecting the user’s intentions from neuronal activity [42]. All these alternative computer access methods are an important key for helping seniors and people with disabilities to interact with smart home environments. There are smart speakers like the popular Amazon Alexa, which uses speech recognition as an intuitive and natural way to interact with other electronic devices [3, 12]. Moreover, Fig. 3.5 depicts an example of a power wheelchair with a hands-free interface that improves the person’s independent mobility [43, 44]. According to the user’s needs, tongue switches, touchscreens, speech recognition, eye tracking, EMG, EOG, and EEG have been explored as hands-free input methods for steering a power wheelchair [45]. The smart house environment uses devices and sensors which are part of the Internet of Things Technology (IoT). There are also health services in this network of objects that gather and share information, integrated by special sensors and care technology that is relevant for householders with disabilities and seniors. Nowadays, remote patient monitoring is possible thanks to IoT bio-sensors to measure important health parameters like blood oxygen saturation, pulse rate, bloody mass index, glucose, cholesterol, sleeping, and movement, among others. Telecare and telehealth services reduce the need for attendance and diagnosis at a clinic, especially for those people with long-term conditions. Nevertheless, there are also potential challenges associated with smart homes for seniors and people with a disability. These can include privacy concerns related to the collection and use of personal data, as well as issues related to affordability and accessibility. Despite these challenges, smart homes provide a promising solution

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Fig. 3.5 Alternative interfaces could operate a power wheelchair

for enabling them to live more independently and safely and improve their QoL. As technology evolves, smart homes will likely become an even more important part of the healthcare sector.

3.5.1 What is the Place of Seniors and People with a Disability in Smart Cities? Smart communities and cities must be inclusive and ensure that all segments of society, including seniors and people with a disability, have equal access to services. Besides, smart cities’ inclusiveness must be conceptualized from the design stage. They must be considered during the conceptualization of the community because, often, they are left out of the benefits of smart technologies [46]. Around 15% of the world’s population live with some form of disability, and they are often excluded from smart city and community planning. Thus, the smart community must create accessible and inclusive smart places that cater to the needs of disabled individuals [47]. Furthermore, they can benefit greatly from smart city technologies, including improved healthcare services and mobility options. Thus, these technologies, such as assistive devices and mobile applications, can improve accessibility and mobility for disabled and senior individuals.

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3.5.2 How are the Seniors and People with a Disability Learning Process Through Technology? The learning process for seniors and people with a disability through technology can vary depending on their needs and abilities. For example, in [39], they proposed using a social connector to engage seniors in activities and keep in touch with their family members. Here are some ways in which they can use technology to enhance their learning [48, 49]: • Access to online courses and resources: Use technology to access online courses and educational resources. They can enroll in online classes or access resources such as eBooks, videos, and podcasts to learn at their own pace and according to their interests. • Assistive technology: Assistive technology can support them in learning. For example, screen readers, speech recognition software, and captioning tools can help those with visual or hearing impairments. Adaptive keyboards, mice, and other input devices can help people with physical disabilities navigate technology more easily. • Virtual learning environments: Virtual learning environments provide them with a more flexible and accessible learning experience. They can participate in online classes, access online resources, and interact with instructors and other students from anywhere with an internet connection. • Teleconferencing: Video conferencing tools like Zoom, Skype, or Google Meets enable them to attend virtual classes or meetings from their households. This can help reduce barriers related to physical accessibility or transportation issues.

3.5.3 How Accessible are Learning Opportunities for Seniors and People with a Disability in Smart Cities? The accessibility of learning opportunities for seniors and individuals with a disability in smart cities depends on a variety of factors, such as the specific technology and infrastructure in place, the availability of resources and support, and the overall commitment of the city to inclusion and accessibility [48–50]. Smart cities may offer various learning opportunities, such as online courses, virtual classrooms, and other forms of digital education. However, these technologies may not always be accessible to seniors and people with limited access to experience technology or who may have physical limitations that make it difficult to use certain devices or platforms [50]. To make learning opportunities more accessible, smart cities may need to invest in assistive technologies and adaptive equipment, as well as provide training and support for seniors and people with a disability who may need assistance with using these

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tools. Additionally, cities may need to prioritize accessibility in the design of physical spaces, such as libraries, community centers, and other learning environments. Here are some considerations for improving the accessibility of learning opportunities for them in smart cities [50]: • Assistive technologies: Smart cities can leverage screen readers, speech recognition software, and captioning tools to make educational materials more accessible. Additionally, adaptive keyboards, mice, and other input devices can help people with physical disabilities navigate technology more easily [40]. • Accessible transportation: Smart cities should offer accessible transportation options to travel to and from learning spaces. This can include accessible public transportation, on-demand ride services, or other accessible transportation options. • Inclusive programs: Educational programs should be designed that consider flexible learning options, provide resources and support for assistive technologies, and create an inclusive and welcoming learning environment. • Physical accessibility: Learning spaces should be designed to be physically accessible. This includes wheelchair accessibility, the availability of ramps and elevators, and the installation of tactile indicators and audible signals for people with visual or hearing impairments.

3.5.4 What does the Job Market Look Like for Seniors and People with a Disability in the Future? The job market for seniors and individuals with a disability will likely undergo significant changes in the future [51]. Here are some trends that may impact the job market for these groups: 1. Technological advancements: The rapid pace of technological advancements may create new job opportunities well-suited for them For example, advances in robotics, artificial intelligence, and telemedicine may create new opportunities for people with physical disabilities. 2. Remote work: The COVID-19 pandemic has accelerated the trend toward remote work, which may benefit individuals who face physical barriers to commuting or working in traditional office settings. 3. Age discrimination: While illegal, it is still prevalent in many industries. However, there is growing recognition of the value that older workers can bring to the workplace, and more companies may begin to prioritize age diversity in their hiring practices. 4. Flexible work arrangements: Flexible work arrangements, such as part-time or freelance work, may become more prevalent. This could benefit seniors who want to continue working but may not want or be able to work full-time.

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5. Workforce diversity and inclusion: Increasing awareness of diversity and inclusion in the workplace may create more opportunities. Companies may prioritize creating more inclusive hiring practices and accommodations for disabled workers. 6. Entrepreneurship: Starting a business may be an attractive option for individuals who want to work for themselves or create a more flexible work arrangement. New technologies and platforms that support entrepreneurship, such as online marketplaces and crowdfunding, may make it easier to start a business. 7. Skills training: As technology evolves, many industries may require new skills and competencies. They benefit from skills training programs to help them adapt to changing job market demands. 8. Social responsibility and sustainability: As consumers become more conscious of social responsibility and sustainability issues, companies may prioritize hiring seniors and people with a disability as part of their commitment to diversity and inclusion. Companies may also create new roles and initiatives focused on social responsibility and sustainability.

3.5.5 What are Some Potential Work Opportunities for Seniors and People with a Disability Who Should Look at Investing Their Time to be Ready When the Time Comes? Seniors and people with a disability who are seeking employment may want to consider investing their time in developing skills and pursuing work opportunities that are in high demand [52]. To that end, there are several potential work opportunities to consider. • For example, with the increasing adoption of remote work policies, seniors and individuals with a disability may focus on developing skills such as digital communication, project management, and virtual collaboration to prepare for remote work opportunities. Additionally, customer service roles may be a good fit for those who prefer working in a customer-facing role, and many customer service jobs can be performed remotely. • Furthermore, healthcare roles such as medical coding and billing, medical transcription, and telemedicine may be in high demand as the healthcare industry grows. These positions may also be well-suited for individuals who have limited mobility. • Another potential avenue is online tutoring and coaching services. These have become increasingly popular in recent years, and there may be opportunities to offer expertise in areas such as language learning, business coaching, or personal development. • Seniors and people with a disability with strong writing and communication skills may want to consider pursuing writing and editing roles. Opportunities may

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include freelance writing, content creation, or copyediting for websites, blogs, and other publications. Additionally, graphic design and digital media roles may be in demand as more companies focus on developing their online presence. Opportunities may include designing graphics for websites and social media, creating digital advertisements, or developing branding materials. • As social media platforms become more central to business and marketing strategies, there may be opportunities for them to work as social media managers. These roles may involve developing content calendars, managing social media accounts, and analyzing metrics. • Accounting and bookkeeping roles may also be in demand as more small businesses and entrepreneurs seek financial support. These roles may be well-suited for seniors and individuals with a disability and strong math skills, and attention to detail. • Finally, the hospitality and tourism industry may offer opportunities for seniors and people with a disability who enjoy interacting with people and working in a fast-paced environment.

3.6 Babies and Children Socializing in Smart Houses Smart cities are meant for everyone. It is crucial to consider life expectancy in smart city innovation to ensure that deployed applications and systems remain best fitted for most people in smart cities. Thus, it is vital to consider children’s input in making smart cities as they are major players and deciders of the smart cities’ success. Children in smart cities have a very important role as they are the main reasons that dictate the success of smart cities in the long term. People are a key component of the overall smart city concept. Their needs remain the foundational point of focus for smart innovations that aim to provide solutions for the citizens’ demands. Children form a big part of the people living in smart cities and the ones that would probably live in them longer [53]. Thus, it is significant to contemplate children’s needs, wants, and adaptations as smart technologies are deployed. Many researchers even call for children to become the main builders of smart cities in the sense that smart cities should mostly focus on addressing current challenges with an eye on the future of the children growing up and living in them [54]. Smart cities not only enable smart technologies but also depend heavily on the citizens’ abilities to adapt, learn, and use smart technologies. Otherwise, the anticipated solutions that smart technologies aim to bring may result in more problems than solutions. Thus, smart cities necessitate smart people, who are smart citizens with the know-how to navigate the deployed smart technologies and services to bring out the proposed efficiency and optimization. It is without a doubt that children also must embark on the learning aspect of smart cities to efficiently adapt and thrive in them. This reality requires that the solutions being deployed in smart cities must consider children’s needs in the short and long term in terms of the added benefits

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and restrictions. It is necessary, for example, to consider the aspect of education in smart cities and what it meant yesterday, what it means today, and what it will mean tomorrow. It is evident that in education today, there are subjects that were maybe relevant in the past but are currently not relevant and will never even be remembered tomorrow due to the advancements in technology and the change of priorities for the future. In courses that used to require a lot of hand calculations in the past, now, with advanced calculators and computing, most tough calculations are getting resolved. Many jobs that higher education in the past used to provide people are now being replaced with people gaining skills in open-source platforms at almost zero cost. Today, some of the best and most popular places where people gravitate to information are not primarily libraries but the internet sites like Wikipedia, google, YouTube, stack overflow, etc. The dynamic shift in what education meant in the past is almost completely different from what it means now, and its focus is gradually changing for the future. Children need to be prepared to facilitate their acceptance of smart city initiatives.

3.6.1 What is the Place of Children in Smart Cities? The place of children in smart cities should be critically assessed as many of the deployed smart technologies may hinder children’s participation in smart cities if they do not possess age-appropriate features that enable children’s growth and adaptation organically. As more autonomous means and ways of doing things in smart cities and providing services continue to increase, it is important to consider how they affect children’s growth and participation in smart cities. It is important for children not to feel overshadowed by the countless innovations developed for smart cities so that they can feel safe and remain confident in their growth [55]. The children’s ways of play should be considered in making smart cities child-friendly as more physical games that were played back then are all being transformed into mental gaming with the innovations in video games to the point where many children are facing a lot of health-related issues due to the non-physicality of their entertainment modes. Figure 3.6 illustrates how valuable and centered children must be for the smart cities to encourage all the initiatives and applications to assess their impact on children’s growth, experience, and acceptance. Most entertainment in smart cities is focused on using mobile devices and screens which most of the time isolates people and children, in this case, from the immediate social reality in the environment they find themselves in. As smart cities invest more and more in tech innovations through smart technologies, there is a tendency for most of them to have disregarded or ignored the street life of the cities themselves to help foster great relationship-building habits among citizens and their environment. Tech innovations are impacting children in ways that maybe were not thought of before as they tend, for the most part, to alienate the children from their immediate surroundings and draw them closer to their long-distance reality through social networks, virtual gaming, augmented reality, and virtual reality. With this effect, it is noticeable that

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Fig. 3.6 Children-centered smart cities applications and systems framework

children engage in conversation with their virtual or digital friends and acquaintances more often than their immediate siblings or parents living close to them [56, 57]. This is a trade-off that society must make, and parents need to deal with it to ensure that the long-term future of children does not underestimate the benefits of social and physical interactions and experiences in the past that helped form relationships and trust among people in the detriment of the virtual digital ones. Thus, to ensure that smart cities are successful in the long term, children must be given the infrastructures and tools they need to shape the smart cities in cities that facilitate their integration and better their lives without compromising the normally required growth cadence. A five-year-old must be given the room for attention, growth, and experience needed for their age in all safety and security while gradually integrating and connecting with others in smart cities. This demand is crucial to evaluate the impact that smart technologies have had on parents bonding with their children in a world where parenting and guarding are being replaced with camera monitoring and gadgets and

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more virtual connections rather than physical ones. Children must be given the tools they need for integration into today’s smart cities and those to come, and this is the responsibility of smart city planners to provide children manual scope of how they practically fit into their overall smart city initiatives. Smart cities should be built and viewed in the eyes of children and how they are empowered in overcoming future anticipated challenges that arise because of the digitization of services and interactions [58].

3.6.2 How is the Children’s Learning Process Through Technology? What does that mean as far as the learning of our Kids? With the advancement of technologies and the deployment of smart technologies in smart cities, it is evident that children in urban areas must meet the challenge of adaptation with the guidance of parents and guardians that are also undergoing the same challenge. Thus, there are no proven strategies for integration in the new concept of smart cities as everyone tries to assimilate to the best of their abilities. It is important to remember the challenges children face, especially with parents that are not tech savvy, especially with several kids being technologically more advanced than their parents. Tech innovations requiring parental guidance are a key demand for smart cities. Still, it is only feasible when parents are equipped enough to understand the danger that their children are exposed to in digital or virtual spaces. The future of cities lies in the inclusive execution of the smart cities’ initiatives with the involvement of all parties planning to enjoy and live in them. Out of all the parties involved with the smart cities’ initiatives, the people that will stay and live longer in the smart cities are constantly overlooked and not yet invited to the table to discuss what matters to them. The future of smart cities might not necessarily be everything we anticipate, but adapting to them will require everyone’s involvement, including the children. The innovation required to meet and exceed the quality of life proposed by smart cities necessitate the revival and mixing of new ideas and the possibility of fixing and responding to the current challenges without intentionally generating many others [59]. Children cannot be left on the sideline, but they must be inspired, trained, and equipped with the intellect, character, and personality to find answers to futuristic problems consciously [59]. Children are to be engaged early on with the understanding of the current challenges about the environment, waste, energy, pollution, climate change, education, economy, sustainability, and nature preservation that the smart cities aim to address. When children get exposed to these issues early on, they trigger a sense of responsibility for them to play their part and begin to brainstorm about possible solutions.

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3.6.3 How Accessible are Learning Opportunities for Children in Smart Cities? How accessible is learning for children learning virtually, getting free education through many avenues like YouTube and boot camping, and what does that mean for children? Many aspects of children’s development in smart cities are changing drastically due to the innovation deployed to optimize some smart cities’ services and processes. One of the aspects that are considerably changing for children is the way children learn and the number of learning avenues available in the digital space nowadays. Children accessing learning platforms in the virtual space tend to leave some children behind in acquiring information. At the same time, other parents face challenges of not knowing how to navigate the virtual spaces to help their children better. It is also important to consider the availability of information in the open source that is rapidly spreading without any clear validation, so the digital space provides an avenue of misinformation that rapidly reaches larger audiences. When children begin to be intoxicated with misinformation, they may end up being a heavy burden for the smart cities and make decisions that may not contribute to the creation of better relationships and trust among citizens. Getting almost free knowledge and education through avenues like social networks leaves many people wondering what the metric of skill-set separation in smart cities would look like. This provides a basis for the trade-off and consideration, especially with education costs increasing to consider whether going through school or focusing on a particular trade is a better investment for the opportunities offered.

3.6.4 What does the Job Market Look Like for Kids in the Future? It is paramount to address this challenge now as more ideas are being considered in building smart cities and managing the resource efficiently to answer to the citizens’ needs. The digitization of smart cities has impacted the educational systems, and every other aspect of the future of smart cities is gradually becoming more digital as time passes. Smart city initiatives must decide and identify the exact role and place that children nowadays will play in smart cities. It is essential that decisions are made with a thorough consideration of what smart cities would mean in 10, 20, or 30 years and whether the systems and technologies being currently deployed would remain sustainable and resilient to stand the test of time or to evolve easily with time. The job market that has embraced automation to facilitate efficiency and optimization should need to be assessed as far as other risks are concerned, especially when it comes to resilience, security, and safety. Many questions may be asked regarding how the automated smart cities would cope with the loss of power, natural disasters, cyberattacks, and less knowledgeable citizens that have embraced automation and put their

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trust in it. These are the factors that our children will potentially face in numbers, so it is important to ensure great contingency plans are deployed and children are trained and prepared for these challenges. Decision-making in the future will predominantly be data-driven decisions, and systems will be created to understand the collected data with specific constraints and in specific environments or scenarios. Thus, smart cities must proliferate to children’s communication medium the key smart cities’ metrics and data-driven criteria for critical systems in ways children can start assimilating and evolving. It is vital to consider also what parameters might need to be adjusted or tuned should any constraint change and ensure that a mitigation process is in place for future adoption. The question is that if systems are automated, how much would human interaction is, and what control would be in place for them to ensure a smooth transition to the next generation? The anticipation of innovation and technologies in the future must trigger even more innovation and growth in other areas of the cities’ social aspects. Work-life balance in smart cities is a key indicator of involvement and engagement of parents and children to ensure that in the transformation process, children are not left alone. There are so many jobs that AI robots would take, so it won’t be feasible for the children to compete for those opportunities in the future as AI robots would ensure a higher throughput in terms of production and delivery. Thus, there will be a trade-off need to be made in compensation by creating more and better opportunities that elevate the level of satisfaction in children for the long term.

3.6.5 What are Some Potential Work Opportunities Children Should Look at Investing Their Time in to be Ready When the Time Comes? As the role of robots continues to rise in smart cities with the greater influence of artificial intelligence, there is a radical shift in almost all the sectors and industries in smart cities to accommodate the deployed technologies and benefit from their proposed automation optimum and efficient value. As a result, the nature of job creation in smart cities will dictate the kind of skill set that must respond to the apparent need. It is important to note that the knowledge of AI and how to interact with it will be fundamental together with knowledge in data science and analytics to make sense of the overall automated processes. Thus, children should be encouraged to explore their software and programming interests and bridge their activities in a way that relates to exploring and understanding the digital world presented to them in terms of applications, systems, and processes. Interested in careers or activities that are routine and can be performed by a robot or automated tend to be a risk because of the thrust in enabling automation wherever possible. Companies are heavily investing in providing automated solutions driven by AI in many of the industries in smart cities to the point of even replacing some of the most

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convoluted and complex tasks with machines to avoid human errors in workplaces and preserve human lives event of a catastrophe. Understanding how to deal with and assimilate the digitization of processes is a key skill to begin to teach children so that they can be prepared to adopt the newer technologies and use them accordingly without putting themselves at risk. From block-chain incorporation in many ways and services that interact with citizens in smart cities to the takeover of automation in the job creation market to the demand for advanced software and programming skills in almost all tech-related job opportunities to a common appetite for experimenting with technology and exploring the digital sphere through digital marketing, it is clear that the citizens of smart cities would need to be soaked in technology and possess the required skill-set if they are to survive in smart cities. Thus, if children in smart cities are to enjoy the benefits therein, they must be encouraged and prepared to find their inner motivation to embrace and trust the digital world with its processes and applications.

3.7 What Types of Job Opportunities will AI and Technologies Eradicate? Artificial intelligence is deployed with many other smart technologies in smart cities to help automatize and optimize services. The evolution of AI has enabled the development of cutting-edge robotics to replace some of the routine and even sophisticated labor [60]. This challenge must be addressed considering the essence and nature of jobs that children today may have to focus on for the future. Will the innovations and dependability of technologies inspire children to consider investing in research and ways to improve on the technologies, or will it be an avenue of comfort for everyone where everything is being presented effortlessly? With AI driving the job markets of the future and making other types of jobs extinct, it is vital to assess whether children will have a say in what they can expect or if they may have to deal with what will be presented to them. Many jobs that help build a society by encouraging interactions among citizens are replaced by having kiosks instead of personnel, chat-bots instead of living customer reps, etc. The digitization of many jobs in smart cities results in the elimination of jobs that encourage the social and physical interactions of citizens, so children in smart cities may miss out on building social skills due to most of the interactions becoming virtual or digital. Besides, the rise of AI and other advanced technologies will likely disrupt various industries and job types. Some of the jobs that are most at risk of being automated or eliminated by AI and other technologies include: • Routine, repetitive jobs: Jobs that involve performing the same task repeatedly, such as assembly line work or data entry, are at a high risk of being automated. • Manual labor: Jobs that require physical labor, such as construction work or manufacturing, may be replaced by machines and robots.

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• Customer service and support: AI-powered chatbots and virtual assistants can handle many customer service tasks, reducing the need for human customer service representatives. • Driving and transportation: Self-driving cars and trucks could eliminate the need for human drivers. However, further research and tests are required. • Data analysis: AI and machine learning algorithms can process large amounts of data and perform tasks such as predictive modeling and data mining, which could make some data analyst jobs obsolete. • Education: Personalized tutoring and educational content. • Healthcare: Patient triage assistant, symptom assessment, and general healthrelated information. • Creative writing: AI-powered chatbots can generate content such as news articles, product descriptions, poetry, etc. • Marketing: For personalized marketing campaigns based on customer engagement. However, while AI and other technologies will certainly change the job market, they will also create new job opportunities in areas such as AI development, data science, and robotics engineering. The key for workers is to develop the skills and knowledge needed to stay relevant and adapt to the changing job market. AI and technologies are likely to replace jobs in areas such as manufacturing, data entry, customer service, data analysis, and administrative tasks. In addition, AI and technologies are expected to reduce the number of jobs in the transportation and logistics sector, both through the automation of vehicles and the introduction of robots and drones for delivery. AI and automation are also likely to have a major impact on jobs in the finance and banking sector, as well as in legal services and healthcare.

3.7.1 What Types of Job Opportunities will be Needed in the Metaverse? The metaverse is an emerging concept that describes a virtual world where people can interact in a shared online space, often using virtual reality technology [61]. As the metaverse develops, it will likely create job opportunities in various fields. Here are some potential job opportunities that may emerge in the metaverse: • Virtual world developers: These professionals will be responsible for creating and maintaining the virtual world infrastructure, designing environments, and developing software applications. • User experience (UX) designers: These professionals will focus on creating engaging and user-friendly experiences within the metaverse, developing user interfaces, and designing virtual interactions. • 3D artists and animators: These professionals will create the characters, objects, and environments that populate the virtual world.

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• Virtual events coordinators: These professionals will plan and organize events within the metaverse, such as virtual conferences, concerts, and other social events. • Virtual goods and services providers: These professionals will provide virtual goods and services to users, such as virtual real estate, digital assets, and virtual currency. • Virtual reality hardware and software developers: These professionals will focus on developing the hardware and software required to access and navigate the metaverse, such as VR headsets, haptic feedback devices, and other peripherals. • Artificial intelligence developers: These professionals will focus on developing the AI algorithms that will power intelligent agents within the metaverse, such as virtual assistants and automated avatars. • Cybersecurity professionals: As with any online space, the metaverse will require cybersecurity professionals to protect against cyberattacks, data breaches, and other security threats. • Social media managers and community managers: These professionals will manage the social media presence and online community of companies, organizations, or individuals who have a presence in the metaverse. As the metaverse continues to develop, additional job opportunities will likely emerge, and many of the job roles may require interdisciplinary skills and expertise. The metaverse is a rapidly developing virtual universe that blends the physical and digital worlds. As such, it can potentially create many new job opportunities. Virtual world designers are responsible for creating the visual and interactive elements of the metaverse. They are responsible for designing the layout of the world, the look and feel of the environment, and the interactions between users. 3D artists are also responsible for creating the virtual world’s 3D elements, such as characters, buildings, and other objects. Virtual reality developers are responsible for creating the software for the metaverse. This includes creating the interface for users to interact with the metaverse and developing the software that powers the virtual world. Virtual world administrators are responsible for the management and maintenance of the metaverse. This includes managing user accounts, ensuring the virtual world is secure, and addressing technical issues. Virtual world strategists are responsible for developing strategies for how businesses can leverage the metaverse. They are responsible for identifying opportunities and developing strategies to take advantage of them. Virtual world entrepreneurs are responsible for developing and launching virtual world business models. They are responsible for identifying potential business opportunities and developing the necessary strategies and plans to make them a reality. Virtual world marketers promote the metaverse and its associated products and services. They are responsible for designing and executing marketing campaigns and managing customer relationships. Virtual world writers are responsible for creating content for the metaverse. This includes writing stories, developing characters, and creating interactive elements. Data scientists are responsible for analyzing the data generated within the metaverse. This includes developing algorithms to identify patterns in user behavior and using machine learning to improve user experience.

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IT professionals are responsible for managing the technical infrastructure of the metaverse. This includes developing and maintaining the servers, databases, and networks that power the virtual world. Artificial intelligence engineers are responsible for developing the AI systems that power the metaverse. This includes creating algorithms to power intelligent agents and developing automated systems to improve user experience. Product designers are responsible for designing the products and services sold within the metaverse. This includes developing the product and service user interface and user experience. Overall, the metaverse has the potential to create a wide range of new job opportunities. These opportunities span a variety of disciplines, from design and art to coding and data science. As the metaverse continues evolving, these opportunities will likely grow in number and variety.

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Chapter 4

Smart Communities

4.1 Empowering Social Communities Recalling Chap. 1, Sect. 1.3, there are different types of communities such as residential, commercial, mixed-use, and academic, among others, and must enhance social relationships through interconnected networks, and urban places, and provide leisure, workplaces, and shopping areas. Furthermore, any smart community can be located in the same geographic area or at least have similar community characteristics, such as household type, climate zone, and similar demographics. Currently, there are online communities in the metaverse in which it is a virtual world where people interact in a shared online space, often using virtual reality technology with their own avatar or persona [1]. Moreover, connected smart communities in developed countries are fundamental for improving the QoL. There are specific governmental and private programs that promote societies to be more connected. Furthermore, it is possible to understand how a community behaves by knowing the communities’ predominant personality traits. For instance, in [2], they analyzed Mexican communities from a worldwide dataset containing 223 countries. They exemplified how there were different predominant personality traits depending on the community, for instance, Tamaulipas is predominantly with agreeableness personality traits rather than Nayarit which is openness. Furthermore, Oaxaca predominates more as a neurotic community than any other Mexican location. Even though, there is an online map available with the personality traits classification from Goldberg’s dataset [3]. Although some communities predominate a particular personality trait, some individuals may have a different trait. For instance, the community is extraverted, but the individual has a neuroticism personality. Thus, specific activities are suggested to this individual. Hence, a way to empower a community is by understanding their general and particular behavior to provide strategies tailored to the specific needs of the individual and the community.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 P. Ponce et al., Data and AI Driving Smart Cities, Studies in Big Data 128, https://doi.org/10.1007/978-3-031-32828-2_4

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4.1.1 Universities and Industrial Parks as Smart Communities Universities and industrial parks are a type of smart community because they are highly networked and often have advanced infrastructure and technologies that enable them to optimize operations and enhance the quality of life for residents. Here are some ways in which universities and industrial parks are embracing smart community technologies [4]: • Smart buildings: Many universities and industrial parks have implemented smart building technologies that use sensors and automation to optimize energy use and improve indoor air quality [5, 6]. • Advanced transportation systems: Universities and industrial parks often have transportation systems, such as shuttle buses or even autonomous vehicles, that can be optimized using real-time data to reduce traffic congestion and improve transportation efficiency [6, 7]. • Advanced energy systems: Many universities and industrial parks are adopting renewable energy technologies, such as solar and wind power, to reduce their carbon footprint and enhance sustainability [5, 6]. • Research and innovation: Universities and industrial parks are hubs of research and innovation, and they often use advanced technologies to drive progress in fields such as biotechnology, nanotechnology, and artificial intelligence [6, 8]. • Community engagement: Universities and industrial parks often have strong ties to the local community, and they use smart community technologies to engage with residents and improve their quality of life [6, 7]. • Smart waste management: Industrial parks often generate large amounts of waste, and many use smart technologies to optimize waste management and reduce their environmental impact [6, 7]. Hence, universities and industrial parks are important examples of smart communities that use advanced technologies to optimize operations, enhance sustainability, and improve the quality of life for residents. By continuing to embrace smart community technologies, these institutions can serve as models for other communities looking to become more sustainable and livable. While universities and industrial parks share many similarities as smart communities, there are also some key differences between them. Here are some of the main differences: • Focus: The primary focus of universities is education, research, and innovation, while the focus of industrial parks is business, manufacturing, and commercial activities. As a result, universities tend to prioritize technologies that enhance education and research, while industrial parks tend to prioritize technologies that improve manufacturing processes and supply chain management. • Governance: Universities are often public institutions overseen by a board of trustees or a similar governing body, while industrial parks are typically owned and

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managed by private companies or developers. As a result, the governance structures and decision-making processes of these communities can be quite different. • Size and population: Universities tend to be larger than industrial parks and often have a much larger population of students, faculty, and staff. This can create challenges and opportunities for smart community technologies, such as transportation systems and energy management. • Community engagement: Universities are often more engaged with the broader community than industrial parks, as they tend to be centers of culture, sports, and other activities open to the public. As a result, universities may use smart community technologies to engage with the community and improve the quality of life for a broader group of people. • Funding: Universities often have access to significant public funding for research and development, which can be used to support smart community initiatives. Industrial parks, on the other hand, are often driven by private investment and business interests, which can limit the scope and direction of smart community technologies. Therefore, by understanding these differences, researchers and practitioners can better design and implement smart community technologies that are tailored to the specific needs and characteristics of each community.

4.1.1.1

Carbon-Neutral Economy for Sustainable Manufacturing

A carbon-neutral economy for sustainable manufacturing refers to an economic system that operates without emitting more greenhouse gases (primarily carbon dioxide) into the atmosphere than it removes [9, 10]. This means that the manufacturing industry must significantly reduce or eliminate its carbon footprint, which is the amount of carbon dioxide released into the atmosphere as a result of its activities. To achieve a carbon-neutral economy for sustainable manufacturing, companies must adopt sustainable practices that reduce their carbon emissions, such as [11, 12]: • Sustainable materials and production processes: Companies can reduce their carbon footprint by adopting sustainable materials and production processes that use fewer resources, generate less waste, and have a lower environmental impact. • Recycling and circular economy: Emphasizing recycling and circular economy can lead to a reduction in greenhouse gas emissions through a reduction in the amount of waste that needs to be disposed of. • Energy-efficient practices: Manufacturers can also reduce their carbon emissions by adopting energy-efficient practices such as using high-efficiency equipment, optimizing processes, and implementing energy-saving measures. • Carbon capture and storage: Some manufacturers can use carbon capture and storage technologies to capture and store carbon dioxide emissions generated during manufacturing processes, effectively removing the emissions from the atmosphere

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4.1.2 Healthcare Buildings as Smart Communities Healthcare institutes are transforming into smart communities by implementing advanced technologies to improve patient care and healthcare outcomes. In recent years, healthcare institutes have been developed as smart communities, with electronic health records, telemedicine, wearable devices, artificial intelligence, the Internet of Things, advanced analytics, robotic process automation, virtual reality, blockchain, and smart hospital design as some of the ways healthcare institutes are being transformed [13]. • Doctors and nurses can access patient information quickly and accurately through electronic health records, leading to more accurate diagnoses and better treatment plans. • Telemedicine uses advanced technologies to provide medical care to patients who cannot travel to a healthcare facility. • Wearable devices can collect data on a patient’s vital signs and physical activity, providing doctors and nurses with important information about a patient’s health. • AI can help doctors and nurses make more accurate diagnoses, predict patients’ risk of developing certain diseases, and even identify potential side effects of medications. • IoT devices can monitor patients’ vital signs, track medication usage, and collect other important health data. • Advanced analytics can be used to identify trends and patterns in healthcare data, which can be used to improve patient care and outcomes. Virtual Reality can simulate medical procedures and surgeries, allowing healthcare professionals to practice and improve their skills. • Blockchain can store and share medical records securely, ensuring patient privacy and data security. Hence, smart hospital designs can use AI and IoT to optimize patient flow, reducing wait times and improving patient care healthcare. Besides, institutes are implementing advanced technologies to improve patient care, reduce costs, and improve healthcare outcomes, transforming into smart communities. With the continued advancement of technology, healthcare institutions are expected to continue leading the smart community movement.

