Internet of Things for Facility Management: Strategies of Service Optimization and Innovation (SpringerBriefs in Applied Sciences and Technology) 3030625931, 9783030625931

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SPRINGER BRIEFS IN APPLIED SCIENCES AND TECHNOLOGY  POLIMI SPRINGER BRIEFS

Nazly Atta

Internet of Things for Facility Management Strategies of Service Optimization and Innovation

SpringerBriefs in Applied Sciences and Technology PoliMI SpringerBriefs

Editorial Board Barbara Pernici, Politecnico di Milano, Milano, Italy Stefano Della Torre, Politecnico di Milano, Milano, Italy Bianca M. Colosimo, Politecnico di Milano, Milano, Italy Tiziano Faravelli, Politecnico di Milano, Milano, Italy Roberto Paolucci, Politecnico di Milano, Milano, Italy Silvia Piardi, Politecnico di Milano, Milano, Italy

More information about this subseries at http://www.springer.com/series/11159 http://www.polimi.it

Nazly Atta

Internet of Things for Facility Management Strategies of Service Optimization and Innovation

Nazly Atta Architecture, Built Environment and Construction Engineering—ABC Department Politecnico di Milano, Milan, Italy

ISSN 2191-530X ISSN 2191-5318 (electronic) SpringerBriefs in Applied Sciences and Technology ISSN 2282-2577 ISSN 2282-2585 (electronic) PoliMI SpringerBriefs ISBN 978-3-030-62593-1 ISBN 978-3-030-62594-8 (eBook) https://doi.org/10.1007/978-3-030-62594-8 © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 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

Preface

The several innovations in the field of Information Management, the establishment of Big Data and Internet of Things (IoT) concepts, as well as the wide development and dissemination of sensing technologies are opening up innovative scenarios for building Facility Management (FM). The IoT—as defined by the International Telecommunication Union (ITU)— is a dynamic global network infrastructure, based on standard and interoperable communication protocols, that enables advanced services by integrating and interconnecting physical and virtual things, giving them unique identities and possibilities of interaction through intelligent interfaces. The application of such a general-purpose technology to the specificity of the FM domain may give rise to new opportunities of innovation of traditional practices. Indeed, IoT technology allows, nowadays, to easily and economically collect data concerning various aspects of the built environment, bringing out new possibilities for a widespread and continuous monitoring. This new availability and accessibility to real-time data and the possibility of integration with different data coming not only from buildings but also from the urban environment that surrounds them opens the door to the development of innovative strategies of services optimization before inconceivable. In particular, today IoT technologies allow to collect real-time data and information on actual conditions and current operational status of the building and its components through sensors and smart devices, achieving a greater awareness and understanding of the behavior of the building itself, as well as of the performance of FM services delivered by/to it. This additional information base may represent a strategic asset and a significant driver for the improvement of cognitive and decision-making processes within the current FM practice. However, what the technology potentially offers, if not supported by a vision of possible models of application to building management, risks to generate a redundancy of data that could become of little use. In this regard, currently, FM stakeholders (e.g., Real Estate owners, FM providers, service suppliers, etc.) are increasingly expressing the need of new methodological tools to support them in understanding the nature and the features of this new v

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Preface

technological offer in order to become promoters of FM innovation, by seizing the opportunity to consciously exploit the IoT potential when applied to their specific sector. Therefore, the present book aims to propose strategies for FM services optimization and innovation, based on innovative models of IoT application within FM processes, able to support FM stakeholders in: • orienting, organizing and managing Big Data flows and their sources (sensor, RFID, etc.); • changing FM services demand/offer and related requirements and, consequently, in developing new approaches to FM agreements; • drawing new supply chains based on network approaches; • outlining new required profiles of competences. Hence, the book provides to FM stakeholders an Analytical-Procedural Framework useful for defining, implementing and requesting IoT-based FM services, consisting of: • an analytical framework for implementing IoT and Big Data technologies within building FM processes; • a definition of tools, strategies and procedures for innovative modalities of FM service provision; • operational support lines for the drafting of FM agreements for IoT-based FM service provision, including new potential organizational models and related novel required competence profiles of facility managers. In particular, the work presented in this book is articulated into nine chapters organized into two parts. The first part introduces the theoretical background. Chapter 1 introduces the topics of Facility Management (FM) and Information Management (IM), analyzing in a critical way the traditional practices that currently characterize IM processes in the FM field and identifying the main related emerging needs and improvement areas, focusing on the potential of Internet of Things (IoT) and Big Data paradigms as enablers of FM processes innovation. Chapter 2 provides a literature review of the concept of “smartness” in the field of built environment, as well as a critical analysis of case studies of Big Data and IoT applications to service management at the urban and building scale in order to identify best practices, trends and recurring models of IoT-based information management. Chapter 3 proposes and discusses four main FM paradigm shifts toward innovative approaches to building and service management, i.e., (i) from static data to real-time data flows; (ii) from linear and siloed to integrated network-based processes; (iii) from static to adaptive systems; and (iv) from a work-intensive to an information-intensive FM scenario. Chapter 4 outlines new available solutions of IoT technology and Big Data management focusing on Information Platforms—enablers of real-time data acquisition, integrated data management, and data analytics—highlighting their innovative capabilities and potential benefits in response to the emerging needs of traditional FM processes. The second part introduces the experimental research.

Preface

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Chapter 5 provides a normalized taxonomy of FM-related information with respect to IoT requirements, in the form of a parameter-sensor matrix. The latter represents a support tool for FM stakeholders to face the current highly variegated market offer of sensors by IT providers, often difficult to understand and compare. Chapter 6 proposes models based on the basic principle of Sensing and Responding (S-R) to enhance the response-ability of systems, as well as a functional model of IoT platform, adapted to the case of building FM, useful to implement the S-R models within FM processes. Hence, the chapter outlines new possible configurations of IoT-based FM processes and organizational models. Chapter 7 proposes innovative FM service strategies based on IoT integration and on the application of S-R models (proposed in Chap. 6), namely: remote monitoring, real-time fault detection and diagnosis; condition-based strategy; predictive strategy; prescriptive strategy. Moreover, the chapter identifies possible scenarios of implementation of the proposed strategies to the management of maintenance, cleaning, waste, space and energy, highlighting related improvements both at strategic and operational level. Chapter 8 reports the application of IoT-based FM strategies to the case study of the new Headquarter (STATUTO 11) of eFM, co-funding company of the present PhD research. The chapter focuses on workplace management, introducing the concept of Smart Digital Workplace, which is based on IoT technologies and involves multi-modal real-time digital communication among employees, as well as new collaborative and creative approaches to work activities. Chapter 9 outlines new possible configurations of supply chain for the IoT-based FM scenario, introducing the IT provider as a new leading FM stakeholder. Then, possible sourcing strategies for IoT services and infrastructure provision are defined, together with the related implications with respect to information responsibility and ownership. Lastly, the chapter introduces an analysis of new profiles of skills and competence for facility managers, required in order to establish profitable win-win relationships with IT providers. Milan, Italy

Nazly Atta

Acknowledgements I would like to express my gratitude to the supervisors of the present thesis—Prof. Cinzia Talamo and Prof. Giancarlo Paganin—for their valuable guidance and constant support, as well as for the opportunities to learn they gave me throughout the PhD experience. I would also like to thank eFM, co-funding company of the present research and, in particular, Giuseppe Capicotto, Daniele Di Fausto, Nicola Martinelli, and Carmela Ventura for their advices which led me to widen the research from various perspectives. A particular thank you to the PhD School and the ABC Department of Politecnico di Milano.

Contents

Part I

Theoretical Background: The Role of ICT, IoT and Big Data within the FM Field

1 Information Management in FM Processes. Traditional Scenario and Future Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Information Management Within Traditional FM Processes . . . . . . . 1.2 Emerging Needs of FM-Related Information Management Practices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Real-Time Data Availability and Accessibility . . . . . . . . . . . . 1.2.2 Network Approach to Information Management . . . . . . . . . . 1.2.3 Data Analytics to Improve Decision-Making . . . . . . . . . . . . . 1.3 Potential of IoT and Big Data Paradigms as Enablers of FM Processes Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Smartness in the Built Environment: Smart Buildings and Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 The Concept of Smartness in the Field of Built Environment . . . . . . 2.2 Smart Cities. Real-Time Dashboards for Integrated Management of Urban Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Smart Buildings. Information Technology for Intelligent Building Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 EU’s FP7 and Horizon 2020 Projects. IoT Application for Advanced Service Management . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Smart Initiatives for Service Innovation: Approaches and Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 FM Paradigm Shifts Enabled by ICTs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 From Static Data to Real-Time Data Flows . . . . . . . . . . . . . . . . . . . . . 3.2 From Linear and Siloed to Integrated Network-Based Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 6 6 7 7 8 9 11 11 14 18 21 31 32 37 37 38

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3.3 From Static to Adaptive Systems: Sensing and Responding (S-R) Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 From a Work-Intensive to an Information-Intensive FM Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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4 Internet of Things, Big Data and Information Platforms for Advanced Information Management Within FM Processes . . . . . . 4.1 Internet of Things (IoT): Evolution of the Concept . . . . . . . . . . . . . . 4.2 IoT Technology for Real-Time Data Acquisition . . . . . . . . . . . . . . . . 4.3 Big Data Features and Potentialities . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 IoT Platforms: Architectures, Functions and Features . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

41 41 42 43 44 48

Part II

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Experimental Research: Proposal of IoT Integration Strategies for Improving FM Processes, Models and Services at the Building Scale

5 IoT-Based Collection of FM Information: Parameters and Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Detecting FM-Related Information Using IoT Technology . . . . . . . . 5.2 Classification of Parameters for Service Management . . . . . . . . . . . . 5.3 Classification of Sensors and IoT Devices for Data Detection . . . . . 6 Sensing and Responding (S-R) Models and IoT Architecture for Advanced FM Information Management . . . . . . . . . . . . . . . . . . . . . . 6.1 Sensing and Responding (S-R) Models for Improving FM Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 IoT Architecture for FM: Fundamental Interconnected Layers . . . . 6.3 Centralized Information Management for Advanced FM Decision-Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 IoT-Based FM Service Strategies and Operational Lines for Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Strategies of Service Management Optimization and Innovation: Integrating IoT and Applying S-R Models . . . . . . . 7.2 IoT-Based Strategies: Improving FM Services . . . . . . . . . . . . . . . . . . 7.2.1 Operation and Maintenance Management . . . . . . . . . . . . . . . . 7.2.2 Cleaning Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.3 Waste Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.4 Space Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.5 Energy and Utilities Management . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

51 51 54 59 65 65 69 72 74 77 77 79 80 84 85 86 87 88

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8 IoT-Based FM Strategies Application: The Case of eFM Headquarter STATUTO 11 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 8.1 Smart and Digital Workplace: The Case of eFM Headquarter STATUTO 11 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 8.2 eFM Advanced Data Management: IoT Technology and Information Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 8.3 eFM IoT-Based Services and Monitoring Tools . . . . . . . . . . . . . . . . . 96 8.3.1 Smart Reservation Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 8.3.2 People Counting Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 8.3.3 Heat Map Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 9 IoT-Based FM Scenario: Supply Chain, Organizational Models and Contracts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 IT Provider as a New Stakeholder of the FM Supply Chain . . . . . . . 9.2 IoT-Based FM: Scenarios of Application and Potential Organizational Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Information Responsibility and Ownership Within IoT-Based FM Invitations to Tender, Contracts and Service Delivery . . . . . . . . 9.4 New Profiles of IoT-Related Skills and Competencies for FM Stakeholders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

103 103 104 107 113 116

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

Chapter 1

Information Management in FM Processes. Traditional Scenario and Future Perspectives

1.1 Information Management Within Traditional FM Processes Facility Management (FM) stakeholders agree that information represents a strategic asset with respect to the efficient building management and they recognize the key role of effective information management systems in order to reach competitive advantages from the available knowledge (Janoskova 2016; Jensen and van der Voordt 2016) concerning different aspects of the building. Academics and practitioners belonging to different business sectors have traditionally defined Information Management (IM) as the process by which relevant information is provided to decision makers in a timely manner, supporting organizations in enhancing their competitive advantage (Davis et al. 1997; Robertson 2005; Morabito 2012). Along with this definition, that seems to be broadly accepted, there are in literature several contributions that underline different aspects and features of IM (Table 1.1). The definitions examined in Table 1.1 bring out the following IM recurring aspects: – First, IM is a conscious process (Hinton 2006). This means that IM should be a systematic process that needs a proper planning phase to structure the process backbone, align interests, set tasks and define procedures, allocate resources and assign roles, functions and responsibilities. – Second, the purpose of IM is to support the decision-making. As Morabito (2012) has stated, information is gathered to be used. More and more in recent years, information has become a crucial asset for organizations and it has a high influence on the effectiveness and success of decision making process. IM helps organizations in identifying problems and understanding the related causes and possible consequences in order to support them in making proper informed decisions. IM assists all the levels of an organization (Hinton 2006); it is not limited to top management but it provides information and insights to all levels, from operative to strategic. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Atta, Internet of Things for Facility Management, PoliMI SpringerBriefs, https://doi.org/10.1007/978-3-030-62594-8_1

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1 Information Management in FM Processes. Traditional Scenario …

– Third, IM should be a circular process (Butcher and Rowley 1998; Choo 2002). The IM cycle is characterized by a circularity of the information flow, allowing a continuous improvement. It is a recursive processes articulated into the broad phases of (i) planning, (ii) implementing, (iii) measuring results and (iv) consequently adjusting strategies or taking corrective actions if the results are not the expected ones, and then (v) enriching knowledge. – Last, IM can be supported by Information Technology (IT) to reach better results and enhance the effectiveness and efficiency of the whole process. In this context, IT refers to the technological infrastructure needed to move large quantities of digitized data and information in an efficient and secure manner from one place or person to another (Barry 1996). Table 1.1 Analysis of Information Management (IM) definitions References

Definition of information management

Keywords

Rowley (1988)

IM includes organization-wide information policy planning, the development and maintenance of integrated systems and services, the optimization of information flows and the harnessing of leading edge technologies to the functional requirements of end-users, whatever their status or role in the parent organization

Integrated systems and services; information flows optimization

Cronin and Davenport (1991)

IM relies on codified knowledge Codified knowledge; Process (symbols, standards, and algorithms) to automation; Information retrieval represent information entities that allow process automation, decision making, information retrieval, etc

Taylor and Farrell (1992)

IM is able to identify, coordinate and Information entities; Value; exploit information entities in an Competitive Advantage organization, using the characteristics of these entities to add value to existing information and to gain competitive advantage over competitors

Rowley (1998)

IM can be viewed as a response to and Information flow; Information control, a search for new and improved means analysis and synthesis of controlling the information explosion and the resultant increasing complexity of decision making by improving the flow, the control, the analysis, and the synthesis of information for decision makers

Back and Moreau (2001)

IM is the use of all agency personnel, Information infrastructure; Resource processes, policies, and technologies coordination that define and comprise the information infrastructure in order to coordinate the use of information from the time it is created until it is no longer useful and eliminated (continued)

1.1 Information Management Within Traditional FM Processes

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Table 1.1 (continued) References

Definition of information management

Keywords

Dias (2001)

In some papers, IM is used as a Information systems; synonym for information systems, Information technology; information technology, data Data management management, systems engineering, among other expressions. In fact, information management is more than that. Modern information management uses information technology, cybernetics, systems engineering, concepts of information and computer sciences, management information systems, engineering, office automation, business and management principles, to plan, manage and control one of the most important resources for survival of an enterprise on the current market-Information

Hinton (2006)

IM can be seen as the conscious process by which information is gathered and used to assist in decision making at all levels of the organization

Ellis and Desouza (2009)

IM is the practice of improving Information accuracy and quality; information usage and flow to add Systemic management value while simultaneously acting as its steward, thus improving analysis, accuracy, systemic management, and quality so that information may act as a catalyst for decision-making

Visser (2012)

IM is a set of solutions that deliver Trusted Information; Information trusted information throughout your Supply Chain; Insights information supply chain and help you analyze your information to gain insights, identify breakdowns, and make better decisions that will optimize your business

Queensland Government (2012)

IM is defined as the means by which an organization plans, identifies, creates, receives, collects, organizes, governs, secures, uses, controls, disseminates, exchanges, maintains, preserves and disposes of its information; as well as any means through which the organization ensures that the value of that information is identified and exploited to its fullest extent

Information Exploitation; Information Value

PAS 1192-2:2013

IM is made up of tasks and procedures applied to inputting, processing and generation activities to ensure accuracy and integrity of information

Tasks and procedures; Information accuracy and integrity

Conscious process; Information gathering

(continued)

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Table 1.1 (continued) References

Definition of information management

Keywords

Oracle (2013)

IM is the means by which an organization seeks to maximize the efficiency with which it plans, collects, organizes, uses, controls, stores, disseminates, and disposes of its Information, and through which it ensures that the value of that information is identified and exploited to the maximum extent possible

Efficiency maximization; Information exploitation; Information value

Gartener Glossary, accessed 2019

IM is a method of using technology to collect, process and condense information with a goal of efficient management. Most large enterprises have a central IM function to facilitate this coordination. The primary technologies needed are contained in a set of modeling tools that either have or interface to a production-worthy repository where the information is stored and managed. The repository and tools must be capable of receiving information in a “top-down,” “bottom-up” or “middle-out” evolutionary manner

Efficient management; Resource coordination; Technologies; Data repository

Hence, it is possible to state that IM is a complex system of sub-processes that involves tools, know-how, and procedures aimed at creating, sharing, using and exploiting information within an organization (Choo 1995, 2002; Dias 2001; Butcher and Rowley 1998; Bytheway 2014). With respect to the FM field, the development and management of the information base is one of the most onerous and complex operations with respect to the FM service provision and therefore, in order to avoid overspending and data loss, it must be properly planned and organized. In particular, the common IM practice within the FM field and, more in general, the Real Estate (RE) management field involves the synergy of several procedures and tools that converge in the pivotal concepts of Inventory Process, Registry System and Information System. Inventory is that systematic, gradual and progressive process of acquisition and management of technical, administrative and legal information aimed at achieving over time a proper RE knowledge (Curcio and Talamo 2013). The collection and the organization of information, carried out according to predetermined shared procedures and reference schemes (Registry System), allow the construction of the Registry of the building. The Registry is an organized system of information, gathered through the Inventory Process, which is necessary to describe the consistency and technical characteristics of the building. The Registry is developed according to the methods of classification and coding of the building and its technical and spatial components (e.g. UNI 8290-1, UNIFORMAT II by ASTM, OmniClass, etc.) defined by the Registry

1.1 Information Management Within Traditional FM Processes

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System. The construction of a Registry can be accomplished through the implementation of an Information System for RE management, i.e. a software tool whose core is represented by a central database able to manage in an integrated way the input–output information concerning FM services. With respect to this framework of methods and tools that supports IM processes within the complex contest of FM services provision, the past and present common practice reveals some inefficiencies and still-open issues (Fransson and Nelson 2000; Ahmed et al. 2017; Støre-Valen and Buser 2017), such as: 1. Slow and resource-consuming processes of data collection and management. Information collection processes are often expensive, they involve a large amount of human resources and have long lead times. Although the potential of digital information systems in streamlining data acquisition processes is commonly recognized, in most of the current FM practices they are not fully implemented also due to the lack of know-how about processes and procedures for managing and updating such digital systems. 2. Not-shared procedures. FM processes involve several tools, procedures and criteria, e.g. system of classification and coding, procedures for collecting information through surveys, performing interventions, data entry, managing on demand intervention requests, etc. The different stakeholders (e.g. clients, property owners and managers, facility managers, asset managers, etc.) usually all use different methods, systems and tools to perform their work and manage their processes, without a common coordination strategy. Instead, the sharing of a common framework of tools and procedures among all stakeholders is fundamental in order to reach the expected efficiency, avoiding undesirable reductions of performance and quality of services. 3. Linear and not-integrated data management processes. The lack of crossdepartmental communication and collaboration is another cause of inefficiency. FM processes are not-integrated and not-coordinated among departments that do not share a unified database architecture and standard procedures and processes to manage data. The current practice of building management is characterized by a “silos” approach (Fransson and Nelson 2000; Ahmed et al. 2017). Indeed, in most of FM realities, the different stakeholders do not share data and do not work synergistically, multiplying in this way the efforts and increasing the costs of information collection. 4. Static and not-updated information. The current FM practice is often characterize by poor quality of information which negatively affects the planning and programming phase of FM processes. The main causes of this issue are: (i) lack of data updating activities on a regular basis, which often lead to the presence of not always reliable data within the database; (ii) “not in time” collection of information, i.e. the information reaches the relevant users but at a wrong time, thus losing its effectiveness. 5. Lack of a systematic approach to feedback information management. The collection and the analysis of feedback information coming both from the strategic level (e.g. reports, benchmarking, etc.) and from the operational level (e.g. closure of

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work orders, customer satisfaction surveys, etc.) is fundamental in order to: (i) improve information bases by adding new data and updating existing ones, (ii) assess performance and (iii) review FM plans and programs. By not defining a systematic process for managing feedback information—clarifying related roles, tools and procedures—facility managers lose the opportunity to enrich their information bases and they run the risk of grounding their decision-making processes on a partial, incomplete and/or outdated vision of their building assets. These criticalities and weaknesses of current IM practices can be partially influenced by the following issues affecting the FM field: – Unclear and blurred contractual clauses about the quality, responsibility and ownership of information both during and at the end of the contract period. – Extremely limited funds are made available by companies for IM investments compared with the funds for other favored activities. This can be due to the difficulty associated with demonstrating the return on investment for IM (Jiao et al. 2013).