4.2 Social, Sustainable, Sensing, and Smart Products The major challenges that the world is facing today including global warming, climate change, resource scarcity, overpopulation, deforestation, energy demands, supply chain tension, security threats, privacy risks, etc. heavily affect smart city initiatives. The very effect of all these catastrophes is more apparent in smart cities where they affect even more people simultaneously. In response to many of the above-mentioned

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challenges, smart cities are adopting the use of technology to help provide optimal and efficient remedies. In this regard, more focus is based on heavy investment in both software and hardware to help develop social, sustainable, sensing, and smart products (S4 products) that render smart cities more appealing and satisfying to mutually work and live in. The S4 products help replace traditional products and provide means to better resource management and service fulfillment by integrating with other products and leveraging data to enable data-driven decision-making and service fulfillment [14]. There are many social products currently in smart cities that are easily used both in connecting the individual to the social digital world and the physical social realm with many relationships and partnerships starting in the virtual space and gravitating towards physical encounters. Smart social products such as social networks are myriad in smart cities, and they demonstrate the extent to which technology and innovation can solve some of the social needs of citizens. It is crucial to identify in all aspects the influence of technology-enabled products in smart cities that possess the smartness and the sensitivity to respond to a natural trigger and provide or execute a needed action to remedy the circumstance without human supervision. These products enable smart cities to address and tackle global challenges and ensure the available resources are managed sustainably to meet the forecasted future demands. As a result, it is paramount to consider the implication, coordination, and integration of all the software and hardware pieces and components that make up the sensible, smart, social, and sustainable products to enable their functionality and operations in fulfilling their objectives. Various products enable smart cities’ initiatives and innovation through smart technologies, and the figure below showcases some of the applications and systems to give a perspective on the variety of applications and potential (see Fig. 4.1).

Fig. 4.1 Ubiquitous social, smart, sensing, and sustainable products in smart cities

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The increasing influence of the development of cutting-edge software and hardware has infected all sectors of smart cities. The utility systems that run the smart cities now experience transformation due to the deployment of IoT devices, the generation of Big Data, and the implementation of ICT infrastructures. Thus, smart technologies propose several S4 products that provide potential benefits due to the interconnectedness of potential rising prospects of merging software and software fragments and mixing content across production schemes, infrastructures, and platforms [15]. Almost all aspect of the cities including manufacturing is becoming smart. All technology companies are gradually exploiting sensors and wireless technologies to collect data at multiple stages of a product’s lifespan [16]. Smart technologies that are experienced in smart cities make various kinds of virtual and physical products and enable them to be reusable, programmable, sensible, memorable, communicable, and traceable. Smart city products permit innovative integrations of both digital and physical extents of advancement in technology to create new manufacturing processes and products [17]. It is therefore compulsory to assess the impact of the prolongation of the rapid progress of smart technologies in the short and long term as it pertains to the kinds of smart sustainable products and their implications in enabling smart cities today [18]. The development of technology innovations that constitutes smart technology products encompasses many different aspects as it pertains to the social, environmental, and economical proportions of smart cities [19]. Nevertheless, the social, environmental, and economical implications drive more creation and deployment of products in enabling profit-oriented goals within the smart cities’ corporate viable policy [17]. Many enterprises and organizations in the private sector in response to the developmental trends of technology incentivize the creation of the social product to enable the stability of the social, environmental, and economical demands of both the cities and citizens to realize corporate sustainability goals [17]. Corporate sustainability goals also continue to be building blocks that propel the realization of various innovative products that provide answers to the various problems and challenges that are encountered in smart cities today. The impact of corporate sustainability on smart technologies cannot be undermined because more and more products in smart cities today are being replaced by products that meet corporate sustainability goals in terms of energy, sustainability, efficiency, and optimization. Smart technologies possess an unswerving substantial impact on economic sustainability that is partially facilitated by trade sustainability policy. Thus, it is clear that smart technologies encompass the ensemble of myriad sensible, social, smart, and sustainable products that help provide resolutions for the global preoccupation within smart cities to fulfilling the demands of their citizens.

4.2.1 Smart Technologies Almost every firm in smart cities aims to change the nature of their business by adopting more of their smart technology advances to improve and optimize their

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processes [17]. Most firms are investing and providing smart management systems in their operations by implanting smart technologies in their machinery and technologies [20]. Services and Products that are not smart are gradually being replaced with those that are fully furnished with smart technologies to deliver fresh functions that improve their usage, design, manufacturing, and delivery [21]. Furthermore, smart technologies offer more opportunities for social economic products that provide the massive potential for enabling new experiences, processes, connections, and infrastructural systems wherein digital sensors, processors, networks, and radio-frequency identification (RFID) tags generate required attributes and properties [20]. The required attributes and properties that these products exhibit constitute what is known by some researchers as digitalized artifacts as they encompassed the necessity and desirability of the products and services to entirely possess the affinity to become digitized [22]. Products in smart cities possess the seven characteristics that associate them with artifacts properties that cement their digital attributions. The seven characteristics include products being addressable addressability, programmable programmability, communicable communicability, sensible sensibility, traceable traceability, associable associability, and memorable memorability. These characteristics are critical to note as improvement and shift to novel aspirations and design continue to become apparent. These characteristics can be easily comprehended when one considers the applications or services that smart technologies products provide. The programmability characteristics have to do with the devices being able to be configured with new changes and reconfigurations for their specific functions. The addressability characteristic pertains to the ability of the devices to respond to messages individually. The sensibility characteristic is the ability of the devices to possess the necessary aptitude to detect, track and react to changes in their immediate surroundings. The communicability characteristic on the other end pertains to the ability of devices to receive from and send messages to other devices in their interactions. The memorability characteristic of the devices pertains to their ability to collect, record and store sensed, created, and communicated information. The traceability characteristic of devices pertains to the ability of devices to chronologically identify, remember, and integrate occurrences and events over time. The associability characteristic pertains to the ability of devices to be identified with other totalities, for example, other places, people, and artifacts. Other characteristics are continuously being added to the seven mentioned earlier like interactivity, distributedness, editability, and openness/re-programmability. These characteristics like the seven mentioned above also pertain mostly to the functionality of the devices or products to accomplish the new improved and optimized services. The editability characteristic pertains to the ability of devices to continuously bring up to date their content, data, and items. The interactivity characteristic on the other end pertains to the ability of devices and products to exploit information using the supple and amenable nature of objects rooted in digital artifacts. The openness/re-programmability characteristic relates to the ability to access and modify the devices’ digital artifacts, while the distributedness characteristic denotes the ability of devices to become borderless regarding digital objects [21] in this chapter.

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The re-programmability characteristic allows for new functionalities and abilities to be added to products and services even after they are produced and deployed to respond to the demands of smart cities and applications. this is due to the data homogenization that many of the IoT devices possess in smart cities as they can produce and handle different types of digital content including text, audio, image, and video in stocking up, dispatching, refining, and displaying employing the same smart cities digital networks and devices. It is also due to the self-referential capabilities that products and services in smart technologies have that enable them to make use of the necessary usage of technological advancement that is enabled [21]. Thus, smart technologies enable the pervasive digital reality in smart cities by integrating digital proficiencies into objects that possessed virtuously physical corporeality in advance. This makes smart technologies the driver of innovation in smart cities as it enables the packaging of various properties implanted into formerly non-digital products and empowers smartness intended for those products. Smart technologies enabling the production of digitized machines through the IoT, Big Data, and ICT technologies are considered to possess potential constructive influences on the environment by decreasing greenhouse gas (GHG) emissions, driving sustainability goals, and so on [17]. Smart technologies continue to presently offer better provisions and promises for smart and automated solutions that optimize production optimization in many different industries, for example, power generation, and agriculture, which in return improve the energy efficiency of the overall smart cities. In the digital realm, different kinds of digital twins can offer real-time information and immediately regulate and improve systems and processes. RFID in smart technologies enable and increases the reusability of construction building blocks and reduces their surplus, consequently supporting the ecological sustainability successes inside the construction sector [23]. Smart technologies facilitate the success of sustainable growth and ecological defense goals by enabling information flow all over production systems and processes. When data is collected in real-time, the manufacturing systems and processes are efficiently and effectively operated and controlled to eventually reduce the energy spent on workpieces, devices, and public premises due to optimizing the operations [24]. Therefore, smart technologies are the enablers of smart cities that drive the reduction of energy consumption and climate change threats, and the elimination of industrial processes’ emissions, electrical grid challenges, and transportation systems overloads [25].

4.2.2 Smart Health Technology Smart health technology is powered by more and more sensible, social, smart, and sustainable products as it is one of the aspects of smart cities that are heavily incorporating various products to enhance the quality of life and wellbeing of citizens. It is easily noticed in smart cities the necessity to avoid depression and drive to alleviate avenues of stress by living healthy lifestyles and optimizing the level of services provided to citizens for their satisfaction. Customers’ and patients’ satisfaction is a

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key driver of many innovations in smart cities that enables the smart cities systems and applications makers to always drive for the continuous improvements of their proposed value. Smart cities seek to improve the quality of life of their citizens and ensure their well-being is ever improving with the deployment of innovative and transforming technologies to optimize, detect, and access factors that limit and hinder citizens’ satisfaction. As a result, many smart city applications have been developed as part of smart health technology and have become a big contributor to smart city initiatives to ensure the continuous improvement of citizens’ quality of life and wellbeing. The increasing number of people in smart cities requires a careful reflection of the health condition of citizens in the short and long term as citizens experience and assume the various changes occurring in smart cities because of new applications, systems, and technologies that they interact with repeatedly to copiously profit from residing in smart cities. Smart cities nowadays are bursting with numerous health-associated IoT devices that power applications and systems to influence and leverage the usage of Big Data and ICT infrastructures to empower diverse services inside the smart city. It is therefore easy to quickly note and discover that smart health technology is really at the epicenter of many smart city applications and systems that tend to shape the future of all smart cities because the well-being and health of citizens are paramount. If smart cities are to proactively respond to the health needs of their citizens over time and meet the increasing concerns of individuals relocating to them, the smart health technology vector within the smart cities must be solidified to meet the proportional healthcare demands. Citizens in smart cities expect the quality of healthcare to keep on improving through both proactive and reactive measures with the use of various applications, systems, and technologies that are constantly deployed to monitor and manage users’ health and provide recommendations toward a better quality of life and wellbeing. There are no opportunities too attractive to justify citizens overlooking their health and care as was recently shown by the Covid-19 pandemic. It is obvious to notice countless smart health IoT components installed and produced in smart cities nowadays such as medical sensors, medical wearable devices, phone health applications, health trackers, smart watches, medical bands, ECG devices, EMG devices, blood glucose, heart rate devices, blood pressure devices, accelerometers, gyroscope, and many other motion sensors devices, etc. The operation and functionality of many of these devices to some extent present some inherited security anxieties aimed at citizens and cause some security-made privacy worries when they are viewed through the lens of empowering privacy-conscious smart cities. All these gears produce data that relates to people’s health and health history, but the data is transferred and shared over numerous paths from systems to systems or devices having different security levels. As devices interact and connect with other devices, they offer ways for the data to be compromised that cannot be ignored particularly when there is a potential for hacks and breaches. The success of smart health technology in smart cities depends on the interconnectedness of IoT, Big Data, and ICT frameworks in obtaining pertinent health data and enabling the development of health systems that answer to the crucial requirements of citizens’ health care

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extending from vital signs tracking in aging individuals to temperature monitoring in newborns [26, 27]. These smart health monitoring devices create opportunities for emerging sustainable products in virtually all the segments of human growth that the acquired data should profit from with appropriate analysis and decision making. The privacy-conscious smart city necessitates more development of smart health applications and solutions that improve the quality of life of citizens and provide them with devices they need to better monitor their health and prevent many health risks. Smart cities need to provide citizens ways to interact with their health status conditions in real time through applications without causing privacy worries. As investments and initiatives in smart health continue to intensify in smart cities, it is paramount to strengthen and enable a robust, balanced, and resilient smart health infrastructure that is affordable to everyone. The smart health network should be able to provide optimal resolutions to both the long- and short-term health requirements of citizens. The developments and investment initiatives include systems such as health embedded systems, artificial intelligence, and machine learning cloud computing. These systems help deal with some individual health concerns among citizens in addition to producing and deriving insights from data that collectively highlight the overall health tendencies in smart cities [27, 28]. As the possibility of more health sensors, applications, and systems are developed and deployed in smart cities, there is a greater concern about collecting individuals’ health information. As the security risks connected to IoT devices increase and are experienced, there is a tendency that more malicious intents may find avenues to compromise and tamper with sensitive data in the overall health system. To accurately track and assess citizens’ health, there is a definite need to identify the concerned individuals, authenticate them and accurately associate them with a particular device by exchanging personal information. This is vital to enable the provision of better-personalized services to individuals. However, there can be a lot of privacy worries should any security incident occur that compromise the smart health systems. Nevertheless, the proposed 3D privacy framework proactively endorses the case of health systems to start dealing with the privacy issues of the smart health technologies sooner than their deployment in smart cities by establishing robust guidelines that address the privacy risks alongside safety and security measures to realize privacy-conscious smart cities. The security concerns about the collection of personal information in smart health technologies, applications, and devices range from the collection of data linked to some smart health devices to some technologies used for connectivity and transfer of information to some health management systems glitches and the overall user interactivity with the used systems, applications, and technologies [26]. Many of these security issues in smart health systems increase privacy concerns amongst citizens due to the constant requirement to identify, verify and authenticate individuals in smart cities before using these systems and applications and before making any reasonable health assessment. Most smart health applications and systems use connectivity protocols such as Bluetooth, NFC, Wi-Fi, ZigBee, BLE, and Satellites while on mobile devices where protection can be attained. The privacy methods of data preservation can use active technologies to help enable a proper privacy-

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aware solution that can incorporate the pseudonyms method [29] and anonymization method [30] together with encryption and blockchain [31, 32] to protect the identity of the individual. This approach however will make it extremely harder to associate individuals with their data appropriately. The main risk to citizens’ health data is the tampering of data to result in wrongly prescribing medicine to a patient. Other worries might be when considering on-body sensors health monitoring that gather data and transfer data in real-time for immediate response to trigger an action. If these body sensors are hacked into and the wrong action is triggered, this might put the patient at more risk and danger [26]. Most adapted health monitoring IoT devices found in smart cities are often used and connected to other IoT devices which exchange data using the same communication media as SMS (short-messaging services), Bluetooth, NFC (near field communication), Wi-Fi, and sometimes even social media medium. These connectivity avenues have shown that they can be breached into and compromised mostly because of an insecure internet connection or corrupted connectivity protocols used in data exchange traffic loads [26]. The question and concern surrounding citizens is how their smart cities run in the first place. This is where smart governance technology comes into play in the overall smart city’s context. This is the sunshade that shelters other smart cities’ technologies to guarantee the objectives of smart cities are met and citizens are satisfied with the outcome. It is crucial and foundational to smart cities because smart governance technology tends to create a medium of interaction between citizens and their smart cities’ leaders in a way that is transparent and traceable. This is what makes it possible for leaders to be questioned regardless of whether they are engaged with their citizens or not. Leaders are somehow compelled to justify their actions and account for their decision in real-time while gauging the sentiment of citizens. Especially when it comes to investments in deployed technologies in smart cities, citizens possess avenues via smart government technology to seek justification to better understand what drives investment in certain projects and not others. These systems are critical in ensuring are addressed efficiently and using optimally using resources. It is due to the increasing number of citizens migrating to cities that the demand for better management methods, protocols, and tools are required to better channel interactions and communications between the different stakeholders in smart cities such as the citizens, government, citizens, and other stakeholders’ concomitant together while empowering smart cities to function to their full capacity [33]. Smart governance technology also includes the usage of IoT, Big Data, and ICT frameworks in enhancing authorized government decision-making to enable the alliance and adaptation of smart cities’ citizens and government to smart cities’ technologies and systems.

4.2.3 Smart Governance Technology Smart governance in the context of smart cities can be demarcated as the potential of a smart city government to utilize intelligent and adaptive resources at its disposal to enable better decision-making about things and processes for the benefit of citizens

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to ensure efficiency and better quality of life in both the short and long term [34]. It is imperative for smart governance in smart cities to be successful because the success of smart cities lies in the way that citizens’ quality of life and wellbeing are experienced. Smart governance technology possesses several components such as data, government, citizens, environment, and technology that enable the smart governance’s ecosystem, which includes ICT-based tools such as social media, to help provide solutions to the numerous challenges requiring better governance like transparency, openness, democratization, citizen engagement, and information sharing. The presence of IoT, Big Data, and ICT frameworks are apparent in smart governance technology, and as such there are opportunities for security glitches that may lead to privacy concerns that should be addressed in smart cities to ensure they become more privacy-aware smart cities. As data and information flow in the smart governance ecosystem, there is a great need to fully understand and distinguished the flow of personal and impersonal data as there is a greater chance of overlap in the services provided where it might be hard for example to separate the opinion of an individual with what they stand for and who they identify with [26, 34]. The role of technology in this space is crucial in enabling the optimization of urban services by providing extra avenues for feedback and interaction with citizens. Many smart cities capitalize on organizations that empower more resilient and transparent smart governance technology that deals with and offers an optimum governing resolution to the citizens’ requirements in the short and long term. There are many explanations nowadays on how to deal with the governance requirements in smart cities such as via different electronic government (e-government) and electronic governance (egovernance) systems and applications. The smart governance technology devices in smart cities possess many privacy concerns, and there is myriad induced security associated problems with both e-government and e-governance smart applications as IoT, Big Data, and ICT bases are interconnected in empowering the realization of connection and the collaboration of the government and citizens as citizens’ views are voiced in smart cities through citizens’ engagement [26, 35]. The 3D privacy framework highlights the privacy concerns about the usage of smart governance applications and technologies that necessitate several measures of control to preserve the privacy of citizens. Many concerns are regarding how to anonymously gauge citizens’ involvement without appropriate evidence that actual citizens are recorded and not fictive records or visitors [35]. Other privacy and security concerns through various data collection processes require the verification of citizens using state IDs or state’s approved documents. It is important in some other cases to identify that a citizen belongs to a smart city and deliver a response to the municipality over secured e-government applications. However, there are still some security problems that arise due to the exchange of some personally sensitive information which is the case with sentiment monitoring applications in smart cities via social media where many privacy concerns are generated [35–37]. Some forms of both identification and authentication are required in many technologies’ solutions about democracy, social inequality, and justice applications, which can affect the citizens’ privacy when compromised [35, 36].

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4.3 Smart Megacities A megacity is a metropolitan area with a population of around 10 million with a densely populated urban area. Megacities are typically characterized by high population density, sizeable economic output, and socioeconomic complexity. Examples of megacities worldwide are shown below with an estimated population. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Tokyo, Japan: Population 37,393,129 Delhi, India: Population 29,399,141 Shanghai, China: Population 26,317,104 Sao Paulo, Brazil: Population 22,043,028 Mexico City, Mexico: Population 21,782,378 Cairo, Egypt: Population 20,484,965 Beijing, China: Population 19,612,368 Dhaka, Bangladesh: Population 18,894,863 Mumbai, India: Population 18,414,288 Osaka, Japan: Population 17,491,297.

On the other hand, it is possible to define Smart megacities as cities equipped with advanced technology to improve citizens’ quality of life. The generated data by citizens is used to improve urban planning. Sensors are installed around the cities to monitor and preserve environmental conditions in the smart city, such as air quality. Besides, social networks play an essential role since they can provide information about the citizen’s needs. The main social networks are Facebook, Twitter, Instagram, LinkedIn, YouTube, Snapchat, Pinterest, Reddit, TikTok, and WhatsApp. Thus, Big data can be used to analyze social networks to improve traffic patterns, identify areas of congestion, and develop strategies to reduce traffic. It can also monitor air quality, identify pollution areas, and develop strategies to reduce emissions. Big data can also identify poverty areas and develop strategies to reduce poverty. Additionally, big data can be used to identify crime areas and develop strategies to reduce crime. Finally, big data can be used to identify areas of health disparities and develop strategies to reduce health disparities. Smart megacities also use technology to improve transportation, healthcare, and education. Smart cities have to tackle the following problems that are in conventional cities. Traffic congestion is a significant problem due to the massive number of vehicles, which can lead to increased air pollution, fuel consumption, and driver stress levels. As a result, the rapid growth of megacities has led to a shortage of housing, which cause a swift rise in housing prices and a decrease in the availability of affordable housing. Pollution is a significant problem in megacities due to the number of vehicles and industrial activities, and hence, it can lead to health problems, such as respiratory illnesses and environmental damage. Dangerous zones are a significant problem in megacities due to the large population and organized crime; this can decrease public safety and increase fear among citizens. Also, poverty is a significant problem in megacities due to the large number of people living in poverty; it can decrement the quality of life and increase social problems. Figure 4.2 shows a megacity structure and the main challenges.

Fig. 4.2 Megacity structure and megacity problems

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4.4 Data from Citizens, Households, and Communities Smart cities live and breathe on the data that is generated via many avenues, and the ability to possess a lot of data-driven decisions in the operation of the cities to meet the demands and requirements of the citizens make all the difference. It is essential to have ways of monitoring the distribution of resources among citizens and ensuring that better services are provided to them in a very efficient and optimized way. The data that is collected from the citizens, households, and communities ranges from impersonal data to personal data, and their use in understanding the operation of services and the smart cities’ environments remain critical to the long-term survival of the smart city’s initiatives [38]. In major smart cities, most impersonal data are collected in the form of real data like road traffic data, weather data, cultural event data, social event data, parking data, library event data, and the form generated data like pollution data, air quality data, and other aggregated service operations’ data. Data has a very big impact on almost all the sectors of smart cities ranging from transportation to health care, security to public safety, budget management to the quality of life, privacy to wellbeing, and so on. Data in smart cities form a beacon of hope for all aspects of the cities as there is a need to understand the inner workings of the cities’ operations to identify loopholes and areas that necessitate improvement. Once the different areas needing improvements or optimization are identified, data monitoring IoT devices are required to ensure that optimization results are affected as more data-driven decisions are taken and implemented in the operation of the smart cities’ services. All the data collected in smart cities are globally categorized as either structured data or unstructured data regardless of the source and purpose. How the data is collected must be appropriate to ensure that the data possesses some representation of the event being captured in the real world to associate some meaning as to what the collected data can be used for. There are several representations of objects, processes, people, and devices in smart cities that can be represented through the collected data in the interconnectedness of the network infrastructures that make up the smart cities’ digital artifacts. The data ecosystem of smart cities comprises tools that capture data such as sensors, tools that store data such as databases, tools that analyze data such as cloud software, and tools that protect data such as security infrastructures with encryptions. The relation between the capturing of real-time data and the analysis of the data gives rise to the category of data known as generated data that provides insights and values to smart cities. The different sources of data in smart cities include cloud computing sources, deployed IoT devices, crowdsourcing, internet, social media, mobile devices, smart cards, vehicles, databases and so much more. As more data is collected from these various sources, the question of categorizing the data become indispensable because people want to know how private and public data are handled. Another concern that is prevalent in smart cities is the idea of the quality of data. It is not because data is collected that it is usable. Sometimes, it takes resources and time to clean up the data before analysis to anticipate any sound insight about what the

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statistic behind the data is reflecting. Another concern in the context of smart cities is how data is aggregated and how data relates to each other through the data workflow of the smart cities going from data collected from individuals to data collected in households to data collected in communities to make up the ecosystem of the data collected in smart cities to optimize its operations.

4.4.1 Personal Data The data collected from individuals mostly pertains to data collected from mobile devices that are associated with them. This data is critical in associating the mobility and the preference of the individual through their query and interests by identifying the services the individuals use most and things and events they react to most of the time. Mobile devices collect numerous data that can provide a better representation of the individual interest in practically all the endeavors that are undertaken [38]. For example, mobility data can be collected from individuals and provide some traceability of the individual based on their location searches and locations presence, and even the mode and time of transportation. The query data can be obtained based on the various purchases that the individual makes, the streaming movies, soaps, or shows the individuals watch and the time and day they watch them, and so on. The individual health data can also be captured when the individuals possess smart Health enabling IoT devices to monitor health trends and activities throughout the days, weeks, months, or years, and enabling recommendations are possible for all these use cases. Other IoT devices are used similarly to collect information about households and provide insight into resource needs.

4.4.2 Household Data Utility services, power, and energy consumptions provide a lot of household information that is used to monitor traffic and presence within the homes and more importantly the number of resources needed to meet the households’ needs. Numerous data are generated and collected per household that reflect the amount of energy and power needed and more importantly the time that it is most needed. For example, the time and days that the households are filled with individuals would result in higher energy and power demands including water demands as individuals in the homes would consume these utility services. Thus, the demand for these services at specific times and days provides the smart cities an insight as to how much energy and water give and take are needed to meet the demand of the household on average [38]. Data also coming from smart devices in the homes and interconnecting via the internet to the individuals in the homes provide a way to associate individual activities with the home smart performance efficiently to ensure no energy is wasted and only enough energy and energy buffers are allocated to homes to meet their needs. Smart

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technologies in homes enable efficiency and optimization by ensuring for example that lights are shut off when the space is not occupied using motion sensor-based technologies. When smart technologies in smart homes are connected and interact via the internet, there is a better opportunity to deeply understand the dynamics of the households and get insights for efficient and optimized configuration and customization of both energy and service demands.

4.4.3 Community Data Data in communities are also collected in similar manners based on what technologies are feasible and deployed in the communities to be able to interact with the data for products and service enhancements. As individuals make up households, so do households make up communities. Thus, the data collection is interlinked and connected to generate what is considered community data. Data about the characteristics of the features that are prominent in the community will be easily collected as it pertains to the local social and cultural dynamics within communities. Data about the communities are mostly aggregated data trends that the IoT devices deployed in the communities can collect to monitor the inner workings of the community services and how the citizens relate to them [38]. For example, camera data are deployed in the communities and at traffic junctions with traffic lights to capture behaviors and trends in recording the community environment. Body cameras on police and patrol agents within a community capture different interactions of the behavior and attitude of individuals living in those communities. Other examples include air quality and noise data in a community can indicate the different prevalent health-related problems and concerns that individuals in those communities can be the victim of. Also, data collected in communities provide insights about what people living in those communities gravitate towards based on the activities and events happening in the communities and capture the time and days of events. These insights provide more recommendations to the cities to better prepare in satisfying the needs of their citizens by providing them with the security and safety their need in vacating from one location to another. These insights help cities in planning for efficient deployment of security personnel and adequate transportation and parking means as well as meet the energy and power demands. Thus, data collected in communities provide insights that characterize the dynamics of the citizens that live in those communities and help highlight what priorities to consider that would meet the demands of the citizens. It is in consideration of the extent of what insights the collected data can provide that the smartest cities are willing to invest in to stand a chance of meeting and exceeding the expectations of their citizens. As more community data are collected within a city and trends are aggregated to highlight the needs and demands of each community within the smart cities, then the smart cities data workflow can be relied upon to help reveal the areas that should be prioritized and invested in to guarantee the quality of life and wellbeing

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Fig. 4.3 Smart cities data collection ecosystem

of citizens. Insights are produced after clear data analysis of clean collected data from the citizens, households, and communities to positively impact the services and operations of smart cities. The following figure shows what can constitute the smart cities’ data collection ecosystem as all the data sources become interlinked to make up the smart city’s big data. As data is collected, analyzed, processed, stored, and archived, there are a lot of security and privacy risks associated with this data. Smart cities are tasked with the ultimate task of protecting and preserving the security and privacy of their citizens’ personal information while optimizing their services and operations to ensure that all risks are mitigated accordingly. These are the issues that are decisive to allow the adoption of many smart cities initiatives among citizens.

4.4.4 Big Data Big Data in smart cities refers to collecting, storing, analyzing, and using large and complex datasets to optimize city operations and services. Smart cities use Big Data to collect and analyze data from various sources, including Internet of Things (IoT) devices, sensors, and cameras. This data is then used to make informed decisions about various issues, including traffic management, energy efficiency, public safety, and healthcare. Big Data can also be used to create predictive models and simulations, which can help city planners to make better decisions about the future of their cities. The main techniques in big data include data mining, machine learning, and predictive analytics. Data mining involves extracting useful information from large datasets. Machine learning is artificial intelligence that uses algorithms to learn from data and make predictions. Predictive analytics is analyzing data to identify patterns and trends and make predictions. Big data analytics also uses data visualization, natural language processing, and deep learning techniques to gain insights from large datasets.

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The Big Data framework is a key pillar of smart cities, as it provides the basis on which the smart cities hope to live and thrive. This basis is the large amount of data that is collected and aggregated to drive many informed decisions in smart cities. Big Data involves ways of managing and treating a huge and multifaceted volume of data of various kinds that are gathered and stored via the countless installed IoT devices in smart cities. Big Data consists of all the different types of data both unstructured and structured that necessitate collection, analysis, use, storage, and disposition. Possessing an enormous amount of data enables the operation of many applications. However, it is very risky to store an enormous amount of data that possess personal information due to many security and privacy issues that may arise regardless of the trends and patterns that can be deduced from it. It is important to understand and address the security and privacy issues that surface in the collection of data, the procession of data, and the storage of data in smart cities to guarantee that there are no additional generated security and privacy risks. Research shows that more than 2.5 quintillion bytes of data are generated per day [26] and it is collected and kept in various forms as part of Big Data. Some of the collected data consists of sensitive and personal information about citizens; many of whom have not agreed to have their data gathered and stored. The unconsented gathering of data generates many privacies worries especially when the stored data and the storage systems are compromised [16]. It is necessary to secure the collected data and the IoT devices that are collecting, processing, storing, and disposing of the data particularly when personal information about citizens is being collected. Anytime there is a menace of the security commission with a possibility of personal data being affected, the trust in technologies and applications is impacted, and citizens become even more skeptical and reluctant to try new technologies regardless of the benefits. The interruption of data during data transfer can occur anytime due to exposure to cyber threats and malicious intent from hackers. Additionally, the use of third-party applications in data analysis can be a channel of data compromise if the software is not well secured. It is also possible for data to be compromised while storing it in devices and systems where duplications can maliciously occur. Many other potential factors can be noticed in smart cities that need to be considered in reducing the number of risks that lead to privacy and security concerns as they relate to Big Data in privacy-conscious smart cities. The complete potential of Big Data is not fully unfolded to empower even superior value in smart cities with the usage of cutting-edge analytics and algorithms that simplify the reclamation of meaningful information. Nevertheless, it must be accomplished with systematic privacy and security reflections that relate to citizens’ data [26, 39]. Big Data comprises data such as call detail records, atmospheric data, e-commerce data, genomic data, medical records, Internet search indexing, military surveillance, video archives, photography archives, sensor network data, social network data, RFID data, and weblogs [26, 39]. Many securities encouraged privacy worries that are generated because of the sensitivity of this data if it is exposed in the process.

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4.5 Reconfigurable Megacities Reconfigurable megacities refer to a concept of urban development that emphasizes flexibility and adaptability in various aspects of city infrastructure, including health, security, environment, energy, and transportation systems. One of the key features of this concept is the idea of no infrastructure ownership, which means that the infrastructure systems of the city are not owned or managed by any single entity but rather are distributed among multiple stakeholders and users. In a reconfigurable megacity, different systems are designed to be modular and interoperable, meaning that they can be easily modified and reconfigured to adapt to changing needs and circumstances. For example, the transportation system may incorporate autonomous vehicles, public transit, and ride-sharing services, all of which can be dynamically adjusted based on real-time traffic and demand data. Health and security systems in a reconfigurable megacity may also be designed to be more responsive and flexible, with advanced sensing and data analytics technologies that can monitor and identify potential health and security risks in real-time. Environmental systems may include advanced monitoring and management technologies for air and water quality, as well as smart energy systems that can balance supply and demand across distributed energy resources. The overarching goal of a reconfigurable megacity is to create a more adaptable and resilient urban infrastructure that can better respond to changing needs and challenges. By leveraging advanced technologies and innovative design approaches, a reconfigurable megacity can improve the quality of life for its residents while also reducing costs and enhancing sustainability. However, implementing such a system can be complex and require significant coordination and cooperation among various stakeholders.

4.6 Global Mitigation of Megacities Global mitigation of megacities refers to efforts aimed at reducing greenhouse gas emissions and addressing climate change in large urban areas, known as megacities, which are defined as cities with populations of over 10 million people. Megacities are significant contributors to global greenhouse gas emissions, and as their populations continue to grow, their impact on the environment is expected to increase. Therefore, global mitigation efforts for megacities often involve implementing sustainable urban development strategies, such as promoting public transportation, increasing energy efficiency in buildings, encouraging the use of renewable energy sources, and reducing waste generation. These efforts aim to help megacities reduce their carbon footprint and contribute to global efforts to address climate change. Moreover, it involves a range of actions and strategies to reduce greenhouse gas emissions and address climate change. Some of these actions include:

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• Sustainable urban development: This involves creating urban environments designed to reduce energy consumption and minimize carbon emissions. Examples of sustainable urban development strategies include promoting public transportation, increasing the use of renewable energy sources, encouraging energy efficiency in buildings, and reducing waste generation. • Energy efficiency: This involves improving the energy efficiency of buildings and appliances, such as using energy-efficient lighting and appliances, building insulation, and smart grid technology. • Renewable energy: This involves increasing the use of renewable energy sources, such as solar, wind, and geothermal power, to reduce the reliance on fossil fuels. • Waste reduction and management: This involves reducing the amount of waste generated by a city, as well as implementing effective waste management practices, such as recycling and composting. • Green infrastructure: This involves incorporating green spaces, such as parks and green roofs, into urban environments, which can help to reduce the urban heat island effect and improve air quality. • Water conservation: This involves reducing water consumption, promoting waterefficient technologies and practices, and improving water management and distribution systems.

4.7 Future Trends in Technologies for Smart Cities Future trends in technologies for smart cities are rapidly evolving, and they hold great promise for improving the quality of life for city residents and enhancing the sustainability of urban environments [40]. Here are some of the major trends and technologies that are likely to shape the future of smart cities: • Internet of Things (IoT): The IoT refers to a network of interconnected devices and sensors that can collect and share data in real-time. In smart cities, IoT devices can be used to monitor everything from traffic patterns to air quality, helping to optimize city operations and improve services for residents. • Artificial Intelligence (AI): AI technologies, such as machine learning and natural language processing, can analyze large amounts of data collected by IoT devices and provide insights to help city officials make better decisions. • Blockchain: Blockchain technology can create secure and transparent systems for managing everything from energy grids to public transportation systems. • 5G networks: The advent of 5G networks promises to enable faster and more reliable connectivity between IoT devices, making it easier to collect and share data in real-time. • Autonomous vehicles: The development of self-driving cars and trucks holds great promise for reducing traffic congestion and improving transportation efficiency in smart cities.

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• Renewable energy: The adoption of renewable energy technologies, such as solar and wind power, can help to reduce the carbon footprint of cities and enhance their sustainability. • Augmented reality: Augmented reality technologies can be used to create immersive experiences that can help city residents better understand the history and culture of their communities. Overall, these and other emerging technologies are likely to play a significant role in developing smart cities in the years to come, helping to create more sustainable and livable urban environments.

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Chapter 5

Smart Communities and Cities as a Unified Concept

5.1 Why Do We Need Smart Communities and Smart Cities Nowadays and in the Future? Communities are groups of people who share common interests, values, and beliefs. They can be physical or virtual and range from small, local groups to large, global networks. As described in the previous chapter (Chap. 4), there are many types of communities. The physical community should consider the urbanism aspect in terms of walkable areas (0.5–1 km) and organized around public transportation. On the other hand, the virtual community shares common characteristics such as household type, climate zone, and similar demographics and depends on the levels of participation because if a community becomes very large, there would be troubles with interactions, and smaller subgroups will be created. Advanced communication systems use technologies to facilitate communication between two or more parties. For designing these systems, various technologies, such as satellite communication, cellular communication, wireless communication, and internet communication, are integrated. Thus, advanced communication systems are used in various applications, such as military, business, and consumer applications; they are also used in various industries, such as telecommunications, healthcare, and transportation. Advanced communication systems are designed to provide reliable, secure, and efficient communication between two or more parties. Manufacturing companies must know the end users’ requirements in real-time to adjust the production lines; hence. As a result, these advanced communication systems are critical in these factories. Moreover, they are designed to be cost-effective and easy to use, like plug-and-play devices. These communication systems send and receive data, a critical component of modern business operations, and companies rely on this information and need to send and receive information quickly and securely.

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This includes everything from customer orders to financial transactions, so data transmission is used in various ways, including email, file transfers, and streaming media. It also means having a reliable network infrastructure in place. Companies need to ensure that their data transmission is secure and reliable, so cybersecurity is a crucial element in this technology. This means using the latest encryption technologies and protected protocols. Smart communities are based on technology that improves the quality of life for their citizens. This technological development includes using technology to improve public safety, transportation, energy efficiency, healthcare, education, and other aspects of citizens’ life. Smart communities use sensors, data analytics, and other sensing platforms to collect and analyze data to make decisions and improve services. Some examples of smart communities include cities using sensors to monitor traffic and air quality or data analytics to improve public safety in real-time. Smart communities also use technology to create more efficient and sustainable energy systems and provide better healthcare and education access [1, 2]. In the future smart communities could send and transmit data about all the needs that they have so they will get an instantaneous response; education will be characterized by virtual reality in which professors will provide additional information when the tutored systems based on AI are not able to understand clearly the interaction with students. However, the decision systems about the community needs will be based on expert systems that will optimize the natural resources and increment the quality of life in a global and local context. The communities will have a connection in a global system. For instance, electrical energy will be managed by one smart grid by continent, so the regulation of electric markets will be updated using real-time information about electricity consumption and generation using microgrids that will cover the world. The energy cost will be reduced since optimized renewable energy sources will be installed. Figure 5.1 illustrates the information collected in a smart city.

5.2 Interconnected Public Outdoor and Indoor Environments by Connected Devices Connected devices in smart cities are becoming more intelligent using a training process that uses end-user information. These devices are based on digital technology that allows connecting with more devices to transmit information that can be used to improve their performance. Besides, these connected devices must be able to change their features to reconfigure their principal functions or create new ones according to the end user’s needs. Connected devices are created using general platforms, but they can be tailored using the information from a specific end-user in a network. Thus, connected devices will be more effective, increasing their life since they are not designed under programmed obsolescence. They are updated using software and hardware components that could be replaced mainly with 3D printers (metal, plastic, etc.) [3, 4].

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Fig. 5.1 Smart communities collected data

Fig. 5.2 Connected devices

Moreover, the new version of the connected devices will be assisted using AI algorithms using the information collected by these devices when operating. They can be controlled remotely or locally. They are typically used to automate tasks, such as controlling lights, thermostats, security systems, and other home appliances, but they can work collectively to achieve a complex task. Connected devices can also monitor citizens’ activity in public places, such as security tracking, energy usage in electric cars, or reminders to take medication during labor hours. These devices are becoming increasingly popular as they offer convenience, help provides good solutions for specific tasks, and improve quality of life [5]. Figure 5.2 describes connected devices in a smart city.

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5.3 The Connected Device and Its Interface for Improving the Quality of Life of Citizens Connected devices and their interfaces can be used to improve the quality of life of citizens in many ways. The Internet of Things (IoT) is a network of interconnected devices that can communicate with each other and exchange data over the internet. IoT technology can create an ecosystem of connected devices that work together to improve various aspects of daily life. For example, connected devices and their interfaces can be used to monitor and control home appliances and other devices remotely, which can provide convenience and energy savings. Homeowners can use smartphone apps, voice assistants, or other interfaces to remotely control smart home devices like thermostats, lighting, and security systems. This convenient feature can enhance the quality of life for homeowners by enabling them to adjust their home environment from anywhere, leading to increased comfort, security, and energy efficiency [6]. Connected devices and their interfaces can also be used to improve healthcare. IoT-enabled medical devices can collect and transmit data on a patient’s health and transmit it to healthcare professionals. This can help improve patient outcomes and reduce healthcare costs by allowing for remote monitoring and earlier intervention in medical conditions [7–9]. In addition, IoT technology can improve transportation by enabling real-time traffic monitoring, optimizing public transit routes, and parking management. This can help reduce traffic congestion and improve public safety, as well as make transportation more convenient and accessible for citizens. Overall, the use of connected devices and their interfaces for improving the quality of life of citizens is a rapidly growing field, with many potential applications across a wide range of industries and use cases. By enabling real-time data collection and analysis, IoT technology can help improve efficiency, convenience, and safety while reducing environmental impact and enhancing the quality of life.

5.3.1 Interfaces for Education Through Social Robotics Interfaces for education through social robotics refer to the use of robots as tools to enhance learning experiences in educational environments. Social robots are designed to interact with humans in a socially engaging and intelligent way, and they have the potential to provide a unique and engaging learning experience for students of all ages [10–12]. One of the key advantages of using social robots in education is that they personalize learning experiences. By interacting with students in a one-on-one or small group setting, social robots can adapt their teaching methods to the specific needs and learning styles of each individual student. For example, a social robot can use nat-

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ural language processing to understand the student’s spoken responses and provide feedback or additional explanation if necessary. Social robots provide a novel and engaging way for students to learn. By creating a social connection with the robot, students may be more motivated to engage in the learning process and be more willing to take risks and explore new ideas. Thus, a social robot that interacts with students in a playful and friendly manner can help to create a positive learning environment. Interfaces for education through social robotics can take many different forms, depending on the specific learning goals and objectives. For example, social robots can be used to teach language skills, math concepts, or scientific principles. They can also be used to teach social and emotional skills, such as empathy and communication.

5.3.1.1

Case Study: Social Robotics for Therapies in Mathematics

The role assignment analysis of an assistive robotic platform in a high school mathematics class is a study that aims to evaluate the effectiveness and usability of a gamified robotic platform as a tool for teaching mathematics. The study assigned different roles for the students in the class, such as team leader, coder, and robot operator, and analyzed how they interact with the robotic platform [13]. They observed and recorded student interactions with the robotic platform, analyzed performance data, and collected feedback from students and teachers. By analyzing the role assignments and the gamification and usability evaluation data, researchers can gain insights into how the robotic platform is being used in the classroom and impacting student learning outcomes. The results of the study can be used to inform the development of future assistive robotic platforms and to help educators understand how best to integrate technology into their teaching methods. By making learning more engaging and interactive, assistive robotic platforms can help students develop a deeper understanding of complex concepts and improve their academic performance.

5.3.1.2

Case Study: Social Robotics for Autism in Mexico

Implementing Robotic Platforms for Therapies Using Qualitative Factors in Mexico is a process that involves the use of robotic platforms to aid in the treatment and rehabilitation of patients. The approach considers the qualitative factors that can impact the effectiveness and success of therapy, such as patient motivation, emotional support, and engagement. Robotic platforms can provide a range of benefits to patients, including increased range of motion, strength, and coordination. They can also offer a more engaging and interactive therapy experience, which can be particularly beneficial for patients who struggle with traditional therapies. To implement robotic platforms for therapies in Mexico, several factors must be taken into consideration. These may include the availability of funding and resources,

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Fig. 5.3 TECO robot for therapies of children with autism

the level of acceptance and support from healthcare providers and patients, and the training and education needed for both patients and healthcare providers. Qualitative factors, such as cultural beliefs and attitudes towards technology, may also play a role in the success of implementing robotic platforms for therapies in Mexico. To address these factors, healthcare providers may need to take a patient-centered approach, involving patients and their families in the decision-making process and tailoring therapy plans to meet their individual needs. In [14], they use signal detection and fuzzy signal detection theories (FSDT) from human psychology as tools for evaluating the effectiveness of social robots used to assist children with autism during therapy (see Fig. 5.3). These theories can help identify how well the robot detects and responds to social interaction clues from the child. Unlike traditional psychophysical approaches, signal detection theory considers observers as both sensors and decision makers, which allows for separate measurements of sensitivity and response criterion. Thus, applying FSDT to social skills can lead to improved design of social robots for autism therapy. A semiautonomous social robot was designed and used to validate the proposal. In addition, the collaboration between healthcare providers, researchers, and technology experts can help ensure the successful implementation of robotic therapy platforms in Mexico. By working together, they can develop and test new technologies, evaluate their effectiveness, and refine therapy programs to ensure that patients receive the best care.

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5.3.1.3

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Social Robotics and the Education Around Sexual and Gender Diversities

Social robotics is a field that involves the development of robots and other artificial agents that are designed to interact with humans in a social or emotional capacity. The use of social robotics in education around sexual and gender diversities is an emerging area of research and development that aims to create more inclusive and supportive learning environments for all students. The use of social robots in education can provide a range of benefits for students, including increased engagement, motivation, and self-esteem. Social robots can be programmed to provide personalized and interactive feedback to students, tailoring their responses to the specific needs and preferences of each student. This can be particularly beneficial for students who may feel marginalized or unsupported in traditional classroom settings. In the context of education around sexual and gender diversities, social robots can provide a safe and supportive space for students to explore their identities and learn about the experiences of others. Social robots can be programmed to provide accurate and up-to-date information on topics related to sexual and gender diversities, such as LGBTQ+ issues, consent, and healthy relationships [15]. In addition, social robots can also help promote empathy and understanding among students. By interacting with social robots representing diverse gender and sexual identities, students can gain a deeper understanding of the experiences of others and develop a greater sense of empathy and respect for diversity. To implement social robotics in education around sexual and gender diversities, collaboration between educators, researchers, and technology experts is needed. The development of effective social robots requires a deep understanding of the needs and preferences of students, as well as the cultural and social factors that may impact their experiences. By working together, educators and technology experts can create social robots that are effective, engaging, and inclusive, promoting a more supportive and equitable learning environment for all students.

5.3.2 Interfaces for Healthcare Gamified interfaces in healthcare are becoming an increasingly popular way to engage patients and encourage healthy behaviors. These interfaces use game-like elements such as points, badges, leaderboards, and challenges to make healthcare more fun and engaging. One example of gamified interfaces in healthcare is in the area of physical therapy. Many patients who have suffered injuries or undergone surgeries require physical therapy to regain their strength and mobility. However, physical therapy can be a long and often tedious process. By using gamified interfaces, physical therapy exercises can be turned into fun and engaging activities that patients are more likely to stick with [16].

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Another example is in the area of chronic disease management. Many chronic diseases require patients to make significant lifestyle changes, such as changing their diet, exercising more, or taking medication on a regular basis. Gamified interfaces can be used to help patients stay motivated and engaged in their treatment plans by providing incentives and rewards for sticking with their prescribed treatments. Gamified interfaces can also be used to help patients manage their mental health. For example, apps that use gamification to teach mindfulness and meditation techniques can help reduce stress and improve overall well-being. While gamified interfaces have shown promise in engaging patients and improving health outcomes, it is important to note that they should not replace medical treatment or professional advice. Additionally, the design of gamified interfaces should be carefully considered to ensure that they are accessible and inclusive to all patients, regardless of their age, abilities, or cultural background [8].

5.3.2.1

Medical Robotics and Patient Rehabilitation

Human-Machine Interaction (HMI) involves the study of how humans interact with machines, including interfaces, feedback systems, and user experiences. In healthcare, HMI plays a critical role in enabling patients to interact with medical devices and systems, such as remote monitoring devices or prosthetic limbs, in a natural and intuitive way. By improving the design and usability of these devices and interfaces, we can improve patient outcomes and overall quality of care. Medical robotics refers to using robots and other automated systems in medical procedures, treatments, and research. Medical robots can perform various tasks, from assisting surgeons during complex procedures to monitoring patients in hospital settings. Robotic technology can improve the accuracy, speed, and safety of medical procedures and can reduce the risk of human error [17]. Patient rehabilitation involves using physical therapy and other interventions to help patients recover from injuries, illnesses, or surgical procedures. Rehabilitation programs can help patients regain strength, mobility, and function and can improve their overall quality of life. Emerging technologies such as HMI and medical robotics can also play a role in patient rehabilitation by providing personalized feedback and assistance to patients during their recovery [9]. By integrating these three fields, we can create new and innovative healthcare solutions that improve patient outcomes and quality of life. For example, a medical robot could assist with physical therapy exercises, while an HMI interface could provide personalized feedback to the patient during rehabilitation. Overall, by harnessing the power of HMI, medical robotics, and patient rehabilitation, we can create a more patient-centered and effective healthcare system.

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Depression Pre-diagnosis

Depression is a common mental health condition that can have a significant impact on a person’s quality of life. Early diagnosis and treatment are critical in managing depression, but many people may not be aware that they are experiencing symptoms or may be reluctant to seek help. Smart homes, which are equipped with various connected devices that can be controlled and monitored remotely, have the potential to be used as enablers for depression pre-diagnosis. By integrating a Patient Health Questionnaire-9 (PHQ-9) assessment tool into a smart home system, individuals could complete the assessment from the comfort of their own homes, without needing to visit a healthcare provider in person [8]. The PHQ-9 is a screening tool that can be used to assess the severity of depression symptoms. By integrating this tool into a smart home system, individuals could complete the assessment using a human-machine interface (HMI), such as a touch screen or voice command system. The assessment results would then be processed by a fuzzy logic decision system (artificial intelligence decision system), which would analyze the data and determine whether the individual is at risk for depression. If the fuzzy logic decision system determines that an individual is at risk for depression, the smart home system could initiate a response. For example, it could alert a healthcare provider, notify a family member or friend, or provide the individual with resources and information about depression and mental health support services. Using smart homes as enablers for depression pre-diagnosis has the potential to improve access to mental health services, reduce the stigma associated with seeking help, and promote early intervention and treatment for depression. However, it is important to note that the accuracy and effectiveness of the fuzzy logic decision system would need to be rigorously tested and validated to ensure that it is a reliable tool for depression pre-diagnosis.

5.3.2.3

Seniors People

The multi-sensor system, gamification, and artificial intelligence can be combined to benefit elderly people. Multi-sensor systems can monitor the elderly person’s environment, activity, and vital signs to detect potential health issues, falls, and other emergencies or to address social isolation and depression [7]. Gamification can be used to engage the elderly person in various activities that promote physical and cognitive health, such as memory games, puzzles, and physical exercises. Besides, gamification can encourage the elderly to engage in social activities and connect with others. For example, the system might present the person with challenges that require them to reach out to friends and family or participate in social activities like group games or outings. The system could also provide rewards or recognition for social engagement, such as badges or points. In the case of isolation, for instance, the system might present the person with challenges that require

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them to reach out to friends and family or participate in social activities like group games or outings. The system could also provide rewards or recognition for social engagement, such as badges or points. Artificial intelligence can analyze data collected by sensors to identify patterns and trends that can help predict potential health issues or patterns and trends related to social isolation and depression. For example, the system might detect that the person is showing signs of increased social isolation or depression over time and could alert caregivers or healthcare providers to intervene. Another example is that the system might detect that the elderly person is becoming less active and spending more time sitting in a particular room. The AI could then use this data to suggest activities that might help the elderly person become more active and engaged. Overall, combining multi-sensor systems, gamification, and artificial intelligence can create a powerful system for promoting the health and well-being of elderly people. By monitoring their environment, activity, and vital signs, engaging them in fun and challenging activities, and using AI to analyze their data, we can help elderly people maintain their independence, stay healthy, and live full and active lives. Furthermore, by encouraging social engagement, providing rewards and recognition for positive behavior, and using AI to detect and respond to changes in behavior, we can help elderly people maintain their social connections and enjoy a higher quality of life.

5.3.3 Interfaces for Energy Savings Gamified interfaces for energy savings refer to the use of game design elements and techniques to encourage people to conserve energy and reduce their energy consumption. The idea is to make the process of energy conservation more engaging and fun, and to motivate people to make behavior changes that can help reduce their carbon footprint and save money on their energy bills [18]. One of the key elements of gamification is the use of rewards and feedback mechanisms to reinforce positive behavior. For example, a gamified energy conservation app might award points or badges for completing energy-saving tasks like turning off lights, adjusting thermostats, or using energy-efficient appliances. These rewards can then be used to unlock new levels, access special features, or compete against other users for high scores. Another important aspect of gamification is the use of social comparison and competition to motivate behavior change. For example, a gamified energy conservation program might include leaderboards or other social features that allow users to see how their energy usage compares to that of their friends, family, or other users. This can create a sense of social pressure to reduce energy consumption and help users feel like they are part of a larger community of people working towards a common goal.

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Gamified interfaces for energy savings can be applied to a wide range of settings, from homes and offices to schools and public spaces. They can be implemented through mobile apps, web-based platforms, or even physical installations like interactive displays or smart home devices. While gamification can be an effective way to encourage energy conservation and behavior change, it is important to note that it is not a silver bullet solution. Other factors like education, social norms, and policy incentives also play a crucial role in shaping energy consumption patterns. Nonetheless, gamified interfaces for energy savings are a promising tool in the fight against climate change and can help create a more sustainable future.

5.3.3.1

Tailored Gamification and Serious Game Frameworks

Tailored gamification and serious game frameworks are designed to improve engagement and motivation for specific users or user groups. In the context of energy savings and connected thermostats, these frameworks can encourage users to modify their behavior and adopt energy-efficient practices [6, 19]. Fuzzy logic, a mathematical framework for dealing with uncertain and imprecise information, can create personalized recommendations for energy savings based on user preferences and behavior patterns. By integrating fuzzy logic into a gamification or serious game framework, the system can provide real-time feedback and suggestions to the user, making it easier for them to adjust their behavior and achieve energy savings. The use of gamification and serious game frameworks can also provide a more engaging and enjoyable experience for users, which can lead to greater adoption and sustained use of the connected thermostat system. By tailoring the game to the user’s preferences and behavior patterns, the system can increase the user’s sense of control and ownership over their energy usage, leading to greater commitment to energy-saving practices. In summary, a tailored gamification and serious game framework based on fuzzy logic can be an effective tool for promoting energy savings in connected thermostats by providing personalized recommendations, real-time feedback, and an engaging user experience.

5.3.3.2

Gamified Energy-Saving Systems Through Human Activity Recognition

Gamified energy savings in buildings based on image depth sensors for human activity recognition is a concept that involves using image depth sensors to track human movement and recognize human activities in a building. This data can then be used to develop gamified systems that encourage energy-efficient behaviors and reduce energy consumption.

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The image depth sensors can track movements such as walking, standing, and sitting and provide data on the location and number of people in a room. This information can be analyzed using machine learning algorithms to identify patterns of human behavior that can be used to develop gamified energy-saving systems [20]. One example of a gamified energy-saving system is a competition that encourages people in a building to reduce their energy consumption. The system can track energy usage and provide user feedback through a game-like interface. Users can compete against each other to see who can save the most energy, with rewards for the winners. Another example is a system that encourages people to take the stairs instead of the elevator. The image depth sensors can track people’s movements and provide feedback on their progress toward their energy-saving goals. The system can also provide rewards for achieving goals and track progress over time. Overall, gamified energy savings in buildings based on image depth sensors for human activity recognition is a promising concept that has the potential to help reduce energy consumption in buildings while making the process fun and engaging for users.

5.3.3.3

A Rapid HMI Prototyping for Connected Thermostats

A rapid human-machine interface (HMI) prototyping based on personality traits and artificial intelligence (AI) for socially connected thermostats is an innovative approach to designing more user-friendly and personalized thermostat systems [21]. The HMI prototyping process uses data on user personality traits and preferences to create an interface tailored to the specific user. For example, a user who values aesthetics and design may be presented with an interface emphasizing visual appeal. In contrast, a user who values simplicity and ease of use may be presented with a more streamlined and intuitive interface. AI in the prototyping process allows for the creation of dynamic and adaptive interfaces that can learn from user behavior and adapt over time. For example, if a user consistently adjusts the thermostat to a specific temperature, the AI may learn this pattern and automatically adjust the temperature to this setting at the appropriate times. The rapid prototyping process allows for quick iteration and testing of different interface designs, which can lead to a more efficient and effective final product. This approach can also help to identify and address potential user issues early in the development process. Overall, rapid HMI prototyping based on personality traits and AI can lead to a more user-friendly and personalized interface for socially connected thermostats, which can improve user engagement and satisfaction with the system.

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Garments and Connected Thermostats

Using deep learning in real-time for clothing classification with connected thermostats is an emerging application of artificial intelligence and the internet of things (IoT). The goal of this system is to optimize home heating and cooling by predicting the clothing of individuals in a home through their connected thermostats [22, 23]. The system uses deep learning algorithms to analyze images from cameras placed in different rooms of the house. These images are then processed and analyzed to determine the clothing individuals wear in each room. The information is then transmitted to the connected thermostats, which can adjust the temperature settings based on the predicted clothing types. Deep learning algorithms are particularly well-suited for this task because they can analyze and interpret large amounts of data quickly and accurately. They can recognize patterns and identify unique features of clothing that may be difficult for humans or other machine learning algorithms to detect. One of the potential benefits of this system is that it can help to reduce energy consumption and costs by optimizing heating and cooling based on actual human behavior. It can also provide more personalized and comfortable temperature settings for individuals in a home, as the system can predict their clothing and adjust the temperature accordingly. However, there are also potential privacy concerns associated with this system, as it requires cameras to be installed in different home rooms. Additionally, there may be limitations regarding the accuracy of the predictions, as deep learning algorithms can sometimes make errors in complex and unpredictable real-world scenarios. Overall, using deep learning in real-time for clothing classification with connected thermostats has the potential to revolutionize home heating and cooling systems, but further research and development are needed to refine the technology and address potential concerns.

5.3.3.5

Thermal Comfort in Buildings

The concept of thermal comfort refers to the state of mind that a person has when they are satisfied with the thermal environment they are in. Achieving thermal comfort in a building is an important aspect of creating a comfortable living environment. With the advent of smart homes and the internet of things, the use of gamification and interfaces is becoming an increasingly popular way to achieve thermal comfort while also promoting sustainable energy practices. A real-time adaptive thermal comfort model for sustainable energy in interactive gamified smart homes is a system that uses gamification and interfaces to promote sustainable energy practices while also ensuring that the occupants of the building are comfortable. The system uses data from sensors to create a real-time model of the thermal environment, which is then used to adjust the temperature and other environmental factors to ensure that the occupants are comfortable [24, 25].

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The system also uses gamification to encourage sustainable energy practices by rewarding users for energy-saving behaviors. The system can track the user’s behavior and adjust the rewards accordingly, creating a personalized experience for each user. For example, a user who consistently saves energy may receive more rewards than a user who is less consistent in their energy-saving behavior. The system also uses interfaces to communicate with the occupants of the building, providing them with information on their energy consumption and offering suggestions on saving energy. The interfaces can be a mobile app, a web portal, or a voice-activated system that the user can control.

5.4 Fundamental and Supportive Technologies (5G, IoT, ICT, AI, Renewable Energy, Blockchain) Many fundamental and supportive technologies are necessary to enable and empower smart city initiatives. To name a few, there is 5G, IoT, AI, ICT, Big Data, Blockchain, renewable energies, etc. that is explored in detail as shown in Fig. 5.4, to give a better view of what constitutes the different flavors of smart cities initiatives. There are some advantages and disadvantages of these technologies that are explored to outline the necessity to properly plan smart cities with the well-being and quality of life of the citizens in mind. Some privacy and security frameworks are also presented in this section to demonstrate the different measures of control and accountability that can be put in place for a sustainable smart city like blockchain, cryptography, anonymizations, and federated learning.

5.4.1 5G 5G is known as the 5th generation wireless communication mobile network that offers a lot of improvements on the previous wireless communication network generations and enables more possibilities of interconnection and communication that powers a lot of the technology-driven innovation and invention of the current and nearfuture era in the global digitization. 5G is a foundational building block of the digital transformation the world is undergoing [27]. It is a major enhancement of all the preceding mobile generation networks that make most of the smart cities’ initiatives possible today. 5G empowers three different conveniences for consumers namely by enabling extreme mobile broadband, massive machine-type communication, and ultra-reliable low latency communication [26]. Extreme mobile broadband provides the possibility of high-speed internet connectivity with superior bandwidth, and a reasonable latency, and enables the capability of UltraHD streaming videos while bringing to life the potential of virtual reality and augmented reality media, and many more benefits.

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Fig. 5.4 The ubiquitous smart cities’ fundamental, supportive, and enabling technologies

The massive machine-type communication, likewise, provides the capability of enabling long-range and broadband machine-type communication with a possibility of a moderate cost-effective price while requiring and consuming less power. It enables the potential of providing a service of high data rate with low power consumption while having extended coverage through a reduced amount of device complexity over several mobile carriers intended for IoT applications. The ultrareliable low latency communication similarly provides the potential of enabling a low-latency and ultra-high reliability connectivity service that is rich in quality of service that cannot be attained with the previous traditional mobile network generation architecture. It is intended primarily for immediate communication that empowers cutting-edge technologies such as smart grids, vehicle-to-vehicle communication, smart transport system, industry 4.0, remote surgery, etc. It is worth explicitly mentioning that 5G is faster than 4G, and it enables many remote-controlled operations over a very reliable network with the capability of zero delays [26]. 5G is largely divided into two fragments. One fragment is known as the 6 GHz 5G and the other is known as the millimeter wave 5G. The difference between the 6 GHz 5G and the millimeter wave 5G is that the 6 GHz 5G operate as a mid-frequency

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band that acts as a midpoint between coverage and capacity to provide a seamless environment for 5G connectivity while the millimeter wave 5G is an indispensable technology of 5G network which enables high-performance network connectivity. 6 GHz 5G spectrum is designed to enable a high bandwidth with enhanced network performance [26]. The 6 GHz 5G has continuous channels that help decrease the necessity for network compression when the mid-band spectrum is not accessible, and it renders the 5G connection affordable to users anywhere and at any time. On the other end, the millimeter wave 5G provides various connection services that incentivize almost all network benefactors to complement this technology in their 5G deployment endeavors and planning. Many service providers already are deploying the millimeter wave 5G, and their simulation data demonstrates that the millimeter wave 5G spectrum could be used even more due to the potential that it possesses even now. The millimeter wave 5G spectrum offers very high-speed wireless communication and an ultra-wide bandwidth that enable the next-generation mobile network [26].

5.4.1.1

What are the Potentials of 5G?

5G is more than an evolutionary improvement of the preceding generation of mobile network communication. It is an intended world-shattering technology that is purposed to eradicate the existing limits of access, performance, bandwidth, and latency restrictions on global connectivity. 5G possesses the capacity to empower essentially novel applications, business models, and industries, and brilliantly advance the quality of life and well-being everywhere through unparalleled use cases that necessitate low latency, high instant data-rate communications, and immense connectivity for innovative applications intended for mobile, smart homes, smart cities, autonomous vehicles, eHealth, and the IoT [28]. 5G delivers a down-link extreme throughput capability of 20 Gbps, and provides, in addition, reliable provisions for 4G WWWW short for the fourth Generation World Wide Wireless Web [29]. 5G is built on Internet protocol version 6 protocol, and enables unlimited internet connection at users’ ease, anytime, anyplace using tremendously high speed, and high throughput with low latency while providing higher reliability and scalability and maintaining a better energy-efficient mobile network communication technology [30].