1.2 Emerging Needs of FM-Related Information Management Practices The lacks and weaknesses of current FM practices—highlighted in the previous paragraph—are related to three main topics that today represent the emerging needs for innovation of FM, namely: Real-Time Data availability and accessibility; Network approach to information management; Data Analytics to improve the decision-making.

1.2.1 Real-Time Data Availability and Accessibility The availability and accessibility to real-time data represent an opportunity to improve and widen the range of FM services and businesses, with significant potential benefits to all the involved FM stakeholders. In particular, the “time” dimension with respect to data availability and accessibility highlights two interesting reading keys: a. Time of information collection. IM processes acquire different meanings according to different timescales. Focusing on the “past”, IM within a FM service means to be able to collect and process data over time to create a constantly growing knowledge base able to describe the history of building life. This knowledge base represents a fundamental condition in order to enable FM managers to estimate, for instance, future building components behaviors and costs to be incurred, but also to develop a framework of indicators for benchmarking past and present performance. As regards the “present”, IM means to be able to collect and

1.2 Emerging Needs of FM-Related Information Management Practices

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process real-time data useful, for instance, in order to: monitor present conditions and current operation state of building components; detect faults and anomalies in real-time; monitor the performance of providers, as well as the progress of planned activities and ongoing expenditures. In this way, facility managers are able to observe and analyze a real-time updated snapshot of the building status and service performance. b. Time of information delivery. This reading key regards the timely access by FM stakeholders to accurate information, i.e. the need of real-time data collection in order to give a proper “on time” response to contingent requests or events (allowing the timely sending of relevant data to the right actor/s in order to support the imminent decision-making and activate timely response actions).

1.2.2 Network Approach to Information Management The network approach to information management is one of the main emerging needs for innovation of the FM sector. The need to overcome linear and not-integrated processes, often characterized by several single decision centers with a very poor cross-communication, represents a key element to reach innovation of FM models. The current partitioned and vertical approach to IM does not allow to seize the opportunity of exploiting potential synergies rising from the information sharing among departments. On the contrary, information could be collected only one time and then shared across the network into a unique data warehouse, available and accessible to all the departments and all the involved stakeholders. Networking is a fundamental IM approach that allows to connect the several nodes of the network (e.g. building components, people, IM systems) in order to exchange and transfer knowledge and information. Also considering the high quantities of data connected to the several building components and to the various operators alternating during the building life cycle, the implementation of a unified central shared database—along with the setting up of standard methods, procedures and processes to manage data— may generate benefits such as: reduction of data and information loss; minimization of data redundancies; optimization of data sharing procedures; reduction of risks of runaway expenditures, etc.

1.2.3 Data Analytics to Improve Decision-Making Data Analytics represents nowadays another key emerging need for improvement of FM practice. Indeed, if on one hand it is essential to have a well-structured information base, always updated and accessible, on the other hand the possibility of analyzing this information base to gain knowledge is even more crucial. Organizations need more and more advanced data analysis solutions that support them in extracting meaning from massive amounts of data coming from different sources and regarding different aspects of the real estate, in order to improve their

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decision-making processes, while reducing the related risks. Data Analytics represents for organizations a new capability of analyzing historical and real-time data in order to reveal hidden patterns and trends, forecast future events and behaviors and consequently assess possible response scenarios.

1.3 Potential of IoT and Big Data Paradigms as Enablers of FM Processes Innovation In the last decade, the ICT (Information and Communication Technology) sector has been interested by a continuous evolution that has led to a progressive development of information management solutions for real-time collection, management and analysis of data flows (Big Data) useful to improve cognitive and decisionmaking processes. Hence, new ICT-based FM innovation scenarios are emerging and they are capable of modifying, in a perspective that could be defined as “smart”, many of the concepts currently referable to FM information management. In particular, the information management with a Big Data perspective and the capabilities of data integration allowed by the Internet of Things (IoT) logics can introduce several profound innovations regarding the ways in which FM services are conceived, requested and delivered (Konanahalli et al. 2018). Indeed, IoT and Big Data paradigms allow to overcome the traditional concepts of inventory process and knowledge base, introducing new paradigms for FM cognitive, decision-making and implementation processes that can now be based on new capabilities of simulation and prediction of phenomena and new adaptive response capabilities of building systems. The plurality of IoT devices now easily available on the market (Vermesan and Friess 2014; Atlam et al. 2018) widens the range of data sources (e.g. fixed, mobile and wearable sensors, RFID tags, GPS, smartphones, etc.) and open up to the topic of multiple connections (Zhou et al. 2016) between objects and people, typical of the IoT concept. Objects with embedded sensors generate data and - by means of communication networks - they can interact among them and with people, exchanging data through smart interfaces (Fortino and Trunfio 2014; Marjani et al. 2017). In light of these novel capabilities offered by IoT and Big Data management, it is possible to highlight the following potential organizational innovations for the FM field: – new information management models based on network approaches aimed at information sharing among FM stakeholders and centralized data capitalization; – provision of improved FM services, based on the proactive and timely response to changes in the reference conditions (due to exogenous or endogenous causes) detected through sensor monitoring systems; – new methods of diagnosis, simulation and prediction of systems behaviors, enablers of data-driven approaches to risk assessment and management (Talamo et al. 2016).

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The achievement of these improvements in the next future strongly depends, firstly, by the ability of Clients and Service Providers to recognize this room for innovation and, secondly, by the development of proper information tools able to support FM stakeholders in the conscious adoption of ICT to improve their processes, services and businesses.

References Ahmed V, Tezel A, Aziz Z, Sibley M (2017) The future of big data in facilities management: opportunities and challenges. Facilities 35(13/14):725–745 Atlam H, Walters R, Wills G (2018) Internet of things: state-of-the-art, challenges, applications, and open issues. Int J Intell Comput Res (IJICR) 9(3):928–938 Back WE, Moreau KA (2001) Information management strategies for project management. Project Manage J 32(1):10–19 Barry RE (1996) Making the distinctions between information management and records management. https://www.mybestdocs.com/imt-arm1.htm. Accessed Jan 2018 Butcher D, Rowley J (1998) The 7 R’s of information management. Manag Inform 5(3):34–36 Bytheway A (2014) Investing in information: the information management body of knowledge. Springer Choo CW (1995) Information management for the intelligent organization: roles and implications for the information professions. In: Digital libraries conference Choo CW (2002) Information management for the intelligent organization: the art of scanning the environment. Information Today, Inc. Cronin B, Davenport E (1991) Elements of information management. Scarecrow Press, New Jersey Curcio S, Talamo C (2013) Glossario del facility management. EdiCom, Milano Davis JH, Schoorman FD, Donaldson L (1997) Toward a stewardship theory of management. Acad Manag Rev 22(1):20–47 Dias C (2001) Corporate portals: a literature review of a new concept in information management. Int J Inf Manage 21(4):269–287 Ellis P, Desouza KC (2009) On information management, environmental sustainability, and cradle to cradle mentalities: a relationship framework. Business Inform Rev 26(4):257–264 Fortino G, Trunfio P (Eds) (2014) Internet of things based on smart objects: Technology, middleware and applications. Springer Science & Business Media Fransson W, Nelson D (2000) Management information systems for corporate real estate. J Corporate Real Estate 2(2):154–169 Hinton M (ed) (2006) Introducing information management: the business approach. Butterworth Heinemann/Elsevier, Oxford, UK Janoskova K (2016) Facility management as an important competitive advantage of companies in international environment. Economics 5:6 Jensen PA, van der Voordt T (Eds) (2016) Facilities management and corporate real estate management as value drivers: how to manage and measure adding value. Taylor & Francis Jiao Y, Wang Y, Zhang S, Li Y, Yang B, Yuan L (2013) A cloud approach to unified lifecycle data management in architecture, engineering, construction and facilities management: Integrating BIMs and SNS. Adv Eng Inform 27(2):173–188 Konanahalli A, Oyedele L, Marinelli M, Selim G (2018) Big data: a new revolution in the UK facilities management sector Marjani M, Nasaruddin F, Gani A, Karim A, Hashem IAT, Siddiqa A, Yaqoob I (2017) Big IoT data analytics: architecture, opportunities, and open research challenges. IEEE Access 5:5247–5261 Morabito V (2012) Business technology organization: managing digital information technology for value creation-the SIGMA approach. Springer Science & Business Media

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Oracle (2013) Information management and big data. A reference architecture. White paper. https://www.oracle.com/technetwork/topics/entarch/articles/info-mgmt-big-data-ref-arch-190 2853.pdf. Accessed Jan 2019 Queensland Government (2012) Information management strategic framework. https://www.qgcio. qld.gov.au/documents/information-management-strategic-framework. Accessed Jan 2019 Robertson J (2005) 10 principles of effective information management. Step Two Designs Pty Ltd. Rowley J (1998) Towards a framework for information management. Int J Inf Manage 18(5):359– 369 Rowley JE (1988) The basics of information technology. Clive Bingley Støre-Valen M, Buser M (2017) Barriers to and challenges of sustainable facilities management practices–experiences from the Nordic countries. Welcome to Delegates IRC 2017:356 Talamo C, Atta N, Martani C, Paganin G (2016) The integration of physical and digital urban infrastructures: the role of “Big data.” TECHNE-J Technol Architect Environ 11:217–225 Taylor A, Farrell S (1992) Information management in context. Aslib Proceedings 44(9):319–322 Vermesan O, Friess P (eds) (2014) Internet of things-from research and innovation to market deployment, vol 29. River publishers, Aalborg Visser S (2012) Information Management. What does it mean to you? IBM Blogs. https://www. ibm.com/developerworks/community/blogs/SusanVisser/entry/information_management_w hat_does_it_mean_to_you?lang=en. Accessed Jan 2019 Zhou K, Fu C, Yang S (2016) Big data driven smart energy management: From big data to big insights. Renew Sustain Energy Rev 56:215–225

Standards and Laws PAS 1192–2:2013 Specification for information management for the capital/delivery phase of construction projects using building information modelling

Websites Gartner Glossary. www.gartner.com/it-glossary/im-information-management. Accessed Jan 2019

Chapter 2

Smartness in the Built Environment: Smart Buildings and Smart Cities

2.1 The Concept of Smartness in the Field of Built Environment The latest years of the 20th century have been characterized by the rise of two significant phenomena, the increasing urbanization and the spread of Information and Communication Technologies (ICTs) (Cocchia 2014). Urbanization has two main implications, on one hand it causes an increase of the cultural level and a growth of the economic conditions of the city, on the other hand the concentration of people within the city causes a variety of technical, social, economic and organizational issues that undermine the economic and environmental sustainability of the city (Neirotti et al. 2014). According to Kim and Han (2012), the growth of urbanization implies increased levels of traffic, pollution, waste, gases emission as well as social inequality which generate negative implications for both the environment and people. Consequently, according to Stratigea et al. (2015), the impacts of the concentration of people within cities are reflected in a higher energy consumptions and pollution levels, increased volume of urban waste, decrease of social cohesion and so on. In the nineties, this awareness begins to animate the debate among policy makers and urban planners, also contextually to the development of the ICT sector. Indeed, the acknowledgment of the significant support offered by ICTs to reduce the aforementioned impacts (European Commission 2014) by improving urban infrastructures and service management represents a great step forward towards sustainable future cities. In those years the concept of Smart City as testing ground to investigate ways to exploit new ICT-based solutions and new approaches to urban planning (Nam and Pardo 2011; Neirotti et al. 2014) spreads globally. In the late nineties, the Smart Growth Movement introduces the paradigm of smart development of urban areas (Bollier 1998; Harrison and Donnelly 2011), understood as conscious development attentive to the topics of environmental, economic and social sustainability. Since those years, the worldwide diffusion of several Smart City projects and initiatives

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has made it possible to experiment and promote the development of new ICT applications to the urban scale. However, this rapid diffusion has often made it difficult to harmonize practices, methods and tools (Cocchia 2014; Neirotti et al. 2014), as can also be observed by analyzing Smart City definitions proposed in the literature (Table 2.1). All the definitions in Table 2.1 underline the key role of ICT as enabler of a greater efficiency in urban service management practices. Despite the variety of approaches, it is possible to identify some recurring topics, such as: environmental and economic sustainability; human and social capital and citizens’ quality of life; resilience; participatory governance; sustainable resource management; advanced ICT-based information management. Table 2.1 Definitions of Smart City Author, year

Smart city definition

Keywords

Giffinger et al. (2007)

“A Smart City is a city well performing in a forward-looking way in economy, people, governance, mobility, environment, and living, built on the ‘smart’ combination of endowments and activities of self -decisive, independent and aware citizens”

Economy; People; Governance; Mobility; Environment; Living

Batty et al. (2007)

“…a virtual city is a city where bricks and mortar, buildings Digital data; Virtual and their materials are represented as polygons and textures, space is digital data. Data is key to our knowledge and understanding of the form of the city but its geometry must be distinguished from its other more substantive attributes which might be both physical and social”

Harrison et al. (2010)

“A city connecting the physical infrastructure, the IT infrastructure, the social infrastructure, and the business infrastructure to leverage the collective intelligence of the city”

IT infrastructure; Social infrastructure; Business infrastructure

Washburn and Sindhu (2010)

“[…] the use of smart computing technologies to make the critical infrastructure components and services of a city—which include city administration, education, healthcare, public safety, real-estate, transportation, and utilities—more intelligent, interconnected, and efficient”

Computing technologies; Urban services

Caragliu et al. (2011)

“A city to be smart when investments in human and social capital and traditional (transport) and modern (ICT) communication infrastructure fuel sustainable economic growth and a high quality of life, with a wise management of natural resources, through participatory governance”

Sustainable economic growth; Resources management; Participatory governance

Gartner (2011)

“A smart city is based on intelligent exchanges of information that flow between its many different subsystems. This flow of information is analyzed and translated into citizen and commercial services. The city will act on this information flow to make its wider ecosystem more resource efficient and sustainable. The information exchange is based on a smart governance operating framework designed to make cities sustainable”

Intelligent information flow; Information exchange

Schaffers et al. (2011)

“Smart Cities are integrated social, physical, institutional, and digital spaces, in which digital components improve the functioning of socio-economic activities, and the management of physical infrastructures of cities, while also enhancing the problem solving capacities of urban communities”

Management of physical urban infrastructure; Problem solving capacities

(continued)

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Table 2.1 (continued) Author, year

Smart city definition

Keywords

Dameri (2013)

“A smart city is a well-defined geographical area, in which high technologies such as ICT, logistic, energy production, and so on, cooperate to create benefits for citizens in terms of well-being, inclusion and participation, environmental quality, intelligent development; it is governed by a well-defined pool of subjects, able to state the rules and policy for the city government and development”

Well-being; inclusion and participation; Environmental quality

EIP-SCC (2013)

“Smart cities should be regarded as systems of people interacting with and using flows of energy, materials, services and financing to catalyse sustainable economic development, resilience, and high quality of life; these flows and interactions become smart through making strategic use of information and communication infrastructure and services in a process of transparent urban planning and management that is responsive to the social and economic needs of society”

Sustainable economic development; Resilience; ICTs; Urban planning and management

Manville et al. (2014)

“…a city seeking to address public issues via ICTs-based solutions on the basis of a multi-stakeholder, municipally-based partnership”

ICTs-based solutions; Multi-stakeholder municipally-based partnership

BSI PAS 180:2014

“Smart Cities is a term denoting the effective integration of physical, digital and human systems in the built environment to deliver a sustainable, prosperous and inclusive future for its citizens”

Integration of physical, digital and human systems

Ratti and Claudel (2014)

“The idea of a senseable city is to engage in a real-time and ongoing loop of information, between the city and its citizens, towards enabling a more sustainable future”

Senseable city; Real-time information

ITU-T (2014)

“A smart sustainable city is an innovative city that uses Quality of life; Urban information and communication technologies (ICTs) and operation and services other means to improve quality of life, efficiency of urban operation and services, and competitiveness, while ensuring that it meets the needs of present and future generations with respect to economic, social and environmental aspects”

Sagl et al. (2015)

“Smart cities are complex and dynamic systems that include a wide range of physical and environmental features, as well as social and human-related components and that require smart technological infrastructures with advanced sensing capabilities. Thus, we can say that the major smart city domains are humans, environment and technology”

Technological infrastructures; Sensing capabilities

ISO IEC (2015)

“A ‘Smart City’ is one that […] dramatically increases the pace at which it improves its social economic and environmental (sustainability) outcomes, responding to challenges such as climate change, rapid population growth, and political and economic instability […] by fundamentally improving how it engages society, how it applies collaborative leadership methods, how it works across disciplines and city systems, and how it uses data information and modern technologies […] in order to provide better services and quality of life to those in and involved with the city (residents, businesses, visitors), now and for the foreseeable future, without unfair disadvantage of others or degradation of the natural environment” Source: http://www.iso.org/iso/smart_cities_report-jtc1.pdf

Economic and environmental sustainability; Collaborative leadership

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2.2 Smart Cities. Real-Time Dashboards for Integrated Management of Urban Services Several cities at the European scale have implemented initiatives of ICT application with different purposes and domains of interests (Dameri 2017; Angelidou et al. 2018). Despite the diversity of intents, they all converge in recognizing the key role of ICTs towards the achievement of the Smart City concept. In particular, one of the technological solutions most adopted by municipalities at the global scale is the Information Platform, which enables to deliver new digital services to citizens and to improve information management at the urban scale (Khatoun and Zeadally 2016; Silva et al. 2018). Through an Information Platform, municipalities can built a structured database—unique, dynamic and updated over time—able to collect, integrate and manage heterogeneous data and information related to different urban services and city domains (e.g. energy, mobility, waste, governance, health, etc.). For instance, the city of Amsterdam has promoted several initiatives (Table 2.2) related to, among others, environmental sustainability, urban mobility and public spaces. All the initiatives exploit the potential of ICTs through the implementation of an open dynamic database connected with sensor-based monitoring systems, allowing an efficient management of spaces, assets and resources by the municipality (Somayya and Ramaswamy 2016). In particular, the city of Amsterdam has developed the Amsterdam Smart City platform (ASC) that is the result of a cooperative venture between the local council, the economic board, citizens and a set of private companies (Putra and van der Knaap 2018). The ASC, with more than one hundred partners organizations, can be meant as a urban innovation eco-system that gathers innovative projects and activities addressing several societal challenges, as environmental sustainability and energy and mobility efficiency (Van Winden 2016; Putra and van der Knaap 2018). Moreover, Barcelona—winner of the European Capital of Innovation (iCapital) Award 2014—has implemented several smart projects across the whole city (Gascó-Hernandez 2018). These projects (Table 2.3) are gathered under the umbrella of the 22@ Barcelona region, sharing a single strategy. The 22@ Barcelona initiative aims to transform 200 hectares of industrial land into an innovative smart district and, along with the 22@ Urban Lab, it represents the main testing ground to trial new technologies applications (e.g. in the fields of Media, ICT, Energy and Design) and to create interesting collaborations and synergies among SMEs, public institutions and academia (Ardeleanu and Pav˘al 2016). The municipality provides shared workspaces to enable Living Labs and, therefore, to stimulate and accelerate industrial and social innovation also through the direct participation of citizens in the development and validation of innovations (Calzada 2018). In order to support the dialogue among citizens, city managers, businesses, research institutions and professionals, Barcelona implemented the Urban Platform which is able to gather data from the urban sensor network and from several smart applications with the aim of improving the management of urban services such as, for instance: waste management (sensors placed in solid waste containers in order to report loading data with the aim of adjusting schedules and routes); management of urban green areas (humidity sensors detect the humidity level and properly adjust the irrigation service in public parks and

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Table 2.2 Examples of smart initiatives and projects implemented in Amsterdam Amsterdam Examples of projects

Description

City-Zen

City-Zen project involves innovations in smart grid, district heating, and green building technology. The objective of the project is to save energy and assess systems and policies efficiency with a multi-energy territorial monitoring system. The project involves the connection of 10,000 residential buildings to a smart grid. In this way, residents gain a higher knowledge about their energy use and consumption useful to adjust, for instance, their behaviors or their energy supply contracts.