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5.4.2 Internet of Things (IoT) IoT short for the Internet of Things is considered largely one of the main enablers or the fundamental building blocks of smart cities. IoT is a system of unified computing devices, automatic and digital equipment, objects, animals, or individuals that possess the capacity to exchange data across a network without needing interaction for human-to-human collaboration or human-to-computer communication. Things in IoT denote any normal or artificial object that can be granted an Internet Protocol address and holds the capability to transfer data information during communication over a network. The data communication includes information from people with things like an implanted heart monitor devices, farm animals with things like biochip transmitters, and cars with things like built-in tire pressure monitors, etc. Enterprises across a range of industries are swiftly employing IoT to operate more efficiently, understand their consumers better, offer improved customer service, enhance decision-making, and elevate the value of the business [41]. IoT possesses the potential to provide smart cities with various avenues necessary for data or information collection to better drive informed decision-making in the provision of services. The data collected through the various kind of IoT devices provide the necessary means of receiving diverse kinds of information that can be aggregated to shed some light on the interdepend ability of various services in smart cities and empower good and informed decision-making in the complete smart cities’ revolution. It is very complex to fully describe IoT and its influences in empowering smart cities, yet its existence is perceived and sensed virtually ubiquitously in all the segments of smart cities. Consequently, IoT establishes the core of the smart city’s enactment since without its developments and distributions over the years, many smart cities’ ingenuities would be virtually unbearable today. The purpose of IoT is to empower ways to gather all kinds of information in innumerable forms both unstructured and structured data. The countless ways of data collection constitute a system empowered by the IoT distribution of devices to guarantee that smart cities have the right instrumentation to facilitate the required interconnection that controls the astuteness of smart cities [31]. The IoT system is constructed with instrumentation that comprises numerous sensors, actuators, apparatus, electronics, networks, firmware, middleware, and software. The data collection apparatus permits the use of several things or devices like computers, wearable devices, smartphones, structures, buildings, homes, vehicles, and energy systems to turn out to be the gatherers of data in smart cities, and intrinsically there is the multitude of data sources and data types that can be enabled and gathered correspondingly. The services provided by the IoT devices are practically ubiquitously nowadays, and they enable the burst of potential relating to what can be empowered in cities to render them smart in the way they operate and how they provide to the requests and requirements of their citizens. The main compensations for having numerous forms of IoT devices are because of availability, affordability, and ease of deployment to empower the gathering of data and substantially intermingle innocuously with the citizens.

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The issue of security of IoT devices cannot be underestimated considering the amount of IoT devices available today and the interaction between them in accomplishing the big task of collecting citizens’ information in smart cities for various reasons and services. The IoT security issues are mainly classified into two layered categories namely the physical security layer and the technological layer. The physical security layer encompasses the security issues that are related to the physical IoT devices, and their locations are considered a point of contact and data gathering. The technological security layer on the other end includes the numerous security issues that are associated with the control, access, and permission to operate the IoT devices and tampering with the collected data. These two security layers in a nutshell yield many IoT-related security concerns in smart cities that need to be addressed and solutions need to be provided to them long before citizens begin to abundantly trust the smart cities’ initiatives and start demanding privacy-aware smart cities that protect their right further. This is the only way that will eradicate the likelihood of many IoT-induced security and privacy concerns amongst citizens [40]. It is important to know that there are not many adopted strong control regulations and approved standards amongst all smart cities concerning the definite data types that can be collected and recorded by the several IoT devices installed in smart cities. For example, with audio recorders that are deployed in smart cities, there are many privacy concerns among citizens as many citizens may not agree to be taped, logged, recorded, or documented in certain surroundings [40]. Likewise, it is apparent the several IoT devices like cameras that are used in image and video collections that are recorded, collected, and documented in smart cities that many times citizens are not even aware that they are being recorded on camera. This is a sign that there are possible IoT-encouraged security and privacy concerns that smart cities must address and appropriately cope with in the exertion of forming privacy-conscious smart cities [40]. Many systems, applications, and technologies are being empowered nowadays in smart cities with the convenience of IoT devices that seem to generate some security and privacy concerns among citizens like motion sensing technology, facial recognition, sound and speech recognition, biometrics capture, and many more because these technologies are mainly associated with a personalized data and information to some degree. There are many IoT-induced security issues relating to the points of data gathering, especially in locations where there are opportunities to amass data from numerous people simultaneously while there may be a need to only perform a specific distinct examination [40]. Hence, the technological IoT security layer demonstrates a superior threat when it comes to security and privacy issues and concerns amongst the citizens especially if a hacker somehow manages to gain control and access to the IoT device at the point of data collection and possesses the ability to access the data and control the IoT device, at that moment citizens’ collected data and information will be in jeopardy. There are many recommendations provided in quest of resolutions about the lessening of security and privacy concerns produced by IoT devices in evaluating the gathered data information close to the point of collection, nevertheless, there are concerns that these resolutions do not completely address or properly respond to the

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core of the security and privacy problems that lie in the principle of the empowered IoT technologies and the constant deployment of newer IoT devices and new IoT use cases in smart cities, to begin with. The utilization of IoT devices in smart cities stands as one of the core assets and enablers of possibilities in smart cities, which conditions future smart cities’ solutions to remain strictly dependent on the fruition and evolution of many novel IoT devices [40].

5.4.3 Information and Communication Technology (ICT) ICT is one more pillar that empowers many smart city initiatives and expansions to guarantee the development of systems and processes that are autonomous, optimal, and efficient [31]. ICT enables the interrelation between IoT and Big Data in the general smart city organization to empower potential resolutions that improve citizens’ well-being and quality of life. It is crucial to contemplate ICT as significant as both IoT and Big Data because it is the avenue of rendering and materializing the informed decision taken through the understanding of Big Data that is gathered with the help of IoT devices to influence and improve citizens’ quality of life in smart cities. ICT like some combination of IoT devices is a framework that networks with the citizens in the manner of instigating unconventionality that does not negatively affect citizens and implementing resolutions that citizens can be in connection with. Practically all the applications, systems, and technologies currently deployed in smart cities are realized through ICT, which facilitates different avenues for citizens to accept and network with the deployed systems and applications. Incidentally, one of the purposes of ICT is to empower citizens’ collaboration and acceptance of applications, systems, and technologies in addition to facilitating the feedback system of citizens’ take on applications, technologies, and systems back to the integrators and inventors to guarantee better ICT organizations are established and installed to materialize the smart city resolutions. ICT remains a big player with a critical role in virtually every smart city segment through its existence in empowering technologies. Intrinsically, there are many concerns regarding security risks that arise in considering ICT and its dependencies and implementation because different clusters of systems networks, contribute to the operation of other systems, and end up sharing data with different security echelons. The mutuality of systems and conventions within the ICT organization generates several security concerns as data transfer is performed in many ways to embody the informed decision-making that produces the effectiveness and efficiency of applications, systems, and technologies in smart cities. These security problems can trigger many other security-induced privacy worries amongst citizens, specifically as soon as there are cracks in communication or when an unauthorized transmission of personal data occurs. Therefore, it is obvious that as the number of systems, applications, and technologies keeps increasing and the deployment rate keeps rising for the developed interconnected technologies in the smart cities with more adoption of ICT, there will be even more security risks and privacy issues that must be considered and resolved

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given citizens’ data protection. The commission in this regard can occur deliberately or involuntarily if robust measures are not taken sooner. Privacy and security issues, like public place facial recognition, pervasiveness in elderly’s health worries, and modification of mobility systems, are instigated by ICT network breaches and continue to be innumerable in smart cities and cannot be overlooked [31, 42]. The potential usage and occurrence of ICT in smart city space is undoubtedly the principal facilitator of smart city networks, and it remains the key layer that intermingles with citizens either in getting information from citizens or performing a service for citizens.

5.4.4 Sensing Platforms A sensing platform collects, processes, and analyzes data from various sensors. These sensors are either physical sensors, such as temperature, pressure, or motion sensors, or digital sensors, such as cameras or microphones. Its purpose is to collect and analyze data in real-time to gain insights into various systems, environments, or processes. Sensing platforms can be used in various applications, from environmental monitoring and industrial automation to healthcare and smart cities. In the healthcare sector, in [32], they proposed a modular system with different sensing methods for hands-free inputs that consider hardware with several possibilities for connection to more electronic devices. In addition, this sensing platform can expand and include new interfaces for different applications, such as environmental control systems. Thus, this platform has been tested in a power mobility application. The evaluation has been done by a standard protocol involving volunteers with severe mobility problems and non-wheelchair users. On the other hand, in the environmental field, human activity is increasing worldwide. In the first instance, this increment is caused due to population growth that leads to a higher demand for natural resources to satisfy as many citizens as possible. This challenge requires many developments in industrialization, urbanization, and technology. This progress carries many activities that are harmful to the planet, for example, deforestation, transportation, energy production, and industrial processes. As a result of these activities, many pollutants are released into the atmosphere, causing environmental alterations and unusual weather conditions. Heat waves, floods, droughts, unusual rainfall patterns, and increments in air pollution are just some examples of phenomena caused due to variations in temperature, humidity, atmospheric pressure, and greenhouse gas emissions (CO2 , CH4 , NOx). Based on this, governments try to implement policies and regulations that allow for controlling and measuring human activity’s environmental impact. To implement these policies, data about atmospheric variables and gas emissions are needed [33, 34]. For this reason, many countries worldwide are deploying measuring stations. The United States, England, India, and Germany are some examples. These are high-quality stations equipped with sophisticated sensors that aim to

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get real-time data. Governments mainly use these measurements to make informed decisions on their policies or regulations; however, other users are also interested in knowing their local environment, which is the case for the industrial or agriculture sectors [35, 36]. They require this information to evaluate their processes’ performance and improve their practices. For this reason, monitoring their local environment is compulsory. One of the main limitations is that these stations have limited coverage and cannot guarantee the reliability of their results in places away from them because the weather conditions change each 3 km considerably. Therefore, the measurements obtained cannot be generalized for locations with no fixed station nearby. The second limitation is the elevated cost of these devices, and since they rely on expensive equipment, trained personnel is required to operate and maintain them. This reduces the possibilities for users who require measuring environmental variables but cannot afford a high-accuracy monitoring device. To address these problems, less expensive stations are available on the market [37, 38]. However, they still require personnel to install, configure and maintain them, and some cannot be connected to the cloud or easily collect the data locally. Therefore, some other solutions have been developed based on low-cost sensors providing a cheaper way to measure the environment. These devices are also small, portable, easy-to-use, and suitable for different scenarios, a good option for users and the public. These low-cost stations measure environmental data and air quality in a particular site using fewer resources and money than professional-grade stations. They are used in many scenarios according to the user’s necessities. For example, in 2019 in Rome [37], LILI-1, a portable station, was developed to measure meteorological parameters and chemical pollutants such as nitrogen dioxide (NO2 ), ozone (O3 ), particle matter (PM), specifically PM10 and PM2.5, temperature, relative humidity, and atmospheric pressure. With a weight of 1.5 kg, an autonomy of 8 h, and a 25 s measuring interval, this device was placed on a bicycle running along a 10-km path. Ultimately, the data was stored on an SD card and compared with a near-fixed station. In 2020, the “Home Health Box” device was developed to monitor air quality in a kitchen [38]. The variables measured were PM 2.5, PM10, CO2 , CO, NO2 , and O3 . This device was equipped with an internal battery with an autonomy of 6 h and a connection to a 120-V wall outlet, a weight of 2 kg, and a 30 s measuring interval. The purpose was to measure the risks of cooking in a closed space; in this case, the evaluation site was a kitchen in Colorado. The data was also stored on an SD card and compared with professional on-site equipment. The previous devices were mainly focused on air quality in the residential sector. But also, some industrial processes require local environmental monitoring. In 2022, a device was created in China to monitor the conditions inside a poultry house [39]. The measurements were CO2 , NH3 , PM2.5, PM10, wind speed, temperature, humidity, and illumination. The values were obtained each 1hr, and this station requires an AC connection. These devices have been developed in the last three years and show some usage examples in different areas; the first is focused on air quality and meteorological

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conditions in Rome, the second focuses on the health risks caused by cooking, and the last one help to measure conditions inside a poultry home. But there are some limitations regarding connectivity, autonomy, and real-time data visualization and collection. The current proposal is centered on designing and implementing a platform to assess environmental conditions indoors and outdoors. This device is primarily focused on two sectors: industrial and agriculture. This low-cost sensor-based platform aims to quickly show the real-time data gathered and send it to a cloud service for further analysis, including a short-term forecast based on historical values. The variables measured are temperature, humidity, atmospheric pressure, PM1, PM2.5, PM10, and CO2 . The meteorological values are obtained from the sensor BME280 from Bosch company. It has an accuracy of ±0.1 ◦ C for temperature, ±3% for relative humidity, and ±1 hPa for pressure. For the particle matter concentration, the sensor PMS5003 from Plantower company was implemented; it has an accuracy of ±10% µg/m3 for PM1, PM2.5, and PM10. Finally, for the CO2 concentration, the sensor SCD30 from Sensirion company was added. Its accuracy is ±30 ppm. The data gathered is stored on a 32GB SD card with a CSV format and sent via WiFi to a channel on ThingSpeak. The user configures the measurement interval with a single button; it can be each 30 s, 1 min, 10 min, 1 h, 12 h, and 24 h. In addition, a 2.4” TFT display is embedded to show the data in real-time. The user can turn it on or off depending on their needs. There is also an RTC equipped to add a timestamp to each measurement performed. This device can be energized with three different power sources: a 120-V wall outlet, a Photovoltaic panel connected to a 12 V gel battery, and an internal Li-Ion rechargeable battery. In terms of autonomy, the device consumes 250 mA in full mode (measuring each 30 s with the display turned on, sending the data via Wi-Fi, and storing it on the SD card). Therefore, with the internal battery, the platform can last up to 40 h. This device is based on two ARM Cortex M3 microcontrollers from ST company. One is in charge of handling all the sensors, setting the interval measurement, and managing the SD module. Meanwhile, the other microcontroller controls the display and sends the data via serial to a Wi-Fi board (ESP8266) connected to a private ThingSpeak channel. The sensors and the display are embedded inside an IP65 ABS case, as shown in Fig. 5.5. The microcontrollers and other components were placed on a designed printed circuit board (PCB) to connect all the elements. The 24 AWG wires between the PCB and the sensors have JST terminals for a plug-and-play assembly. In addition, they are wrapped with aluminum foil to cut out external interfaces and covered with thermofit for additional protection. These internal wire connections, the two designed-PCB, and the Wi-Fi module can be observed in Fig. 5.6. The physical measurements of this platform are 22×15×10 cm with a weight of 1.5 kg. The data in the cloud can be exported to MATLAB, where all the analysis is performed. Regarding forecasting, time series analysis techniques are used to predict

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Fig. 5.5 Environmental forecast prototype: a platform that assesses indoor and outdoor environmental conditions

Fig. 5.6 Internal wire connections: a PCB and Wi-Fi module, b PCB and internal battery

new values using historical data. Autoregressive (A-R) and autoregressive integrated moving average (ARIMA) models are implemented for such prediction. Since this platform is a prototype focused on industrial and agricultural processes, some limitations are involved. First, this device has not been tested on many outdoor scenarios, and they may lead to inaccurate data, mainly because low-cost sensors have some limitations in extreme weather conditions. As a second point, Wi-Fi connections are not always available, especially in remote places. Therefore, SD card storage is the only option to get the data. Regarding remote areas, if a sensor fails and there is

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no wireless connection, there is no way to notify of that failure to the users resulting in missing data. Finally, since the variables measured vary stochastically, the forecast methods may be more dispersive when abrupt changes occur.

5.4.5 Renewable Energy 5.4.5.1

Smart Energy Technology

Smart energy technology is the principal vehicle that propels nearly all the other smart city technologies in terms of guaranteeing proficiency, optimization, and efficiency in smart cities. The increasing number of citizens in smart cities demands a vigilant deliberation of energy generation, distribution, and consumption to optimally meet and exceed the demands of citizens in addition to cautious attention to energy storage and conversion avenues to assist in the long term as the tendency of energy demand continues to upsurge. Smart energy technology is the heart and soul of virtually many if not all smart city apparatuses for example IoT devices, digital road signs, gadgets, vehicles, parking meters, delivery robots, servers, street cameras, stations, drones, kiosks, etc. These apparatuses necessitate power for actuation, propulsion, and operation accordingly to accomplish their potential and render their service. Intrinsically, there cannot be any smart city deprived of smart energy technology and strategy to meet the energy demands of the city and its citizens. There are many sources of renewable energy that smart cities can leverage and expand simultaneously to offload the current smart cities grid as shown in the figure below (Fig. 5.7). For smart cities to possess great smart energy technology, the IoT, Big Data, and ICT frameworks together must play a critical role in attaining the required energy generation, distribution, consumption, and storage capacity with the ability to analyze waste energy data. It is only through evaluating the properly gathered data and making informed decisions that efficiency and optimization can drive the complete smart energy technology organization in meeting and exceed the energy demands of cities and citizens. The core components in smart energy technology comprise possessing cutting-edge usage of technology, competent energy conversion, sustainable energy consumption, and a completely clean environment to guarantee a non-negative impression on the environment and enhancement in citizens’ quality of life and well-being [43–45]. As smart cities capitalize on systems and technologies that enable a more vigorous and resistant smart energy infrastructure that deals and offers optimum solutions to the energy demands of cities in both the short and long term, there are resolutions that help deal with the energy requirements in smart cities, for instance, the expansion of renewable energy sources (RES) for power generation intended for many other smart cities’ systems, applications, and technologies.

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Fig. 5.7 The renewable energy sources

5.4.5.2

Security and Privacy Issues

Many efforts and investments are poured into RES to help enable the potential to produce sufficient clean power to meet and exceed the power requirements and energy demands of smart cities in the long term. However, these investments do not necessarily address the security issues and privacy allegations that global smart energy technology causes. There are numerous induced security-associated privacy issues from IoT, Big Data, and ICT bases involved, and as such the recently proposed 3D privacy framework acclaims dealing with the privacy concerns of the technologies and their apparatuses before installing them in smart cities by establishing the required principles that aim at reducing privacy worries of citizens to enable the creation and appearance of privacy conscious smart cities. Many security concerns are found in the tracking and distribution of energy inside smart cities on the way to individuals and households to the point where the energy used up is regularly traced and measured. Countless privacy control issues arise in correlating the consumption of energy to the number of people present in the households with the conception that more individuals in the home would generally use more energy. Therefore, linking the magnitude and times of energy consumption occurring during the times of the day to the presence of people in the home turn out to be very nerve-wracking particularly when the data relating to the energy use is unswervingly connected with the home’s owner or renter in the smart energy network. The predilection of individuals to pick a particular type of energy source from RES and the requirement for people to produce

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off-grid power and subsidize some energy to the grid continue to create security and privacy tensions. These tensions are because of the collection and transfer of information relating to which houses contribute a larger amount of power, particularly when IoT, Big Data, and ICT bases are involved in enabling the services [46, 47].

5.4.5.3

Privacy Framework Assessment

As a result of the numerous development and deployment of off-grid power contributions to the grid, the need to monitor and manage sensitive information that is shared amongst the different energy benefactors in the network continues to generate risky privacy apprehensions that are easily intensified when there is a breach event or a cyber threat attack [47]. It is therefore important to consider the 3D Privacy Framework recommendation to properly strive to assess the smart energy systems, applications, and processes in smart cities to ensure that there are proper control mechanisms in place and that the privacy concerns are addressed. This approach will require a thorough look into what energy data is collected from houses and how the collected data is managed to guarantee that privacy worries are lessened and addressed appropriately with regulations. The 3D privacy framework classifies smart energy technology in its quadrant 7 which pertains to a medium privacy risk quadrant that necessitates a clear watch over how data is managed in this space to avoid putting people at risk. The medium risk requires the acceptance of guiding principles in data collection and protocol to remedy security issues before the collection of more sensitive data [43].

5.4.6 Artificial Intelligence (AI) A sustainable future that the smart cities project necessitates efficiency and optimization in almost all its processes and systems that AI can enable and facilitate. As smart cities transition from visionary perception to realization, artificial intelligence takes a central role in enabling all the integrated components. AI enables the advanced technologies required to boost the effective strategies and optimization needed throughout all the smart cities sectors’ operations [48]. Various technologies are swiftly emerging as the means of achieving net-zero pledges, realizing smart cities, and rewarding clean and sustainable efforts in response to the present demands. Thus, smart city development and AI are becoming increasingly interconnected as Gartner’s study [49] showed in its 2018 prediction that AI will become a crucial component of 30% of smart city applications by 2020. This is up from just 5% a few years ago, and the impact of AI taking over in 2022 is evident with more and more autonomous technologies being deployed and] enabled yearly ranging from autonomous vehicles to chatbots to telemedicine to recommendation systems, etc. The use of AI is increasingly acknowledged as the not-so-secret element enabling main energy providers to achieve their lowest carbon footprints to date, in addition

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to unmatched efficiency and appealing profitability. The gathering and processing of enormous volumes of data from a variety of areas, from metropolitan growth and electricity allocation down to manual operations like city services, is what enables the smartness of a city. Hence, the coined word “Smart City”. The real work in enabling AI lies in building and maintaining sensor setups, equipment, and other systems intended to increase the sustainability and efficiency that is necessary for smart cities. One of the main avenues to make a city smarter and more sustainable is to change the strategy that controls its utility operations. In this segment, AI solutions have already made substantial progress and are on the road to monopolizing the problem-solving approach to address future operation issues. The question that is often as is whether the utility operation would work just fine with AI without frequent human intervention. In the long term, this is the approach that smart cities tend to explore to enable the building of resilient systems that can autonomously operate. The influence that cutting-edge technologies are already having on the business excites many greatly as many CEOs of AI startups developing software for the utility sector are pumped to raise to the challenge and deliver the missing piece in the sustainable development pursuit [48]. One practical example of AI supporting smart city utilities, in this case, is that of the Nvidia Metropolis platform application that makes use of sophisticated video analytics to enhance public services, logistics, and other related areas [48]. Consistent with Nvidia, this platform application is intended to improve public services for both citizens and communities, enhance infrastructure, and grow more sustainable cities. The platform gathers information from various sensors and other IoT devices across the city to deliver insights that could help with better asset security, supply planning, traffic control, and disaster response [50]. Along the same lines of thoughts, another example of AI application is the project run by Xcell Security House and Finance SA in Africa that promises the construction of the first cognitive AI-managed power plant in the world that is aimed at helping to leverage expertise and provide quality services to Africa. This project is a proper example of how AI can be used globally to solve some of the most challenging hardships that the world is facing. This is an innovative approach to creating more smarter cities in Africa by evidently propelling the utility growth in West Africa, which in return can help enhance other sectors dependent on it. This project aims to use cutting-edge sensor systems and methodologies that incorporate knowledge and experience into every aspect of the facilities’ operations as the first attempt to build and empower an AI-powered factory from the scratch. Many stakeholders would have access to the facility-scale data in an expedited way in case of anything that can go wrong. This will result in a much safe plant environment that is more risk-mitigating and that maximizes both productivity and efficiency. The promise of AI and what it can facilitate are countless. In the integration of software and hardware lies the enabling power of AI in coordinating processes and facilitating decision-making by using pattern recognition to provide insights into data science and analytics that empower the next sets of innovations. It is paramount to consider a hybrid coexistence of both humans and AI in the decision-making approach for many future innovations. AI is almost everywhere, from the simplest

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of tasks to the most sophisticated ones; it has demonstrated that it can be relied upon to the point of even forgetting when its implementations are flawless. Through AI, smart cities are becoming smarter, and their services and operations are optimized and efficient. With more investments in AI, smart cities can be sure that many challenges that they currently face are only for a moment as more data is collected and analyzed to lead to an AI insight and resolution eventually. As more successes are explored, more trust will be allocated to AI, and more people will eventually gravitate towards it for optimum resolutions.

5.4.6.1

Embedded AI Applied in Computer Vision

Embedded artificial intelligence (AI) is a rapidly evolving field that revolutionizes computer vision applications. Computer vision is the process of enabling computers to interpret and analyze visual data, such as images or videos. By using embedded AI in computer vision, it is possible to create more efficient, accurate, and powerful systems that can recognize, classify and track objects in real-time [23, 51]. Embedded AI uses deep learning algorithms, which are a type of machine learning technique that involves training a neural network to recognize patterns and features in data. This training process involves feeding large amounts of data into the neural network to enable it to learn and adapt to new data. Once trained, the network can be embedded into a computer vision system to perform complex tasks such as object detection, recognition, and tracking. One of the main advantages of embedded AI in computer vision is that it can enable real-time decision-making. By embedding an AI system into a camera or sensor, for example, the system can analyze data and make decisions in real-time. This is particularly useful in applications such as security, where quick decisionmaking is critical. Another advantage of embedded AI in computer vision is that it can enable more efficient and accurate data processing. Traditional computer vision systems can be slow and resource-intensive, making them difficult to implement in certain applications. By using embedded AI, it is possible to reduce the processing time and increase the system’s accuracy, making it more effective and efficient. Applications of embedded AI in computer vision include autonomous vehicles, security and surveillance, medical imaging, and industrial automation. These applications require fast and accurate visual data analysis, achieved through embedded AI.

5.4.6.2

AI Applied in the Architectural and Construction Field

Architects are increasingly utilizing artificial intelligence (AI) in their designs, which allows them to create more innovative, sustainable, and efficient buildings. AI in architecture involves various techniques, such as machine learning algorithms, that assist architects in the design process, from the initial concept to construction [52].

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The most significant advantage of AI in architectural design is that it enables architects to process vast amounts of data, including the environment, site, building materials, and other relevant factors, to make informed design decisions. Using machine learning algorithms, AI can identify patterns and relationships in the data to aid architects in designing the building. AI can also optimize building performance by analyzing and modeling energy consumption and environmental impact and identifying areas for energy savings and indoor air quality improvements. Generative design is a primary application of AI in architecture, where algorithms generate and explore numerous design options based on design constraints and parameters. AI assists architects in generative design, enabling them to explore various possibilities and identify the best design solutions. VR/AR technology is another AI application in architecture. Architects can use it to create immersive and interactive models of their designs and simulate the building’s user experience. This helps architects identify potential issues and make necessary design changes before construction. AI can assist construction by using sensors and data to monitor the building’s progress and performance. The data can analyze construction materials and processes, identify potential delays, and enable real-time monitoring and optimization of the construction process. Furthermore, the construction industry is being revolutionized by artificial intelligence (AI), which is making significant strides in various areas. AI is used differently, from design and planning to construction and post-construction monitoring. Here are some examples of how AI is applied in the construction sector: • Design and Planning: AI can detect design errors and suggest design modifications before construction begins by analyzing building plans during the design phase. • Project Management: By analyzing vast amounts of data, AI can identify and mitigate project risks, as well as assist in tracking the progress of a construction project, allowing project managers to make informed decisions. • Construction Process: AI can automate various tasks, such as bricklaying, significantly reducing the construction timeline. AI-powered robots can work 24/7 without breaks, increasing productivity and reducing labor costs. • Safety: I can monitor and analyze data from various sensors on construction sites to identify potential safety risks. This includes analyzing data from wearable devices worn by construction workers to detect potential safety hazards and alert workers to take action. • Quality Control: It can analyze construction materials to ensure they meet the required standards. AI-powered sensors can detect defects in materials before they are used in construction, saving time and reducing wastage. • Post-construction Monitoring: It can monitor buildings’ performance after construction completion, identifying maintenance issues, energy inefficiencies, and other potential problems. This data can help property owners and managers optimize building performance, reduce energy costs, and improve occupant comfort.

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Thus, AI is transforming the construction sector, enabling faster and more efficient construction projects, improving safety, and ensuring quality control. As AI technology continues to advance, it is expected to play an even more significant role in the construction industry in the future.

5.4.6.3

AI Applied in the Urban Planning

Urban planners are increasingly using artificial intelligence (AI) to optimize the design and management of cities, improving decision-making in urban planning [53]. Here are some of how AI is being applied in urban planning: • Predictive Modeling: By analyzing large amounts of historical data, AI algorithms can develop predictive models that forecast various urban phenomena, such as traffic flow, energy usage, and population growth. These insights can help city planners make informed decisions about infrastructure investments, zoning regulations, and transportation planning. • Land Use Planning: AI can analyze various datasets, such as satellite images, urban maps, and zoning laws, to determine the best land use. AI can also optimize the placement of public facilities, such as schools and parks, ensuring accessibility for all residents. • Transportation Planning: AI can optimize transportation systems by analyzing traffic patterns, transit usage, and other data. Real-time traffic management systems can also be developed using AI to reduce congestion and improve road safety. • Environmental Sustainability: AI can optimize energy use in buildings, reducing emissions and energy costs. Environmental data, such as air quality and noise pollution, can also be analyzed by AI to inform urban planning decisions that promote sustainability and improve quality of life. • Disaster Response: AI can help in emergency response by analyzing data from social media, news reports, and other sources to identify areas affected by disasters and prioritize relief efforts.

5.4.6.4

AI in the Metaverse

The metaverse refers to a virtual world where users can interact with a computergenerated environment and other users in real time. The metaverse concept has gained significant attention in recent years, and artificial intelligence (AI) is expected to play a key role in its development [54]. Here are some potential ways in which AI could be applied in the metaverse: • Intelligent virtual assistants: AI-powered virtual assistants could assist users in navigating and interacting with the metaverse, providing personalized recommendations and improving the overall user experience.

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• Natural language processing: AI could enable natural language processing, allowing users to communicate with virtual characters in the metaverse more intuitively and naturally. • Dynamic content generation: AI could generate dynamic, interactive content within the metaverse, creating a more immersive and engaging user experience. • Realistic graphics and physics: AI could enhance the realism of graphics and physics within the metaverse, creating a more immersive experience for users. • Virtual marketplaces: AI could create virtual marketplaces within the metaverse, allowing users to buy and sell virtual goods and services. • Personalized experiences: AI could be used to create personalized experiences for users within the metaverse, tailoring content and recommendations to each user’s individual preferences and behavior. • Social analytics: AI could analyze social behavior within the metaverse, identifying patterns and trends in user behavior and preferences. • Intelligent moderation: AI could moderate user-generated content within the metaverse, detecting and removing inappropriate or offensive content. • Virtual economy management: AI could manage the virtual economy within the metaverse, monitoring supply and demand for virtual goods and services and adjusting prices accordingly. • Metaverse governance: AI could govern the metaverse, ensuring that users adhere to community guidelines and preventing the spread of misinformation and harmful content.

5.4.7 Blockchain The Urbanization of cities in becoming Smart Cities necessitates a new type of builder and skill set. In this regard, there is a clear demand for Software Architecture to play a vital role in enabling the new era of smart cities and meet the sustainability demands of the almost immediate future to ensure resilience in times of uncertainty and both stability and reliability in times of demands [55]. Thus, one of the technologies that promise the enhancement of both the quality of life and well-being of citizens in smart cities is none other than Blockchain. The word “blockchain” appears or sounds new, but the idea behind it is almost like it has been in everyone’s head in facilitating transactions that must be transparent and immutable to ensure traceability.

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What is Blockchain?

As a technology or even technique, blockchain is a distributed digital ledger approach that ensures the records of transactions in a persistent, transparent, and public manner. It is considered an append-only ledger. By an append-only ledger, we mean it is a system in which one can add data, but one cannot change data within. The overall blockchain system is enabled through a mechanism where consensus is created between scattered or distributed parties that do not necessarily need to establish trust in any other form because they solely rely on the mechanism on which they based their consensus [55].

The simple process of a blockchain transaction from the initiation to the completion follows the seven steps outlined in the figure below (Fig. 5.8). There are many applications and use cases that are leveraging blockchain technology today that are worth noting as more and more applications are being developed and deployed in smart cities. One of the blockchain implementations in smart cities is Crypto Currency. Crypto Currency is an interesting use case of blockchain because it is considered the implementation of blockchain. The cryptocurrency system relies basically on the challenge that no one acting individual or component in a network can resolve the challenge consistently without others in the network consenting to the resolution. Consequently, there is a component of unpredictability that randomizes the blockchain method and guarantees that nobody can trick the blockchain into accepting a certain entry on the record that other participants on the ledge disagree on. The information on the transaction network is distributed throughout the network, and everyone on the network has a copy of the information on the network. Consequently, the ledger on the network is maintained, updated, and completely verified to ensure the updates are valid because it is impossible to defraud or alter after the fact. After the updates are approved, the peer-to-peer networks are consistently updated for everyone, and each party is made aware of the ledger and possesses a copy of all past and current transactions [56]. The existing privacy model elaborated in blockchain confines access to data information to the participants involved and reliable third parties. The obligation to transparently broadcast all transactions impedes many privacy assurance methods necessary to eliminate users’ skepticism. Nevertheless, privacy can still be preserved by interrupting the transfer of information to unintended places and safeguarding public keys anonymously [56]. The community can perceive that an individual is transferring a certain sum of money to somebody else without the necessary information connecting the transaction to anybody. Many people still have the money hype from Cryptocurrency when thinking of blockchain since cryptocurrency is the most famous implemented example intrinsically tied to blockchain technology. Nevertheless, there are way more benefits that blockchain technology possesses and enables. From a technolog-

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Fig. 5.8 The illustration of the blockchain process

ical point of view, blockchain is another great tool for humans, robots, and other identities to use in trading at scale and make trade more distributed and transparent.

5.4.7.2

Benefits and Use of Blockchain in Smart City

There are many benefits and use of blockchain as discussed below in many sectors of smart cities in general ranging from energy distribution and conservation to food and supply chain to health care and real estate etc. When it comes to energy efficiency, there is great potential to leverage blockchain and enable an even more reliable energy ecosystem. Energy efficiency is immense in Smart City, and it is the vehicle that drives most innovations as more and more systems are aiming to be as efficient

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as they can be. The energy ecosystem of smart cities includes things and systems like smart grids, electric cars, solar panels, etc., and many renewable energy sources when considering different energy perspectives [57]. There are many avenues where the distributed network system can be identified for example all the batteries powering the electric vehicles can be essentially considered to be in a distributed network system to enable most functionalities of the electric vehicles. A smart city can essentially implement a network system with Blockchain technology that may require peak conditions or consumption for all the cars, batteries, and solar panels to alleviate the grid by providing excess energy to the grid using by-direction techniques. As vehicles plugin to consume electricity, they can reverse electricity back to the grid to support the electric grid. The same analogy goes for individual houses’ solar panels and batteries. This is made possible when the electromechanical systems are installed in-house to generate power automatically and autonomously, distribute power, the store needed power in local batteries, and offload excess power to the grid based on the batteries scattered throughout the grid for other purposes. In this case, the automated energy grid system maintains the energy balance and provides the efficiency and optimization needed for the energy system. It is an exciting concept to create a sustainable and efficient society where more and more renewable energy is harnessed and used to power our most needed systems. Renewable energy makes the difference and supports the power grid load during the peak of energy usage or unfavorable weather conditions as well as provides the envelope of reliability for the future demands of smart cities. Food: When it comes to the food industry, it is important to remember the number of times e-coli outbreaks have occurred to make a sense of how important it is to monitor the food network and ensure its safety. One of the e-coli outbreaks pushed the Centers for Disease Control and Prevention (CDC) heavily recommend food markets and individuals to the trash and get rid of their lettuce because they could not trace which farmer in the network was contaminated at the time. This particular use case helps amplify an opportunity where blockchain can be of great use. The inability to trace back an outbreak would not have happened in Smart cities that possess a blockchain system implemented in their food distribution system. The key characteristics of blockchain are the transparency and security of the ledger. Assuming farmers or food providers and distributors are using smart agriculture technologies to grow their crops, then in this case each food producer in the food production and distribution system would have had a way to store all the information about their food supply and provide food safety through a secure and transparent public ledger. Once the outbreak of any food-related disease occurred, the smart city could easily read and access ledger transaction information and pinpoint exactly when and where that incident occurred based on the recorded ledger. This will create trust in the food system and provide information to the scientists to solve and prevent any future food-related issues.

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Supply Chain: When it comes to the supply chain, it is important to explore another key advantage of Blockchain which creating trust in the system. The query of whether someone can trust someone else if they have never had a chance to meet and know each other is among the endeavor that Blockchain aims to resolve. Blockchain shines when it comes to trusting an unknown trade partner while using its technology because of the immutability of transactions and the verification of updates within the network to authorize the ledger. Some countries have started to adopt or recognize cryptocurrency as their legal tender, and some are missing the opportunity to get more on the underline technology of Blockchain. When Smart Cities deploy blockchain technology to their supply chain, blockchain will increase the efficiency and validity of the transaction, streamline the supply chain process, and improve the overall public trust. For instance, there are many supply-chain conflicts nowadays that can benefit from blockchain especially when it comes to mineral tracing and resource allocation. One example is the mineral conflict of the columbite-tantalum resource in the Democratic Republic of Congo which is greatly needed for electronic device manufacturing and can benefit from a blockchain-enabled supply chain where all the stakeholders and critical players can be identified to create better traceability of the resources. The current supply chain tends to go from Congo (Africa) to China (Asia), and then to the USA(America) with most of the players at the source completely unknown. In the blockchain-enabled supply chain, the information regarding the extraction of the mineral in this can will be recorded in the ledger, and every step and transformation of the mineral will be registered through the network. At the end of the process, consumers will confidently trust that their gadgets are not using bloody minerals or child labor minerals. Thus, it is important to note how blockchain in the supply chain enhances and solves many societal problems and restores public trust in both transactions and interactions.

Real Estate: When it comes to real estate, at this point the benefits of blockchain can be easily identified as transparency and traceability with security are paramount to enable what is known as smart contracts. There are many papers works required in the process of purchasing a house with many partakers to identify, notify and provide valid information. This process is so tiresome especially since all the involved documents need proper verification. The housing real estate process requires several legal documents to be reviewed by the involved parties and verified. Trust is paramount in this process from the buyers to the lenders to the title company and even underwriters to establish and complete a closing on the properties. With a blockchain-enabled smart contract in smart cities, every needed paperwork and party involved are appended into

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the ledger of that particular transaction after a process of verification and confirmation that is secure and transparent. Once the property has been assigned and transferred to an involved party, the process becomes very streamlined and a significant reduction in processing time for the next time the property must be transferred to other personnel as most of the information would be already verified and stored in the ledger transaction. Thus, blockchain would help reduce the time needed to complete any other transactions on the property and improve trust in the overall real estate property acquisition process.

Health Care: In considering the benefits of blockchain in health care, health care is one of the sectors that blockchain provides unlimited potential ranging from the public health perspective to the doctor and patient relationship. The health care sector requires trust and transparency plus traceability to ensure the well-being and quality of life of the patient and even their providers. The health care system can be a great benefactor of blockchain to enhance trust and transparency as the recent covid-19 pandemic has strained the system to the point that has affected many people’s trust and confidence in the system. Covid-19 has reduced many people’s confidence in the health care system, and it has provided a lot of challenges in the health care management, distribution, and prioritization that can be addressed with blockchain due to the transparency of information in the ledger. Thus, there is an opportunity to incorporate blockchain technology into the healthcare systems to help alleviate legacy healthcare systems that proved faulty during the recent pandemic [1]. Though the implementation of blockchain technologies in health care is challenging, it is indispensable for urban planners to push to incorporate blockchain in the digitization of the health care systems for trust and confidence to be restored in smart cities.

5.5 Bridging the Gap Between Smart Communities and Cities Bridging the gap between smart communities and cities from an educational perspective involves equipping individuals with the knowledge and skills necessary to participate in the planning, design, and implementation of smart community and city initiatives. Smart communities and cities rely on a multidisciplinary approach that encompasses urban planning, engineering, information technology, public policy, and social

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sciences. Therefore, education in these areas is essential to ensure that the future workforce is equipped with the necessary skills to create and implement smart community solutions [58].

5.5.1 Architectural and Urbanism Perspective From an architectural and urbanism point of view, the primary challenge in bridging the gap between smart communities and cities lies in the integration of smart technologies with the built environment. This integration must be designed to meet the specific needs of each community or city, while also providing a framework for the seamless integration of future technologies. Architects and urban planners must work closely with technology experts and other stakeholders to design and implement smart solutions that are sustainable, inclusive, and responsive to the needs of the community or city. This involves creating an environment that is conducive to innovation, experimentation, and collaboration, and that fosters a culture of continuous improvement and adaptation. Some of the key areas that must be addressed in bridging the gap between smart communities and cities from an architectural and urbanism point of view include: • Connectivity: The integration of smart technologies with the built environment requires a high degree of connectivity. This involves designing and implementing a robust and reliable network infrastructure that can support a wide range of smart devices and applications. • Data Management: The collection, storage, and analysis of data is a critical component of smart communities and cities. Architects and urban planners must work closely with data experts to design and implement effective data management systems that can support real-time decision-making. • Sustainability: Smart communities and cities must be designed with sustainability in mind. This involves incorporating renewable energy sources, green building materials, and other sustainable practices into the built environment. • Inclusivity: Smart solutions must be designed to meet the needs of all members of the community, regardless of age, gender, or ability. This involves designing for accessibility and ensuring that smart solutions are inclusive and equitable. • Flexibility: The integration of smart technologies with the built environment requires a high degree of flexibility. This involves designing solutions that can adapt to changing needs and emerging technologies, and that can be reconfigured as required. Therefore, bridging the gap between smart communities and cities from an architectural and urbanism point of view requires a collaborative and multidisciplinary approach. Architects and urban planners must work closely with technology experts and other stakeholders to design and implement smart solutions that are sustainable, inclusive, and responsive to the needs of the community or city.

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5.5.2 Engineering Perspective From an engineering perspective, the gap between smart communities and cities can be bridged in several ways. Here are some examples: • Developing standards: Standards need to be developed to ensure compatibility between the various systems used in smart communities and cities. This includes not only technical standards but also standards related to data privacy and security. • Integration of systems: Engineers can work to integrate various systems in a city or community to ensure that they work seamlessly together. This includes integrating systems related to transportation, energy, water, waste management, and communication. • Use of sensors and IoT: The use of sensors and the Internet of Things (IoT) can help to gather data from various sources in a city or community. This data can be analyzed to identify trends and patterns, and used to improve the functioning of the various systems in a city or community. • Smart grids: Engineers can develop smart grids that use advanced technology to manage energy distribution and reduce waste. This can help to reduce the carbon footprint of a city or community. • Infrastructure planning: Engineers can work with architects and urban planners to ensure that smart technology is integrated into the design of new infrastructure projects. This can include designing buildings with smart sensors and energyefficient systems or designing roads and transportation systems that incorporate smart technology. • Innovation: Engineers can innovate and develop new technology to bridge the gap between smart communities and cities. This could include developing new materials, sensors, or software systems that can be used to improve the functioning of a city or community.

5.5.3 Information Technology Perspective Bridging the gap between smart communities and cities from an information technology (IT) perspective involves developing and implementing advanced technological solutions to improve the functioning of cities and communities. IT solutions can improve transportation, communication, energy usage, public safety, health, and many other aspects of urban life. One important aspect is to ensure that smart communities and cities are connected through the use of advanced communication technologies. This involves the development and implementation of high-speed broadband networks and wireless infrastructure to support connectivity and the exchange of data among different systems and devices. This is essential for enabling real-time monitoring and control of various urban systems and services.

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Another important aspect of IT is the use of sensors and data analytics to optimize resource use, improve decision-making, and predict and prevent failures in various systems. For example, sensors can be used to monitor air quality, water usage, and energy consumption, allowing cities to make data-driven decisions to optimize resource use and reduce waste. IT solutions can also be used to enhance public safety through the use of advanced surveillance and monitoring systems, as well as the deployment of smart emergency response systems. For example, video surveillance and facial recognition technologies can be used to detect and prevent crime, while smart emergency response systems can be used to quickly deploy emergency personnel and resources to respond to natural disasters, accidents, or other emergencies. In addition to developing and deploying these technological solutions, bridging the gap between smart communities and cities from an IT perspective also involves addressing cybersecurity concerns. The use of advanced IT solutions requires the protection of data and critical infrastructure, which is a key challenge in the era of cyber threats. Effective cybersecurity measures must be implemented to ensure that smart communities and cities are protected against cyber-attacks and data breaches. Therefore, bridging the gap between smart communities and cities from an IT perspective involves developing and deploying advanced technology solutions to improve urban life, while also addressing cybersecurity concerns to ensure the protection of critical infrastructure and sensitive data.

5.5.4 Manufacturing Perspective It leverages advanced manufacturing technologies to drive innovation, improve productivity, and enhance sustainability in both smart communities and cities. Here are some ways in which manufacturing can bridge the gap: • Digital Twins: Develop digital twins of manufacturing processes and equipment to enable real-time monitoring, predictive maintenance, and performance optimization. This can lead to improved operational efficiency and reduced downtime. • Circular Economy: Implement circular economy principles that focus on reducing waste and maximizing the value of resources. This includes practices such as product redesign, material reuse, and recycling. By doing so, manufacturers can contribute to the sustainability of smart communities and cities. • Smart Manufacturing: Implement smart manufacturing practices that enable manufacturers to optimize production processes, reduce waste, and improve product quality. This can be achieved through the use of advanced technologies such as IoT, automation, and data analytics. • Collaboration with Communities: Foster collaboration between manufacturers and communities to identify opportunities for joint innovation and problem-solving. This can lead to the development of pr.

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• Workforce Development: Develop a skilled workforce that is equipped to operate and maintain advanced manufacturing technologies. This includes providing training and development opportunities that enable workers to adapt to the changing manufacturing landscape.

5.5.5 Public Policy Perspective It involves developing and implementing policies that support the integration of smart community technologies and practices into city governance and operations. This requires a collaborative and coordinated approach between the different levels of government, community stakeholders, and technology providers. Some key considerations for bridging the gap between smart communities and cities from a public policy perspective include: • Policy development: Governments need to develop policies and regulations that support the adoption of smart community technologies and practices. This includes developing standards and guidelines for data privacy and security, creating incentives for private-sector investment, and providing funding for research and development. • Collaboration: City governments need to work collaboratively with stakeholders such as community groups, businesses, and technology providers to ensure that smart community technologies and practices are implemented in a way that aligns with the needs and values of the community. • Citizen engagement: Public participation is crucial for successful smart community initiatives. Citizens need to be engaged in the planning and implementation process to ensure that their needs and concerns are addressed. • Data governance: Effective data governance is essential for ensuring that data collected from smart community technologies is used in a responsible and ethical way. Policies need to be developed to ensure that data is secure, private, and accessible. • Capacity building: Governments need to invest in the development of human capital and institutional capacity to ensure that city officials have the knowledge and skills necessary to manage smart community technologies and practices.

5.5.6 Educational Perspective It involves equipping students with the knowledge, skills, and competencies needed to design, build, and manage smart communities and cities. This requires a multidisciplinary approach that integrates engineering, urban planning, computer science, and social sciences.

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Educational programs can range from primary and secondary schools to higher education institutions and vocational training centers. These programs should focus on developing skills in areas such as data analysis, communication technology, urban planning, and sustainability. One of the primary objectives of smart community education is to develop the next generation of smart community leaders who can drive the development and implementation of smart community initiatives. To achieve this, educational institutions should provide students with hands-on experience in designing and implementing smart community projects. Moreover, it is essential to provide education and training that is accessible to a diverse range of individuals. This can be done through online education platforms, community workshops, and vocational training programs. Institutions can also provide training for professionals already working in the field to stay up-to-date with the latest technologies and innovations, allowing them to provide better services and support for their communities. Additionally, educational institutions can work closely with industry partners to align their programs with the latest developments and trends, providing students with practical skills and experience that they can apply to real-world problems. In addition to providing technical skills, education should also focus on fostering a culture of innovation and entrepreneurship. This can involve providing opportunities for students and community members to collaborate and develop creative solutions for smart community challenges. Thus, bridging the gap between smart communities and cities from an educational perspective requires a comprehensive approach that addresses the current and future needs of communities, fosters collaboration, and interdisciplinary approaches, and provides students and professionals with the necessary knowledge and skills to meet the challenges of building and managing smart communities and cities.

5.5.7 Social Sciences Perspective It involves understanding how people interact with and within their communities and how technology can be used to enhance those interactions. Social sciences include fields such as sociology, psychology, anthropology, and political science, among others, that are concerned with understanding human behavior and social structures. From a social sciences perspective, bridging the gap between smart communities and cities requires understanding the needs and perspectives of different stakeholders in the community, including residents, local businesses, and government officials. It also involves considering the social and cultural contexts of the community and how technology can be used to support and enhance existing social structures. For example, an anthropological perspective may involve understanding the cultural practices and values of the community and how they shape perceptions of technology and its use. A political science perspective may involve considering how policy decisions and regulations can support the development and adoption of smart

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technologies in the community. A psychological perspective may involve understanding how technology can affect social behavior and interaction, both positively and negatively. Hence, this perspective involves understanding the human and social dynamics of the community and using that understanding to develop and implement smart technologies in a way that supports and enhances the community’s social structures and well-being.

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Chapter 6

Current Smart Communities and Cities

6.1 Current Smart Cities There are many smart cities worldwide, with various levels of development and implementation of smart technologies. Besides, different organizations use different criteria to define and rank smart cities. However, here are some of the cities that are often considered to be among the top smart cities in the world [1]: • Singapore: Singapore has been a leader in smart city technology for years, with initiatives like the Smart Nation program, which aims to use technology to improve the lives of citizens and businesses. • Tokyo: Tokyo is often cited as one of the world’s most livable cities, thanks partly to its extensive public transportation network and innovative use of technology to manage everything from waste disposal to emergency services. • London: London has been implementing several smart city projects, including using sensors to manage traffic and air quality and the installation of smart streetlights that can adjust their brightness based on the level of pedestrian and vehicle traffic. • New York City: New York City has implemented some smart city initiatives, including the LinkNYC program, which provides free Wi-Fi and other services through a network of kiosks. • Seoul: Seoul has been implementing smart city technologies for years, including a network of sensors that monitor air quality and traffic, and an app that allows citizens to report issues like potholes and broken streetlights. • Copenhagen: Copenhagen is often cited as one of the most sustainable cities in the world, thanks in part to its extensive network of bike lanes and its use of technology to manage energy and water resources. • Helsinki: Helsinki has implemented many smart city projects, including a system that allows citizens to use their smartphones to pay for public transportation and access other services.

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• Barcelona: Barcelona has implemented several smart city initiatives, including a network of sensors that monitor air quality and noise levels, and an app that allows citizens to report issues like graffiti and potholes. • Dubai: Dubai has implemented many smart city projects, including using drones to monitor and manage traffic, and the installation of smart streetlights that can adjust their brightness based on pedestrian and vehicle traffic. • San Francisco: San Francisco has implemented some smart city initiatives, including the use of sensors to manage parking and traffic and the development of a mobile app that allows citizens to report issues like potholes and broken streetlights. Throughout the book, the definition and concept of smart cities and communities have been detailed. Here are some key factors that make a city or community smart: • Information and communication technology (ICT) infrastructure: A smart city needs a robust ICT infrastructure, including high-speed internet access, wireless connectivity, and sensors that collect data from the city’s physical assets. • Smart governance: A smart city needs an efficient and effective governance structure that enables it to use data and technology to make better decisions and improve services for its citizens. • Sustainable development: A smart city needs to focus on sustainable development, including reducing energy consumption, promoting renewable energy sources, and reducing waste. • Quality of life: A smart city needs to focus on improving the quality of life for its citizens, including providing access to quality healthcare, education, and cultural amenities. • Mobility: A smart city needs to focus on improving mobility and transportation for its citizens, including reducing traffic congestion, promoting sustainable modes of transportation, and providing efficient public transportation. • Safety and security: A smart city needs to focus on improving safety and security for its citizens, including using technology to monitor crime and traffic accidents and providing emergency services quickly. • Environmental sustainability: A smart city needs to focus on environmental sustainability, including reducing pollution and greenhouse gas emissions, promoting sustainable development, and protecting natural resources. • Citizen engagement: A smart city needs to engage citizens in the decision-making process and use technology to promote transparency and accountability in governance. • Data-driven decision-making: Smart cities use data to make informed decisions and improve services. This involves collecting and analyzing data from various sources, including sensors, social media, and citizen feedback. • Efficient services: Smart cities use technology to optimize urban services, such as traffic management, waste collection, and public safety. • Innovation: Smart cities encourage innovation and experimentation, with a focus on finding new and creative solutions to urban challenges.

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• Integrated Systems: Smart cities use integrated systems that connect various components of the city’s infrastructure, such as transportation, energy, and public services. This allows for greater efficiency and better coordination of services. • Technology infrastructure: Smart cities rely on advanced technology infrastructure, such as high-speed internet, sensors, and smart devices, to support their digital services. • Data Analytics: Smart cities use data analytics to monitor and analyze data from various sources, such as traffic sensors, weather sensors, and social media, to gain insights into the city’s operations and to identify areas for improvement. Therefore, either a smart city or a smart community uses technology and data to improve the quality of life for its residents while promoting sustainability, efficiency, and innovation.

6.2 Some Conventional Indicators of Smart Cities Developing and preserving smart cities requires specific indicators that allow for measuring the quality of the smart city. Thus, when defining smart cities, several essential keywords are life quality, technology, people, government, economy, sustainability, resources, health, safety, and protection [2–4]. One of the most advanced smart city is Songdo, South Korea. Located in Incheon, Songdo is a city designed to be a model of sustainability and efficiency that improves the communication between the citizen’s needs and actions. It is necessary to have various smart technologies, including a high-speed fiber-optic network that can transmit and receive a large amount of data quickly, intelligent traffic systems, and sensors that monitor air quality, water usage, and energy consumption. The city also has a range of green spaces for ludic activities that promote creativity and innovation, including parks, gardens, and wetlands. The public transportation network provides a service that is effective for citizens. Songdo is also an active economic hub since it is home to numerous innovative businesses, including several technological startups. Today, it is necessary to define the smart city and sustainability using unified frameworks that are overlapped to transform conventional cities into smart cities using sustainable conditions and regulations. The sustainable frameworks comprise education, culture, science, innovation, economy, energy, transportation, water and waste management, built environment, natural environment, well-being, health, safety, and natural environment as elements that must be evaluated in a smart city [5]. Some conventional indicators of smart cities are divided according to the needs of citizens; this indicator can be seen as a reference to evaluate the performance of the smart city and how the needs of citizens are fulfilled [6]. Moreover, these indicators are aligned with the quality of life. Below is a list of indicators to evaluate smart cities (this list is based on the information presented in [6]. A simple evaluation could include three main labels bad, regular, and good; the labels illustrate the performance of the smart city. However, the crisp numbers can be used to assess the smart city in a precise scale.

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Table 6.1 Smart city main indicators Main indicator 1. Availability of essential healthcare within a 500 m radius 2. Promotion of healthy lifestyles through policy 3. Transportation fatalities per 100,000 people 4. Number of incidents per 100,000 people 5. Cybersecurity level of the computers used in the city’s operations 6. Level of data protection in the city 7. Percentage of the population with access to public transportation within a 500 m radius 8. Length of bicycle paths/lanes compared to total street length (excluding motorways) 9. Percentage of the population with access to public amenities within a 500 m radius 10. Accessibility of educational resources in the city, both physical and digital 11. Proportion of schools with environmental education programs 12. Percentage of commercial/public ground floor surface compared to the total ground floor 13. Amount of green space (in hectares) per 100,000 population 14. The proportion of the city’s total energy consumption that comes from renewable sources 15. Annual CO2 emissions per person in tonnes 16. The fraction of renewable fuels used in local freight transportation 17. Per capita direct material usage in the city 18. Daily per capita water consumption 19. The percentage of water lost from the total water consumed 20. Population density in individuals per square kilometer 21. The proportion of food consumed that is produced within 100 km of the city 22. Maximum temperature difference city-countryside in summer months 23. Annual per capita generation of municipal solid waste 24. Percentage of the population exposed to nighttime noise levels above 55 dB(a) 25. The percentage of the city’s solid waste that is recycled 26. The number of open government datasets available per 100 k population

It is recommended to use the label and the crisp number. The general label could be colored like a traffic light (green, yellow, and red) to indicate quickly the indicators that need to be improved (see Table 6.1). On the other hand, specific indicators evaluate participative governance practices and citizens’ responsibilities, and these indicators are connected strongly with social factors in smart cities. Besides, social inclusion indicators need to be promoted in emerging and developing countries because they are key elements that dramatically improve the quality of life. The social inclusion indicators must gain more popularity to be integrated as leading indicators in all smart cities. Some studies present a complete evaluation of social indicators that help transform conventional cities into smart cities [7]. These social indicators could accelerate the creation of smart cities. The leading smart city indicators are presented below:

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• Sustainability is related to using renewable energy sources such as wind and solar and how they are manipulated. • Quality of life: citizens’ safety, comfort, education, health services, and convenience. • Economic prosperity: it is defined as the number of work opportunities as well as economic growth. In addition, the amount of salary per year could be an indicator. • Participation: the degree to which citizens participate in decision-making regarding the smart city. • Innovation: the city’s ability to develop, test, and implement new solutions. Response time is considered. • Connectivity: the city’s ability to connect and share data across various networks and technologies. • Mobility: the shortage of public and private transportation; accessibility and availability of transportation systems and services.

6.3 Types of Certifications Certifications are a way to measure and communicate the environmental, social, and economic performance of buildings and cities. Here are some of the most common types of certifications: • LEED [8, 9]: Leadership in Energy and Environmental Design (LEED) is a green building certification program developed by the United States Green Building Council (USGBC). It provides a framework for buildings to be designed, constructed, and operated in an environmentally responsible and sustainable manner. LEED certification is based on a point system that evaluates a building’s performance in areas such as energy efficiency, water conservation, indoor air quality, and materials selection. • ISO: The International Organization for Standardization (ISO) has developed a series of standards related to sustainable development of cities. ISO-37120 provides a framework for cities to measure and compare their performance in terms of sustainability and livability [10]. ISO-37122 establishes a framework for measuring cities’ performance in economic development, environmental sustainability, social inclusion, and governance [11]. ISO-37123 guides the development and implementation of smart community services [12]. • Passivhaus [13, 14]: Passivhaus is a certification for buildings that meet strict energy efficiency standards. The Passivhaus standard requires buildings to have a very low energy demand and to be well-insulated and airtight. Passivhaus buildings rely on natural sources of heat, such as the sun and human activity, to maintain a comfortable indoor temperature. • BREEAM [15, 16]: Building Research Establishment Environmental Assessment Method (BREEAM) is a certification developed by the Building Research Establishment (BRE) in the UK. It evaluates a building’s performance in areas such

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as energy efficiency, water conservation, and indoor air quality. BREEAM also takes into account the building’s impact on the surrounding environment and the community. These certifications provide a way to measure and communicate a building or city’s performance in terms of sustainability, energy efficiency, and other important criteria. They can also help building and city managers make decisions that will improve the environmental, social, and economic sustainability of their projects.

6.3.1 LEED Specific certifications can be a complement support when a smart city is designed and evaluated. Below are some certifications used when a smart city is built or assessed. Since 38% of the greenhouse gases produced in the US today are coming from buildings, it is vital to regulate buildings in smart cities. To solve this significant carbon footprint in American buildings, architecture companies from the United States work with construction managers and environmental lawyers to create LEED (Leadership in Energy and Environmental Design). According to the Sustainable Investment Group, the U.S. Green Building Council was established by this collaboration and it was later created the LEED rating system. In 1998, the first LEED certificates were granted. The four primary LEED certification levels for buildings are Certified Silver, Gold, and Platinum. Reduced energy consumption and waste, effective resource management, and lower operating costs are the foundation for achieving certification, and LEED also provides a complete framework that helps to reduce carbon emissions. LEED certification is a globally recognized green building certification system that provides third-party verification of a building’s sustainability performance. LEED certification is based on a point system, with points awarded for meeting specific criteria in energy efficiency, water efficiency, materials and resources, indoor environmental quality, and innovation. The benefits of LEED certification include improved energy and water efficiency, reduced operating costs, improved occupant health and productivity, and reduced environmental impact, benefiting building owners, occupants, and the environment. To obtain LEED certification, a project must apply to the US Green Building Council, which evaluates it using the LEED rating system. Projects that meet the criteria are awarded one of four levels of certification: Certified, Silver, Gold, or Platinum, based on the number of points earned. LEED certification is valid for three years, after which the project must undergo recertification by submitting an application to the USGBC and being reviewed by a third-party certification body. Here are some more details on the benefits of LEED certification: • Improved energy and water efficiency: LEED-certified buildings are designed to use energy and water more efficiently, which helps reduce operating costs and lower greenhouse gas emissions. Energy-efficient buildings also have a positive

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impact on the environment by reducing the amount of fossil fuels burned for electricity generation. • Improved occupant health and productivity: LEED-certified buildings prioritize indoor environmental quality, which can improve occupant health and productivity. This includes providing access to natural light, improving air quality, and using non-toxic building materials. • Reduced environmental impact: LEED-certified buildings are designed to reduce environmental impact. This includes using sustainable materials, reducing waste during construction, and designing buildings with a smaller carbon footprint. • Marketing and branding advantages: LEED certification can help improve a building’s marketability and brand recognition. This is because LEED certification is widely recognized as a standard for sustainable building design. Regarding the certification process, it is worth noting that there are different types of LEED certification, including for new construction, existing buildings, interior design, and neighborhood development. The certification process can vary slightly depending on the type of project. Additionally, the certification process involves earning points in different categories, such as energy and water efficiency, indoor environmental quality, and sustainable site development. The number of points required for each level of certification varies depending on the type of project.

6.3.2 ISO 37120, 37122, 37123 In the context of smart cities, the most commonly used standards are ISO 37120 Sustainable Development of Communities: Indicators for City Services and Quality of Life, ISO 37122 Smart Community Infrastructure, and ISO 37123 Smart Community Services. These standards provide a comprehensive framework for cities to measure and compare their performance in various areas, such as energy, water, waste, transport, health, safety, education, and the environment. Additionally, the ISO 37125 Smart City Framework guides how to develop and implement a comprehensive smart city strategy, which can help cities effectively implement smart city initiatives. ISO 37120 is an international standard for sustainable cities that provides cities with a framework for measuring their performance in key areas of urban sustainability, such as economic, environmental, social, and governance indicators. The standard is intended to assist cities in measuring their progress toward achieving the United Nations Sustainable Development Goals (SDGs) and to provide a basis for city comparison. It includes indicators for air quality, water and sanitation, energy, transportation, green spaces, and public safety, among other things. The standard also specifies how to collect and analyze data, as well as how to use the results to inform decision-making. ISO 37122 is an international standard that provides a comprehensive framework for measuring smart city performance. It covers four main areas, including economic

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development, environmental sustainability, social inclusion, and governance. This standard includes a set of indicators and metrics that cities can use to evaluate their performance in each of these areas. It also explains how to collect and analyze data, as well as how to use the results to inform decision-making. ISO 37122 is designed to help cities become more efficient, effective, and equitable. In addition, ISO 37120 provides a framework for cities to measure and compare their performance in terms of sustainability and livability. This standard defines a set of indicators that can be used to measure the performance of cities in various aspects, such as economic, environmental, social, and cultural. Specifically, the economic indicators in ISO 37120 measure the performance of cities in terms of economic development, employment, and income. By adopting both ISO 37122 and ISO 37120, cities can comprehensively evaluate and improve their performance in various areas of smart city development. These indicators include Gross Domestic Product (GDP), Gross Value Added (GVA), employment rate, and median household income. Environmental Indicators: ISO 37120 defines a set of environmental indicators that measure the performance of cities in terms of air quality, water quality, waste management, and energy efficiency. These indicators include the air quality index, water quality index, waste management index, and energy efficiency index. Social Indicators: ISO 37120 defines a set of social indicators that measure the performance of cities in terms of health, education, safety, and housing. These indicators include life expectancy, literacy rate, crime rate, and housing affordability. Cultural Indicators: ISO 37120 defines a set of cultural indicators that measure the performance of cities in terms of cultural heritage, cultural diversity, and cultural activities. These indicators include the number of cultural heritage sites, events, and activities [10].