Vehicle2Grid

Vehicle2Grid is a smart city initiative developed to use electrical cars as back-up power during outages and to provide solutions for storage of renewable electricity. The project developed a solution for the storage of the surplus of energy generated from renewable sources (e.g. from sun and wind) during the whole day by using as storage (for later uses) the batteries of Electric Vehicles that act as an energy buffer. The stored energy is then released in the evening to meet peak demands, reducing the costs related to the management of peak loads.

Amsterdam Arena Innovation Centre (AAIC)

The Amsterdam Arena Innovation Center (AAIC) - located in the ArenA stadium - was established in 2015 as a Living Lab for Smart City innovation to support the development of smart applications for cities and citizens. According to this purpose, the ArenA was equipped with sensors that collect data from the stadium itself and from the surrounding neighborhood. These data are made available in order to be used for different projects at different scales. One of these project is the Mobility Portal, which through web and mobile interfaces supports users to identify different available transport options, providing also real-time information about waiting times and crowding levels.

References https://amsterdamsmartcity.com/ http://www.cityzen-smartcity.eu/home/demonstration-sites/amsterdam/ http://www.amsterdamvehicle2grid.nl/ http://amsterdaminnovationarena.com/

other green areas); energy management (urban metering devices detect the demand of gas, water or power supply); mobility management (street sensors can reveal the realtime condition and occupancy of parking spaces and loading areas); environmental monitoring and control (sensors to monitor air quality and noise pollution). Another example of a city that has implemented ICTs in order to optimize the services offered to citizens is London. The Greater London Authority (GLA) and, above all, the office of Mayor of London have been the coordinators of the Smart London programme. This programme involves the strategic development and the operative implementation of several smart initiative (Table 2.4) mainly concerning the sectors of health, transport, waste, energy and water, aimed at supporting the city of London in dealing with environmental challenges, finding innovative smart solutions to optimize resources and improve services (Willems et al. 2017). In particular, the GLA has invested its technological capital in the creation of the London Datastore (Barns 2018). This huge data archive is made available free of charge by the London municipality to citizens and anyone who intends to develop services

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Table 2.3 Examples of smart initiatives and projects implemented in Barcelona Barcelona Examples of projects Description SIIUR Project

The SIIUR (Integral Solution for Urban Infrastructures) project is a urban Living Lab developed with the collaboration of Barcelona City Council, the Catalan Institute for Energy (ICAEN) and the Catalonia Energy Efficiency Cluster (CEEC). The aim of the project is manifold: to reduce maintenance costs of street lighting; to better satisfy the needs of citizens; to improve energy efficiency; to reduce energy consumptions; and to reduce energy pollution. SIIUR project involved, among others, the deployment of street lamps with LED technology to reduce cost and pollution. Lamps are equipped with sensors that collect environmental data (e.g. temperature, humidity, noise and pollution) and detect presence of people/objects. These data are sent to the central monitoring and control centre to otimize services.

Districlima

Districlima is a project implemented in 2002 with the aim of creating a district heating and cooling network for use in heating, air conditioning and sanitary hot water. In 2005, after the awarding of a public tender, a second stage started along with the extension of the network to the 22@ Barcelona district. This urban network of centralized production and distribution of thermal energy, not only enables to achieve a greater energy efficiency system limiting the environmental impact, but also it offers to its users various benefits related to the economic expenditure, the continuity, security and quality of the supply. In particular, the main environmental advantages of Districlima compared with conventional systems, can be summarized as follows: (i) urban solid waste and other residual energy sources are used within a high performance energy equipment, allowing in this way the minimization of the fossil origin primary energy consumption; (ii) reduction of refrigerant losses into the atmosphere; (iii) reduction of greenhouse effect gas emissions; (iv) noise and vibration reduction in buildings connected to the system; (v) reduction of maintenance costs and savings in bills

References http://www.22barcelona.com/content/view/41/427/lang,en/ de Barcelona, A. (2013). Barcelona Smart City Tour. Barcelona: Ayuntament de Barcelona, Report http://www.c40.org/profiles/2014-barcelona https://www.construction21.org/france/data/exports/pdf/districlima-urban-network-of-heat-andcold-in-barcelona-and-sant-adria-de-besos.pdf

for citizens. According to an open-government logic, the London Datastore collects and store data about different city domains and aspects of interest, such as: public transport, traffic, economy, population, atmospheric emissions, taxes, neighborhood rental ratios (Barns 2018). Mobility is perhaps the main investigated field: plans for optimizing the existing public transport network, real-time parking occupancy monitoring, electric bike sharing systems are just some of the winning initiatives already in place.

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Table 2.4 Examples of smart initiatives and projects implemented in London London Examples of projects

Description

Transport and Mobility Management – Cycle Hire application informs users about where they can get or drop off a bike. The database of Cycle Hire is also connected to the TfL (Transport for London) website and to mobile applications, providing users with useful real -time information, journey planning and mapping, etc. – Countdown service by London Buses provides live bus arrival information via fixed and mobile web, via SMS and via roadside signs – Oyster system provides TfL with several information about customers’ travels (collected and used in accordance with data protection standards) useful, not only to have a feedback of the rail service performance, but also to improve the operational planning RE:FIT Programme

RE:FIT, jointly funded by the Greater London Authority (GLA) and the European Union European Regional Development Fund, has the aim of supporting organizations and public bodies to retrofit energy efficiency measures in buildings. In particular, the provided support tools are: (i) RE:FIT London Programme Delivery Unit, that is an expert team providing free end to end support needed to starting, running and implementing innovative projects; (ii) RE:FIT framework of energy service companies, which support organizations and public bodies with the procurement of retrofit services and works.

References https://www.london.gov.uk/what-we-do/environment/smart-london-and-innovation/smartlondon https://www.asmartercity.london/

Furthermore, also the city of Santander can be considered a pioneer case of Smart City, mainly thanks to the SmartSantander project which represents one of the first European initiative that involves a massive and widespread deployment of connected IoT sensors on the urban territory (Hamalainen and Tyrvainen 2016). In particular, the SmartSantander platform involves a wide set of applications addressing communities of users, industries, professionals, SMEs, research institutions, etc. that seek to use the platform and the whole experimental facility for deploying, testing, assessing and validate new ICT-based services and applications in various fields (Díaz-Díaz et al. 2017) (Table 2.5).

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Table 2.5 Examples of smart initiatives and projects implemented in Santander Santander Examples of projects

Description

Transport and Mobility Management

– More than 200 inductive sensors installed across the city to measure traffic density – Information panels have been installed at selected city points, providin real-time information about traffic and on-street praking lots availability. – Smart tags have been installed at bus stops in order to provide data about the municipal bus service lines, stops, waiting times and other information like eventual delays, etc. All these data are made available also on the SmartSantanderRA app.

Waste Management

Deployment of about 3000 devices able to collect in real-time information about: the state of waste disposal points (e.g. volumetric sensors on bins to detect their level of filling); the real-time GPS tracking and monitoring of the progress state of the waste collection works (also collecting information about vehicle routes, fuel consumption, etc.)

Water Management

About 1000 devices (e.g. .remote domestic meter-reading devices, network flow sensors, water pressure sensors, water level sensors, etc.) have been installed in the main sewers in order to provide information regarding the operating state of water supply and sewerage systems. The SmartWater app allows users to access real-time information about, for instance, their water consumptions, water quality (e.g. pH value), etc. The SmartWater app si also useful to for informing citizens about programmed water interruptions in supply, emergency interventions, etc.

References Gutiérrez Bayo, J. (2016). International Case Studies of Smart Cities: Santan-der, Spain. Inter-American Development Bank Sanchez, L., Muñoz, L., Galache, J. A., Sotres, P., Santana, J. R., Gutierrez, V., … & Pfisterer, D. (2014). SmartSantander: IoT experimentation over a smart city testbed. Computer Networks, 61, 217–238

2.3 Smart Buildings. Information Technology for Intelligent Building Management The several experimentations in the field of Smart Buildings at the European level carried out in recent years represent an evidence of the increasing interest by stakeholders in testing innovative solutions for enhancing the efficiency and sustainability of buildings, their systems and services (Jadhav 2016; Moseley 2017). In particular, currently the majority of experimentations focuses on: (i) innovative ICT-based solutions for an efficient building energy management (Hannan et al. 2018; Marinakis et al. 2018; Molina-Solana et al. 2017; Ock et al. 2016); and (ii) smart materials and technical elements for improving the performance of building technological solutions (Casini 2018; Madad et al. 2018; Parisi et al. 2018; Cuce and Riffat 2017; Al-Obaidi et al. 2017). Despite current experimentations are often limited to the vision of the building as an envelope (e.g. smart façades, hi-tech materials, etc.) (Iommi 2018; Juaristi et al. 2018; Gallo and Romano 2017; Kim and Kim 2017; Rezaei et al. 2017), there is a growing interest expressed by facility managers in projects concerning: (i) the connection of the building with its technical components and its occupants; and (ii) the exploitation of real-time data to improve the adaptive capacity of the building systems. In fact, these two topics animate the current international Smart Building debates, also given the related high expectations in terms of improvement of buildings knowledge and responsiveness. However, the experimentations in this regard require significant efforts for the definition and development of innovative models for systematically applying new technologies to traditional

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building management processes; models that go beyond mere data acquisition but are able to reconfigure current FM procedures and innovate the planning, programming and control of service activities (Talamo et al. 2016). The following examples of smart building best practices highlight possible scenarios of IoT application to improve the management and control of buildings. In particular, one of the most outstanding project at the European level is represented by The Edge, a 40,000 m2 office building (completed in 2015) located in the Zuidas business district in Amsterdam (Nambiar et al 2018; BREAM website). It was designed for the global financial firm and main tenant Deloitte with the aim of creating a Smart Building meant as a new working environment enabled by IoT technologies, supporting collaboration and promoting sustainability (Araszkiewicz 2017). This building exploits technologies to offer innovative advanced services (Table 2.6) to its occupants. Another virtuous example is represented by The Crystal (completed in 2012), a site of 18,000 m2 , owned and managed by Siemens (thecrysta l.org). In particular, The Crystal is part of the Green Enterprise District policy that is a regeneration project of the Mayor of London in East London that aims to create a lowcarbon economy district (Ryser 2014; thecrystal.org). The Crystal is an all-electric Table 2.6 Examples of The Edge smart solutions The Edge, Amsterdam Examples of solutions

Description

Façades and Roof

Photovoltaic panels are installed on the roof and across the south façade, allowing The Edge to produce more energy than it consumes. Moreover, rainwater is collected on the roof and used to flush toilets and irrigate green terraces and other garden areas. In addition, an underground aquifer thermal energy storage generates the energy required for heating and cooling, and a heath-pump is also applied to this storage system to allow a efficiency increase.

Smart Parking System

The smart parking solution involves the use of Near-Field Communications (NFC) technology and a smart phone application that displays free parkings. In particular, the parking system is integrated with the building access and users’ management system in order to help users to chose the best option to park their car.

Mobile App as Users’ Interface Thanks to The Edge app, employees have access to a set of innovative services, for instance: they can find free desks, they can report some intervention requests, they can modify the temperature and the lighting level of each space, etc.. The data generated by building digital systems and mobile apps, regarding different aspect of the building (from occupancy patterns to energy use and consumptions), have a key role in order to: enhance the knowledge of the workplace, analyze recurring trends and reveal correlations and consequently maximize the timely response of the building systems. References Araszkiewicz, K. (2017). Digital technologies in Facility Management—the state of practice and research challenges. Procedia Engineering, 196, 1034–1042. (www.breeam.com/offices/theedge-amsterdam)

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building equipped with: solar heating, automated building management systems, rainwater harvesting, and black water treatment. The Building Management System (BMS) connect over 2500 sensors and devices (on lighting terminals, windows, blinds and heating, etc.) (thecrystal.org) and it is able to remotely control building systems reaching the maximum comfort (e.g. heat, light, ventilation) while reducing energy consumptions (Table 2.7). Moreover, the WaterPark Place in Toronto (completed in 2014), conceived by EllisDon (general contractor) in collaboration with Cisco (IT provider) (CISCO 2015), exploits the network-connected technologies with the aim of delivering better experiences to users, providing a better work environment and reducing the energy consumption (Table 2.8). Also the MEDIA-TIC building in Barcelona represents a good example of technological and sustainable architecture. This building, inaugurated in 2010, was digitally designed and built using CAD-CAM processes (Velikov and Thün 2013). Designed by Cloud-9, the MEDIA-TIC building has the shape of a cube and is formed by iron beams covered in a translucent plastic coating of inflatable bubbles, which allows a glimpse of the fluorescent structure of the building (Monticelli 2015). This innovative covering, made by ETFE (Ethylene Tetrafluor Ethylene), is activated by means of luxometer sensors and it acts as an external covering and mobile sunscreen that helps light to penetrate, by automatically activating the chamber inflation/deflation devices according to the intensity of the solar energy (Monticelli 2015).

Table 2.7 Examples of The Crystal smart solutions The Crystal, London Examples of solutions

Description

Lighting System

According to the intensity of the natural daylight and the occupancy, the light brightness and color of every LED lamp is constantly monitored and automatically adjusted.

Ventilation and Heating System The building management system maximizes free cooling. The building is naturally ventilated using motorized opening vents in the facade and in the roof, avoiding in this way the use of air conditioning when and where possible. The air conditioning cooling is mainly taken from surroundings through a ground source heat pump. The system pumps the heat from the building to the ground on hot days of the cooling season and from the ground to the building on cold days of the heating season. Intelligent Energy Centre

A significant part of the electrical power produced in this all electric building is generated by photovoltaic roof panels. The building has an intelligent Energy Centre that is able to extensively monitoring the energy in The Crystal (along with the battery storage load and demand, etc.) in order to intelligently control the consumption and to ensure a continuous efficiency.

References https://www.thecrystal.org/about/architecture-and-technology/ https://www.e-architect.co.uk/london/siemens-crystal http://www.siemens.co.uk/en/news_press/index/news_archive/the-crystal-takes-shape.htm

2.4 EU’s FP7 and Horizon 2020 Projects …

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Table 2.8 Examples of the Waterpark Place smart solutions Waterpark Place, Toronto Examples of solutions

Description

Smart PoE Lighting System

Thanks to embedded sensors, each LED terminal can autonomously assess the levels of daylight coming through the windows and, according to the records, automatically reduce its power levels and brightness to properly complement the natural lighting. Employees can also use their smart phones to turn lights on/off or to dim them.

Smart HVAC System

Waterpark Place has a smart fresh-air ventilation system that is part of the smart and connected HVAC system and it is able to provide fresh air on demand. Carbon dioxide sensors placed in return air ducts can adjust the system parameters according to the detection of the occupancy (based on data from the sensors on lights and from smartphones GPS triangulation).

Self-service Parking

The building has a smart self-service parking system with ticket dispensers, automated gates and payment kiosks, reducing operational expenses and enhancing user experience. Moreover, all these systems are all connected to the Cisco IP network of the building to allow a centralized monitoring of the parking use profile.

References http://www.ellisdon.com/wp-content/uploads/2016/03/ellisdon_voc-case-study.pdf

2.4 EU’s FP7 and Horizon 2020 Projects. IoT Application for Advanced Service Management In order to reach a more comprehensive understanding, together with the literature review and the survey of smart cities and buildings case studies (reported in the previous paragraphs), this paragraph introduces an analysis of projects funded in recent years by the European Commission concerning the topic of IoT adoption for the optimization of service management. In particular, by analyzing the projects funded under the “7th Framework Programme for research and technological development (FP7)1 ” and “Horizon 20202 ”, through the dedicated tool of CORDIS website (cordis.europa.eu), it was 1 The

7th Framework Programme for Research and Technological Development (FP7) lasted from 2007 to 2013, and provided EU Research Funding for a total budget of over e50 billion. The Framework Programme for Research has two main strategic objectives, first, it aims to strengthen the scientific and technological base of European industry and, second, it aims to support and improve its international competitiveness. Moreover, FP7 is a key tool to meet the Europe’s employment needs and quality of life (europa.eu). In particular, among the key thematic areas of the FP7 Cooperation, there is the FP7—ICT. According to CORDIS website, ICTs have a significant impact on three key fields: (i) productivity and innovation, by facilitating creativity and management; (ii) modernization of public services as health, education and transport; and (iii) advances in science and technology, by supporting cooperation and access to information (cordis.europa.eu). 2 The European Union in January 2014 launched the 7-year (2014–2020) re-search program Horizon 2020. Its key goal is to strengthen innovation mainly by supporting collaboration between the public and private sectors. Among the main objectives of Horizon 2020, it is possible to mention: (i) boosting the industrial leadership and competitiveness of Europe by stimulating leadership in enabling technologies, improving the access to risk finance, and stimulating innovation in SMEs; (iii) increasing the contribution of research to the resolution of key societal challenges. To reach this goal, the technological component has a key role, indeed—as stated by the European Commission— ICTs underpin innovation and competitiveness across private and public sectors and enable scientific progress in all disciplines (ec.europa.eu).

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possible to select some projects paradigmatic and representative of current ICT and IoT applications for an advanced and integrated management of physical and digital assets both at the building and urban scale. The analysis of the projects from CORDIS website has highlighted six reading keys in which it is possible to articulate the projects. These reading keys are representative of current trends and current scenarios of change of FM services enabled by ICTs. In particular, they are (Table 2.9): a. Real-time data for cities security & safety and prompt response to emergencies (Table 2.10); b. Real-time data and intelligent networks for resource efficiency and advanced energy management (Table 2.11); c. Real-time data and intelligent networks for smart urban mobility (Table 2.12); d. Network approach and information platforms for inclusive and participa-tive processes (Table 2.13); e. Network approach and information platforms for circular economy (Table 2.14); f. Data analytics for data-driven decision making (Table 2.15). In particular, Table 2.9 shows examples of EU-funded projects for each identified reading key. Tables 2.10, 2.11, 2.12, 2.13, 2.14 and 2.15 introduce an overview of the analyzed EU-funded projects, highlighting information about coordinating country, EU programme and topic, total cost, EU contribution and funding scheme, as well as a brief description of the aims, objectives and added value of each project.

Table 2.9 Reading keys and EU-funded projects Reading key

Projects

1. Real-Time Data for cities security & safety and prompt response to emergencies

E-SPONDER. A holistic Urban Safety; Minimizing approach towards the development Uncertainty; Real-Time of the first responder of the future Resources Synchronization

http://cordis.europa. eu/project/rcn/ 94833_en.html http://www.e-spo nder.eu/

VITRUV. Vulnerability Identification Tools for Resilience Enhancements of Urban Environments

Urban Planning; Vulnerability; Resilience; Security and Safety

http://cordis.europa. eu/project/rcn/ 98970_en.html

L4S. Learning 4 Security

Transportation Sector; Crisis Management; Collaborative Decision-making

http://cordis.europa. eu/project/rcn/ 92869_en.html http://cordis.europa. eu/result/rcn/196 146_en.html

ETA4B. Energy Trusted Advisor for Buildings

Energy Efficiency; Customized Energy Performance; Big Data Analysis

http://cordis.europa. eu/project/rcn/196 416_en.html

EDI-NET. The Energy Data Innovation Network; using smart meter data, campaigns and networking to increase the capacity of public authorities to implement sustainable energy policy

Building Energy http://cordis.europa. Management Systems eu/project/rcn/200 (BEMS); Smart Energy and 163_en.html Water Meter; Big Data Analytic Technologies

2. Real-time Data and intelligent networks for resource efficiency and advanced energy management

Keywords

References

(continued)

2.4 EU’s FP7 and Horizon 2020 Projects …

23

Table 2.9 (continued) Reading key

3. Real-time Data and intelligent networks for smart urban mobility

4. Network approach and information platforms for inclusive and participative processes

Projects

Keywords

References

E2SG. Energy To Smart Grid

Smart Grids; Electrical Energy Management; Environmental Awareness; Distributed Data Exploitation

http://cordis.europa. eu/project/rcn/201 959_en.html

AMBASSADOR. Autonomous Management System Developed for Building and District Levels

Smart Districts; Energy Optimization; Flexibility of Systems; Real-Time Adaptive and Predictive Behavioral Models

http://cordis.europa. eu/project/rcn/105 668_en.html http://cordis.europa. eu/result/rcn/197 097_en.html

OPTICITIES. Optimize Citizen Urban Mobility; Mobility Mobility and Freight Management Public Policies; in Urban Environments Multimodal Urban Dataset

http://cordis.europa. eu/project/rcn/111 158_en.html http://cordis.europa. eu/result/rcn/176 015_en.html

MoTiV. Mobility and Time Value

Transportation infrastructure; transport Management; Value of Travel Time (VTT); Multi-modal Journey Planner

http://cordis.europa. eu/project/rcn/211 697_en.html

SMApp. Smart Mobility Application To Improve Traffic Management And Planning

Traffic Management; Mobility and Infrastructure Planning; Prediction of Road Infrastructure Needs; Traffic Flows Simulations

http://cordis.europa. eu/project/rcn/210 390_en.html http://iceacsa.com/ web2017/gestionde-infraestructuras/

OPEN4CITIZENS. Empowering Open Data; Data citizens to make meaningful use of accessibility; Stakeholders open data Engagement; Participation and Inclusion; Co-creation and Co-design

http://cordis.europa. eu/project/rcn/200 250_en.html

CITI-SENSE. Development of sensor-based Citizens’ Observatory Community for improving quality of life in cities

Citizenship Co-participation; Community-Based Environmental Monitoring and Information System; Societal Involvement; Data Repositories; Sensing Technologies; Information and Communication Technologies

http://cordis.europa. eu/project/rcn/106 482_en.html http://cordis.europa. eu/result/rcn/175 088_en.html http://www.citi-sen se.eu/

SMARTER TOGETHER. Smart and Inclusive Solutions for a Better Life in Urban Districts

Living labs; Inclusive Solutions; Smart Data management platform

http://cordis.europa. eu/project/rcn/199 963_en.html (continued)

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Table 2.9 (continued) Reading key

Projects

Keywords

References

5. Network approach and information platforms for circular economy

BAMB. Buildings as Material Banks: Integrating Materials Passports with Reversible Building Design to Optimize Circular Industrial Value Chains

Construction and Demolition Waste; Resource Efficiency; Materials Passports; Reversible Building Design

https://cordis.eur opa.eu/project/rcn/ 196829_en.html https://cordis.eur opa.eu/result/rcn/ 202000_en.html

SCOT. Smart CO2 Transformation

CO2 -as-a-resource; Environmental Performance; Renewable Energies; Joint Action Plan

https://cordis.eur opa.eu/project/rcn/ 111299_en.html https://cordis.eur opa.eu/result/rcn/ 197021_en.html

BIOSKOH. BIOSKOH’s Innovation Stepping Stones for a novel European Second Generation BioEconomy

Circular Bio-Economy; Second Generation (2G) Bio-Refinery; LCA

https://cordis.eur opa.eu/project/rcn/ 204326_en.html

ExtraLytics. Big Data Analytics for Real Estate

Real Estate; Big Data Analytics; Prediction Models; Data-Driven Analytics; Efficient Decision Making

https://cordis.eur opa.eu/project/rcn/ 193793_en.html

CityPulse. Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications

ICT-enabled Services; Smart City Applications; Internet of Things (IoT); Large-Scale Data Analytics

https://cordis.eur opa.eu/project/rcn/ 109806_en.html https://cordis.eur opa.eu/docs/pro jects/cnect/5/609 035/080/deliverab les/001-609035CIT YPULSED22rendit ionDownload.pdf https://cordis.eur opa.eu/docs/pro jects/cnect/5/609 035/080/deliverab les/001-D51v18Are s20144032292.pdf

GDC. A Genetic Data CUBE—An innovative business model applied to predictive and prescriptive analytics, exploring Big Data and empowering cloud-services and urgent computation

Business Intelligence; Predictive Analysis; prescriptive analysis; Big Data Analytics; Decision-making Optimization

https://cordis.eur opa.eu/project/rcn/ 198412_en.html https://cordis.eur opa.eu/result/rcn/ 186166_en.html

URBAN SENSING. Urban Sensing through User Generated Contents

City Planning; Urban Management; User Generated Content (UGC); Visualization Tools; Human-Centered Approach

https://cordis.eur opa.eu/project/rcn/ 104744_it.html https://cordis.eur opa.eu/result/rcn/ 151562_en.html https://cordis.eur opa.eu/result/rcn/ 176075_en.html http://urban-sensin g.eu/

6. Data Analytics for data-driven decision making

2.4 EU’s FP7 and Horizon 2020 Projects …

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Table 2.10 Reading key 1—analyzed EU-funded projects Reading Key 1_ Real-Time Data for cities security & safety and prompt response to emergencies General information

Brief description

E-SPONDER Coordinated in Greece From 2010-07-01 to 2014-12-31 EU Programme FP7-SECURITY EU Topic SEC-2009-4.2-01—First responder of the future Total Cost EUR 12 542 904,30 EU Contribution EUR 8 790 044 Funding Scheme CP-Collaborative project (generic)

“The E-SPONDER platform is a suite of real-time data-centric technologies able to provide actionable information and communication support to first responders that operate on critical infrastructures during abnormal events. Main project objectives: (i) improvement of data collection technologies installed both on portable and fixed platforms; (ii) implementation of Data Analytics tools of real-time data processing in order to provide real-time decision support information; (iii) making these resources readily available to commanders through the use of easily accessible web-portals; (iv) providing significant information and decision support, based on ICT, to the first responders” (cordis.europa.eu) References http://cordis.europa.eu/project/rcn/94833_en.html http://www.e-sponder.eu/

VITRUV

Coordinated in Germany From 2011-05-01 to 2014-04-30 EU Programme FP7-SECURITY EU Topic Planning, (re)design, and (re)engineering of urban areas to make them less vulnerable and more resilient to security threats Total Cost EUR 4 520 921,80 EU Contribution EUR 3 339 898 Funding Scheme CP-Collaborative project (generic)

“The objective of VITRUV is to develop software tools which support urban planners to consistently integrate security issues into the considerations made in the long and complex urban planning process. The tools will enable planners to: (i) make well-considered systematic qualitative decisions (concept level); (ii) analyze the susceptibility of urban spaces (e.g. building types, squares, public transport, and their functionalities) with respect to new threats, and (iii) perform vulnerability analyses of urban spaces by computing the likely damage on individuals, buildings, traffic infrastructure ” (cordis.europa.eu) Reference http://cordis.europa.eu/project/rcn/98970_en.html

L4S

Coordinated in Greece From 2009-07-01 to 2011-07-31 EU Programme FP7-SECURITY EU Topic Security systems integration, interconnectivity and interoperability: Modelling and simulation for training Total Cost EUR 3 471 413,41 EU Contribution EUR 2 415 768,13 Funding Scheme CP-FP—Small or medium-scale focused research project

“The project aims to identify the factors that inhibit the effective collaboration in crisis situations within transport sector to outline strategies to reduce these risks. The aim is to develop an Advanced Collaboration in Crisis Management—ACCM Framework. The latter supports collaborative decision making processes and collaboration of competencies for the specific challenges of crisis management in the transportation sector, overcoming information asymmetries. ” (cordis.europa.eu) References http://cordis.europa.eu/project/rcn/92869_en.html http://cordis.europa.eu/result/rcn/196146_en.html

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Table 2.11 Reading key 2—analyzed EU-funded projects Reading Key 2_ Real-time Data and intelligent networks for resource efficiency and advanced energy management General information

Brief description

ETA4B

Coordinated in Italy From 2015-05-01 to 2015-10-31 EU Programme H2020-EU.2.3.1—Mainstreaming SME support, especially through a dedicated instrument; H2020-EU.3.3-SOCIETAL CHALLENGES—Secure, clean and efficient energy EU Topic Stimulating the innovation potential of SMEs for a low carbon energy system Total Cost EUR 71 429 EU Contribution EUR 50 000 Funding Scheme SME-1—SME instrument phase 1

“ETA4B (Energy Trusted Advisor for Buildings) project promotes the development of an embedded energy optimization system collaborating with existing devices, providing citizens with innovative applications and services in the field of energy efficiency of buildings. The solution includes a multi-protocol electronic unit to be installed in buildings, providing an open-framework web-based platform with energy-related data flow for supporting the decision making process for end users. ” (cordis.europa.eu) Reference http://cordis.europa.eu/project/rcn/196416_en.html

EDI-NET

Coordinated in United Kingdom From 2016-03-01 to 2019-02-28 EU Programme H2020-EU.3.3.7—Market uptake of energy innovation EU Topic Enhancing the capacity of public authorities to plan and implement sustainable energy policies and measures Total Cost EUR 1 558 800 EU Contribution EUR 1 558 800 Funding Scheme CSA—Coordination and support action

“The Energy Data Innovation Network (EDI-Net) will use smart energy and water meter data to accelerate the implementation of sustainable energy policy. The core of EDI-NET is the analysis of smart meter data from buildings, from renewable energy systems and from Building Energy Management Systems (BEMS) using Big Data analytics technologies. ” (cordis.europa.eu) References http://cordis.europa.eu/project/rcn/200163_en.html

E2SG

Coordinated in Italy From 2012-04-01 to 2015-03-01 EU Programme FP7-JTI EU Topic Energy Efficiency Total Cost EUR 34 032 714 EU Contribution EUR 5 683 465 Funding Scheme JTI-CP-ENIAC—Joint Technology Initiatives-Collaborative Project (ENIAC)

“The target of E2SG project is to devise and design mechanisms and policies to assemble, monitor and control smart grids. E2SG aims at addressing most of the challenges entailed in evolving the concept of smart-grid to the level needed by both the industrial players in the society of the next decades, and the uprising environmental awareness which will lead to the increasing exploitation of removable energy sources” (cordis.europa.eu) Reference http://cordis.europa.eu/project/rcn/201959_en.html

AMBASSADOR Coordinated in France From 2012-11-01 to 2016-10-31 EU Programme FP7-JTI EU Topic Interaction and integration between buildings, grids, heating and cooling networks, and energy storage and energy generation systems Total Cost EUR 9 619 373,97 EU Contribution EUR 6 499 513 Funding Scheme CP-IP—Large-scale integrating project

“The major objective of the AMBASSADOR project is to develop energy solutions for Smart Districts, both for electric and heating district networks. Descriptive and predictive behavioral models will allow to find optimal supply/demand balancing. Accordignly, Building Energy Management Systems (BEMS) will establish proper energy schemes in real-time” (cor dis.europa.eu) References http://cordis.europa.eu/project/rcn/105668_en.html http://cordis.europa.eu/result/rcn/197097_en.html

2.4 EU’s FP7 and Horizon 2020 Projects …

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Table 2.12 Reading key 3—analyzed EU-funded projects Reading Key 3_ Real-time Data and intelligent networks for smart urban mobility General information

Brief description

OPTICITIES

Coordinated in France From 2013-11-01 to 2016-10-31 EU Programme FP7-TRANSPORT EU Topic Managing integrated multimodal urban transport network Total Cost EUR 12 862 075,16 EU Contribution EUR 8 997 972 Funding Scheme CP—Collaborative project (generic)

“The aim of OPTICITIES is to optimize urban mobility from a user perspective. In particular, a Multimodal Urban Dataset is developed starting from all transport mode data (historical, real time, predictive). European cities provide the Multimodal Urban Dataset content via a standardized interface: the Urban Mobility Portal. Hence, service providers can plug in and deliver services exploiting these available data” (cordis.europa.eu) References http://cordis.europa.eu/project/rcn/111158_ en.html http://cordis.europa.eu/result/rcn/176015_en. html

MoTiV

Coordinated in Slovakia From 2017-11-01 to 2020-04-30 EU Programme H2020-EU.3.4.—SOCIETAL CHALLENGES—Smart, Green And Integrated Transport EU Topic Shifting paradigms: Exploring the dynamics of individual preferences, behaviours and lifestyles influencing travel and mobility choices Total Cost EUR 1 930 838,75 EU Contribution EUR 1 930 835,50 Funding Scheme RIA—Research and Innovation action

“MoTiV main goal is to contribute to advance research on Value of Travel Time (VTT) by introducing and validating a conceptual framework for the estimation of VTT through a European-wide data collection. Data are gathered through the MoTiV smart phone app. The project explores emerging views of VTT that consider not only its economic dimension, but also motivations, preferences and behaviors linked to the broader concept of individual well-being” (cordis.europa.eu) Reference http://cordis.europa.eu/project/rcn/211697_ en.html

SMApp

Coordinated in Spain From 2017-05-01 to 2017-08-31 EU Programme H2020-EU.2.1.1.—INDUSTRIAL LEADERSHIP; H2020-EU.2.3.1.—Mainstreaming SME support; H2020-EU.3.4.—SOCIETAL CHALLENGES—Smart, Green And Integrated Transport EU Topic Small business innovation research for Transport and Smart Cities Mobility Total Cost EUR 71 429 EU Contribution EUR 50 000 Funding Scheme SME-1—SME instrument phase 1

“In order to analyze and predict road infrastructure needs for both public and private players, it is essential to extract large amounts of accurate data about volume, direction, intensity and distribution of road traffic. SMApp will integrate data from ALPR cameras to automatically reconstruct traffic flows between origin and destination nodes of any given road network. Infrastructure planning simulations based on accurate traffic flow distribution can support the optimization of road infrastructure planning” (cordis.europa.eu) References http://cordis.europa.eu/project/rcn/210390_ en.html http://iceacsa.com/web2017/gestion-de-inf raestructuras/

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Table 2.13 Reading key 4—analyzed EU-funded projects Reading Key 4_ Network approach and information platforms for inclusive and participative processes General information

Brief description

OPEN4CITIZENS Coordinated in Denmark From 2016-01-01 to 2018-06-30 EU Programme H2020-EU.2.1.1.—INDUSTRIAL LEADERSHIP—Leadership in enabling and industrial technologies-ICTs EU Topic Collective Awareness Platforms for Sustainability and Social Innovation Total Cost EUR 1 999 411,25 EU Contribution EUR 1 999 411,25 Funding Scheme RIA—Research and Innovation action

“The project aims to show the potential of open data to citizens, by creating open playgrounds where citizens, students, experts, start-up companies, academia and public institutions can collaborate to generate meaningful applications. The O4C lab represent the playground to co-create new solutions for existing or future services based on the use of data. These include data coming from the existing networks of devices, sensors and microcomputers part of the city infrastructures, and user-generated data (deriving from citizens’ voluntary publication of information).The O4C lab can be meant as an integrated system of engagement, participation and co-design tools” (cordis.europa.eu) Reference http://cordis.europa.eu/project/rcn/200250_en. html

CITI-SENSE.

Coordinated in Norway From 2012-10-01 to 2016-09-30 EU Programme FP7-ENVIRONMENT EU Topic Developing community-based environmental monitoring and information systems using innovative and novel earth observation applications Total Cost EUR 12 341 563,29 EU Contribution EUR 8 968 282 Funding Scheme CP—Collaborative project (generic)

“CITI-SENSE develops a community-based environmental monitoring and information system using innovative and novel Earth Observation applications. In particular, CITI-SENSE rests on three pillars: technological platforms for distributed monitoring; information and communication technologies; and societal involvement. Among the project outcomes: participatory methods, data management strategies, and applications to facilitate exploitation of the data and information for policy” (cordis.europa.eu) References http://cordis.europa.eu/project/rcn/106482_en. html http://cordis.europa.eu/result/rcn/175088_en. html http://www.citi-sense.eu/

SMARTER TOGETHER

Coordinated in France From 2016-02-01 to 2021-01-31 EU Programme H2020-EU.3.3.1.3.—Foster European Smart cities and Communities EU Topic Smart Cities and Communities solutions integrating energy, transport, ICT sectors through lighthouse (large scale demonstration—first of the kind) projects Total Cost EUR 29 119 448,36 EU Contribution EUR 24 742 978,64 Funding Scheme IA—Innovation action

“SMARTER TOGETHER project involves Lyon, Munich, Vienna, Santiago de Compostela, Sofia, Venice, Kyiv and Yokohama. These cities are complemented by business partners from energy, mobility and ICT sectors, and leading European research and academia organizations. SMARTER TOGETHER delivers five clusters of co-created, smart and integrated solutions: (i) Living labs for citizen engagement; (ii) District heating for low energy districts; (iii) Holistic refurbishment for low energy districts addressing public and private housing; (iv) Smart Data management platform and smart services; (v) E-mobility solutions for sustainable mobility” (cordis.europa.eu) Reference http://cordis.europa.eu/project/rcn/199963_en. html

2.4 EU’s FP7 and Horizon 2020 Projects …

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Table 2.14 Reading key 5—analyzed EU-funded projects Reading Key 5_ Network approach and information platforms for circular economy General information

Brief description

BAMB

Coordinated in Belgium From 2015-09-01 to 2019-02-28 EU Programme H2020-EU.3.5.4.—Enabling the transition towards a green economy and society through eco-innovation EU Topic Moving towards a circular economy through industrial symbiosis Total Cost EUR 9 933 112,13 EU Contribution EUR 8 858 763,02 Funding Scheme IA—Innovation action

“The BAMB (Buildings as Material Banks) project implements the principles of the waste hierarchy in order to improve the value of materials used in buildings for recovery. This is achieved by exploiting ICTs that enable to develop and integrate two complementary value adding frameworks: (i) materials passports and (ii) reversible building design. These frameworks will be able to change conventional (cradle-to-grave) building design, so that buildings can be transformed to new functions (extending their life span) or disassembled to building components or material feedstock that can be up-cycled in new constructions (using materials passports). In this way, continuous loops of materials are created while large amounts of waste will be prevented” (cordis.europa.eu) Reference https://cordis.europa.eu/project/rcn/196829_en.html https://cordis.europa.eu/result/rcn/202000_en.html

SCOT

Coordinated in Belgium From 2013-10-01 to 2016-09-30 EU Programme FP7-REGIONS EU Topic REGIONS-2012-2013-1—Transnational cooperation between regional research-driven clusters Total Cost EUR 2 373 853,53 EU Contribution EUR 2 140 400 Funding Scheme CSA-CA—Coordination (or networking) actions

“The SCOT project focuses on recycling and utilization of CO2 through its transformation into valuable products via chemical or biological technologies. CO2 is no longer considered as a waste but as an efficient resource enabling industries to: (i) reduce dependency on fossil fuels and primary raw materials for the production of industrial and transportation fuels, basic chemicals, and building materials; (ii) increase the use of renewable energies from intermittent sources (e.g. solar, photovoltaic, or wind) by providing a solution for electricity storage, via the conversion of CO2 into gaseous or liquid fuels in periods where potential production exceeds demand on the grid and would otherwise be wasted” (cordis.europa.eu) References https://cordis.europa.eu/project/rcn/111299_en.html https://cordis.europa.eu/result/rcn/197021_en.html

BIOSKOH

Coordinated in Italy From 2016-06-01 to 2021-05-31 EU Programme H2020-EU.3.2.6.1-Sustainable and competitive bio-based industries and supporting the development of a European bio-economy; H2020-EU.3.2.6.3-Sustainable bio-refineries EU Topic BBI.VC1.F1—From lignocellulosic feedstock to advanced bio-based chemicals, materials or ethanol Total Cost EUR 30 122 313,75 EU Contribution EUR 21 568 194,13 Funding Scheme BBI-IA-FLAG—Bio-based Industries Innovation action-Flagship

“BIOSKOH project involves a two stage investment process and development path to realize the largest (110 kton) second generation (2G) bio-refinery in Europe. It starts from a brown-field industrial site in the eastern part of the Slovak Republic to realize the 1st stage Flagship plant to produce 55 kton of cellulosic ethanol per year for EU bio-fuel mandates. Partners include the full value chain starting from land owners and feedstock producers, supply chain experts and an agronomical research partner to set-up a new biomass value chain exploiting large amounts of currently unused crop residues (kton/year), and developing newly grown dedicated crops on marginal land (total circa 320 kton/year), thus revitalizing the regional economy” (cordis.europa.eu) Reference https://cordis.europa.eu/project/rcn/204326_en.html

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Table 2.15 Reading key 6—analyzed EU-funded projects Reading Key 6_ Data Analytics for data-driven decision making General information