6.3.3 Passivhaus The project must meet the criteria for the same level of certification or higher to maintain its certification. It is essential to mention that some indicators and standards have been developed worldwide, such as Passivhaus and Deutsche Gesellschaft für Nachhaltiges Bauen e.V. Passivhaus is an energy-efficient building standard developed by the Passivhaus Institute in Germany that is used frequently. It is based on energy conservation principles and airtight construction and is designed to reduce energy consumption and improve indoor air quality. Passivhaus buildings are intended to decrease energy usage by up to 90% by using high insulation levels, airtightness, and thermal bridging. Passivhaus structures are also intended to be comfortable and healthy, with high indoor air quality and low noise levels. German Society for Sustainable Building The Deutsche Gesellschaft für Nachhaltiges Bauen (DGNB) is a non-profit organization in Germany that encourages green building practices. The DGNB, founded in 2007, promotes sustainable building materials, energy efficiency,

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and green building technologies. The DGNB has created a certification program for buildings that meet their sustainability targets, and they have certified over 1,000 buildings in Germany. The DGNB not only promotes expanding Passivhaus standards in Germany but also endorses using renewable energy sources in buildings. To this end, the organization has developed certification systems for both Passivhaus buildings and buildings that meet their standards for renewable energy use.

6.3.4 BREEAM BREEAM (Building Research Establishment Environmental Assessment Method) is a widely recognized sustainability assessment method and certification program for buildings. It was first developed in the UK in the early 1990s by the Building Research Establishment (BRE), a leading research organization in the construction industry. BREEAM provides a comprehensive framework for assessing and certifying the environmental performance of new and existing buildings across various stages of their life cycle, from design and construction to operation and maintenance. It evaluates buildings on various criteria, including energy and water use, materials and waste management, indoor environmental quality, transport, land use and ecology, and management processes. BREEAM uses a scoring system that rates the building’s performance against benchmarks, with higher scores indicating better environmental performance. BREEAM certifications are available at various levels, including Pass, Good, Very Good, Excellent, and Outstanding, depending on the building’s score. BREEAM has been adopted in many countries worldwide, including the UK, Europe, Asia, the Middle East, and the Americas. It is commonly used in commercial, residential, and public buildings and is recognized by governments, developers, and investors as a leading standard for sustainable buildings.

6.4 Proposed Connected Model in Current Smart City IoT There are several proposed connected models for smart cities, and the specific model used can vary depending on the city’s needs and resources. Here are some examples of proposed connected models: • Sensor-based model: This model relies on a network of sensors installed throughout the city to collect data on various parameters such as air quality, traffic flow, and energy consumption. The data is then analyzed to improve services and make the city more efficient.

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• Platform-based model: This model uses a digital platform to integrate various city services and allow for seamless communication between different agencies and departments. The platform can also collect and analyze data to improve services and enable better decision-making. • Citizen-centric model: This model puts the needs and concerns of citizens at the center of smart city planning. It involves active engagement with citizens to identify their priorities and involve them in decision-making processes. This model emphasizes transparency, accessibility, and inclusivity. • Data-driven model: This model relies on data analysis and machine learning to optimize city services and make them more efficient. It involves collecting large amounts of data from various sources and using advanced analytics tools to identify patterns and make predictions. • Collaborative model: This model involves collaboration between various stakeholders, including citizens, private sector companies, and government agencies, to develop and implement smart city solutions. It emphasizes partnerships and cooperation to address complex urban challenges. These are just a few examples of the proposed connected models for smart cities. Ultimately, the success of a smart city depends on the ability to integrate technology, data, and citizen engagement to improve the quality of life for residents and create a more sustainable and resilient urban environment. Furthermore, other connected models that consider IoT and the specific model used can depend on the city’s needs and resources. Here are some examples of proposed connected models in smart city IoT: • Centralized Model: In this model, a centralized system is used to manage and control all the IoT devices installed in the city. The system can collect data from different sensors, analyze the data, and provide insights to city managers for decisionmaking. • Edge Computing Model: This model involves processing data at the network’s edge, i.e., closer to the IoT devices. This reduces latency and bandwidth requirements and makes the system more scalable and efficient. • Distributed Model: In this model, the IoT devices are distributed across the city, and each device has its processing power. The devices can communicate with each other to share data and coordinate their actions. • Hybrid Model: This model combines centralized, edge computing, and distributed models to create a more robust and scalable smart city IoT system. This model can offer the benefits of all the models and address their limitations. • Open Data Model: This model makes data collected from IoT devices available to the public, developers, and businesses to create new services and solutions that benefit the city and its residents. IoT for smart cities is a concept that uses the Internet of Things (IoT) to create an ecosystem of connected devices that work together to improve the efficiency and livability of urban areas. It can create a more efficient, connected, and sustainable urban environment. IoT-enabled smart cities can use connected devices to monitor

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and manage traffic, energy, water, and waste systems, as well as provide citizens with real-time information about their cities. Smart cities can also use IoT to improve public safety, reduce pollution, and develop more effective public services. Connective data can also improve urban planning and development decisions in IoT-enabled smart cities. IoT for smart cities typically involves using sensors and other connected devices to gather data on different aspects of the city, such as traffic flow, air quality, noise levels, energy usage, and water consumption. This data is then analyzed to identify patterns and trends and used to inform decision-making by city officials and other stakeholders. For example, IoT technology can monitor traffic flow and adjust traffic signals in real-time to optimize traffic flow and reduce congestion. It can also monitor air quality and alert city officials and citizens when pollution levels exceed safe levels. Other examples of IoT technology for smart cities include smart lighting systems that adjust lighting levels based on occupancy, weather sensors that can automatically adjust irrigation systems in public parks, and waste management systems that can optimize collection routes based on real-time data. Overall, IoT technology has the potential to make cities more efficient, sustainable, and livable by providing real-time data and insights that can be used to inform decision-making and improve city services. In today’s smart city IoT, a disconnected model does not rely on a central server or cloud-based platform to store and process data. The data is instead stored and processed locally on the device. This model is commonly used in lighting systems, parking management, and smart waste management applications, where data is collected from sensors and then analyzed and acted upon locally. This concept is advantageous because it eradicates the need for a centralized server, which can be expensive and difficult to maintain. It can also offer more secure data storage and faster response times. Cyber-physical systems (CPS) are a type of technology that combines physical and digital components to create a system that can interact with its environment. CPS’s elements [11, 12]: • Sensors: Sensors collect data from the physical environment and transmit it to the cyber system. • Actuators: Actuators are used to control the physical environment based on the data received from the cyber system. • Network: The network connects the cyber system and the physical environment. • Control System: The control system is used to process the data received from the sensors and generate the appropriate control signals for the actuators. Software: Software provides the logic and algorithms for the control system. Cyber-physical systems (CPS) serve as a bridge between the physical and digital worlds, enabling the seamless integration of physical components, such as sensors, actuators, and machines, with digital components, such as software, networks, and databases. This integration enables the automation of complex processes, the optimization of resources, and data collection for analysis. CPS have numerous appli-

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cations, including smart homes, smart cities, industrial automation, and healthcare, and they can improve safety, efficiency, and productivity in many industries. In the context of smart cities, CPS have the potential to create a more efficient and sustainable urban environment. Specifically, CPS can be used to monitor and control traffic, optimize energy usage, and improve public safety. By leveraging CPS technology, cities can automate and streamline complex systems, resulting in significant benefits for citizens and the environment. Overall, CPS technology has the potential to revolutionize the way cities operate, making them more efficient, sustainable, and livable. Additionally, CPS are able to collect data from different sources, such as sensors and other data sources, to provide real-time information about the city’s operations. This data can be used to make informed decisions on improving the city’s infrastructure and services. A smart city based on cyber-physical systems is a city that uses a combination of physical infrastructure and digital technology to create a more efficient, sustainable, and livable urban environment. Cyber-physical systems combine physical infrastructure with virtual information (models), such as roads, buildings, and other dynamical infrastructure, and digital technology, such as sensors, networks, and software. This combination of physical and digital technology allows for the collection and analysis of data from the physical environment, which can then be used to improve the city’s efficiency, safety, and sustainability. For example, a smart city based on cyber-physical systems could use sensors to monitor traffic patterns and then use this data to optimize traffic flow. It could also use sensors to monitor air quality and then use this data to reduce air pollution [17–20]. Additionally, a smart city could implement digital technology to improve public safety by monitoring crime and providing real-time alerts to citizens. Finally, a smart city could use digital technology to improve energy efficiency by monitoring energy usage and providing incentives for energy conservation. A smart city based on cyber-physical systems can provide a more efficient, sustainable, and livable urban environment. By using digital technology to monitor and analyze data from the physical environment, cities can improve their cities’ efficiency, safety, and sustainability. On the other hand, a digital twin is a digital model of a real physical entity. It is a dynamic digital model that relies on sensor data to understand the entity’s state and simulate its behavior. Digital twins are used to analyzing and predict physical assets’ performance, processes, and systems. They can be used to optimize operations, reduce costs, and dynamically improve customer experiences [21, 22]. Singapore is at the forefront of the development of digital twin smart cities. The Singapore government has launched the Smart Nation initiative, a comprehensive effort to leverage technology to enhance the lives of its citizens and businesses. The initiative includes the development of digital twins of the city, which are essentially digital replicas of the physical infrastructure and services of the city. These digital twins can monitor and manage the city’s infrastructure, services, and resources in real time. The use of digital twins allows the government to gain better insights into the city’s needs and respond quickly to any changes. Furthermore, the data collected by the digital twins can be used to improve the city’s services and infrastructure. For

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instance, the digital twins can be utilized to monitor traffic patterns, detect areas of congestion, and suggest solutions to reduce traffic [21–23]. By embracing the digital twin concept as part of the Smart Nation initiative, Singapore is pioneering an innovative approach to urban management. This approach can greatly enhance the livability, sustainability, and efficiency of the city, as well as serve as a model for other cities around the world. By developing these digital twins, Singapore is well-positioned to take advantage of emerging technologies, enabling it to become a truly smart city leader. Additionally, digital twins can monitor air quality, water quality, and energy usage. This data can be used to improve the city’s sustainability and reduce its environmental impact. Digital twins in smart cities could be able to promote and push applications that are crucial in smart cities; these applications are presented below. The smart cities of 2050 will see a significant transformation in various areas. Autonomous transportation will become the norm, with driverless cars, buses, and other modes of transport reducing traffic congestion, improving safety, and lowering emissions. Smart buildings equipped with sensors and internet-connected will be more energy-efficient and responsive to changing conditions. In addition, smart infrastructure will monitor and manage city resources such as water, electricity, and waste, enabling cities to become more sustainable and efficient. Furthermore, smart energy systems will generate, store, and distribute energy more efficiently, lowering energy costs and increasing sustainability. Smart healthcare systems will be used to monitor and manage the health of citizens, leading to reduced healthcare costs and improved quality of life. Smart education systems will be implemented to provide personalized learning experiences for students, improving educational outcomes and reducing inequality. Finally, smart governance systems will be adopted to enhance the efficiency and transparency of government services, reducing corruption and improving the quality of life. By integrating these smart technologies, cities can create more sustainable, efficient, and equitable urban environments, benefiting citizens and the environment. The smart cities of 2050 will revolutionize how people live and interact with their surroundings and pave the way for a brighter and more sustainable future.

6.4.1 Mexico as a Study Case for Medium-Sized Companies for Deploying Renewable Energy Today’s world faces many environmental, social, and economic challenges as climate change, social inequalities, and poverty, to name a few. The rapid growth of population, urban areas, and manufacturing companies are linked to these challenges since these increments are related to a major use of resources, including those to generate energy. There are four key challenges that the world’s energy industry confronts today: oil scarcity, energy security, environmental degradation, and the growing energy needs [24]. Different analyses of the future of energy show that it is

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Fig. 6.1 Description of the S4 Framework

possible to achieve energy access and energy security simultaneously while avoiding negative environmental impacts [25], such as climate change and global warming. Thus, the use and development of technologies and policies are fundamental to achieving these objectives. Renewable technologies appear as a viable option to satisfy the increasing electricity demand in a sustainable and climate-friendly way, especially for the industrial sector since it consumes almost 42% of the world’s total electricity production and produces 30% of the total greenhouse gas emissions. Mexico is considered one of the countries with the highest levels of solar radiation. Thus, Mexican companies have a great opportunity to diversify their energy sources while saving money by incorporating solar technologies, especially PV panels, into their operations. Regardless, the lack of information in the country does not allow them to analyze their social and technical needs and make decisions about PV solar energy systems. Pérez et al. [26] proposed an S4 Framework that contains the sensing, smart, sustainable, and social features that small or medium-sized companies must consider when installing, operating, and disposing of PV systems in Mexico. The objective of the framework is that companies analyze each feature and its function in each stage of the PV system and choose the features that best meet their needs. The S4 features are divided according to their function, as observed in Fig. 6.1. The S4 Framework considers all the elements involved in PV systems connections: energy source and other meteorological variables that may affect the functioning of the system; PV modules; inverters; energy storage if applicable; energy management system; smart meters, and the type of connection, stand-alone or grid-connected. The

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Framework analyzes the life cycle of the PV system from the evaluation/diagnosis to installation, operation, and disposal, considering in this last stage a substage of reuse in case some elements can be reused in other installations. Furthermore, the sensing, smart, sustainable, and social features are presented in each stage of the system. A summary of how companies can use this Framework is presented below: 1. Identify the technical and social needs of the manufacturing company, and thus, determine if it is feasible to install the PV system. 2. Select the PV panel, inverter, and structure that best meets the company’s needs. 3. Select the type of PV system: standalone or grid-connected, if standalone, select the energy storage system. 4. Select the sensing, smart, sustainable, and social features. 5. Install the PV system and the S4 features. 6. Operation, maintenance, and monitoring of the system. 7. End of the PV systems life cycle: evaluate if there are elements that can be reused, if not, dispose of them according to current regulations. This S4 Framework was developed mainly for being used for small and mediumsized manufacturing companies interested in installing PV systems in the Mexican context, but it can also be adopted and used in other environments, for example, to install PV systems in communities living in energy poverty.

6.4.2 Solar Energy Implementation in Manufacturing Industry Using Multi-criteria Decision-Making Fuzzy TOPSIS and S4 Framework The demand for electrical energy has increased exponentially since the population and automation in the industry have grown. Electrical energy is considered one of the most critical inputs of industrial production and its development. Industry consumed almost half of the electricity produced worldwide and therefore, it is one of the biggest greenhouse gas emitters. Electricity is used in industry for operating motors and machinery, lighting, computers and office equipment, and heating, cooling, and ventilation. Because of the recent growth in conventional fuel prices and environmental impacts, companies are starting to produce electricity on-site by installing renewable energy instead of purchasing it directly from electric utilities [27]. Solar photovoltaic (PV) energy is a promising option to be applied in the industry because it is abundant, accessible, clean, and does not make any noise or pollution. It also has had a price decrease in the last years, showing quick payback and long-term savings. Currently, there is a lack of information about the technical and social factors that a company must consider to make an effective decision based on their needs regarding PV solar energy systems and how to assess and select solar energy companies. Assessing a company about these decisions can be complex since several criteria

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with different hierarchies among decision-makers are involved and the lifespan of the PV system must be considered, from analysis/diagnosis to installation, operation, and disposal. In [27], they carried out an analysis of the multi-criteria decision-making methods used in the literature to solve renewable energy problems. After this analysis, it was decided to use the TOPSIS method because it showed to be quite advantageous in resolving problems with various attributes since it has a simple process and procedure complexity remains the same regardless of the increase in the number of attributes. Fuzzy sets were also included, as linguistic variables can easily be converted to fuzzy numbers, they tolerate uncertainty and deal with incomplete and uncertain information The fuzzy decision-making approach (Fuzzy TOPSIS) proposed in this work deals with the assessment of solar companies using the S4 framework in which the sensing, smart, sustainable, and social features are labeled with linguistic values that allow evaluating the companies using fuzzy values, and thus, select the best alternative for manufacturing companies that want to install solar PV systems. Using the Fuzzy TOPSIS methodology, this paper considers the S4 features as benefits, and decision-makers from manufacturing companies evaluate solar panel companies considering the S4 framework to choose the best alternative that meets the manufacturing companies’ needs. The Fuzzy TOPSIS approach proposed can be observed in Fig. 6.2. The blue boxes are the steps that have to be made by the decision-makers. For the case study, three Mexican solar panel companies were analyzed according to the S4 Framework. Furthermore, three different manufacturing companies with different needs participated in the study. With the S4 features chosen and knowing the products and services offered by the three solar panel companies, C1, C2, and C3, three decision-makers, D1, D2, and

Fig. 6.2 Fuzzy TOPSIS approach for solar PV systems

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D3, from three manufacturing companies, M1, M2, and M3, evaluate the products and services offered by the solar panel companies considering four benefit criteria, Sensing (S1), Smart (S2), Sustainable (S3), and Social (S4). After running the fuzzy TOPSIS method proposed, it could be observed that the resulting solar panel company was indeed the best alternative for each manufacturing company. The results obtained confirm that decision-making is an important stage of solar energy deployment and operation. Moreover, selecting the S4 features and implementing this multicriteria methodology can provide a complete evaluation of the solar energy system according to specific companies’ needs. Thus, a correct company selection that can fulfill the need is achieved.

6.4.3 Energy Simulations for Understanding Building Behavior Energy simulations and thermal comfort are important tools for understanding the behavior of buildings and their impact on the environment. Energy simulations use computer models to predict how a building will perform in terms of energy usage based on building materials, insulation, heating and cooling systems, and occupant behavior. This allows architects, engineers, and building managers to optimize a building’s design and operation for energy efficiency, which can reduce its environmental impact and save money on energy costs [28–31]. Thermal comfort, on the other hand, measures how comfortable occupants are in a building’s indoor environment. It is influenced by various factors, including air temperature, humidity, air velocity, and radiant heat. By analyzing thermal comfort data, building managers can adjust building systems to improve occupant comfort, leading to increased productivity, better health outcomes, and reduced energy consumption. Together, energy simulations and thermal comfort analysis can help building managers optimize building performance and create more sustainable and comfortable indoor environments. This is particularly important as buildings account for a significant portion of global energy consumption and greenhouse gas emissions. Ladybug Tools is a free and open-source suite of plug-ins for the 3D modeling software Rhino that allows designers, engineers, and architects to perform environmental analysis for buildings. The toolset is primarily focused on simulating building performance, particularly related to daylighting, energy use, and thermal comfort. One of the key components of Ladybug Tools is EnergyPlus, a building energy simulation software developed by the U.S. Department of Energy. EnergyPlus is a whole-building simulation software that models energy use and thermal performance of buildings, allowing designers to assess how different design options and strategies can impact energy use and performance. Ladybug Tools provides a user-friendly interface to EnergyPlus, allowing designers to create EnergyPlus models from 3D models of the building easily. The toolset

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also includes several other plugins that can be used for simulation and analysis, such as Honeybee, which allows for dynamic daylighting analysis, and Butterfly, which can simulate air flow and wind patterns. Galapagos is a plugin for Grasshopper that performs evolutionary optimization to help designers find optimal solutions to design problems. Galapagos can be used to optimize a building’s geometry or parameters for energy efficiency, thermal comfort, and other performance criteria. When used together, Ladybug Tools, EnergyPlus, and Galapagos can provide a powerful set of tools for designers to optimize building performance. Ladybug Tools can be used to create 3D models and run simulations of environmental conditions, while EnergyPlus can be used to simulate energy consumption and performance. Galapagos can then be used to optimize the design for the best performance based on the results of the simulations. Furthermore, Figs. 6.3, 6.4, 6.5 and 6.6 depict four examples of how architectural students use the Ladybug tools software for the site analysis and environmental analysis applied to the envelope to determine the best strategies for reducing energy consumption and increasing thermal comfort.

Fig. 6.3 Radiation analysis for a residential building located at Monterrey, Mexico

Fig. 6.4 Best building placement using Galapagos and minimum incident radiation in Monterrey, Mexico

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Fig. 6.5 Thermal comfort analysis for a commercial building located at Queretaro

Fig. 6.6 Thermal comfort analysis for a healthcare building located at Estado de Mexico

6.4.4 Net Zero Buildings Net zero buildings, also known as zero-energy buildings, are buildings that generate as much energy as they consume over the course of a year. This means that the amount of energy they use is offset by the amount of renewable energy they produce. Net zero buildings can be achieved through a combination of energy efficiency measures, renewable energy sources, and energy storage systems. To achieve net zero status, a building must be designed and constructed to minimize energy consumption through measures such as high-performance insulation, efficient lighting systems, and optimized heating and cooling systems. This reduces the building’s energy demand, which can be further reduced through the use of energy-efficient appliances and equipment. Renewable energy sources, such as solar panels, wind turbines, or geothermal systems, are used to generate the energy required by the building. The energy generated is used to meet the building’s energy demand, with any excess energy being stored in batteries or fed back into the grid for use elsewhere. Energy storage systems, such as batteries, can also be used to store excess energy for use during periods of high demand or low renewable energy availability.

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Net Zero Smart Factories

They are manufacturing facilities that operate with a net zero carbon footprint. These facilities use smart technologies and sustainable practices to minimize energy consumption and greenhouse gas emissions while optimizing production and reducing waste [32]. There are a growing number of net zero smart factories around the world. The Schneider Electric’s Smart Factory in Lexington, Kentucky, USA produces electrical equipment and uses IoT technology to monitor and optimize energy use and production processes [33]. The Siemens’ Amberg Electronics Plant in Amberg, Germany produces automation systems and uses advanced technologies such as 3D printing and IoT to optimize production and reduce waste [34].

6.4.5 Sustainable Campus A gamified smart grid implementation is a strategy for promoting sustainable energy use in a campus or community by integrating gamification principles into the design of a smart grid system. The idea is to incentivize and engage users to reduce energy consumption, increase energy efficiency, and promote renewable energy sources [35]. Pico, nano, and microgrids are different levels of smart grid systems that can be implemented in a campus or community, depending on the scale and energy needs of the area. A pico-grid is designed for a small-scale system, such as a single floor, building, or home, while a nano-grid can serve several buildings or a small community. A microgrid is a larger-scale system that can power a neighborhood or an entire campus. Implementing a smart-sustainable university campus can involve integrating different technologies and infrastructure, such as pico, nano, and microgrids. These grids enable renewable energy sources and distributed generation, which can help reduce the reliance on traditional energy sources and decrease carbon emissions. A gamified HMI can be a valuable tool for implementing a smart and sustainable university campus. Gamification can encourage and motivate students, faculty, and staff to adopt sustainable behaviors and practices that reduce energy consumption and promote sustainability.

6.5 The Future of Connected Citizens, Communities, and Cities This book explains and details the characteristics of a connected citizen, a connected community, and a connected city. Furthermore, it describes the advantages and limitations of using data and AI in the context of smart cities. Thus, the future

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of connected citizens, communities, and cities will likely be shaped by emerging technologies and innovative solutions that improve people’s quality of life, increase sustainability, and enhance efficiency. Some of the key trends in this area include: • Internet of Things: The network of physical devices, vehicles, home appliances, and other items embedded with sensors, software, and connectivity. In smart cities, IoT devices can collect data, automate processes, and improve efficiency in transportation, energy management, and public safety. • Artificial Intelligence: AI is a rapidly evolving field that enables machines to perform tasks that typically require human intelligence, such as speech recognition, image analysis, and decision-making. AI can help cities automate processes, analyze data, and improve services, such as personalized healthcare and intelligent transportation systems. • Data Analytics: Smart cities generate vast amounts of data from sensors, cameras, and other sources. Analyzing this data can provide insights into urban patterns, resource consumption, and other key areas. Data analytics can help cities optimize services and resources and make data-driven decisions. • Electric Mobility: As cities become more congested and air pollution becomes a growing concern, electric mobility solutions such as electric cars, bikes, and scooters are gaining popularity. Smart cities can leverage these solutions to improve public transportation and reduce the environmental impact of transportation. • Blockchain: Blockchain is a decentralized, secure ledger technology that enables secure, transparent transactions. In smart cities, blockchain can be used to securely manage digital identities, verify transactions, and improve transparency and accountability in government. • Augmented and Virtual Reality: AR and VR are immersive technologies that can provide citizens with more engaging, interactive experiences. Smart cities can use these technologies to enhance public services, such as virtual tours of city landmarks and training programs for emergency responders. In the context of smart cities, personalized gamification can encourage citizens to participate in initiatives that promote sustainability, improve public services, and enhance civic engagement. For example, a smart city could use personalized gamification to motivate citizens to reduce their carbon footprint by using public transportation, conserving energy, or recycling. To implement personalized gamification in smart cities, several steps are typically involved. These include: • Data collection: Smart cities generate vast amounts of data on citizens’ behaviors and preferences, including information from sensors, social media, and other sources. This data can be used to create a personalized profile for each user, which can then be used to tailor gamification experiences. • Gamification design: Once user profiles have been created, gamification designers can use this information to design personalized gamification experiences. This may involve creating challenges or competitions that are specifically targeted to users’ interests or behaviors.

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• Implementation: Once the gamification design has been finalized, it can be implemented using various channels, such as mobile apps, social media, or digital signage in public spaces. • Feedback and iteration: To ensure that the gamification experience is effective, it is important to gather feedback from users and continuously iterate on the design. This can involve monitoring user behavior, analyzing data, and making changes to the gamification experience as needed. Personalized gamification in smart cities has the potential to engage citizens and encourage them to adopt more sustainable and civic-minded behaviors. However, it is important to ensure that these gamification experiences are designed in a way that is ethical, transparent, and respects user privacy [36]. Human-machine interfaces for socially connected devices enable people to interact with connected devices and appliances naturally and intuitively. As the number of connected devices in our homes, communities, and cities increases, these interfaces become increasingly important. Human-machine interfaces for socially connected devices can be found in smart households, communities, and cities. In smart households, these interfaces allow people to control and interact with various devices and appliances in the home, such as lighting, heating, security systems, and entertainment systems. For instance, individuals can use voice-activated assistants like Amazon’s Alexa or Google Assistant to control various devices in the home. Additionally, connected thermostats can optimize energy usage by learning a user’s habits and preferences. These interfaces also enable individuals to connect household devices to the wider community, allowing them to share resources, communicate with neighbors, and participate in community initiatives. In smart communities, these interfaces can connect individuals with a range of services and infrastructure in the community, such as public transportation, traffic management, waste management, and emergency services. For example, mobile apps can provide real-time information about public transportation, and sensors can monitor traffic flow and adjust traffic signals to optimize traffic flow. Moreover, these interfaces can connect individuals with community resources and services such as job training, education, and health care. In smart cities, these interfaces can connect individuals with a range of services and infrastructure in the city, such as public transportation, traffic management, waste management, and emergency services. Moreover, these interfaces can promote civic engagement and enable citizens to participate in decision-making. For instance, mobile apps can gather feedback and input from citizens, and virtual town hall meetings can be held to discuss community issues. To make human-machine interfaces for socially connected devices more effective and user-friendly, developers use various technologies, such as natural language processing, computer vision, and machine learning. Natural language processing allows devices to understand and respond to voice commands, while computer vision enables devices to recognize and respond to visual cues, such as hand gestures. Machine learning enables devices to learn from user interactions and adjust their behavior over time.

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Overall, human-machine interfaces for socially connected devices are an important and rapidly evolving area of technology that has the potential to transform the way individuals interact with the world around them, from their homes to their communities and cities. Thus, the future of connected citizens, communities, and cities will likely be shaped by emerging technologies that improve efficiency, increase sustainability, and enhance the quality of life.