Brief description

ExtraLytics Coordinated in United Kingdom From 2014-11-01 to 2016-04-30 EU Programme H2020-EU.1.1-EXCELLENT SCIENCE—European Research Council (ERC) EU Topic ERC Proof of Concept-2014 Total Cost EUR 148 377 EU Contribution EUR 148 377 Funding Scheme ERC-POC—Proof of Concept Grant

“ ExtraLytics addresses the lack of data-driven analytics by giving consumers and investors a tool for better understanding properties, their location, their neighborhood, and their investment potential compared to other properties. ExtraLytics introduces analytics and prediction models into DIADEM’s platform for accurate big data extraction from the web” (cordis.europa.eu) Reference https://cordis.europa.eu/project/rcn/193793_en.html

CityPulse

Coordinated in United Kingdom From 2013-09-01 to 2016-09-30 EU Programme FP7-ICT EU Topic ICT-2013.1.4— reliable, smart and secure Internet of Things for Smart Cities Total Cost EUR 3 695 059 EU Contribution EUR 2 522 475 Funding Scheme CP—Collaborative project (generic)

“CityPulse aims to develop a framework for semantic discovery and processing of large-scale real-time IoT and relevant social data streams for reliable knowledge extraction in a real city environment. The scalable, adaptive and robust framework provides: large-scale data analytics for resource efficient event detection in multiple data streams; semantic description frameworks and analytics tools to provide machine-interpretable descriptions of information; creation of real-time smart city applications ” (cordis. europa.eu) References https://cordis.europa.eu/project/rcn/109806_en.html https://cordis.europa.eu/docs/projects/cnect/5/609 035/080/deliverables/001-609035CITYPULSED22 renditionDownload.pdf https://cordis.europa.eu/docs/projects/cnect/5/609 035/080/deliverables/001-D51v18Ares20144032292. pdf

GDC

Coordinated in Italy From 2015-09-01 to 2016-02-29 EU Programme H2020-EU.2.3.1—Mainstreaming SME support, especially through a dedicated instrument H2020-EU.3.6-SOCIETAL CHALLENGES—Europe In A Changing World—Inclusive, Innovative And Reflective Societies EU Topic INSO-10-2015-1—SME business model innovation Total Cost EUR 71 429 EU Contribution EUR 50 000 Funding Scheme SME-1

“GDC (Genetic Data Cube) is a data driven innovative business model powered by real-time preference/usage/feedback data coming from end-users. The project aims to develop world-class data systems to empower multiple services and facilitate SMEs as well large enterprises in diagnosing and solving relevant business challenges. Big Data Analytics stand at the forefront to data management for economic and financial forecasting in order to have a picture of the business opportunity.” (cordis.europa.eu) References https://cordis.europa.eu/project/rcn/198412_en.html https://cordis.europa.eu/result/rcn/186166_en.html (continued)

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Table 2.15 (continued) Reading Key 6_ Data Analytics for data-driven decision making General information

Brief description

URBAN SENSING

“The project aims to develop a platform extracting patterns of use and citizens’ perceptions concerning city spaces, through analysis of User Generated Content (UGC) shared by the inhabitants over social networks and digital media. Urban Sensing provides indexes and dynamic maps depicting citizens’ shared perceptions and opinions regarding public services, urban spaces, time-based events, etc. A wide range of indicators is defined in order to provide an insight into understanding how public policies, spatial interventions, events and transformations are perceived within a city, and at the same time it will give hints to designers, developers and entrepreneurs adopting a more human-centered approach toward the cities’ evolution” (cordis.europa.eu) References https://cordis.europa.eu/project/rcn/104744_it.html https://cordis.europa.eu/result/rcn/151562_en.html https://cordis.europa.eu/result/rcn/176075_en.html http://urban-sensing.eu/

Coordinated in Italy From 2012-10-01 to 2015-02-28 EU Programme FP7-SME EU Topic SME-2012-1—Research for SMEs Total Cost EUR 1 442 895,81 EU Contribution EUR 1 140 734,80 Funding Scheme BSG-SME—Research for SMEs

2.5 Smart Initiatives for Service Innovation: Approaches and Considerations From the analysis of virtuous case studies of Smart Cities, Smart Buildings and EU-funded projects, it is possible to outline some recurring approaches to the implementation of initiatives and projects: – holistic approach. In order to implement projects and derive significant results it is necessary to have a holistic approach characterized by the integration of expertise and the sharing of resources and means; – inclusive approach. In order to successfully implement projects it is necessary to involve from the early design stage all the concerned stakeholders including endusers, whose feedbacks are essential especially to assess the project applicability; – integrated approach. In order to manage in an integrated way the set of FM services and perform a centralized data management it is fundamental to establish an efficient information platform, interoperable with other systems, allowing stakeholders communication and collaboration. Although it is possible to identify recurring approaches in ICT implementation, the two scales of application (city scale and the building scale) have different levels of maturity of ICT applications to improve Facility Management (FM) services. For what concerns the city scale, the management of urban services has already been positively affected by the adoption of ICTs and it is possible to observe recurring innovative tools (e.g. information platforms) and semi-consolidated models for managing information with the aim to improve the management of urban FM services

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(e.g. energy management; mobility management, traffic control, etc.) and to enhance the real-time responsiveness and adaptability of urban systems. With respect to the building scale, today there is a strong interest in applying IoT technologies and Big Data management to the building scale but there is not a fully experimented and proven ground yet. ICT applications to the building scale often involve only the envelope of the building (e.g. smart faÇades) without deepening the possibilities offered by IoT in terms, for instance, of interaction between the building and its occupants to identify use patterns, users’ preferences, etc. Nevertheless, it is possible to observe how the advanced way of collecting and managing real-time data enabled by IoT is defining some paradigms shifts that can mark the transition from a work-intensive to an information-intensive FM scenario, outlined and deepened in the next chapter.

References Al-Obaidi KM, Ismail MA, Hussein H, Rahman AMA (2017) Biomimetic building skins: an adaptive approach. Renew Sustain Energy Rev 79:1472–1491 Angelidou M, Psaltoglou A, Komninos N, Kakderi C, Tsarchopoulos P, Panori A (2018) Enhancing sustainable urban development through smart city applications. J Sci Technol Policy Manag Araszkiewicz K (2017) Digital technologies in facility management—the state of practice and research challenges. Procedia Eng 196:1034–1042 Ardeleanu A, Pav˘al O (2016) Industrial heritage in the smart city context. Sci Bull “Petru Maior” Univ Tîrgu Mure¸s 13(2):43–46 Barns S (2018) Smart cities and urban data platforms: designing interfaces for smart governance. City Cult Soc 12:5–12 Batty M, Hudson-Smith A, Milton R, Dearden J (2007) Virtual cities: digital mirrors into a recursive world. UCL Working Papers Series, paper 125, Dec 07 Bollier D (1998) How smart growth can stop sprawl. Essential Books, Washington, DC Calzada I (2018) (Smart) Citizens from data providers to decision-makers? The case study of Barcelona. Sustainability 10(9):3252 Caragliu A, Del Bo C, Nijkamp P (2011) Smart cities in Europe. J Urban Technol 18(2):65–82 Casini M (2018) Active dynamic windows for buildings: a review. Renew Energy 119:923–934 CISCO (2015) IoE benefits transform the commercial high rise. https://www.ellisdon.com/wp-con tent/uploads/2016/03/ellisdon_voc-case-study.pdf. Accessed 2019 Cocchia A (2014) Smart and digital city: a systematic literature review. In: Dameri RP, RosenthalSabroux C (eds) Smart city. How to create public and economic value with high technology in urban space. Springer Cuce E, Riffat SB (2017) A smart building material for low/zero carbon applications: heat insulation solar glass—characteristic results from laboratory and in situ tests. Int J Low-Carbon Technol 12(2):126–135 Dameri RP (2013) Searching for smart city definition: a comprehensive proposal. Int J Comput Technol 11(5):2544–2551 Dameri RP (2017) Smart city implementation. Progress in IS. Springer, Genoa, Italy Díaz-Díaz R, Muñoz L, Pérez-González D (2017) Business model analysis of public services operating in the smart city ecosystem: the case of SmartSantander. Future Gener Comput Syst 76:198–214

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Nambiar R, Shroff R, Handy S (2018) Smart cities: challenges and opportunities. In: 2018 10th international conference on communication systems & networks (COMSNETS). IEEE, pp 243– 250 Neirotti P, De Marco A, Cagliano AC, Mangano G, Scorrano F (2014) Current trends in smart city initiatives: some stylised facts. Cities 38:25–36 Ock J, Issa RR, Flood I (2016) Smart building energy management systems (BEMS) simulation conceptual framework. In: 2016 Winter simulation conference (WSC). IEEE, pp 3237–3245 Parisi S, Spallazzo D, Ferraro V, Ferrara M, Ceconello MA, Garcia CA, Rognoli V (2018) Mapping ICS materials: interactive, connected, and smart materials. In: International conference on intelligent human systems integration. Springer, Cham, pp 739–744 Putra ZDW, van der Knaap WG (2018) Urban innovation system and the role of an open web-based platform: the case of Amsterdam Smart City. J Reg City Plan 29(3):234–249 Ratti C, Claudel M (2014) Architettura open source. Verso una progettazione aperta. Einaudi Rezaei SD, Shannigrahi S, Ramakrishna S (2017) A review of conventional, advanced, and smart glazing technologies and materials for improving indoor environment. Sol Energy Mater Sol Cells 159:26–51 Ryser J (2014) Planning smart cities….sustainable, healthy, liveable, creative cities…or just planning cities? In: REAL CORP 2014–PLAN IT SMART! Clever solutions for smart cities. Proceedings of 19th international conference on urban planning, regional development and information society. CORP–Competence Center of Urban and Regional Planning, pp 447–456 Sagl G, Resch B, Blaschke T (2015) Contextual sensing: Integrating contextual information with human and technical geo-sensor information for smart cities. Sensors 15(7):17013–17035 Schaffers H, Komninos N, Pallot M, Trousse B, Nilsson M, Oliveira A (2011) Smart cities and the future internet: towards cooperation frameworks for open innovation. The future internet assembly. Springer, Berlin, Heidelberg, pp 431–446 Silva BN, Khan M, Han K (2018) Towards sustainable smart cities: a review of trends, architectures, components, and open challenges in smart cities. Sustain Cities Soc 38:697–713 Somayya M, Ramaswamy R (2016) Amsterdam Smart City (ASC): fishing village to sustainable city. WIT Trans Ecol Environ 204:831–842 Stratigea A, Papadopoulou CA, Panagiotopoulou M (2015) Tools and technologies for planning the development of smart cities. J Urban Technol 22(2):43–62 Talamo C, Atta N, Martani C, Paganin G (2016) The integration of physical and digital urban infrastructures: the role of “Big data”. TECHNE-J Technol Archit Environ 217–225 Telecommunication Standardization Sector of International Telecommunication Union (ITU-T). Focus Group on Smart Sustainable Cities (2014) An overview of smart sustainable cities and the role of information and Communication technologies. Focus group technical report Van Winden W (2016) Smart city pilot projects, scaling up or fading out? Experiences from Amsterdam. In: Regional studies association annual conference, Graz Velikov K, Thün G (2013) Responsive building envelopes: characteristics and evolving paradigms. In: Trubiano F (ed) Design and construction of high performance homes, pp 75–92 Washburn D, Sindhu U (2010) Helping CIOs understand “smart sustainable city” initiatives. FORRESTER Willems J, Van den Bergh J, Viaene S (2017) Smart city projects and citizen participation: the case of London. Public sector management in a globalized world). Springer, Gabler, Wiesbaden, pp 249–266

Standards and Laws BSI PAS 180:2014. Smart Cities. Vocabulary

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Websites The Crystal website https://www.thecrystal.org/. Accessed 2019 The Edge, Amsterdam—BREAM website https://www.breeam.com/case-studies/offices/the-edgeamsterdam/. Accessed 2019

Chapter 3

FM Paradigm Shifts Enabled by ICTs

3.1 From Static Data to Real-Time Data Flows The future FM scenario involves dynamic data, such as transactional data,1 realtime data2 and streaming data3 , which are continuously flowing inside the network, across interconnected communication channels. The dynamic nature of streaming data represents a potential for innovation of the FM sector. The current FM practice is characterized by process inefficiencies often related to: lack of information, poor quality of information, not-updated information, and/or not-validated information. Relying on a structured dynamic database constantly updated, even in real time, facility managers have the opportunity to improve current processes of building management. The literature and case studies review at the international level (Chap. 2) highlights how today the focus is on real-time data visualization tools and real-time monitoring systems, which are able to display relevant information supporting facility managers to make time-sensitive decisions. Real-time data allow to develop reliable information bases that can be exploited in order to: (i) calibrate FM strategies on the basis of the real needs expressed by the building; (ii) optimize the operational service delivery, acting only when and where necessary and, thus, minimizing the waste of resources; (ii) improve performance control systems (e.g. SLA—Service Level Agreement and KPIs—Key Performance Indicators) by increasing the accuracy and reliability of measurements.

1 Transactional

data, i.e. data updated over time, changing their values from time to time at each update. The frequency of the updates can be periodic, regular or variable. 2 Real-time data, i.e. data that are immediately delivered after their collection without delay. 3 Streaming data, i.e. flows of data in motion continuously generated by various sources. In particular, data streaming concerns the transfer of data at a high constant velocity whereas data are continuously received in real time without delay. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Atta, Internet of Things for Facility Management, PoliMI SpringerBriefs, https://doi.org/10.1007/978-3-030-62594-8_3

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3.2 From Linear and Siloed to Integrated Network-Based Processes From the analysis of literature and the review of smart city and smart building case studies in Chap. 2, a common feature emerged: the sharing of information as a strategy that allows managers and decision-makers belonging to different departments to work synergistically, exploiting and enriching the same common information base, even if they are distant on the territory or even if they have different roles, functions, purposes and interests. The network approach to data management can be achieved by the implementation of a unique shared integrated system for data management, i.e.: a unified database architecture, along with standardized taxonomies and management procedures to manage data (data collection, transfer, storage, etc.), shared among all the FM operators. Such a unique shared system for data management acts also as a portal to integrate all the involved FM stakeholders and to disseminate and share information. In fact, the development of this kind of centralized information platform allows to overcome “silos” of information, promoting participation and inclusion, as well as networking and information sharing, in order to establish a stimulating dialogue among FM stakeholders.

3.3 From Static to Adaptive Systems: Sensing and Responding (S-R) Principle The establishment of concepts such as “adaptive systems” and “sensing and responding” have begun to change the perception of buildings as static entities, introducing a vision of buildings that are able to respond to changing conditions overtime. Indeed, several concepts that relate to innovative and interconnected buildings are now challenging the traditional vision of buildings towards more dynamic and interactive systems. In particular, both buildings and their surrounding physical environments can be equipped with sensing technologies able to collect and share data concerning their status, use, conditions, etc. The collection and analysis of such data make it possible to increase the pro-active response of buildings in case of changes in reference conditions, also predicting anomalies before they occur in order to act on time and preventing faults. In this regard, nowadays the principle of Sensing and Responding (S-R) is an increasingly mentioned paradigm that involves a vision of building services able to proactively respond to the emerging needs—changeable over time—of the building itself and of its occupants. The development of responsive and dynamic buildings involves technologies able to provide building systems with the ability to adapt to changes of external and internal reference conditions by reacting in real time (activating predetermined reactions to observed conditions). Indeed, as

3.3 From Static to Adaptive Systems: Sensing and Responding …

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also highlighted by the literature and case studies review (Chap. 2), the main characteristic of adaptive building systems is represented by the flexibility and responsiveness of the systems to different usage contexts and environmental contexts, thus, to different types of solicitations coming from people, natural events, and building systems themselves.

3.4 From a Work-Intensive to an Information-Intensive FM Scenario This last paradigm shift summarizes the process of change, linked to the digital transformation, that the FM field is undergoing today. The smart city and smart building best practices analyzed in Chap. 2 show how today new information technologies allow facility managers to: (i) rely on a novel real-time data availability and accessibility; (ii) implement an integrated network approach both to data management and supply chain management; (iii) develop adaptive response models of the building, its systems and services towards flexibility and adaptability to changing conditions. These new capabilities can mark the transition of the FM sector—its processes, models and services—from a work-intensive to an information-intensive scenario. In light of these premises, in order to derive maximum benefit from this transition phase towards an information-intensive FM scenario, it is fundamental to: (i) accurately analyze the current technological offer, understanding its potential and limits; and (ii) develop application models of this technological offer (and related implementation procedures) in order to reshape current information management practices (see Part II).

Chapter 4

Internet of Things, Big Data and Information Platforms for Advanced Information Management Within FM Processes

4.1 Internet of Things (IoT): Evolution of the Concept Mark Weiser, chief scientist at the American Xerox PARC, in his seminal paper “The Computer for the 21st Century” published in 1991 in Scientific American, stated that “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it” (Weiser 1991). He was the first to predict that the introduction of the Information Technology (IT) solutions would substantially change the daily life of people, their social relationships and their everyday actions within their work environment. The term Internet of Things (IoT) is introduced and discussed for the first time in 1999 by Kevin Ashton, executive director of the Auto-ID Lab at MIT, which opens the doors to a new way of controlling and acting on the surrounding environment and it has gained overtime more and more attention within academic and industrial fields (Santucci 2009). In 2005, the IoT is formally accepted and adopted by the International Telecommunication Union (ITU) in the ITU Internet Report. IoT technology, through an integrated system of small devices and sensors placed on objects, allows new ways of communication among people and things. In particular, today IoT promises to create a global network that supports ubiquitous computing, term introduced by Maerk Weiser in 1991 (Weiser 1991; Bandyopadhyay et al. 2011; Darianian and Michael 2008), and context-awareness, term introduced by Schilit and Theimer in 1994 that identifies a context of awareness and recognition among devices (Schilit and Theimer 1994). In particular, this intelligent environment enables everyday objects: to understand the environment in which they lay and to interact with other objects and with people. In the 20th Tyrrhenian Workshop on Digital Communications in 2009, the IoT has been described as the pervasive presence all around us of smart things (equipped with e.g. RFID tags, sensors, actuators, etc.) that, by using unique addressing schemes, are able to work together and cooperate with other near intelligent objects, by acquiring, collecting and sharing huge amount of data (Big Data) in order to accomplish common purposes (Giusto et al. 2010). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Atta, Internet of Things for Facility Management, PoliMI SpringerBriefs, https://doi.org/10.1007/978-3-030-62594-8_4

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Today, the IoT is commonly understood as a dynamic global network infrastructure based on standard and interoperable communication protocols, that enables advanced services by integrating and interconnecting physical and virtual things, giving them unique identities and possibilities of interact through intelligent interfaces (Van Kranenburg 2008; Zavazava 2015). In light of this perspective, the advent of Big Data and the employment of IoT sensing technologies are outlining innovative scenarios and new research areas concerning information gathering methods, knowledge management and decision-making techniques, with specific declinations when applied to the context of Real Estate managment. Indeed, these developments can offer new possible forms of integration between physical and digital urban infrastructures. On the basis of this integration, several innovative scenarios can be outlined, aiming at supporting the management of buildings and services offered by/to them.

4.2 IoT Technology for Real-Time Data Acquisition The application of sensing technologies to the building can lead to the creation of a dynamic and distributed network of sensors, tags and smart devices—placed or embedded on building components. These IoT devices are connected to each other, they are aware of the context in which they are placed and they can detect, collect and transmit real-time data (Sezer et al. 2017) to other devices and visualization software, thus being able to communicate with each other and with people. According to Sezer et al. (2017), the IoT systems may have different structural layouts, nevertheless it is possible to identify the following fundamental components: (i) sensors placed-on or embedded-in objects (things); (ii) computer systems which collect, store, process and use collected data; (iii) a communication network that connects objects among them and with computer systems, allowing the interaction among all the involved components. Current sensor technologies allow to collect data in real time regarding several different aspects of the built environment (energy, occupancy, air quality, etc.), identifying novel modalities to measure, quantify and continuously monitor both building and environmental conditions. In particular, the number of IoT connected devices is projected to amount to 21.5 billion units worldwide by 2025 (IoT Analytics 2018). All these sensing devices, by collecting data (e.g. temperature, humidity, lighting, energy consumptions, occupancy, air quality, noise pollution, etc.), generate a knowledge base useful to better understand buildings and their behaviors and to monitor and control the flows of matter, energy and people that occur within them. Beside sensors, another largely used data source for monitoring the built environment is represented by smartphones, which can be considered multi-purposes portable sensors (Talamo et al. 2016) since they are equipped with, for instance, accelerometers, microphones, Wi-Fi, GPSs, etc. The access to these data represents a great opportunity to monitor users’ behaviours, as well as to monitor and control in real-time the intensity of use of building spaces, equipment and services by users.

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4.3 Big Data Features and Potentialities The concept of Internet of Things (IoT) is strongly linked to the concept of Big Data management. In fact, the countless IoT sensors and devices collect and generate massive amounts of data, commonly called Big Data due to their peculiar characteristics. Indeed, Big Data are characterized by high volume, variety, velocity, variability, veracity, value and virality (Table 4.1) and they require innovative, advanced and sustainable forms of information processing in order to increase the existing knowledge bases and, consequently, optimize decision-making processes (De Mauro et al. 2016; ISO/IEC 20546:2019). Big Data features (Table 4.1) highlight an extremely complex scenario that requires innovative and advanced tools capable of properly manage data in order to enrich existing knowledge bases and to develop new ones. This need for innovative forms of data gathering and processing opens up further fields of innovation that refer to the concept of IoT Platform. Table 4.1 The “Vs” of big data “V”

Big data features

Volume

The first major feature of Big Data is the high volume generated by both the considerable size and the high amount of data. Big Data have orders of magnitude of Petabyte (PB = 10ˆ15) and Exabyte (EB = 10ˆ18). Such considerable data volume challenges the traditional information management systems, such as relational databases, and it raises new data management issues (e.g. long time queries, need of scalable storage functions, unstructured data management, high management costs, etc.).

Variety

The variety that characterizes Big Data is referred to: (a) the multiplicity and heterogeneity of the sources; (b) the wide variety of typologies and shapes of data; and (c) the diversity of possible data formats. (a) Among data sources, it is possible to mention: sensors, RFID tags, logs or accesses to the public web, social media, social networks, databases and data storage systems, business applications, media, archives, etc.. (b) Among the different types and shapes of Big Data (related to data sources), it is possible to mention: txt, csv, PDF, Word, Excel, ppt, HTML, XML, JSON, etc. (for digital documents); printed or scanned paper documents (for archives); images, video, audio, live streams, podcasts, etc. (for media); SQL, NoSQL, Hadoop, doc repository, etc. (for databases and data storage systems); etc.. (c) For what concerns data formats, it is possible to identify three typologies: structured data, semi-structured data, and unstructured data. Structured data are data that respect a precise schema, such as the alphanumeric string that forms the tax code. Semi-structured data do not have predefined schemes but they, however, follow some grammar rules, such as the tags of HTML language or XML files. Unstructured data have a free /open structure, e.g. free text, videos, voice messages, images, etc. (continued)

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Table 4.1 (continued) “V”

Big data features

Velocity

The characteristic of velocity, referred to Big Data, acquires the following main meanings: data in motion; velocity of data production, collection and storage; data lifetime; and real-time data analysis. Indeed, Big Data are data packages in real-time motion and transmission, that means fast data streams transmitted from one source to one or more destinations through a communication network. These data can also be stored and processed in real-time. Moreover, the concept of “data lifetime” refers to the period of time in which data, starting from their generation, remain significant and therefore, have the right to remain stored withi the database.

Variability Variability may refer to changes in data semantic, format or quality. For instance, variability can stand for: constantly change of the meaning of data (e.g. meaning of words change according to the context); variable data structure; variable data interpretation according to different users; etc.. Veracity

Veracity may refer to the variable nature of the quality of data. Data has a noisy nature, which can sometimes limit their trustworthiness.

Value

The new availability of information represents an added value for companies, as well as a useful support tool for optimizing decision-making processes. Indeed, a “data driven” decision-making is able to orient towards efficient strategies aimed at enhancing the business value, as well as, at seizing new market opportunities.

Virality

Virality refers to the speed at which data can spread through a network. It describes how quickly data is spread and shared across networks (e.g. calculating the rate of spread of data), mainly people to people (P2P) networks.

4.4 IoT Platforms: Architectures, Functions and Features An IoT environment is characterized by different key components (Table 4.2) that operate synergistically in a coordinated way in order to achieve multiple common goals and objectives. Within an IoT environment, IoT Platforms enable the connection among the different IoT elements (e.g. sensors, devices, visualization tools, etc.), horizontally integrating a wide and diverse range of data sources and end-user applications. In particular, according to Scully (2016), a full-scale IoT Platform architecture (Fig. 4.1) is articulated in eight components performing eight different macro-functions (Table 4.3): Connectivity and Normalization; Device management; Database; Processing and action management; Analytics; Visualization; Additional tools; External interfaces (Scully 2016). IoT platforms have a modular expandable structure constituted by layers (Table 4.3) that can increase in number and complexity overtime according to the client’s needs and purposes. Among the features of IoT platforms, it is possible to mention (Zarko et al. 2017; Kondratenko et al. 2018):

4.4 IoT Platforms: Architectures, Functions and Features

45

Table 4.2 Description and main functions of IoT environment key components IoT key component

Description and function

Physical things

Tangible physical objects, such as, in the case of a building, the technical elements as lightning terminals, doors, windows, etc..

Sensors, devices and actuators

Sensors and smart devices that can be embedded-in or placed-on physical things. They are able to sense the physical environment and to act on the physical environment (actuators), providing physical thingswith the ability to detect the surrounding environment and to consequently change the value of their reference parameters.

Virtual things

Virtual Things are: (i) the digital identities of Physical Things (virtual translation of Physical Things) which become digital virtual objects; and (ii) virtual items and tools, such as digital room reservation register, digital agendas, digital building registry, digital expense log, etc..

People

The stakeholders that interact with the built environment. In the case of an office building, for instance: occupants, users, visitors, FM managers, owners, service providers, etc..

Network

Networks are the connector elements of IoT components and act as glue among them. Networks have the goal of providing a pervasive connectivity that acts as a link between physical reality (tangible environment of physical things) and virtual reality (intangible environment of virtual things).

Platform

An IoT Platform acts as an intermediary between physical things, digital things, people and computer programs, allowing them to communicate. Moreover, it provides multiples functions, such as, among others: data analytics, connection to external databases, access to device, building systems responses activation (e.g. activation of fire alarms, windows shading systems, etc.), change of systems reference parameters (e.g. changing intensity or brightness of a lightning terminal), etc..

Services, applications and tools

Digital building services that can be managed through smart interfaces (e.g. web and mobile applications). They are mainly based on data visualization tools and remote monitoring and control systems.

– Scalability, i.e. the ability of the platform to sustain over time the expansion of systems and increasing numbers of connected devices without progressively losing performance and quality of provided services. – Multi-tenancy, i.e. the ability of the platform to host multiple companies. Multitenant IoT architectures are provided with an extension, based on an identity and authority management system, that supports multiple user access to the platform so that multiple companies can act on the platform at the same time, visualizing data and managing devices.

46

4 Internet of Things, Big Data and Information Platforms …

Fig. 4.1 IoT platform architecture by Scully. Source Scully (2016) (in IoT analytics website)

– Interoperability, i.e. the ability of the platform to guarantee the co-operation and data exchange among its different layers and with other platforms and software. Nowadays there are several IoT platforms offered on the market, such as, among others: – – – – – – – – –

Libelium IoT Sensor Platform by Libelium (libelium.com); IBM Watson IoT Platform by IBM (ibm.com); Autodesk Fusion Connect by Autodesk (autodesk.com); Intel IoT Platform by Intel (intel.it); Cisco Jasper Control Center by Cisco (cisco.com); Oracle IoT Platform by Oracle (oracle.com); Kaa IoT Platform by Kaa (kaaproject.org); Microsoft Azure IoT Suite by Microsoft (azure.microsoft.com); Ericcson IoT Accelerator Platform by Ericsson (ericsson.com).

All these platforms shared the features mentioned before, indeed they all have the ability to: connect and manage devices, collect and manage data, perform data analytics, enable data visualization, offer cloud services (e.g. data storage & back up, software applications, etc.). All of the cited platform solutions available on the market are modular, scalable, expandable and interoperable (capable of integrating data sets from external sources and systems). These platforms are able to process and analyze data from IoT devices and to display them on web or mobile interfaces.

4.4 IoT Platforms: Architectures, Functions and Features

47

Table 4.3 The eight IoT platform components according to Scully (2016) IoT platform component

Description

Connectivity and normalization

The Connectivity and Normalization component is fundamental, firstly, to ensure interoperability, thus a proper interaction of the platform with sensors and devices. Secondly, since data are often heterogeneous and they may support different communication protocols, an effort of harmonization (shared roles for communication) becomes fundamental in order to normalize data, making them interoperable. Through normalization, data can be stored in a unique database for further processing and analyses.

Device management

This component of the platform has the objective of guaranteeing the correct functioning of the connected devices over time. It also takes care of updating the management software of the devices.

Database

The database is the central element of the platform. It stores data, including real-time data flows coming from sensors and smart devices. Among its main characteristics, there are: scalability, scheme flexibility and interoperablity with other databases and data analysis tools and software.

Processing and action management This component involves data processing and analysis. Moreover, this component is based on "event-action-trigger" rules, which allow the automatic real-time execution of predefined actions, based on data coming from sensors and devices. Analytics

This component of the platform performs cognitive computing, which enables to generate context-aware insights from Big Data, leveraging on autonomous reasoning and continuous learning based on ever-growing knowledge bases.

Visualization

The component involves data visualization tools and smart interfaces, e.g. web and mobile applications that display interactive dashboards or other graphical representations (e.g. charts, diagram, 3D models, etc.) showing historical and real-time data, as well as calculated indexes and indicators.

Additional tools

The IoT Platform can include a varied range of supplementary management tools (e.g. access management tools, reporting, editor tools, apps, etc.) which can be useful to the managers to better monitor, control and manage the platform itself and all its components.

External interfaces

This part of the platform aims to ensure the integration of the platform itself with third-party systems through intelligent interfaces by means, for instance, of Application Programming Interfaces (APIs), Software Development Kits (SDKs), etc. In particular, these tools allow the platform to share its own data and to access and use external data.

48

4 Internet of Things, Big Data and Information Platforms …

Moreover, through Application Programming Interfaces (APIs) they allow to create and offer digital applications and services. From the analysis of case studies of implementation in real contexts of the aforementioned platforms available on the market, also with reference to the case studies analyzed in Chap. 2, it is possible to outline three different implementation scenarios of increasing complexity: – Implementation of platform segments. In this case, the client implements only few segments (layers) of the whole platform, e.g. sensors and a software for data visualization in real-time for performing real-time monitoring. This implementation scenario is one of the most common nowadays, and it demonstrates the growing interest, expressed by municipalities, in experimenting the IoT technology to exploit its capabilities and assess its potential and, then, over time add other platform components and functionalities. – Implementation of the whole platform. In this case, the client implements an entire platform solution, as it is offered on the market by the IT provider, with all its predefined modules and functionalities. – Implementation of customized platforms. In this case the client can customize an existing platform offered on the market by IT providers. The client, according to its needs and purposes, can create its tailored platform version, e.g. selecting the set of layers, adding specific functionalities and software, selecting real-time data visualization tools, etc. In light of the performed analyses (see also Chap. 2), it is possible to state that currently there is a technology market that offers information management services in various sectors and fields including building management and facility management. The over-availability of these scalable IoT services and solutions can be transferred to the building FM sector. However, despite the growing interest in the implementation of IoT platforms for FM, applications are still experimental and not much supported by reference rules, tools and practices shared among FM and IoT stakeholders. Hence, the advanced capabilities offered by IoT technologies for improving information management need to be properly supported by new logical frameworks, models of application and procedures that can act as common shared reference tools for stakeholders towards the harmonization and consolidation of the IoT-based FM scenario.

References Bandyopadhyay S, Sengupta M, Maiti S, Dutta S (2011) Role of middleware for internet of things: a study. Int J Comput Sci Eng Surv 2(3):94–105 Darianian M, Michael MP (2008) Smart home mobile RFID-based internet-of-things systems and services. In: Proceedings of the conference on advanced computer theory and engineering (ICACTE’08). IEEE, Phuket, 20–22 Dec 2008, pp 116–120 De Mauro A, Greco M, Grimaldi M (2016) A formal definition of big data based on its essential features. Library Rev

References

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Giusto D, Iera A, Morabito G, Atzori L (2010) The internet of things. In: 20th Tyrrhenian workshop on digital communications, Springer, New York Kondratenko Y, Kondratenko G, Sidenko I (2018) Multi-criteria decision making for selecting a rational IoT platform. In: 2018 IEEE 9th International conference on dependable systems, services and technologies (DESSERT). IEEE, pp 147–152 Santucci G (2009) From internet of data to Internet of things. In: Proceedings of the international conference on future trends of the internet, 28 Jan 2009 Schilit B, Theimer M (1994) Disseminating active map information to mobile hosts. IEEE Network 8(5):22–32 Scully P (2016) 5 things to know about the IoT platform ecosystem. IoT analytics. https://iot-ana lytics.com/5-things-know-about-iot-platform/. Accessed 2019 Sezer OB, Dogdu E, Ozbayoglu AM (2017) Context-aware computing, learning, and big data in internet of things: a survey. IEEE Internet of Things J 5(1):1–27 Talamo C, Atta N, Martani C, Paganin G (2016) The integration of physical and digital urban infrastructures: the role of “Big data”. TECHNE-J Technol Archit Environ 217–225 Van Kranenburg R (2008). The internet of things: a critique of ambient technology and the all-seeing network of RFID. Inst Netw Cult Weiser M (1991) The computer for the 21st century. Sci Am 265(3):66–75 Zarko IP, Soursos S, Gojmerac I, Ostermann EG, Insolvibile G, Plociennik M, Bianchi G (2017) Towards an IoT framework for semantic and organizational interoperability. In: 2017 Global internet of things summit (GIoTS). IEEE, pp 1–6 Zavazava C (2015) ITU work on internet of things. In: Presentation at ICTP workshop

Standards and Laws ISO/IEC 20546:2019 Information technology. Big data—Overview and vocabulary

Websites IoT Analytics (2018) State of the IoT 2018: number of IoT devices now at 7B— market accelerating. https://iot-analytics.com/state-of-the-iot-update-q1-q2-2018-number-ofiot-devices-now-7b/. Accessed 2019

Chapter 5

IoT-Based Collection of FM Information: Parameters and Sensors

5.1 Detecting FM-Related Information Using IoT Technology Stakeholders involved in building management practices are currently recognizing the potential offered by ICT solutions, related to the capabilities of processing both static information (e.g. from existing databases, statistics, etc.) and dynamic information (e.g. from sensors and IoT devices) coming from heterogeneous sources and concerning several aspects of the building. Current FM practices are often characterized by a fragmentation of knowledge and by the lack of integration among FM stakeholders and departments. This fragmentation may lead to the absence of a clear strategy for acquiring relevant information. On the contrary, the perspective of IoT-based innovation of FM practices involves the creation of an integrated information sharing environment able to: – develop a unique and shared building life cycle database—reliable, flexible and constantly growing thanks to feedback information from diagnostics, interventions and users’ feedbacks—set on unified data collection, communication and processing protocols, capable of providing over time technical, behavioural and performance data on building components and technological systems (e.g. lifecycle degradation profiles, maintenance cycles, costs, etc.); – integrate, during the whole duration of the building process, multiple and heterogeneous data and information coming from different stakeholders and from different sources (e.g. technical documents, databases, simulations, monitoring and control processes, operators, etc.) and centralize them in a single platform. In this way a single detected data can be exploited in several processes by different actors for different uses and purposes; – achieve an improvement in the accuracy of building and services performance control activities, by continuous monitoring—through KPIs (Key Performance

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Atta, Internet of Things for Facility Management, PoliMI SpringerBriefs, https://doi.org/10.1007/978-3-030-62594-8_5

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5 IoT-Based Collection of FM Information: Parameters and Sensors

Indicators)—the satisfaction of SLAs (Service Level Agreements) and performing a dynamic internal and external benchmarking on key aspects (e.g. energy consumptions, economic expenses; service quality; etc.); – increase the accuracy and reliability of the failures forecasts, improving the “condition-based” response capacity of building components and systems; – integrate applications and tools regarding different building aspects (e.g. energy management, maintenance management, occupancy, asset management, workplace management, waste management, etc.)—strongly heterogeneous but united by the same knowledge base in a collaborative logic of information sharing. The application of IoT and Big Data management paradigms to the building scale consists in a structured approach to data management through the implementation of a distributed system of sensors and smart devices, which are able to detect in realtime a plurality of parameters, in the form of data flows. These data, if appropriately contextualized and analyzed, could describe building behaviours in relation to various field of interest (e.g. energy, occupancy, asset management, maintenance, etc.). Sensors, tags and smart devices can gather real-time data, such as, among others: temperature, humidity, people flows, energy flows, air quality, acoustic noise, etc. (Table 5.1), opening up new possibilities for the collection and management of FMrelated data.

Table 5.1 Examples of parameters, units of measurement, detection methods and sensing devices Parameter

U. M.

Detection methods

Sensing devices

Temperature

[°C] [K]

Measurement of the amount of heat present in a system. Temperature sensors are divided into two types: “contact” (physical contact with the object) and “contactless” (do not need physical contact; they measure the temperature through the measurement of convection and irradiation).

Digital Thermometers Calorimeters Thermocouple Contact sensors Contactless sensors

Humidity

[%]

Measurement of the degree of humidity of Hygrometers air or mass by detecting the amount of Moisture sensors water vapor. It is possible to measure: absolute humidity, relative humidity, mass ratio, etc.

Pressure

[Pa] [Bar]

Measurement of the force exerted by liquids or gases. Then, pressure is calculated as force per unit of surface.

Barometers Piezometers Diaphragms Pressure gauge Load cells (continued)

5.1 Detecting FM-Related Information Using IoT Technology

53

Table 5.1 (continued) Parameter

U. M.

Detection methods

Sensing devices

Occupancy and motion

–

Generation of a signal when the presence of people and/or objects is detected. Detection of the movement of people and/or objects.

Presence sensors Kinetic sensors Radar Electric eye Infrared camera Video camera

Location

–

Outdoor locations: GPS sensors provide information about location and speed of a receiver on the ground in an external environment. Indoor locations: RFID technology or infrared-based positioning systems or radio frequency (RF) technologies detect position in internal environment.

GPS sensors RFID devices Infrared-based positioning systems Radio frequency detectors

Speed and acceleration

[m/s] and [m/sˆ2]

Measurement of the speed of the linear motion. Measurement of the angular velocity of rotational motions and their inclination with respect to reference axes.

Accelerometers Gyroscopes

Air-flows and water-flows

[mˆ3/h] and [l/h]

Measurement of: Linear flow; Volumetric flow rate; or Mass flow rate

Flow-meters Displacement gauges Static gauges Pressure gauges Pitot tube

Sound and vibration

[Hz] [mm/s]

Measurement of sound levels and conversion of data to digital or analog signals. Measurement of the reflected signals of the energy generated by a propagating source, in a fluid or mass, in the form of a pressure wave. Measurement of the cyclic shift in time of an object around a central static position.

Microphones Geophones Hydrophones Vibrometers Stroboscopes

Light (visible and not visible)

[candle/ mˆ2] [lux]

Visible light: measurement of the luminous intensity (or luxmeters that measure the illumination) [lux]. Non-visible light: detection ma measurement of not visible thermal radiation of the infrared spectrum emitted by the warm sources.

Photo-detectors Photometers Luxmeters Infrared detectors

Detection and measurement of the presence of one or more types of gas within an environment.

Chemical sensors Gas sensors (e.g. oxygen, carbon monoxide/dioxide, hydrogen)

Gases and gaseous [%] chemical mixtures

54

5 IoT-Based Collection of FM Information: Parameters and Sensors

5.2 Classification of Parameters for Service Management In order to understand the extent of the Information Need related to FM processes, a general FM delivery process1 has been outlined and broken down into elementary activities. In particular, the FM process is mapped assuming the perspective of the facility manager. The mapped general FM delivery process (Fig. 5.1) is articulated into two level of different complexity. The higher level consists of the following six primary processes: identifying service strategy, analyzing requirements, developing solutions, performing services, monitoring service provision, and assessing and reviewing. While the lower level is articulated into secondary processes resulting from the breakdown of the six higher level processes. Then, the mapped FM process has been analyzed highlighting data, information and document necessary2 for performing each identified secondary action of the primary processes (Table 5.2). Starting from the defined FM Information Need (Table 5.2), it is possible to extract those data and information that can be collected through advanced solutions based on ICT and IoT. In particular, among the IoT-detectable FM-related data, it is possible to mention: (i) ID, Inventory and Access data (e.g. log-ins registered by mobile applications, inventory data collected through smart tags, etc.); (ii) User generated data (e.g. data from smart-phones, web and mobile applications, social media, feedback information; etc. useful to understand user’s behaviors and preferences); (iii) Performance data both at the operative level (e.g. real-time condition of assets and equipments; profile of use of assets and equipments by users; number and frequencies of failures; etc.) and strategic level (e.g. real-time measurement of KPIs and assessment of SLAs).