References 1. Méndez, J., Ponce, P., Medina, A., Meier, A., Peffer, T., McDaniel, T., Molina, A.: Humanmachine interfaces for socially connected devices: from smart households to smart cities. In: Multimedia for Accessible Human Computer Interfaces, pp. 253–289 (2021) 2. UNE UNE 178201:2016 Ciudades inteligentes. Definición, atributos y.... https://www.une.org/ encuentra-tu-norma/busca-tu-norma/norma?c=N0056504 3. Midor, K., Płaza, G.: Moving to smart cities through the standard indicators ISO 37120. Multidiscip. Aspects Prod. Eng. 3, 617–630 (2020) 4. Lai, C., Jia, Y., Dong, Z., Wang, D., Tao, Y., Lai, Q., Wong, R., Zobaa, A., Wu, R., Lai, L.: A review of technical standards for smart cities. Clean Technol. 2, 290–310 (2020) 5. Advertorial, F.: Ciudades Inteligentes: 5 proyectos que delinean el futuro colaborativo. https:// www.forbes.com.mx/ciudades-inteligentes-5-proyectos-futuro/ 6. Bosch, P., Jongeneel, S., Rovers, V., Neumann, H., Airaksinen, M., Huovila, A.: CITYkeys indicators for smart city projects and smart cities. CITYkeys Report (2017) 7. Malek, J., Lim, S., Yigitcanlar, T.: Social inclusion indicators for building citizen-centric smart cities: a systematic literature review. Sustainability 13, 376 (2021) 8. ExakTime LEED Certification Requirements, Rating System, and Benefits. ExakTime (2020). https://www.exaktime.com/blog/leed-certification-requirements/ 9. USGBC LEED rating system | U.S. Green Building Council. https://www.usgbc.org/leed 10. ISO ISO 37120:2018. ISO (2019). https://www.iso.org/standard/68498.html 11. ISO ISO 37122:2019. ISO (2019). https://www.iso.org/standard/69050.html 12. ISO ISO 37123:2019. ISO (2019). https://www.iso.org/standard/70428.html 13. Moreno-Rangel, A.: Passivhaus. Encyclopedia 1, 20–29 (2020) 14. Institute, P.: Passivhaus Institut. https://passiv.de/de/02_informationen/01_wasistpassivhaus/ 01_wasistpassivhaus.htm 15. Kubba, S.: Handbook of Green Building Design and Construction: LEED, BREEAM, and Green Globes. Butterworth-Heinemann (2012) 16. NBS What is BREEAM? NBS. https://www.thenbs.com/knowledge/what-is-breeam 17. Wolf, W.: Cyber-physical systems. Computer 42, 88–89 (2009) 18. Karnouskos, S.: Cyber-physical systems in the smartgrid. In: 2011 9th IEEE International Conference on Industrial Informatics, pp. 20–23 (2011) 19. Shi, J., Wan, J., Yan, H., Suo, H.: A survey of cyber-physical systems. In: 2011 International Conference on Wireless Communications and Signal Processing (WCSP), pp. 1–6 (2011) 20. Khan, F., Kumar, R., Kadry, S., Nam, Y., Meqdad, M.: Cyber physical systems: a smart city perspective. Int. J. Electr. Comput. Eng. 11, 3609 (2021) 21. White, G., Zink, A., Codecá, L., Clarke, S.: A digital twin smart city for citizen feedback. Cities 110, 103064 (2021) 22. Xia, H., Liu, Z., Maria, E., Liu, X., Lin, C.: Study on city digital twin technologies for sustainable smart city design: a review and bibliometric analysis of geographic information system and building information modeling integration. Sustain. Cities Soc. 104009 (2022)

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23. Ruohomäki, T., Airaksinen, E., Huuska, P., Kesäniemi, O., Martikka, M., Suomisto, J.: Smart city platform enabling digital twin. In: 2018 International Conference on Intelligent Systems (IS), pp. 155–161 (2018) 24. Dorian, J., Franssen, H., Simbeck, D.: Global challenges in energy. Energy Policy 34, 1984– 1991 (2006) 25. Gielen, D., Boshell, F., Saygin, D., Bazilian, M., Wagner, N., Gorini, R.: The role of renewable energy in the global energy transformation. Energy Strategy Rev. 24, 38–50 (2019) 26. Pérez, C., Ponce, P., Meier, A., Dorantes, L., Sandoval, J., Palma, J., Molina, A.: S4 framework for the integration of solar energy systems in small and medium-sized manufacturing companies in Mexico. Energies 15, 6882 (2022) 27. Ponce, P., Pérez, C., Fayek, A., Molina, A.: Solar energy implementation in manufacturing industry using multi-criteria decision-making fuzzy TOPSIS and S4 framework. Energies 15, 8838 (2022) 28. Méndez, J., Peffer, T., Ponce, P., Meier, A., Molina, A.: Empowering saving energy at home through serious games on thermostat interfaces. Energy Build. 263, 112026 (2022). https:// linkinghub.elsevier.com/retrieve/pii/S0378778822001979 29. Méndez, J., Ponce, P., Meier, A., Peffer, T., Mata, O., Molina, A.: Empower saving energy into smart communities using social products with a gamification structure for gamified human– machine interfaces within smart homes. Int. J. Interact. Design Manuf. (IJIDeM) 1–25 (2022) 30. Méndez, J., Medina, A., Ponce, P., Peffer, T., Meier, A., Molina, A.: A real-time adaptive thermal comfort model for sustainable energy in interactive smart homes: part I. Smart Multimed. 13497 (2022). https://doi.org/10.1007/978-3-031-22061-6_17 31. Medina, A., Méndez, J., Ponce, P., Peffer, T., Meier, A., Molina, A.: A real-time adaptive thermal comfort model for sustainable energy in interactive smart homes: part II. Smart Multimed. 13497 (2022). https://doi.org/10.1007/978-3-031-22061-6_18 32. Bartolucci, L., Cordiner, S., Mulone, V., Santarelli, M., Lombardi, P., Arendarski, B.: Towards net zero energy factory: a multi-objective approach to optimally size and operate industrial flexibility solutions. Int. J. Electr. Power Energy Syst. 137, 107796 (2022) 33. WIRE, B.: Schneider electric lexington smart factory among first in the world to be named a sustainability lighthouse by world economic forum (2021). https://www.businesswire.com/ news/home/20210927005592/en/Schneider-Electric-Lexington-Smart-Factory-AmongFirst-in-the-World-to-be-Named-a-Sustainability-Lighthouse-by-World-Economic-Forum 34. Lal, R.: The Digital Factory – Siemens: Electronic Works Amberg. Case - Faculty & Research. Harvard Business School. https://www.hbs.edu/faculty/Pages/item.aspx?num=53442 35. Pérez, C., Méndez, J., Rivera, A., Ponce, P., Castellanos, S., Peffer, T., Meier, A., Molina, A.: Gamified smart grid implementation through pico, nano, and microgrids in a sustainable campus. Smart Multimed. 13497 (2022). https://doi.org/10.1007/978-3-031-22061-6_10 36. Méndez, J., Medina, A., Ponce, P., Peffer, T., Meier, A., Molina, A.: Evolving gamified smart communities in Mexico to save energy in communities through intelligent interfaces. Energies 15, 5553 (2022). https://www.mdpi.com/1996-1073/15/15/5553

Chapter 7

Demand Side Management and Transactive Energy Strategies for Smart Cities

7.1 Introduction At the beginning of the 20th century, a city served as a regional point of convergence for humans to come together, trade, and satisfy their day-to-day requirements. Most cities were isolated agglomerations of buildings connected only by the trade and road networks between each other, with their energy requirements being satisfied by regional availability of resources. Today, cities have transformed to host a vast array of networks that all utilize energy in various forms, with energy demand per capita reaching new highs thanks to personal electronic devices, electrification of vehicles and public transit systems, and distributed energy production from wind farms and solar panels. This change necessitates novel ways of managing the supply and demand of energy across urban spaces. Effective satisfaction of day-to-day energy demands within the city is made possible through new technologies making efficient usage of chemical energy within fuels, networks moving the energy from source to site, equipment converting energy to other usable forms, and devices storing the energy. In some exceptional cases, such as in Brasilia, Putrajaya, and Toyota Woven City, it was possible to start from a blank slate and design and expand the energy infrastructure in a structured and systematic manner. However, the vast majority of cities come with a set of outdated systems and networks which were designed and implemented to satisfy demands and supplies from previous decades. Prohibitive investment, installation, and maintenance costs of wholesale updates required to the preexisting network infrastructure means that the network and the network operators are constantly one step behind the changing demand and supply curves. In addition to providing a stable energy service under increasingly difficult conditions, the most pressing need to look at more efficient methods of energy management is driven by the looming anthropogenic climate change crisis. Cities and their everincreasing energy requirements have been one of the major drivers for the increasing

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Fig. 7.1 Electricity demand and generation profiles between 13 March 2023 and 20 March 2023 for Texas as seen by ERCOT

greenhouse gas (GHG) emissions and associated impacts of climate change and thus there is an urgent need to identify practices that would alleviate and arrest these events. Increased integration and usage of renewable technologies and Distributed Energy Resources (DERs) in the electricity mix has been proposed as one of the main solutions to wean society away from fossil fuels and reduce GHG emissions. However, renewable resources suffer from intermittency and unpredictability issues when compared to coal-fired power plants. This is evident from Fig. 7.1, which shows the variation in solar and wind energy generation in Texas. The figure also shows the significant changes in net energy demand over 24 h and from day-to-day. The peaks in end-user consumption (typically seen during the morning and evening) and variable outputs from renewable resources highlight the need for approaches to shift and match demand and supply and to reduce the disparities between the peaks and valleys. The need for approaches that strategically enable network owners and operators to handle the changes becomes prescient within this context. These strategies require management of both the utility infrastructure and the supply infrastructure, including upgrades of the transmission and distribution networks. Equally important is the need for management of energy demand and lifetime maintenance of the associated devices and resources. This is the role that Demand-Side Management (DSM) has come to fill. Demand-Side Management, as the name suggests, involves change in the demand side consumption behavior in terms of both power and energy. It includes permanent improvements to the system configuration via strategic upgrade and installation of

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Fig. 7.2 DR approaches based on time of day as adapted from [1]

new hardware as well as temporary deviations from standard system operation as a response to market prices and occupant requirements. Due to increasing demand for optimal DSM strategies, project proposals and analysis of the different configurations have been increasing over the last decade. Figure 7.2 shows the various grid objectives for the temporary changes in operations possible depending on the time of day using Demand Response (DR)-based management strategies. As DSM covers such a broad range of topics, a similarly large number of methods of implementation have also been proposed in the literature. Each implementation comes with its own set of stakeholders, market conditions, and proposals for how to best manage the systems. This chapter aims to broadly cover all of these aspects, highlighting the salient lessons learned and future actions required to make DSM ubiquitous in society. For these reasons, the chapter has been structured as follows: Sect. 7.2 discusses the various stakeholders that are expected to participate within a DSM framework as well as their roles and requirements. This is followed by Sect. 7.3 which details some of the existing frameworks for DSM and Transactive Energy and proposals on the implementation of DSM. Section 7.4 briefly describes the intersectionalities between smart grids and DSM frameworks. Section 7.5 covers the modeling requirements and algorithms for DSM from the different stakeholder perspectives. The associated implementation issues, data requirements, need for market models, and enabling technologies like IoT (Internet-of-Things) and blockchains are detailed in Sect. 7.6. The chapter concludes with Sect. 7.7 which briefly discusses the barriers, some solutions, and future lines of research that will enable translation of DSM from a great idea to reality.

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7.2 Stakeholders and Their Roles DSM uniquely affects each participant in the existing supply-demand pipeline, including utility grids, end-users, and intermediary energy aggregators. Providing different DR programs to change user consumption offers new avenues for the endusers to retain decision-making autonomy while potentially making a profit or reducing energy costs. At the same time, it offers the utilities and the entities maintaining the electricity grid with improved predictability of consumption and elimination of severe peaks. Most importantly, it offers opportunities for new entities to enter the market with different business models and services that could aid both the supply and demand.

7.2.1 Utility Grid, DSOs, and TSOs The traditional entities that provide energy, be it electrical, thermal, or as fuel, have much to gain from the implementation of DSM. The three entities at the production, transmission, and distribution side are [2]: 1. Generation Actors/Utilities/Fuel suppliers: One or more entities which have the necessary infrastructure to provide fuel or convert fuel to usable forms of energy. These entities are able to operate independently or through coordination and are regulated by specific operating rules. 2. Transmission Grid Operator (TSO): The entity that is responsible for operating, maintaining, and developing the transmission system for a control area and its interconnections. 3. Distribution Grid Operator (DSO): The entity that is responsible for operating, maintaining, and developing the distribution system for a control area and its interconnections. In this chapter, we commonly refer to these entities together as “the grid”. The electrical utilities, TSOs, and DSOs stand to reap the majority of the rewards on the demand side in a typical DSM implementation as most gains of DSM are to the electrical grid. DSM alleviates and provides better predictability of the consumer requirements to these entities, enabling better usage of their infrastructure and improved scheduling and dispatch. Localized changes in the consumption can also be facilitated through DSM, which could potentially increase the lifetime of the grid infrastructure by keeping within their optimal operational limits. It would also help these entities identify strategic improvements and additional reinforcement to the grid without having to upgrade the entire network. Until recent times, the supply-demand equation has remained unchanged. In terms of relinquishing control of grid management and DSM to other entities, DSOs have not typically encouraged individual users to participate in distribution system management programs as an over-saturation of users runs the risk of making the system

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less resilient. However, only relying on several centralized entities to manage the distribution systems has often resulted in unpredictable or irregular demand and can be responsible for increased economic and environmental burdens on the grid. This may be owing to both the difficulty in predicting energy demands as well as the noted increase in severe weather events causing devastating power outages in recent decades. This disruption in energy services to consumers has led to consumers seeking carbon-intensive alternatives. For example, the threat of power loss due to wildfires, earthquakes, and other natural disasters led to a 22% increase in generators in California in 2018, with approximately 85% of these generators using diesel as the primary fuel type [11]. Per kilowatt, this results in higher carbon emissions while the supplementary generators are being used, which is not ideal in the larger picture of tackling climate change. As we move towards a true smart grid, there is a need to integrate users into the existing conventional approaches such that they could better coordinate and communicate with DSOs and TSOs. With the advent of IoT (Internet-of-Things) devices and sensors, the measurement and communication quality and frequency will only improve. However, to apply DSM, a clear understanding of the load composition of the different sectors and geographical groupings will be required. This brings up questions on the type of investments that need to be made in the grid on the demand side which could offset some of these emissions and outages. Ownership of sensors, metering infrastructure, and data and privacy issues also necessitate the need to integrate users and novel approaches.

7.2.2 End-Users/Buildings In lieu of making behavioral changes for optimal energy demand, DSM offers endusers a chance to move from a passive consumer to an active prosumer. As a prosumer, the end-user not only consumes energy but produces it and is able to resell excess energy back to the grid, thus permitting a reduction in energy bills while simultaneously maximizing user comfort. To achieve this, the end-user would have to make some modifications to their premises, including the installation of DERs, smart meters, and better monitoring and control infrastructure that would facilitate the successful application of the DSM strategies. In some cases, owners can best manage their assets including multiple buildings while other programs allow for individual occupants to participate in DSM. This would only be further helped by working alongside aggregators, the new third-party entities mentioned in the next subsection.

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7.2.3 Aggregators and Other Service Providers “Aggregators” are a relatively new group of third-party entities that emerged and continue to evolve following the liberalization of the energy market. The aggregators play the role of intermediaries between the grid and the end-users who ensure that the promised changes in consumption happen according to the rules dictated by either the market or the entities that control the market. In this sense, aggregators could be an entity related to the already existing utilities/DSOs/TSOs, while in other scenarios, aggregators are entirely separate entities contracted by utilities to provide agreed-upon services during critical times of need. They are expected to play an important role in facilitating implementation of DSM and are already doing exactly that in some countries in Europe (Belgium, Germany, Switzerland, Sweden etc.). Their role would also be vital to ensure successful management of multiple endusers in situations where individual end-users are too small to participate on their own and need to pool together efforts to enter the market. Aggregators would, thus, help maintain the delicate balance by providing the grid with an agglomeration of end-users whose control actions are verifiable while offering the end-users better revenue streams and new business models. For the purposes of this chapter, we define the aggregator as the following: Aggregator: Any entity that is able to provide certain services by playing an active role in the retail or wholesale market using clusters of demand-side assets. Each cluster will consist of end-user assets (buildings, Electric Vehicles (EVs), DERs, and Renewable Energy Resources (RERs)). Each asset owner signs a contract informing them of the entry guidelines, financial remuneration and rules of participation. The aggregator does not need to be an existing grid entity. However, all aggregators will need to have the necessary intelligence and infrastructure to aggregate, disaggregate, control, and monitor the behavior of assets to successfully fulfill the services promised.

Coordination between the grid, aggregators, and end-users requires multiple mechanisms of control, negotiation, and monitoring to work successfully. Some elements of these already exist, especially on the supply side. However, there is a need to assess the existing infrastructure and modernize it for successful implementation. New control architectures and validation mechanisms need to be researched with the directionality of data and energy flows clearly elucidated, which will be highlighted further in the following section.

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7.3 Existing DSM and Transactional Energy Frameworks Demand-Side Management, as mentioned previously, constitutes a set of strategies defined to modify user energy demand (electrical or thermal) in order to satisfy any number of goals. Within that context, most literature focuses on the implications of DSM from an electrical perspective. However, in the last few years, integration of thermal components, District Heating Networks (DHNs), and other Power-to-Heat (P2H) equipment have seen a steady increase. Many implementation configurations have been proposed by research and industrial working groups to forward the implementation of DSM across the world. Four such popular ones found in academic literature which have attracted industrial collaboration are: (i) Microgrids, (ii) Energy Hubs, (iii) Virtual Power Plants (VPPs) and (iv) Transactive Energy Frameworks. These configurations, while effective, have not yet gained widespread integration across society and thus we are still seeing further research and implementation efforts to develop best practices and global standardization.

7.3.1 Microgrids Microgrids have traditionally been defined as LV (Low-Voltage) or MV (MediumVoltage) clusters of DERs that behave as a single producer or load from a market and grid perspective. Chapter 1, Sect. 1.3.1 briefly described its usage in a study case for a Mexican University (Tecnologico de Monterrey, CCM). Microgrids are also unique in their ability to connect and disconnect (“island” themselves) from the rest of the grid via the Point of Common Coupling (PCC). This also means that operation and control of the local distribution network of the microgrid becomes incredibly important for maintaining the voltage and power within acceptable limits. Hierarchical control and energy management of microgrids is foreseen by studies to fill these requirements. Hierarchical control systems would require thoughtful partitioning of the various devices and software for enhanced security and functionality. Also, each level would require different time responses, communication bandwidths, and clear flow of information.

7.3.2 Virtual Power Plants Virtual Power plant or VPP was a concept propounded to increase DER participation in markets using either software systems, pricing programs, or a combination thereof, to ultimately optimize and manage generation from the DERs. Thus, DSM and DR is embedded within the fundament of VPPs. The key distinction between VPP and microgrids is that VPPs are not limited by location and connection to the

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local distribution network. Any two houses subscribing to the same program can be within one VPP. VPPs are not limited by hardware limitations and face fewer regulatory hurdles and grid management issues as VPPs depend solely on a participant’s agreement to the program. In doing so, the participant acknowledges having the minimum hardware required to maintain active participation in the program.

7.3.3 Energy Hubs Energy Hubs was a concept first presented in the Vision of Future Energy Networks project by ETH Zurich, TU Delft, ABB, Siemens, RWTH Aachen and other partners. The goal was to develop scenarios on how transmission and distribution systems should function in the next 30–50 years considering technical feasibility, economic prosperity, and environmental sustainability. An energy hub was defined by the project as any unit where multiple energy carriers can be converted, conditioned, and stored. The size of an energy hub can very between a building to entire communities to cities. As energy hubs focus on energy inputs and outputs, the concept enjoys more degrees of freedom compared to microgrids but has higher geographical specificity when compared to VPPs. Energy hubs also allow for increased integration of non-electric flows.

7.3.4 Transactive Energy Ongoing studies, experiments, and pilot tests have revealed a need for a synergistic incorporation of industrial practices with knowledge from academia. Lack of standardization across the various domains has revealed flaws within the DSM implementations proposed and adjustments that might be required for improvements. Attempts are being made today to further clarify the minimum requirements that would make DSM viable. A prominent approach which has seen quite a lot of concerted effort from industry, government, and academia is Transactive Energy, proposed in 2011/12 by the USbased Grid-Wise Architecture Council (GWAC). GWAC defines Transactive Energy as “a system of economic and control mechanisms that allows the dynamic balance of supply and demand across the entire electrical infra-structure using value as a key operational parameter [15].” Transactive Energy takes the best elements from many of the above-mentioned concepts and attempts to put them together in one overarching coherent structure, informed by industrial experience. Thus, Transactive Energy offers guiding principles to enable successful implementation of intelligent and interactive systems. Concurrent approaches similar to Transactive Energy have also been seen in other parts of the world, with tailor-made, location-specific solutions such as the Toyota Woven City and “Society 5.0” in Japan.

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The frameworks discussed in the previous paragraphs offer guidelines to empower the utilities, DSOs, and TSOs to maximize their profits and portfolios while also offering improved infrastructure management. However, many of the approaches have been developed by prioritizing particular players or networks within the energy supply-and-demand pipeline. VPP and Transactive Energy explicitly aim to give more independence and autonomy to the end-user, while microgrids and energy hubs focus more on creating entities that allow for better supervision and management of the electric network, often without consideration for the end-user. Owing to these often conflicting objectives, there is a need to study the development and implementation of DSM from different perspectives to better understand the full picture and arrive at a happy middle ground that ensures higher chances of adoption in cities. With this in mind, it is possible to break down the design and implementation of DSM into categories based on the frameworks’ requirements. These steps are necessary for DSM to go from a concept to realization, and thus we address the interoperability and coordination architecture, control architecture (including modeling methodology), communication architecture, and hardware implementation within DSM frameworks in the subsequent sections.

7.4 Enabling DSM Through Smart Grids Successful implementations of DSM require the presence of multiple different systems and domains working concurrently and communicating seamlessly with each other. Awareness of the systems, the control architectures, and the communication infrastructure (hardware, software and data requirements) is expected to prove pivotal. This falls squarely within the field of smart grids and cyber-physical representations of the cities. There is considerable overlap between the world of smart grids and DSM. Smart grids cover all measurable assets within the urban environment while DSM is narrower in scope and focuses only on the assets that allow the stakeholders control and manage their consumption profiles. Thus, effective design and operation of DSM strategies in a modern city can be strengthened by the presence of and an in-depth understanding of smart grids. Smart grids span multiple domains and operate across several physical and virtual dimensions. This is usually resolved by defining reference models and appropriate layers which enable clear definition of each dimension of the framework. Two big examples that can be used as reference models are the EU Smart Grid Reference Architecture Model (SGAM) [12] and the GWAC Transactive Energy (TE) Framework model. Both models have subdivided their respective frameworks into distinct layers. For example, the SGAM proposes 5 layers (Component, Communication, Information, Function and Business Layer), the GWAC TE framework proposes eight layers classified by 3 different categories: technical, informational and organizational.

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Fig. 7.3 The Smart Grid Architecture Model (SGAM) as developed by the CEN-CENELEC-ETSI (European oganisations for electrotechnical, telecommunications and other technical standardization) Smart Grid Coordination Group

The SGAM goes further by defining two additional dimensions: zones and domains (as can be seen in Fig. 7.3). The zones (Process, Field, Station, Operation, Enterprise and Market) are derived from the typical layers of a hierarchical automation system and the domains reflect the different stages of power generation: Bulk Generation, Transmission, Distribution, DER and Consumer Premises/Consumption. This type of top-down planning and management clarifies the minimum requirements for both existing and prospective participants and provides an avenue to make necessary modifications. For example, the SGAM layers provide each participant with clarity on the specific domain and zone that they are situated in. It also clarifies the preferred communication protocols, the data models that will be required by their algorithms, the market guidelines, and the business models that are possible. An additional benefit is that this informs planners, policymakers, and legislators such as governmental bodies (both local and national), and other maintainers/coordinating bodies (like the European Network of Transmission System Operators for Electricity (ENTSO-E) and Electric Reliability Council of Texas (ERCOT)) on how the market might evolve. This structure also informs them on the corrective mechanisms and new infrastructure that might be needed in the future. Thus, the checks and balances that would make a smart grid implementation successful would also lay the foundations for a functioning DSM environment. Coordination and cooperation between the players in both fields would only prove mutually beneficial, and could provide impetus to development of new technologies, algorithms, and business models.

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7.5 Algorithms and Modeling There is a need to consider the requirements for the different domains within cities and the electrical network. Each domain has developed over the years with varying standards, working practices, and control philosophies owing to the different requirements of the users within those domains. As a result, smart grid infrastructure has to be integrated into each domain. A variety of different algorithms and modeling methodologies that will be integrated within smart grids are expected to benefit DSM and thus are addressed in this section. For the purpose of ease, the domains have been divided based on the stakeholder description: Bulk Generation, Transmission and Distribution under “Grid perspective”, DER and aggregation of consumer premises fall under the “Aggregator perspective” and each consumer premise has been placed under the “Building perspective”.

7.5.1 Grid Perspective The grid consists of the generation, transmission, and distribution systems (listed as Bulk Generation, Transmission and Distribution in the SGAM domains). Secure operation of power systems have always been a major concern, and requires a comprehensive understanding of the systems and their operating status. Power System State Estimation (PSSE), used to ascertain the states of the transmission system, has played a very important role in secure operation of the systems ever since they were first proposed and implemented in the the 1970s [13]. Through the implementation of SCADA (Supervisory Control And Data Acquisition) systems and transmission-level PSSE and energy management systems, operation and management of transmission systems became possible. However, traditionally distribution systems had been run with very little automation. The simplicity of system operation took precedence over its optimality. Other sensitive and intricate factors that have limited the implementation of SCADA systems and PSSE within the distribution systems include low X/R ratios, differences in topology between transmission and distribution systems, unbalanced phases, and a larger number of nodes. The DSO has been concerned with and is expected to perform many actions including lowering operating costs, reducing power losses, congestion management, and maintaining voltage regulation by controlling the output power and voltage with DERs and voltage regulators. In addition to energy-consuming end-user equipment and devices, often referred to generally in this chapter as “loads”, secondary circuits and auxiliary service equipment including shunt capacitors, voltage regulators, electric vehicles, primary feeders, laterals, distribution transformers, and other Renewable Energy Resources (RERs) are all essential components of distribution networks. With the rising number of DERs that are being installed, the potential for DSM increases and concurrently, the need for better understanding and monitoring

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Fig. 7.4 A schematic showing the different stakeholders: grid (Generation, TSO and DSO), aggregators and end-users, and the flow of measurement and information

of the resources in the Distribution System. Estimating the states of the grid to plan the controllable loads as best as possible is crucial as DSM introduces bidirectional flows to the distribution system. The complex landscape currently managed by the DSO and the lay-of-land with DSM and the new stakeholders integrated is elucidated in Fig. 7.4. This highlights the importance of installation of new infrastructure that will make monitoring and controlling of the systems possible. This would also improve both Distribution System State Estimation (DSSE) and Optimal Power Flow (OPF) as well as associated calculations, enabling better control policy creation for the controllable loads. The following subsections will thus briefly detail the different DSSE approaches available to maintain grid stability.

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7.5.1.1

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Conventional State Estimation

For optimal efficiency, the grid attempts to produce as much energy as will be consumed at any given time. However, matching energy supply and demand is becoming increasingly difficult as the energy supply shifts to more distributed generation. These increased power injections and consumptions at the various nodes of the distribution system makes the already difficult task of DSSE and maintaining grid stability and reliability more difficult. Initially, the focus was solely on increased monitoring with control still maintained and supervised by a central controller. Due to the larger number of nodes, cost of additional infrastructure and topology of the distribution system, optimizing the location of the measurement devices has also become high priority owing to investment and operating cost limitations, ease of deployment and maintenance. Although the transformation has not been uniform across the different countries, there has been a clear devolution to smaller networks (usually each with their own set of central controllers) which in turn hierarchically provide information to a higher layer thus ensuring localization of data transfer and improving data bandwidth between the local (LANs) and the wider area networks (WANs). In the past few decades, many electric utility companies have advanced toward automating distribution systems, making the system more robust, effective and reliable. This has been traditionally done in two ways: with increased installation of Phasor Measurement Units (PMUs) and Advanced Metering Infrastructure (AMI). SCADA systems, Remote Terminal Units (RTUs), PMU devices and Geographical Information System (GIS)-based devices are the preferred choices for devices used at the grid-level for State Estimation (SE), complemented by AMI such as Smart Metering devices at the building level, measurements at the substations and pseudomeasurements (predictions made from a priori knowledge of the grid). The PSSE problem can be solved in many different ways (static, dynamic, robust, voltagebased, branch-current-based etc.) as summarised in the Table 7.1 and is solved predominantly using Weighted Least-Squares (WLS), Least Median of Squares (LMS), Least Trimmed Squares (LTS) Generalized Maximum-Likelihood (GM) and Least Absolute Value (LAV) estimators. Faster sampling and higher accuracy of data can be acquired from distributed PMUs installed across parts of the grid. However, this heavily relies on the presence of data acquisition devices that have been installed across the network. This is as yet

Table 7.1 Structure of DSSE formulation [14] Approach State variable Voltage-based Current-based

Magnitude and phase angle Real and imaginary parts Magnitude and phase angle Magnitude and phase angle, real and imaginary parts

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not the case. Load models and dynamic simulations and validations are made possible using this approach. Most DSSE approaches today attempt to utilize the PMU measurements alongside the SCADA measurements to provide better state estimation. However this comes with its own set of new problems. There are differences in data collection frequencies between the SCADA system, PMUs and the AMIs installed. In addition, there might be bad data issues, network communication issues and changing topology which deteriorate DSSE accuracy. DERs and DR programs are also expected to impact DSSE by exposing the problem to price-sensitivity, uncertainty and variability in the customer signals. This impact is also made apparent from the information flow of DSSE informing grid EMS in Fig. 7.4. DERs will also affect the voltage profile of the distribution system. Better observability and higher quality of the data collected from PMUs, RTUs and AMIs will greatly help the state estimation problem. However, the deeply interlinked questions of the cost of infrastructure purchase/installation (even with the advent of µPMUs), the question of ownership of data from AMIs and other sensors from prosumers and end-users and the potential of exposing the system to cyber-attacks are driving the studies on alternate approaches and attempts at transitioning to a decentralized and/or distributed implementations.

7.5.1.2

Hybrid State Estimation

The availability of powerful PMU devices and equally capable algorithms makes DSSE possible, however, due to the high costs of buying purchasing and installing PMUs, is still limited in practical power systems. Thus, power systems may not be observable in all situations. This has led a large body of the research on distribution network state estimation, energy management and power flow calculations to look at pseudo-measurements and hybrid methods. The research work on hybrid SE is broadly categorized as either indirect or direct. Indirect methods utilize SCADA measurements to run the conventional SE. The PMU measurements are subsequently incorporated using a post-processing operator. In direct methods, both SCADA and PMU measurements are simultaneously utilized in the measurement vector of SE.

7.5.1.3

Optimal Power Flow, Demand Response and DSSE

Optimal Power Flow (OPF) problems are used to generate control policies for the controllable loads and devices subject to the grid constraints. In OPF problems for distribution networks, most frameworks assume complete availability and observability of network states, which, as has been explained previously, is not always the case. This is only bound to increase with the influx of DSM and DR programs. However, state estimation, OPF and DR can be used to inform and enrich each other when used in tandem with the monitoring infrastructure being installed by the various stakeholders.

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The OPF problem is a non-convex NP-hard1 problem [18] that is frequently solved using Stochastic Optimization algorithms like Particle-Swarm Optimization (PSO) and Genetic Algorithms (GA) or variations of Mixed-Integer Linear Programming (MILP) and Mixed-Integer Non-Linear Programming (MINLP). The optimization objective functions can vary depending on goals such as reducing distribution system losses, minimization of costs (operating, depreciation, and maintenance), and maximization of distribution system operator’s revenue. The problem is typically constrained using the power (active and reactive), distribution line voltage and voltage regulator constraints (load tap changer transformer constraints) in addition to constraints on operation of the systems themselves, such as the auto-transformer, feeder voltage regulator, smart inverter, and capacitors. The OPF problem is a problem associated with the supply and distribution within the grid. DR programs on the other hand affects the demand side of the grid. DR programs encourage consumers to shift their consumption behavior during certain hours and can be incentive-based or price-based, depending on the relationship between the grid, the energy service provider, and the consumer. In incentive-based programs, consumers agree to make changes in energy consumption behaviors according to terms set in a pre-determined contract with the grid operator in exchange for financial incentives. Price-based DR programs provide customers with pricing schedules to help incentivize consumption during periods of high energy supply and likewise penalize energy consumption during periods of constrained supply by affixing an increased price (e.g., Time-of-Use (ToU) pricing, Real-Time Pricing (RTP)) [17]. State Estimation, whether at the transmission or distribution level, can be utilized for different aspects of grid-level energy management, including load forecasting, economic dispatch, and OPF. Reference [19] shows how the OPF can be improved using state estimated measurements. Studies such as [16] have shown how a Demand Response Enabled Load (DREL) could be used to generate pseudo-measurement generation for DSSE and estimate the states of a distribution system. More details on the algorithms and calculations that may be implemented for the EMS/DR programs by a DSO or another central entity will be provided in Sect. 7.5.2. Such combinations of DSSE, OPF and DR help reduce the uncertainties and improve the observability over a period of time and across the distribution system while also addressing data incompatibility and availability issues. This is also hoped to create opportunities, new implementations and business models that will allow for further investment and installation of better hardware and software that will enable more accurate estimation of the grid variables, creating a positive feedback loop. This work is still in varying stages of development across the globe and will require an investment of time and resources before a mature implementation is possible.

1

NP-hard problems are a class of problems for which there is no conclusive proof that a polynomial time solution exists

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7.5.2 Aggregator Perspective With the proliferation of smart meters and distributed local generation from renewables, management of groups of prosumers becomes very vital to the health of the distribution network as well as the transmission networks. As prosumers are merely consumers who have the opportunity to both consume from and produce energy for the grid, management of their bidirectional flows and aggregation of the resultant consumption patterns would only be possible through an entity which has a much clearer picture of the demands within the local network or with an understanding of the requirements of the market (which serves as a proxy of the requirements of the network). As the number of prosumers increase and the services multiply, modeling complexity and pricing strategies would also increase in number. This necessitates the aggregator to be able to possess the required computational power and algorithmic capabilities to simultaneously participate in the market and provide control actions to their prosumers. It is within this scope that third-party entities can play a role in providing meaningful services to both the prosumers and the grid at large. This role could also be filled by the utility/DSO/TSO; however, in a liberalized energy market, opportunities for actors other than the DSOs and TSOs is increasingly likely. Regardless, the aggregator will need to possess either oversight, communication services and computational nous (or combinations thereof) to provide each prosumer with a constructive course of action. As a result, an aggregator can provide services both to the utility and the end-user. Modeling the interactions between multiple buildings, electric vehicles, and DERs for optimization of the network will vary depending on the use-cases and services available. As computational costs decrease, machine learning approaches including reinforcement learning, neural networks , and support vector machines have been used to make forecasts regarding energy consumption or determine optimal decisions for energy management. While machine learning approaches are becoming increasingly popular for aggregators in the European market, heuristic methods and conventional optimization-based approaches are also being studied depending on the service and the temporal scale required. To quantify the availability to provide DSM services, measuring an aggregator’s energy flexibility becomes crucial for providing an effective way of balancing supply and demand. Prosumers have been mentioned in studies as a potential source of the flexibility that the electricity infrastructure requires. However, the concept of flexibility remains quite abstract and the current set of services, rules, and regulations prevent a majority of small prosumers from participating or providing any sort of value on an individual basis. Therefore, clarity on what flexibility implies in the market, how it can be provided as a prosumer, and the ground rules of participation need to be established. The aggregator can greatly facilitate and accelerate this process by providing the means of gathering overall flexibility volumes from small prosumers, providing them with the chance to enter markets and unlock their flexibility value. Significant work has been put into this across various parts of the world. The European Commission has given the idea of aggregators special attention

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because it believes that they can manage demand flexibility, such as load shedding and improved load profiling, for a variety of power system entities, including utilities, DSOs, independent system operators, capacity markets, and energy service companies. The concept of flexibility offered by the aggregator within this chapter has been defined as: Flexibility: Any amount of energy or power that loads (end-user buildings and systems) and DERs are able to supply, consume, or defer for a specific time period. The goal could be local system efficiency maximization, cost reduction, improvement of regional network states via incentivized change, or marketimposed incentives through a set of service(s) that ensures that multiple actors could provide a smoothening of the load curve and reduction of uncertainty in the electricity grid.