Fig. 5.1 FM delivery process 1 The

approach followed to map the FM process is the one proposed in 2008 by Keith Alexander in the report “Facilities Management Processes EuroFM Research Monograph”. This report is the result of a research project on FM Processes, started in 2007, made by EuroFM (the European FM platform organization that brings educators, researchers and practitioners in the field of Facility Management, coming from 23 different European countries, together. Its aim is to bring forward the FM profession and to come to a better mutual understanding by learning and sharing FM knowledge) and financially supported by RNG (Research Network Group). 2 It is important to underline that the aim of the developed analysis is not to draw a complete and exhaustive framework of data, information and documents required in order to implement the FM process, but what is shown in Table 5.1 is a non-exhaustive but sufficient list of data, information and documents that allow to plan and to start the implementation of FM processes and services.

Analyzing Architectural, • requirements equipment, • technological, • management • assessments • and site surveys • • • • • • • • • • •

• Expenditure capacity • Budget for service and for activities • Strategic reports regarding: operating costs; space utilization and costs; benchmarks

Indentify financial limits

Location; “As built” status Gross volume and surface area, divided according to the intended use (refer to standards if applicable) General characteristics of component parts (i.e. position inside the building, drawings, technical datasheet, instructions for maintenance issued by manufacturer, etc.) Specifications, submittals file/vendor data, finishes/materials list, space typologies, operating schedules, spaces demands and operating hours Level of compliance with legal and regulatory requirements (objectives to be attained) For equipment: identification, location and description supported by an appropriate coding system Status of distribution systems and data concerning consumption (i.e. energy, water, etc.) Resource consumptions Materials Property and real estate waste Indoor environmental quality Status of maintenance upgrading in accordance with pre-determined operational specifications Technical documents relating to the installation, operation and maintenance of systems and equipments Cognitive purposes Types of management services to be activated Technical characteristics of the elements and their criticality in relation to the building, the function it performs, the faults that may affect them and related risks (continued)

Organization’s needs (demand) and policies Quality targets Productivity targets Service Level Agreements (SLA) Due Diligence

• • • • •

Identifying business needs

Identifying strategy

Data, information and documents

Secondary processes

Primary processes

Table 5.2 Information need of FM delivery process

5.2 Classification of Parameters for Service Management 55

Secondary processes

Generating options and Estimating costs

Primary processes

Developing solutions

Table 5.2 (continued)

(continued)

• Data about maintenance activities already performed (history of the components) • Assessment of efficiency, functionality and compliance with applicable rules and standards • Residual service life, for each component, predicted in accordance with age, quality and use conditions, and in relation with the initially foreseen service life • Types of management services to be activated • Technical specifications: especially concerning equipments and building services in order to identify characteristics and ‘established operating conditions’ • Repair or replacement costs (i.e. estimates in accordance to official or regional price list) • Cost for unavailability and/or down-state: cost estimates, at least for critical components, arising from the down-state of the components or from their inability to provide the services for which they are intended to (i.e. costs for liabilities, damages, damage to the corporate image, etc.) • Information about critical construction techniques • Instructions for inspections, operation manuals and maintenance manuals • Experience and recommendations of the builder/manufacturer useful to develop an appropriate maintenance plan

Data, information and documents

56 5 IoT-Based Collection of FM Information: Parameters and Sensors

Performing services

Primary processes

Checking works

Undertaking works

• • • • •

• • • • • • • • • •

Evaluating and selecting options

Mobilizing workforce

• FM organizations plan • Strategic FM projects

Defining evaluation criteria

Parts lists, inventories, repair requirements Procedures and schedules Filled work orders Actual provider register Provider evaluation report

FM reporting Laws and regulations Approved demands list FM standards Investment projects list Projects descriptions Projects plans FM standards regarding (operations and maintenance, space, cleaning, security, etc.) FM procedures for evaluation of facilities and buildings Procedures for auditing of service qualities according to SLA and KPIs

Data, information and documents

Secondary processes

Table 5.2 (continued)

(continued)

5.2 Classification of Parameters for Service Management 57

Evaluating and reviewing

• Report on performance and cost • Possible penalties and incentives

Reporting and adjusting payments (possible)

Adjusting solutions

• • • • • •

• • • • • • •

Processing feedback information

Reports about the evaluation of the planning of the facility services Work orders Information about FM teams Facilities evaluation report and customer satisfaction Surveys reports Reports about the measure of the service results according to SLA and KPI FM reports about operations and maintenance data (i.e. fulfilled activities, status data, condition data, emergency and break downs, corrective actions, etc.) FM reports about service requests of end users and change requests of end users Reports about complaints (i.e. cleaning, malfunctions, etc.) Reports about security issues and moves Building register Strategic space plan Space utilization report (occupancy and use of space)

• Reports on performance and cost, inspections, compliance survey

Measuring compliance

Quality targets, Productivity targets, Service performed, contractual obligations, Perceived quality, delivered quality and efficiency of work process (KPI and SLA), customer satisfaction

• • • •

Checking performance and costs

Monitoring services provision

Data, information and documents

Secondary processes

Primary processes

Table 5.2 (continued)

58 5 IoT-Based Collection of FM Information: Parameters and Sensors

5.3 Classification of Sensors and IoT Devices for Data Detection

59

5.3 Classification of Sensors and IoT Devices for Data Detection In the form of a Parameters—Sensors Matrix, Table 5.3 proposes a classification system of an example set of parameters detectable through sensors, articulated into basic parameters (fundamental physical quantities) and derived parameters, according to different objects of survey (e.g. air, fluids, surfaces, etc.). Moreover, in order to identify—for each derived parameter—the possible sensing devices (e.g. sensors, meters, tags, etc.) able to detect and/or measure them, Table 5.3 also provides a normalized taxonomy of the example set of parameters and related sensors. Such a tool may represent a support for FM stakeholders to handle the highly variegated offer of sensors by different IT providers, that is often difficult to understand and compare. It must be stressed that the tools of measurement (column “Devices” in Table 5.3) can: – vary according to methods and technology of measurement. There are, in fact, different technologies for detecting a single parameter (e.g. to detect motion it is possible to use: infrared sensors; Video Motion Detection—VMD; thermal cameras, radar, etc.); – be traditional measuring instruments (e.g. thermometers, barometers, hygrometers, etc.) which acquire data transmission and communication capabilities as they are coupled to intelligent transducers and they are inserted into a communication network, as the Wireless Sensor Network3 (WSN) for instance. Thus, they acquire communication skills that make them able to exchange and share information; – be embedded in building components and equipments (e.g. doors; windows; lighting bodies; etc.) or installed on them; – have different locations according to the specific features of the application context and to the purposes of the application.

3A

Wireless Sensor Network (WSN) is a network whose nodes are made up of sensors, able to collect data from the surrounding environment and transfer them to other nodes for the local data exploitation, or to a gateway that collect data and transfer them to a broader network. This network appears the most suitable in the case of buildings since it is very flexible and it does not have physical limits, such as cabling.

Parameter

Temperature

Pressure

Cod

1

2

Fluids

O2

Surfaces

O3

Air

Fluids

O2

O1

Air

Object of survey

O1

Cod

Table 5.3 Parameters—Sensors Matrix

Acoustic pressure

2.P4 Absolute pressure

Gauge pressure

2.P3

2.P5

Absolute pressure

2.P2

Mean radiant temperature

1.P6 Atmospheric pressure

Radiation (infrared) temperature (NC)

1.P5

2.P1

Contact temperature

1.P4

Contact temperature (immersion)

Wet bulb temperature

1.P2

1.P3

Dry bulb temperature

Derived parameter

1.P1

Cod

Pa; bar

Pa

Pa; atm; mmHg; bar

Pa; atm; mmHg; bar

Pa; atm; mmHg; bar

°C °F K

°C °F K

°C °F K

°C K

°C K

°C K

M. U.

Thermocouple

1.O1.P1.D5

Manometer (continued)

Acoustic pressure sensor 2.O2.P5.D1

Microphone 2.O1.P3.D2

Pressure sensor

Manometer

Barometer

Globe thermometer

Infrared thermometer

Contact thermometer

Immersion thermometer

2.O1.P3.D1

2.O1.P3.D1

2.O1.P2.D1

2.O1.P1.D1

1.O3.P6.D1

1.O3.P5.D1

1.O3.P4.D1

1.O2.P3.D1

Wet bulb temperature sensor

Thermistor

1.O1.P1.D4

Psychrometer

Thermostat

1.O1.P1.D3

1.O1.P2.D2

Pyrometer

1.O1.P1.D2

1.O1.P2.D1

Thermometer

Devices

1.O1.P1.D1

Cod

60 5 IoT-Based Collection of FM Information: Parameters and Sensors

Parameter

Humidity

Cod

3

Table 5.3 (continued)

Air

Solids

O4

O1

Object of survey

Cod

Absolute humidity Relative humidity

3.P2

Acoustic pressure

2.P10 3.P1

Traction (on the solid)

2.P9

Acoustic pressure

2.P7 Compression (on the solid)

Gauge pressure

2.P6

2.P8

Derived parameter

Cod

%

g/m2

Pa

Pa

Pa

Pa

Pa; bar

M. U.

Humidistat Hygrometer Relative humidity sensor Capacitive atmospheric humidity sensor

3.O1.P2.D2 3.O1.P2.D3 3.O1.P2.D4 3.O1.P2.D5

(continued)

Psychrometer

3.O1.P2.D1

Absolute humidity sensor

Acoustic pressure sensor 3.O1.P1.D1

Microphone

Extensometer

2.O4.P10.D2

Load cell

2.O4.P9.D3 2.O4.P10.D1

Pressure transducer

2.O4.P9.D2

Extensometer

2.O4.P8.D3 2.O4.P9.D1

Load cell

2.O4.P8.D2

Acoustic pressure sensor Pressure transducer

2.O4.P8.D1

Idrophone

2.O2.P7.D2

Pressure sensor

Devices

2.O2.P7.D1

2.O2.P6.D1

Cod

5.3 Classification of Sensors and IoT Devices for Data Detection 61

Parameter

Speed

Cod

4

Table 5.3 (continued)

Fluids

Solids

O4

O2

Surface

O3

Air

Fluids

O2

O1

Object of survey

Cod

4.P3

Linear Speed

Linear acceleration

Tied relative humidity (on the surface)

3.P11 Linear speed

Free relative humidity (in the pores)

3.P10

4.P2

Tied absolute humidity

3.P9

4.P1

Free absolute humidity (in the pores)

Relative humidity

3.P7 3.P8

Absolute humidity

Relative humidity

3.P5 3.P6

Absolute humidity

Derived parameter

3.P4

Cod

3.O4.P9.D1

g/m2

Flux meter Venturi meter

4.02.P3.D2

Anemometer

Anemometer

(continued)

Relative humidity sensor

Relative humidity sensor

Absolute humidity sensor

Absolute humidity sensor

Relative humidity sensor

Absolute humidity sensor

Relative humidity sensor

Absolute humidity sensor

Devices

4.O2.P3.D1

4.O1.P2.D1

m/s

4.O1.P1.D1

m/s2

3.O4.P11.D1

m/s

%

3.O4.P10.D1

3.O4.P8.D1

g/m2

%

3.O3.P7.D1

3.O3.P6.D1

g/m2 %

3.O2.P5.D1

3.O2.P4.D1

g/m2 %

Cod

M. U.

62 5 IoT-Based Collection of FM Information: Parameters and Sensors

Parameter

Motion

Cod

5

Table 5.3 (continued)

Solids

O4

Living beings

O5

Fluids

Solids

O4

O2

Object of survey

Cod

Dynamic viscosity Kinematic viscosity Absolute fluency Kinematic fluency

4.P13 4.P14 4.P15 4.P16

5.P2

5.P1

Inclination

Inclination

Linear acceleration

Angular acceleration

4.P12

4.P17

Linear acceleration

Kinematic fluency

4.P8

Angular speed

Absolute fluency

4.P7

4.P11

Kinematic viscosity

4.P6

4.P10

Dynamic viscosity

4.P5

Linear speed

Linear Acceleration

4.P4

4.P9

Derived parameter

Cod

4.O4.P16.D1 4.O5.P17.D1 5.O2.P1.D1 5.O4.P2.D1

m2 /s m/s2 θ, rad θ, rad

Inclinometer

Inclinometer

Accelerometer

Flux meter

Flux meter

(continued)

Viscosity meter 4.O4.P15.D1

4.O4.P14.D2

Viscosity meter Flux meter

4.O4.P13.D2 4.O4.P14.D1 m*s/kg

m2 /s, Stokes

Gyroscope

Accelerometer Flux meter

4.O4.P12.D1

rad/s2

Gyroscope

4.O4.P13.D1

4.O4.P11.D1

Pa*s, PI

4.O4.P10.D1

Velocity meter

rad/s

Tachymeter 4.O4.P9.D2

Flux meter

4.O4.P9.D1

m/s2

m/s

4.O2.P8.D1

m2 /s

Flux meter

Viscosity meter

4.O2.P7.D1

4.O2.P6.D2

Viscosity meter Flux meter

4.O2.P6.D1

m*s/kg

m2 /s, Stokes

4.O2.P5.D2

Venturi meter Flux meter

4.O2.P5.D1

4.O2.P4.D2 Pa*s, PI

Flux meter

4.O2.P4.D1

m/s2

Devices

Cod

M. U.

5.3 Classification of Sensors and IoT Devices for Data Detection 63

Cod

Parameter

Table 5.3 (continued)

Object of survey

Living beings

Cod

O5

Angular movement

5.P4 Linear movement

Linear movement

5.P3

5.P5

Derived parameter

Cod

p

Counter people Photocell Video camera

5.O5.P5.D2 5.O5.P5.D3 5.O5.P5.D4

Encoder Movement sensor

5.O5.P5.D1

5.O4.P4.D2

Video camera Movement sensor

5.O4.P4.D1

5.O4.P3.D2 θ, rad

Movement sensor

5.O4.P3.D1

p

Devices

Cod

M. U.

64 5 IoT-Based Collection of FM Information: Parameters and Sensors

Chapter 6

Sensing and Responding (S-R) Models and IoT Architecture for Advanced FM Information Management

6.1 Sensing and Responding (S-R) Models for Improving FM Management The current technological scenario, outlined in Part I of the present Thesis, outlines innovative perspectives for an advanced management of tangible and intangible assets, based on the principle of Sensing and Responding1 (S-R). As shown by the analysis of case studies of ICT and IoT implementation to the urban and building scale (Chap. 2), the application of automated response mechanisms to the occurrence of predefined events, according to a S-R logic, has the potential to make buildings dynamic and adaptive. The S-R principle has been applied in the Control Theory2 for several decades. However, in current FM practices the development and implementation of advanced detection systems to acquire real-time 1 The

term Sensing and Responding as a business concept was theorized by Stephan H. Haeckel (1999) together with the concept of “adaptive loop". The so-called SIDA loop (“Sense, Interpret, Decide, Act” loop) is formalized starting from the Sensing and Responding principle and it is a process characterizing the adaptive systems which actively exchange information with their components and their environment, they process what they have acquired and learned and then they accordingly adapt themselves in response. 2 The Control Theory concerns the broad area of continuously operating dynamic systems and their constant control over time reached by means of controllers. A controller is an element able to continuously monitor the controlled process variable (PV), and to compare the detected values with the reference or set point (SP). The difference between SP and PV gives as a result the so-called error signal (or SP-PV error) and it represents the difference between the actual and the expected value of the process variable. Then, this error signal is applied as an input to generate a control action aimed at bringing the controlled process variable to the same value as the set point (thus, bringing the actual measured value to be the expected one). This process is commonly called feedback control and it is continuous and it involves the use of a sensor (or multiple sensors or a sensor plus an actuator) as controller which takes measurements and calculate the required adjustment, thus the needed control action/s to do in order to keep the measured value of the variable equal to the expected one or, at least, to keep it within a set predefined range of tolerance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Atta, Internet of Things for Facility Management, PoliMI SpringerBriefs, https://doi.org/10.1007/978-3-030-62594-8_6

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data from buildings, as well as the development and implementation of intelligent programming systems to set control/response programs, are very rare. This is mainly due to the fact that these innovative concepts—with great potential for improving current FM practices—are very demanding. Indeed, with respect to S-R application in FM, if on one hand the advantages and benefits are advisable (e.g. cost reduction through better forecasting and planning, adaptive execution and dynamic allocation of resources to needy/high-impact areas, etc.), on the other hand there are limitation to the adoption of such theories due to two main reasons: – nowadays it is cheaper to collect data, rather than to perform data interpretation and analysis that would enable the effective application of S-R models. One of the main limits to the use of Big Data for the automated management of services is that the data analysis models, used to activate predetermined reactions, have not known yet a development and a cost reduction comparable to the one that, instead, has characterized the sensors development and their market expansion (Kumar et al. 2016; Talamo et al. 2016); – the current scenario of FM is characterized by a lack of possible models of application of the S-R principle to FM processes (also with reference to the different phases of service implementation, i.e. design, planning, programming and delivery). As an attempt to fill this gap, the following S-R models—based on IoT integration in FM processes—can be proposed (Talamo et al. 2016): – – – – – –

automatic sensing and responding; sensing and responding with constraints; sensing and responding with external decision and proposal validation; sensing and knowing; sensing and knowing in emergency; sensing and learning/self learning.

The proposed six models (Table 6.1) are characterized by increasing complexity with respect to the temporal dimension and the gradualism of the implementation. These models are meant to process heterogeneous datasets including: static data (e.g. data from databases); direct dynamic data (e.g. real-time data from sensors and other IoT devices); indirect/processed dynamic data (e.g. statistical analysis, comparisons, etc.); feedback data (from the use phase of the building, e.g. consumption, etc.).

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Table 6.1 S-R models for the management of buildings and urban infrastructures. Adapted from Talamo et al. (2016) Model

Description

Examples of application

Automatic sensing and responding

APPLICATION: non-complex systems having few variables to measure and control and in which it is not expectable a propagation of effects of the actuating to other parameters or conditions. When a monitored threshold is reached, then a predefined automatic procedure is activated. AIM: to create the conditions for an automatic adjustment of non-complex processes in domains that are restricted to few variables.

-Switching off of air conditioning systems when it is reached a defined temperature in the rooms; -automatic activation of dimming systems in relation to direct solar irradiation; -automatic shutdown of rotating equipments (e.g. gas turbines for energy production) when defined vibration levels are reached; -automatic real-time reporting of the available parking place on a publically accessible portal; -automatic reporting of the location and availability of cars (car sharing) on a publically accessible portal.

Sensing and responding with imposed constraints

APPLICATION: bounded systems of low-medium complexity, in which they are well known the cause - effect mechanisms linked to a priori predictable conditions. AIM: automatic activation of a procedure conditioned by the simultaneous satisfaction of one or more predefined constraint conditions. The aim of the constraint conditions is to avoid that the automatic activation of the procedure can cause consequences on other elements.

-Activation of work orders—subordinated by the assessment of budget constraints - when preset conditions are achieved; -automatic activation of gas-discharge fire extinguishing system, subordinated to the ascertainment of the absence of people in the place.

Sensing and responding with APPLICATION: medium external decision and proposal complexity systems, in which validation it is difficult to predict a priori all the interaction scenarios of the system with other potentially connected systems. AIM: to manage the risk linked to the uncertainty generated by gaps of knowledge and/or interpretation of contexts and phenomena.

-Accident detection systems in highway with activation of a traffic control mechanisms from a decisional command center; -systems for pollutants monitoring in urban areas with actions on the management (limitation, partly or completely block) of cars traffic. (continued)

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Table 6.1 (continued) Model

Description

Examples of application

Sensing and knowing

APPLICATION: complex realities, referable to a predefined number of domains whose behavior can be represented and predicted on a statistical basis. AIM: to allow, on the basis of the knowledge coming from the analysis of heterogeneous data, the adoption of choices by appropriate decision makers (individual or networked).