By reducing or increasing the power demand during each period of a specific planning horizon, the aggregator collects demand-side flexibility provided by clusters of end-users and makes it available for system service requirements. This makes it possible to balance the demand and supply, minimize peak demands, and deal with the intermittent nature of renewable energy sources. It is crucial to investigate various options for integrating end-users and aggregating them in local energy markets, as well as offering new mathematical and optimization tools for the decision-making processes of the energy community, in which storage systems play a crucial part. Flexibility can be quantified through different calculation methodologies and can be called upon by the market through many different approaches. The simplest mode of activating an end-user asset is through Direct Load Control which has been detailed in Sect. 7.5.2.1.

7.5.2.1

Direct Load Control

Direct Load Control (DLC) is when an aggregator has the ability to directly control certain loads in the distribution system. This could be the result of either an aggregator-run OPF calculation or another optimization or consensus mechanism, depending on the market and the role of the aggregator in the market. A commercial aggregator with few loads of its own would require explicit contract with the prosumer who owns the system to directly activate and deactivate loads within their cluster. DLC would be the best-case scenario for any aggregator, but especially for those with knowledge of the grid requirements. Hence, this would be the preferred route for DSOs and Distribution Network Operators (DNOs) that also act as aggregators. This is because, a directly controllable load would, within technical limits of the system, serve as a reliable, observable and controllable asset as opposed to other participants who may or may not activate/participate in a DR program based on their availability and discomfort over a time-period.

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Commercial aggregators’ ability to aggregate and disaggregate uses non-OPF market-focused mechanisms. These are usually classed as either price-based or incentive-based DR programs and are solved through approaches that can be broadly classified as optimization-based, learning-based and game theory-based approaches.

7.5.2.2

Optimization-Based Approaches

Optimization and Linear Programming (LP)-based approaches for control of groups of end-users and their behavior have been proliferating over the last decade with the increasing need to prevent peaks in parts of the grid through certain periods of the day if similar strategies are instituted for users of the same group and within a particular locality. However, aggregator-level optimization usually differs from end-user optimization as the objective function and the constraints imposed differ. Prosumers that subscribe to a service with a commercial aggregator don’t have to be in the same part of the grid either. As a result, the aggregator optimization approach does not necessarily need to account for OPF considerations or perform state estimation. Prosumer/end-user optimization is also typically performed with the goal of minimizing personal objectives, such as operating cost, energy consumption, or personal discomfort. The aggregator, depending on the service it provides, has to perform optimization on the cluster’s chosen market service (whether that is price-based on incentive-based). This may prove to be sub-optimal to some of the users in the cluster when compared against a stand-alone minimization of operating costs and the financial remuneration to the prosumer will differ owing to the value of service offered. Table 7.2 reveals the wide range of strategies taken by the aggregators to provide DSM services. MIP or MILP, heuristic optimization algorithm, Particle

Table 7.2 Studies of DSM using aggregators and their approaches References Approach Market [20] [21] [22]

[23] [24] [25] [26] [31] [32]

Constrained NSGA-III Particle swarm optimization Non-cooperative stochastic game and boosting tree-based Deep Q-network (DQN) Math-heuristic optimization algorithm MILP Acceptance learning Algorithm 2.0 (MILP) Batch RL (Fitted Q-iteration) Stochastic optimization Non-cooperative game theory

Real-Time Pricing (RTP) Day-ahead market Dynamic price-based

Day-ahead market Day-ahead market Day-ahead market ToU (Time of Use) Day-ahead market Capacity-based Day-ahead market (Incentive-based)

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Swarm Optimization, Genetic Algorithm and Stochastic optimization are some of the approaches favored by aggregators who use optimization-based approaches.

7.5.2.3

Game Theory-Based Approaches

Game theory-based approaches involve solving a distributed problem that consists of three elements: Players, their strategies and the utility function/payoff. Each game has its own set of rules, and game theory-based approaches still require knowledge of mathematical modeling and models for each player. Games where players can work together are called cooperative games, while non-cooperative games refer to those where the players all work individually to maximize their returns. Aggregators may choose to employ cooperative or non-cooperative game theory-based environments to maximize the payoff or minimize costs and arrive at a point which maximizes benefits all players. This methodology can be used in DSM to solve distributed implementations of DR and cater to the unique capabilities of each prosumer. References [32–34] are examples of implementations of DSM which utilize game-theoretic approaches to solve the aggregation problem.

7.5.2.4

Learning-Based Approaches

The differences in the DERs available in cities and the variations in customer requirements mean that the man-hours required to create accurate models for each participant in a grid continues to be a major stumbling block with prohibitive time and economic costs using conventional modeling approaches. Learning-based approaches have been increasingly used by aggregators as opposed to the model-based optimization approaches in situations where data and infrastructure is available as it reduces the dependence on expert knowledge and the need to model the systems individually. In addition, approaches like Reinforcement Learning (RL) and Deep Learning allows an agent to learn a control policy by interacting and adapting to the environment rather than spending hours performing system identification and modeling steps. Studies such as [26] highlight the advantages of a model-free approach that does not require system identification using RL-based aggregation and DSM. However, it also highlights the need for appropriate data acquisition hardware, data quality requirements, and clarity on the communication protocols which are vital for smooth operation of this approach. Currently, the state of grid infrastructure and prosumer infrastructure for data acquisition, monitoring, and control serves as a significant barrier to this approach being used widely. More details on infrastructure and implementation issues can be found in Sect. 7.6.

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7.5.3 End-User and Building Perspective As per the International Energy Agency (IEA), the buildings and building construction sectors combined are responsible for almost one-third of total global final energy consumption and nearly 15% of direct CO2 emissions. In the US alone, buildings in the services sector, which includes commercial, institutional, and public buildings, together account for approximately 18% of energy consumption while 22% of the energy consumption is attributed to the residential sector, thus totaling approximately 40% of the energy consumption of the United States [27]. In the commercial sector, thermal conditioning and lighting are even more significant for offices in the US, with HVAC (Heating, Ventilation and Air Conditioning) and lighting account for approximately 70% of total energy consumption. Minimizing energy consumption in buildings is the crucial objective at the building level, and it is necessary to examine residential, commercial, and industrial buildings such that the appropriate solutions are prioritized based on the potential impact to building energy consumption while considering the range of different occupants and services provided. In addition to heating and cooling needs, household appliances are another contributor to residential energy consumption and thus are frequently considered in energy modeling. Studies on neighborhood-level energy management studies have typically classified televisions, computers, and lighting as non-flexible loads, while batteries, EVs, dishwashers, and washing machines were generally considered flexible loads as they can be adjusted to operate during non-peak hours, as shown in Fig. 7.5). Several principal flexible loads, including refrigerators, freezers, clothing washers and dryers, and dishwashers together are estimated to account for approximately 45% of energy consumption from appliances in households [28]. As these devices are becoming increasingly efficient, integrated smart controls or phone applications are often used to promote energy savings. For example, smart refrigerators can send

Fig. 7.5 Different types of loads: Flexible and Non-flexible loads

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an alert if the refrigerator door is open, dishwashers or washing machines can be scheduled to operate during off-peak hours, and clothing dryers can use integrated sensors to stop when clothes are dry. Smart devices allow for scheduling or remotely stopping or starting energy activities for grid services while giving the consumer control to ensure that occupant needs and preferences are met. Building energy consumption and energy efficiency can be improved through both permanent and temporary measures. Permanent modifications include everything from upgrade of building envelope and insulation to change of the heating and electrical systems within the building, while temporary measures fall under the umbrella of the DSM. Both permanent and temporary measures require modeling and simulations to inform the decision-making. However, system design optimizations and associated models require a different level of detail and have different objectives than the ones required for DSM programs. DSM programs require the systems to change behavior for a set period of time and thus put a big premium on the time taken to solve the problem at hand. The change in behavior would only be possible if the solutions are found and distributed to each device within the allowed time-step defined within the rules and regulations defined by the program. As a result, DSM optimizations and calculations always tend to walk the fine line between computational speed and accuracy or performance of solutions. This time limitation is not something new as all control problems face the same compromise between accuracy and speed. If the problem is solved fast and and is able to update the set points fast enough with the changing states, the quality of the solution can be sacrificed. This can be performed through very detailed optimizations or basic correlational approaches. The following sections shine light on the conventional approaches, optimization-based approaches, and the changes that machine learning methods have brought to both the problem solving and the modeling of each component.

7.5.3.1

Conventional Building Management Approaches

Building requirements change based on the requirements of the residents, but also change temporally across short time and across the seasons based on the geographical locale. However, since the invention of thermostats, programmable PID controllers and algorithms have been the primary mode of control of various components in the HVAC system. Typically, a controller adjusts several internal variables to provide different flow rates, humidity, and temperatures of the heat transfer fluid (air or water depending on the building distribution system). These variables are adjusted to maintain or achieve a “setpoint”, which represents the target value of the variable. For example, the controllers tend to use dead-bands to control the temperature or humidity setpoints within the building envelope. In its simplest form, the vast majority of controllers within buildings still use static setpoints and dead-bands, thus assuming that the user demands and requirements do not change over time. As such, most commercial buildings including offices, government, and institutional buildings aim to satisfy approximately 80–90% of building occupants by keeping the indoor air

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Fig. 7.6 Indoor operation temperature range as per the Adaptive Method (ASHRAE 55)

temperature of the building between a specific range. This has also been studied and integrated into standards such as the ASHRAE Standard 55—Thermal Environmental Conditions for Occupants [30], which utilises the 80–90% limits as shown in Fig. 7.6. These ASHRAE guidelines among others establish acceptable indoor air temperature ranges that satisfy most people within a building. Modern iterations of controllers have started implementing changes in the setpoints based on the number of occupants, meteorological considerations, and seasonal variations. However, the limits mean that it is acceptable, not necessarily optimal, for enhancing productivity and comfort. There is more that can be done to improve the energy efficiency within buildings while providing more comfort to the users. Considering the effects of individual occupant preferences and behaviors and integrating them within the control algorithms is one such way. Cultural differences, age, gender, clothing, activity, and proximity to vents and windows can all affect a person’s comfort levels within a building. Simulation studies such as [29] and others have shown that occupants engaging in energy-savings behavior, such as turning off lights and using optimal thermostat temperatures, can save up to 50% of energy consumption for a single-room office while those engaging in wasteful behavior can increase energy consumption by approxi-

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mately 90%. It is thus important to understand how to best model occupant behavior in buildings and validate these models in real-life cases as simulation studies do not necessarily translate to actual reductions. Traditional ways of modeling and optimization with buildings, using linear or non-linear programming, further increase the modeling complexity. This has brought other alternative approaches such as use of heuristic approaches and Machine Learning (ML)-based approaches to the fore. In addition to optimal thermal preferences, occupant preferences within buildings also include changes in lighting conditions and flexible appliances such as computers, kitchen hardware, washing machines and audio-visual equipment. Accounting for these other devices and incorporating their behaviors into the optimization will provide a holistic view of the building consumption and also provide more options for alternate strategies, especially if some of these devices can be moved away from standard modes of operation.

7.5.3.2

Optimization of End-User Behaviour

Analyzing the opportunity to optimize household devices and building systems for energy consumption while considering the end-user has attracted a lot of interest in the last decade. Many methods have been proposed and implemented by different studies, but the predominant approaches can be broadly classed into (i) the heuristic and meta-heuristic-based algorithms (such as GA or PSO), (ii) mathematical programming problems (including linear or nonlinear methods), (iii) ML, fuzzy logic, and other data-driven approaches, and (iv) hybrid algorithms using game theory and the aforementioned methods. The optimization is performed by providing the forecast of the setpoints for the different electrical or thermal controllable loads that can used to achieve DSM. Household appliances that comprise the different controllable loads include, but are not limited to, heat pumps, electrical water heaters, electric vehicles, refrigerators, HVAC systems, and energy storage systems. There are also uninterruptible loads including dishwashers, washing machines, and dryers. Some loads are not controllable, like lighting, computers, and televisions. To modify the load profile, the optimization of the HEMS (Home Energy Management System) uses economic signals from the different market models or pricing schemes of DR programs such as, ToU, RTP and critical peak pricing. The system constraints are represented using models of appropriate levels of detail. These models are created using either physics-based white-box models, data-driven black-box models, or hybrid grey-box models that incorporate a mix of physicsbased parameters and data. The model detail greatly affects the speed of convergence towards the optimum and time taken to reach it. Thus, the choice of the type of system model and the objective function determines how effective a building or neighborhood can be in the DR market.

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White-Box Models

White-box models are physics-based models that utilize the parametric data regarding the building design, construction materials, equipment data, operation schedules, and the geographical data to create a facsimile of the building or system in question. As the models are based on the underlying physics, interpretation of the models and the underlying phenomena is quite straightforward and each parameter’s role is easy to identify and adjust if necessary. Decades of research and expertise makes applying white-box models across different domains possible. Also, white-box models allow for easier representation of non-linear dynamics and associated correlations due to the presence of many solvers and environments capable of using white-box models for simulation and optimization. White-box models used for DSM and DR usually fall into either simulation models or forecasting/predictive models. Simulation models usually consist of highly detailed models and utilize toolboxes such as EnergyPlus, TRNSYS, Labview, and MATLAB to represent the dynamics. Predictive models predominantly make use of of Linear Programming (LP) approaches and employ either linear correlations or piecewise linear approximations to represent the dynamics. This allows for faster computation times and the ability to correct quickly and multiple times over a period of time. However, white-box models are very time-intensive to construct as each buildings and system will have specificities that will need to be carefully incorporated. The quality of the model will also depend on the ability of the user to find data of appropriate quality and their knowledge to use that data in the best manner possible. Finally, all models are approximations of the real system and even the best white-box models fail to replicate the exact behavior of the system.

7.5.3.4

Black-Box Models

Black-box models are generalized models that derive meaningful correlations and representations from data without explicit domain knowledge. Instead, operational data is used to create and train the model and the models are then tested against other datasets to determine their robustness and ability to accurately represent or forecast the building or system they represent. Black-box models have been getting a lot of attention owing to their lack of domain specificity and relative ease in model creation when compared to white-box models. In 2020, [4] cited over 9,576 studies on machine learning in buildings that included both black-box and hybrid grey-box methods. However, they also noted that the rate of industry adoption of these approaches is almost zero. This is a huge stumbling block that needs to be addressed if data-driven energy management methods were to succeed. Black-box models do offer the model creators with the facility to adapt quickly and integrate new devices, especially those that have the potential to change the user consumption patterns. Good examples as listed in Sect. 7.5.3.1 offer ways to

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differentiate between the impact of controllable and non-controllable loads within these appliances and modify occupant behavior to a certain extent. To best exploit energy flexibility at the occupant level, modeling must consider the different preferences of many different types of occupants in terms of the times of day in which they would wish to perform certain tasks. Black-box models that provide simple correlations between the primary energy sources and the building envelope could be used to bypass the complexity and time consumed to develop models for the ventilation systems. The inherent downside of black-box models lie in their dense nature and the resulting difficulty in inferring a physical meaning to the correlations identified. This can result in models which might fit the data well in simulation but could prove to be unusable for control and DSM purposes outside of the explicit boundaries that they were created for.

7.5.3.5

Grey-Box Models

Grey-box models are hybrid models that take elements from both white-box and black-box models. Grey-box models combine parameters and other physics-based information from white-box models to inform the model creation process but still interprets time-series data like black-box models. At each time-step, incremental corrections are made that can reduce the error between the real time-series data and the data that is simulated or calculated by the physical model. These models tend to be more accurate and have lower data requirements as they utilize the physical parameters for each specific use-case in question. Examples of grey-box models are thermal network models (RC models), regression models, and Fourier series models. As grey-box models are grounded in the physical meaning of the buildings and systems, the methodology used is easier to reliably replicate and extend to other systems and buildings. However, this also means that there needs to be an expert involved in the process of modeling and full automation of the process is very difficult. It would be possible only if the expert maintains different catalogues and databases regarding the various HVAC configurations, material used subdivided by sector, and other specific information regarding the built environment. This can get difficult and will require resources that may make this approach difficult to scale without allocation of appropriate resources towards creation and maintenance of the catalogues in question.

7.6 Implementation and Associated Data Requirements Implementation of DSM frameworks is a process that needs to run parallel to the design of the various mechanisms which have been discussed in the previous sections. The implementation process would also often provide valuable feedback that would ameliorate and inform the prerequisites required within the design stage. This has

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been reflected in some of the smarter Architecture Models and scientific literature that have been created in recent times. A mature implementaton of a DSM framework will require multiple rounds of iterative improvement on a variety of aspects. From the global system perspective, changes required for the implementation to be successful can be broadly divided into 3 major categories: 1. Availability and installation of new technology, algorithms and smart devices 2. Agreement on degree of autonomy of the stakeholders 3. Availability of new economic and market mechanisms. In addition, bidirectional communication across the network is essential. With that in mind, the next subsection will elaborate on the technology and devices that would form the Internet-of-Things (IoT) network that will enable the technology to communicate coherently. How this bidirectional communication is achieved and the frequency of communication will be decided by the degree of autonomy agreed upon between the stakeholders. A brief summary of the centralized, decentralized and distributed approaches will be presented in Sect. 7.6.2. Finally, the driving force for both of the former elements to be used for DSM is expected to be provided by the economic and market mechanisms. Blockchains are expected to play a major role in this, and thus the final subsection will elaborate briefly on blockchains and the market models available.

7.6.1 IoT and Standards Buildings have seen very non-uniform deployment of sensors within the various sectors housed within them. Devices developed for use with the telecommunication and IT sector such as smartphones, personal computers, and other end-user-oriented smart devices that connect to the internet have seen the biggest influx of sensors with them, followed by the electrical metering infrastructure and a selection of household appliances and thermostats. However, these different sectors rarely communicate with each other. Also, the deployment of these systems, sensors and associated data reconciliation varies depending on the building type. Studies have shown the need for a holistic integration of different sensors that form a complete interconnected network for optimal energy management within the building envelope. This is where IoT and interconnected standards are expected to change the way energy management and control is implemented in the coming years. IoT represents all networks and infrastructure that enable communication between humans (and their physical and psychological needs) and the digital world. Within cities, IoT networks encompass the building and built environment sector, the transport sector, the electricity grid, systems that provide thermal comfort, potable water, the waste management sector and the telecommunication sector that empowers all the data transfers across the various institutions in society. Within the context of DSM, all sectors mentioned are relevant. However, the built environment, the transport sector

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and the electricity grid are the most immediate sectors affected. A well-thought-out IoT network that covers these 3 primary sectors will provide enough scalability to allow for further integration of the other sectors within a DSM framework. To make sense of the datastreams coming from the various sources and create a meaningful representation of the system, IoT frameworks utilize semantic models that provide a human and machine-readable representation of the network. A semantic model of appropriate complexity created for commercial and institutional buildings grounded in the regional building construction and system installation standards would be useful in reducing the time required to develop the building data models and associated DSM models. These ontological models provide information on the sensors available and the data types, providing information to create a coherent building data model consisting of both static and dynamic data of the building. This data model, in turn can be used to develop models for DSM, regardless of whether they are white, grey, or black-box models. Creation of a complete building data model require ability to communicate within and across domains. Hence, identification of the standard communication protocols used in the different sectors and the silos that they are best suited to would also go a long way in developing DSM programs for buildings. Work of this nature has already commenced as can be seen in works such as [35] and Fig. 7.7, although cross-domain collaboration is not uniform across the table. In the commercial sector, targeting the operations of HVAC, domestic hot water, and lighting can have a strategic, significant impact on reducing total energy consumption of the building. The HVAC sector predominantly uses BACNet, Modbus and other wired protocols over the wireless protocols prevalent in the telecommu-

Fig. 7.7 The different alliances catering to the different siloed domains within the urban environment, and the multiple alliances in the horizontal telecommunication domain attempting to connect across the silos as adapted from [35]

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nication networks and phones. The systems across the various domains and their associated siloed communication standards and protocols need channels that allow for inter-domain communication to make DSM possible. Studies such as [10] have shown that creation of appropriate middleware that could facilitate transfer of data across the different silos would go a long way in dissipating many of the issues faced by technicians across different sectors when trying to integrate across to others. Another major shortcoming at present is the non-uniform presence of sensors and networks within the same sector. A good example of this can be found within the thermal systems in buildings. There are stark differences in the number of sensors and measurements across the residential, commercial and institutional usage-types. On one hand, in the commercial and institutional buildings, there are a large number of sensors present for measuring the state of HVAC systems. However, there is very little standardisation of sensor measurements owing to differences in system configurations and depending on when the system was commissioned. On the other hand, the system configurations in residential buildings are simpler, but they eschew sensor measurement for economic reasons. Finally, although occupant thermal comfort should be central to all buildings, there is very little in the way of consistent user occupant feedback across all usage types. Data collection from temperature, occupancy, and movement sensors would help monitor building energy consumption and inform ideal operational conditions. Identification of the minimal number of additional sensors required to make a meaningful improvement in the quality of forecasts and optimization of the building load consumptions (both electric and thermal) would improve efficacy of DR programs and reduce market uncertainty. Regular HVAC maintenance and upgrade and an integrated Building Automation System (BAS) that allows for fault detection and predictive maintenance are also key steps to ensure the best energy performance of buildings. Finally, one looming question hanging over the IoT sector in general has been the question of the costs associated with retrofitting the existing urban landscape with a newer generation of sensors and actuators where some exist and proposing a whole new set of sensors where none exist. Due to the compartmentalization of the different sectors and the financial capabilities of the different stakeholders, answering the question of who pays for the new technology through novel financial remuneration mechanisms will prove essential to the success or failure of IoT towards DSM frameworks.

7.6.2 Degree of Autonomy Depending on the aggregator role, availability of sensors, and network security concerns, various control and communication architectures exist to best facilitate the needs of the network. The three different control and communication architectures proposed for networks are: centralized, decentralized and distributed. Hierarchical architectures are usually a subset of these architectures created to tackle a geographi-

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Fig. 7.8 Centralized, Decentralized and Distributed architectures

cal or temporal constraint. Centralized architectures are exhibited in the conventional supply-demand pipelines that are already in place today. This requires the presence of a central operator that has access to all the data and systems and who makes all the decisions. At present, utilities and the various system operators (DSOs and TSOs) play the role of such central entities for various parts of the supply-demand chain. With the advent of more DERs and automated BEMS, maintaining a centralized architecture will be more difficult due to increasing demands that it will put on the communication infrastructure. However, centralized architectures have still been proposed in many studies and remain applicable for small communities and microgrids that frequently island themselves from the main network. An example of a centralized architecture has been shown in Fig. 7.8 with the central node highlighted in green. More and more, the trend has been towards either a decentralized or a distributed architecture as this eliminates the need for a central operator and reduces stress on the communication and computational infrastructure. These two architectures also enable the creation of new market models (such as P2P markets, imbalance markets, and ancillary service markets) and consensus mechanisms which make them attractive. Reference [3] partitions the different types of decentralized implementations by varying amounts of dependency: fully dependent, partially independent and fully independent. As per the authors, a fully dependent system allows each participant to make their own decisions, unlike in a centralized system where the central entity makes the decision for all entities in the network. However, each entity in a fully dependent system only communicates with a central entity like a centralized system. The partially dependent system allows communication of all entities with each other (including the central entity), allowing for more scalable and manageable computations. In the fully independent system, the central entity is not required anymore and

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all the entities interact with each other and attempt to minimize their consumption on the basis of a game-based consensus mechanism. The fully independent decentralized system defined by [3] is virtually the same as a distributed system. However, for communities and entities that provide services in a DSM framework, there is always a need for a supervisory node which verifies successful fulfillment of the services promised by the others, as has been mentioned in [5]. The presence of a supervisory node either through a system operator or an aggregator makes the solution a decentralized one with certain nodes becoming more central than others. This is shown in Fig. 7.8 where the orange nodes are the central nodes that supervise each agglomeration. A truly distributed system will need a virtual aggregator that provides the supervisory capabilities of an aggregator while maintaining parity for all participants, as can be seen from the example of a distributed network shown in Fig. 7.8. This is where blockchains come to the fore, to serve as the technological solution to provide this supervisory service. Blockchains are distributed ledgers that allow all parties equal access and visibility while providing opportunities to reach consensus in a transparent manner. The specific implementation of blockchains and similar virtual aggregation provides a higher degree of autonomy to all participants and is anticipated to be the way forward for a truly distributed and fair DSM-enabled market. Blockchains and the different market models that are possible for DSM/TEFs are discussed subsequently in Sect. 7.6.3.

7.6.3 Blockchains and Market Models There are many different kinds of DSM possible, as has been made obvious in studies like [6]. However, when one looks at the market side of how these actions can be implemented, they can be broadly seen as either Energy Efficiency Models or Demand Response Business Models as was proposed in 2015 by [7] and reiterated in [8]. These market models have predominantly favored entities with large loads that can be moved or shifted. This is also reflected in the minimum capacities that are often required as prerequisite to participate in the existing market models. Reference [7] shows that even for Demand Response Market models such as ancillary services, the primary transaction driver is reliability, very little notice is given for many of the activation calls available, and a there is a requirement for high precision/fast communication infrastructure. All of this creates a high barrier for urban and residential users from participating in the market. Recent efforts by industry actors such as ENTSO-E as well as a market reform towards economy-driven and load-based services have allowed for better opportunities for end-user prosumers to better participate in the market. But, the market mechanisms at use leaves much to be desired in terms of how to best utilize the untapped potential within the residential, mixed, and urban assets. With the impetus of increased digitalization with IoT and the direction clearly focused towards increased decentralization of energy production and institution of advanced control mechanisms, there is a need for a scalable, secure, sustainable,

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transparent, transaction mechanism/market. Blockchains allow for the formation of cooperative networks between parties and allows for secure transactions, thus standing out as one of the best candidates for such a transaction platform. Blockchains are a type of distributed ledger technology that is used to connect a large number of anonymous nodes without the need for a central controlling agent. Although, blockchain is in its infancy in the energy market context and requires more detailed validation, blockchain could offer key features such as (i) ability to create consensus (ii) encryption of information/transactions while maintaining a detailed log, and (iii) creation of smart contracts between entities without the need for a central entity or a central storage/server infrastructure. This has the potential to facilitate increased and more diverse user participation from across the different domains and fairness for each user, while also increasing opportunities for creation of reliable, transparent, and traceable custom contracts. Simultaneously, it also allows the utilities and grid operators to better manage and operate the grid and tailor services based on the requirement of each region or country. Blockchain could thus allow prosumers to not only provide ancillary and balance services to the larger market but also open P2P markets that allow microgrids and smaller agglomerations to self-optimize. Blockchain implementations would enable agent creation for prosumers and participants regardless of their size and offer. Automating the process with agent-based coordination and communication could also prevent issues usually observed with non-automated DR and DSM implementations such as diminishing user participation over time. These agents would be able to appropriately tailor the utility functions and provide vital information to the system installers and operators on what measurements (and as a direct consequence, the sensors) are missing and what data is required so that any particular agent can meaningfully participate in the market. The biggest boost that blockchain can provide is the computational and organizational flexibility that it can provide to the different markets. Blockchain presents a distributed verification mechanism that would be available to each participant, thus enabling markets (regardless of whether they are P2P or standard capacity markets) to function across different timescales without the need for a central entity. It also provides a strong cyber-physical foundation that ensures that system operators can meaningfully inform users on what products they can subscribe to and how to increase their participation. Blockchains’ encryptions may also be used to provide privacy and fairness to user transactions regardlesss of the architecture chosen for the market model. Finally, blockchains can offer security and alleviate privacy and data ownership concerns if they are created with attention to detail on the data structures and the encryption and decryption algorithms [9]. However, this is not a mandatory requirement of blockchains and is something that needs to be studied in greater detail. Better understanding from validation and iterative improvement of blockchain implementations and community-driven P2P markets is expected to inform this conversation in the coming years. Multi-year follow-ups of such implementations would also provide a better glimpse on the fairness of the incentives provided to the participants and the resulting shift in their behavior.

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7.7 Barriers, Potential Solutions, and Future Lines of Research There have been a significant number of studies proposing DSM frameworks or usecases over the last decade. However, the number of studies that validate the proposed theoretical frameworks outside of lab environments and then go on to be scaled up and integrated at a community/neighborhood level are scarce. Thus, its implementation is hard although they are accessible to the end-users. This lack of pilot programs and platforms for validation in residential, mixed, or commercial settings has been the chief stumbling block preventing broader adoption of DSM in society. However, this is also due to the inherent complexity involved in planning and executing such a project. It would require significant collaboration between various actors from multiple sectors to successfully pull off such a project. Quite often, some of these actors have never worked together before. As a result, there are often miscommunications owing to decades of build-up of sector-specific silos, including vernacular and existing practices. These have to be studied, understood, and corrected to make DSM possible. Weak political will, laws, and regulation has also disincentivized change from both the supply and demand side. However, the greenhouse gas targets established in the Paris Agreement by governments (national and regional) and organizations (public and private) have forced the hand in recent times. With the targets looming, there has been a renewed urgency in addressing the need for rapid decarbonization, increased electrification, and retrofit of the urban infrastructure. This has increased pressure and action from politicians, policy makers, and businesses and is expected to further open up avenues and opportunities to implement DSM within the next generation of cities. To this end, the last few years have seen an increase in the number of projects that bring together academics and industry across multiple disciplines to make testing and validation of DSM possible, driven technologically by the timely advancement in IoT and blockchain. To continue moving forward, one of the biggest obstacles is thoroughly understanding and quantifying the existing problems within our cities and the associated IoT requirements and standardization of communication protocols which will be necessary across all sectors. Validation and improvement of existing IoT appliances in potential application areas also remains to be fully addressed. This may be done with new hardware, but also through the creation of APIs and middlewares that connect the systems, prosumers, and buildings to each other, thus enabling communication and coordination. As we transition to this more distributed architecture, there is further need to consistently test the assumptions regarding the remuneration and consensus mechanisms proposed alongside the market models to provide incentives to the various actors and stakeholders. This will provide vital information on the capability of various end-users and systems participating and highlight the potential for regionspecific adjustments, entry guidelines, and incentives so as to make DSM attractive and financially feasible for all parties involved. A temporal analysis will also reveal

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the medium- and long-term impact of automated and non-automated methods of DSM and how we might adjust our approaches with this feedback cycle. Leveraging the abilities of data-driven approaches to reduce the modeling load could also greatly speed up research and real-life applications while yielding new ideas and unique causative links. At the same time, understanding and identifying the underlying physical meanings of these connections in a multitude of situations would help identify the boundaries of DSM from a systemic and an economic perspective. Finally, consistent dialogue between the policymakers, utilities, consumers, and system manufacturers that demystifies the concepts and technologies and communicates the advantages and limitations to broader society would go a long way in alleviating the fears and understanding the social barriers. Government-funded flagship pilot projects can be used to help foster participation and gain public traction. Being a part of flagship neighborhood- or city-level pilot projects would also inform the end-users on the potential benefits, such as reduced energy costs, and what they would need to do to be able to access these benefits. Including end-user feedback will also help researchers understand the flaws in their assumptions and work on correcting them. This is an effective way to drive up the installation rates of IoT networks, retrofit of buildings with more efficient systems, reduce GHG emissions, and ensure increased institution and participation within DSM markets within today’s cities.

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