-Management of critical situations at the territorial scale through the elaboration and the comparison of alternative scenarios; -design of new infrastructures and simulation of their interactions with the existing; -construction of dashboards for effective decision-making at the territorial scale; -setting and constant updating (benchmarking) of sets of indicators for the assessment of integrated infrastructure performances.

Sensing and knowing in case of emergency

APPLICATION: exceptional events characterized by several variables and by behaviors that are not stable and not always predictable on a statistical basis. AIM: to increase resilience, allowing decision makers to manage risks in real-time through the adoption of choices based on: constantly updated data, instantaneous simulations, detection of the overcoming of threshold conditions.

-Shared control rooms (e.g. among civil defense, infrastructure managers, public security, health, etc.) for the management of crisis situations; - real-time coordinated management of critical infrastructures (e.g. communications, transports, health, energy, etc.) in case of emergency.

Sensing and learning/self learning

APPLICATION: complex realities involving large number of data, respect to which correlations between different variables and quantities subjected to monitoring shall be identified. AIM: to build—through the data analysis or the application of genetic algorithms based on the self-learning approach—interpretative models of complex networks.

-Integrated management of critical infrastructures (e.g at the urban and suburban scale) based on the knowledge about past, present and predictable future events; -detection of multi-risk scenarios in contexts characterized by high complexity and high level of systems interconnection.

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6.2 IoT Architecture for FM: Fundamental Interconnected Layers The proposed S-R models need an IoT platform in order to be implemented. Such advanced information management tool is required in order to collect and manage Big Data, enabling the transfer of the S-R models to the FM field. On the basis of the finding and results of the literature and market review of current IoT solutions (carried out in Part I—Chap. 4), it is possible to outline an IoT Architecture suitable to the management of FM services at the building scale, articulated into the following fundamental layers: Sensors and Devices layer; Gateway and Network layer; Platform layer, including Service Management layer and Service Analytics layer; Application layer (Fig. 6.1). This modular structure can range from the simplest case made up by the Sensor and Devices layer together with the Gateway and Network layer and a software for real-time data visualization—enabling to perform a continuous monitoring of relevant key parameters—to the most complex case in which the whole infrastructure is implemented in order to perform predictive analyses (e.g. Sensing & Knowing and Sensing & Learning—see para 6.1), allowing building systems to become responsive and adaptive. In particular: • Sensors and Devices layer. Sensors3 and IoT devices allow to extend communication capabilities to objects and things and, consequently, to acquire, collect, gather and process data from the physical world.

Fig. 6.1 IoT Architecture for FM

3 The

Institute of Electrical and Electronics Engineers (IEEE) provides, in the standard “IEEE P1451.6 Terms and Definitions”, the following definition of sensor: “an electronic device that produces electrical, optical, or digital data derived from a physical condition or event. Data produced from sensors is then electronically transformed, by another device, into information (output) that is useful in decision making done by intelligent devices or individuals (people)” (IEEE P1451.6). Beside sensors, there are Radio-Frequency Identification (RFID) tags and readers, cameras, GPS, NFC readers etc.; IoT devices can also be wearable sensors, smart watches, LED lights, smart phones, etc.

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• Gateway and Network layer. Sensors and devices at the building scale can be connected to the network4 by using gateways (acting as bridge between devices and network). When connected to the network, devices can communicate using a common language, i.e. network protocol.5 • Platform layer. This layer is made up by a unique central database6 that is dynamically updated in real-time with data coming from sensors and devices through the network. It can perform Big Data storage, processing and visualization and it feeds the Service Management and the Service Analytics layers, exchanging data and information. • Service Management layer. This layer can consist of a Building Management System (BMS) able to monitor and control equipments, technical elements (e.g. doors, windows, etc.), mechanical and electrical systems (including electric power control as lighting control, HVAC systems, fire alarm system, lifts and elevators), furniture, etc. This layer is responsible of activating response interventions and activities, on the basis of the results of data analyses coming from the Service Analytics layer.

4 A networks is a set or a collection of computers or/and other hardware and machines linked together

and interconnected through communication channels that allow sharing of information. There are different typologies of network technologies and they can be classified mainly according to: (i) connection method (wired and wireless network) and (ii) scale (PAN, HAN, LAN, MAN, WAN). 5 Network Protocols can be classified in open and proprietary. On one hand, open protocols (e.g. Internet Protocol - IP) ensure interoperability across heterogeneous devices, thus improving also the scalability of the network itself. On the other hand, proprietary protocols facilitate the customization and the differentiation of offers by companies. 6 It is important to stress that significant and unavoidable decisions have to be made in the design phase of the dynamic database (core of the Platform), such as: which data to store, for how long, and at what level of aggregation. For what concern “which data and for how long they have to be stored”, it is possible to identify two main categories of data: transient data and recurring data (Badiru Adedeji 1996). Transient data appear once in the analytical process and they are not needed again. Therefore, transient data should not be permanently stored in the database, it would be a useless effort. On the contrary, recurring data appear in the analytical process so much frequently that they need to be permanently stored in the database. This last category of data can be further articulated into static data and dynamic data. Recurring data that are static retain the same values every time they are encountered in the analytical process (e.g. date of birth). On the other hand, recurring data that are dynamic can take on different values each time they are encountered in the analytical process (e.g. current age). It is also appropriate to take into account: first, the lifetime of data, so the period of time in which data remain significant and therefore, have the right to remain stored; second, the possible legislative or regulatory requirements that, consequently, dictate the length of time in which data should remain available to be extracted or analyzed; third, the length of data storage may also vary according to the needs of the company. As regards the level of aggregation of data, it can vary according to the purpose of the analyses. However, the decision related to the level of data aggregation has to be carefully made by the company on the basis, for instance, of a cost–benefit analysis. High levels of aggregation may require lower cost of data storage but their weakness may lies in a possible loss of information. While lower levels of aggregation ensure precise data but, usually, they require a high frequency of detection in order to obtain significant results, therefore they may have higher cost of data storage.

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• Service Analytics layer. This layer performs analyses on data and information (stored/in streaming) present in the database (Platform layer) to improve FM processes both at the strategic and operational level (exchanging the analyses results with the Service Management layer). Among the analyses that can be performed, it is possible to mention: descriptive,7 predictive8 and prescriptive9 analyses. • Application Layer. This layer acts as a dynamic and interactive interface. In particular, interfaces can vary according to the identity of the two extremes of the

7 Descriptive Analytics is performed to provide insight into the past and give an answer to questions

like “What has happened?” and “What is happening?”. Therefore, the aim of Descriptive Analytics is to observe and summarize raw data to deliver information understandable and interpretable by analysts, managers, etc. Hence, Descriptive Analytics allows us to learn from past behaviors and understand how they might influence future behaviors, events or outcomes. This group is based mainly on statistical analyses, thus it includes data analysis techniques as: classification (aimed at discovering rules which define if an item belongs to a particular subset or class of data), association (aimed at searching for patterns with a high probability of repetition), sequence (used to relate events in time), and cluster (used to group together a set of objects according to their similarity or, for instance, proximity to each other). Descriptive Analytics can also evolve into Diagnostic Analytics, aimed at answering to the question “Why did it happen?” understanding, thus, the causes of past events and behaviors. 8 Predictive Analytics is performed to understand and predict future events and answer to the question “What could happen?”. Indeed, Predictive Analytics is applied to try to estimate the likelihood of a future outcome, in other words, to identify, given parameters and historical trends, the best outcome to future events. Moreover, on the basis of the analyses results, it suggests decision options to best take advantage of a future opportunity or to try to mitigate a future risk. Predictive analytics exploits Big Data to identify patterns and it applies statistical models and algorithms to capture relationships between various data sets in order to build new models that correlate unrelated variables. Predictive Analytics includes different typologies of data analysis, such as: decision trees, data visualization, neural networks - that is the development of non-linear predictive models that are capable of learning how different combinations of variables affect the data set (OgrajenSek 2003)—and other machine learning techniques (e.g. genetic algorithms). The concept of machine learning explores the study and construction of algorithms that can learn from and make predictions on data, but can also represent the ability of computer systems to improve their performance by observing and analyzing data, without follow explicitly instructions. 9 Prescriptive Analytics is performed to advice on possible outcomes and it may answer to questions like “What should we do?”, “How should we respond to these predicted future events?”, and “How can we benefit from these predictions?”. Indeed, Prescriptive Analytics tries to quantify the effect of possible future decisions in order to inform about their related possible outcomes before these decisions are really made. Therefore, by predicting what may happen in the future and the related causes, it provides recommendations and advices concerning actions and strategies that can positively exploit and take advantage of the predictions, showing also the implications of each possible

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communication process. Hence, it is possible to identify three typologies of interface: Machine to Machine (M2M),10 Human to Machine (H2M)11 and Machine to Human (M2H).

6.3 Centralized Information Management for Advanced FM Decision-Making In light of the layered structure (introduced in Para 6.2) of the IoT platform for FM, Fig. 6.2 shows the stages of the related information management process articulated according to each layer.

Fig. 6.2 Stages of the IoT-enabled FM information management

decision, thus mitigating future risks. In light of this, it is important to stress the role of human participation in the process of interpretation of prescriptive analytics results. Indeed, the machine have the potential to suggest actions to human operators who then must discretionally decide to take or not to take the recommended actions. Prescriptive analytics includes algorithms based on large hybrid data sets (combinations of structured and unstructured data), business rules, and mathematical models. It continuously includes new data to re-predict and re-prescribe, improving the accuracy of prescriptions. 10 M2M interfaces refer to direct communication between devices using wired or wireless communication channels. This kind of interface enables, for instance, sensors to communicate the data that they have recorded (e.g. temperature) to the proper application software that will exploit those data. 11 H2M interfaces, also known as user interfaces, allow interactions between humans and machines, permitting an effective operation, monitoring and control of the machine by the hands of the human and, simultaneously, the machine provides to the operator (M2H) useful feedback information that will support his decision making process. Basically, H2M interfaces consist of a hardware and a software that allow to translate user inputs in signals for machines that, in turn, provide the required result to the user.

6.3 Centralized Information Management for Advanced FM …

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In particular: • Data Detection. At this stage, IoT devices gather user data, sensor data and machine data - in the form of real-time data flows - regarding the physical environment. The detected data are made available and displayed within the central database to the different stakeholders of the platform, to be used with different purposes both at a strategic (aggregated data) and operational (single data) level. • Data Transmission. In this stage, single data are collected, combined and transmitted over the network to the central database (Platform layer). In order to ensure the possible integration of heterogeneous data (in terms of format and shape, since they come from different sources or because they are detected with different technologies), it is necessary to normalize data using a common language that enables interoperability both among sensors/devices and between the layers of the platform. In particular, interoperability12 can be defined as the ability of different information technology systems, software, devices, etc. to communicate with each other exchanging data. • Data Storing and Processing. At this stage, information is stored and processed, offering to stakeholders an accessible and always updated knowledge base concerning several interrelated aspects of the building (e.g. operational data, economic data, administrative data, maintenance data, energy consumptions, etc.). Such a repository represents the foundations of an integrated information sharing environment able to optimize and innovate current FM practices, overcoming the “silos” approach to data management (according to which the different FM departments and stakeholders have their own databases and do not communicate and coordinate with each other, losing the opportunity to exploit convenient synergies that could reduce or optimize data acquisition costs and reduce unnecessary data redundancies). • Information Exploiting. At this stage, collected data and information are exploited to optimize the design, planning, programming and/or delivery of FM services. For instance, the collected real-time data and information can be exploited in order to: (i) provide a framework of the present conditions of building systems; (ii) identify users’ behaviors and preferences; (iii) assess performance of the ongoing services, identifying any deviations between expected and actual performance; (iv) detect in real time any faults in the equipment and identify the related causes; 12 The Healthcare Information and Management System Society (HIMSS) in 2013 proposed three levels of information technology interoperability: Foundational, Structural, and Semantic (HIMSS 2013). In particular, according to HIMSS 2013, Foundational interoperability is the basic one, meant as the data exchange from one system to be received by another. Structural interoperability is the medium level and it defines the format (or packaging) of data exchange, defining the syntax of data exchange. Therefore, it allows systems to interpret the exchanged data. Semantic interoperability is the highest level, it is meant as the ability of systems to exchange information, interpret the exchanged information and make effective use of it (e.g. use of information in computable algorithm). Indeed, Semantic interoperability is concerned not only with the data syntax but also with the semantics (data meaning) transmission, linking each information package to a shared vocabulary and then associating it to an ontology, thus creating an interoperable information package.

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(v) create a knowledge base to perform data analytics (in the following layer) with the aim, for instance, to reveal hidden patterns in equipment’s faults or to estimate the residual useful life of building systems and components, etc. • Data Analytics. At this stage data and information are processed according to algorithms and mathematical models (including clustering, descriptive and predictive analyzes, models for finding correlations, etc.). Data modeling tools also allow to perform scenario simulations. This stage is fed by the normalized and structured data deriving from the Platform layer and it serves the layers of information exploiting and use, allowing to extract meaning and insights from data. • Use of information and Feedback Information. This stage concerns the interaction between the building, its systems and occupants. This interaction is made possible by the use of smart interfaces that allow - through data visualization tools - to display, share and query the unique central database updated in realtime. Moreover, by using smart interfaces (web or mobile), occupants can activate actions (e.g. switching on or off a system, opening or closing a certain access or a window, changing the temperature of a room, booking a certain space or desk, etc.) or the building systems themselves can activate predefined actions—according to the S-R models—which are displayed on the smart interfaces and, if necessary, authorized by the building managers always through the same interfaces. Applications and smart interfaces allow building managers, not only to offer digital services to building users, but also to collect useful feedback information from the users themselves. In particular, smart applications can collect and gather direct feedback information (e.g. request for intervention, reporting of an anomaly, evaluation of a service, etc.) or indirect feedback information (e.g. number of accesses to a certain service, number and type of room booked by users, time spent in a certain room or area of the building, etc.). These feedbacks are fundamental to implement a virtuous process of continuous service improvement.

References Badiru Adedeji B (1996) Project management for research. Springer, Netherlands Haeckel SH (1999) Adaptive enterprise: creating and leading sense-and-respond organizations. Harvard business press Healthcare Information and Management System Society—HIMSS (2013) Definition of Interoperability. Available at: https://www.himss.org/library/interoperability-standards/what-is-interoper ability Kumar P, Martani C, Morawska L, Norford L, Choudhary R, Bell M, Leach M (2016) Indoor air quality and energy management through real-time sensing in commercial buildings. Energy Build 111:145–153 Ograjenšek (2003) Use of customer data analysis in continuous quality improvement of service processes. In: Proceedings of the seventh young statisticians meeting, pp 51–69 Talamo C, Atta N, Martani C, Paganin G (2016) The integration of physical and digital urban infrastructures: the role of “Big data”. TECHNE J Technol Architect Environ 11:217–225

References

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Standards and Laws IEEE P1451.6 Terms and Definitions. Available at: https://grouper.ieee.org/groups/1451/6/TermsD efinitions.htm

Chapter 7

IoT-Based FM Service Strategies and Operational Lines for Implementation

7.1 Strategies of Service Management Optimization and Innovation: Integrating IoT and Applying S-R Models Starting from the Sensing and Responding (S-R) models introduced in Chap. 6, it is possible to develop new strategies for the management of IoT-based FM services (Fig. 7.1), able to improve and innovate current practices of service planning, programming and delivery. In particular, the following innovative service strategies, based on S-R models and IoT application, can be proposed (Fig. 7.1): – – – – –

Remote Monitoring and Control; Real-Time Fault Detection and Diagnosis; Condition-based Maintenance Strategy; Predictive Maintenance Strategy; Prescriptive Maintenance Strategy.

These strategies, described in Table 7.1, are enabled by the implementation of the IoT technological infrastructure outlined in Chap. 6. In particular, with reference to Fig. 7.1, Table 7.1 shows the parallelism between the S-R models and the innovative FM service strategies, highlighting related key features.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Atta, Internet of Things for Facility Management, PoliMI SpringerBriefs, https://doi.org/10.1007/978-3-030-62594-8_7

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7 IoT-Based FM Service Strategies and Operational Lines for Implementation

Fig. 7.1 Innovative FM service strategies based on IoT integration Table 7.1 Innovative strategies for IoT-based FM services S-R model

Description

Innovative FM service strategy

Description

Sensing

Real-time monitoring of parameters and conditions by one or more sensor or IoT device.

Remote monitoring and control

Automated communication process by which measurements and data, coming from a variety of fixed/mobile sensors, are remotely collected and transmitted to a receiving system for monitoring and control.

Automatic sensing and responding

Automatic adjustment of non-complex processes (no propagation of effects) in domains that are restricted to few variables. When a condition, monitored and controlled by one or more sensors, is reached then a predetermined automatic procedure is activated.

Real-time fault detection and diagnosis

Continuous monitoring and control of a system and automatic real-time identification of occurring faults, pinpointing also the type of fault and its location. This strategy involves three approaches: – real-time monitoring and control of parameters; – automatic detection of faults by (i) detecting discrepancies between the sensor readings and expected values or (ii) detecting when residual goes above a certain predefined threshold; – automatic fault isolation to categorize the type of fault and its location in the building systems Real-time sensor-based fault detection and diagnosis strategy is able to: determine the state of a system over time, as well as to detect the existence, determine the type, and quantify the severity of faults in real-time.

Sensing and responding with constraints

The activation of a predetermined procedure, based on a detected measurement, is conditioned by the simultaneous satisfaction of one or more predefined imposed constraint conditions. It is applied to bounded systems of low-medium complexity, in which they are well known the cause—effect mechanisms linked to some predefined predictable conditions.

Condition-based maintenance strategy

Real-time monitoring of the actual condition of the asset to determine when and what maintenance intervention needs to be performed. Condition-based maintenance uses real-time data to prioritize and optimize maintenance resources. By monitoring and controlling the state and conditions of the system, it is possible to properly schedule maintenance, or other actions to be taken to prevent consequential damages. Such a strategy allows to perform interventions exactly and only when they are actually necessary. In particular, through Condition-based Maintenance, the interventions are requested only when certain detected parameters or indicators show signs of decreasing performance or upcoming failure. Thus, maintenance is done on an as-needed basis, so it can be proactively scheduled when needed. (continued)

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Table 7.1 (continued) S-R model

Description

Sensing and responding with external decision and proposal validation

Mechanism of S-R with constraints but it does not involve the automatic implementation of the response because it has to be submitted to verification and validation by a decision maker.

Sensing and knowing

Construction of information frameworks based on the real-time monitoring and analysis of heterogeneous data in order to describe realities and events that can be represented and predicted on a statistical basis.

Sensing and knowing in emergency

In case of exceptional events—characterized by behaviors that are not stable and not always predictable on a statistical basis—this model involves the construction of information frameworks based on continuous monitoring and analysis of datasets in order to represent, interpret and predict these events/phenomena.

Sensing and learning/self learning

Construction of cognitive, interpretative and descriptive frameworks that are based on the interpretation and analysis of a large number of data, in order to identify correlations between different variables.

Innovative FM service strategy

Description

Predictive maintenance strategy

Predictive maintenance strategy is designed to determine the condition of systems or assets in order to predict when maintenance should be performed. It is a maintenance strategy driven by predictive analytics, performed for predicting failures or anomalies. This approach allows to detect in advance failures, prevent them and define a proper convenient scheduling of interventions.

Prescriptive maintenance strategy

Prescriptive maintenance is a maintenance strategy—based on the use of cognitive and prescriptive analytics—that involves: (i) the continuous monitoring of systems conditions; (ii) the prediction of asset failures and (iii) the provision of potential solutions as an option to the end users. Prescriptive maintenance is able to predict the failure, determine why it will happen, and consequently identify and propose suitable possible solutions to prevent it (eliminating/reducing the releted possible consequent risks).

7.2 IoT-Based Strategies: Improving FM Services Below, some possible application scenarios of the IoT-based FM strategies to improve FM service management are proposed, focusing on the following services: Operation and Maintenance, Cleaning, Waste management, Space management and Energy and Utilities management.

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7 IoT-Based FM Service Strategies and Operational Lines for Implementation

7.2.1 Operation and Maintenance Management The new IoT-based data availability opens up interesting scenarios of optimization for building maintenance. Indeed, on one hand, the accessibility to real-time data and the possibility to integrate these data with other data coming not only from the building itself but also from the urban environment that surrounds it, opens the door to the development of innovative strategies of interventions before inconceivable. On the other hand, data coming from the phase of use of buildings (e.g. consumptions, feedbacks from planned interventions or corrective interventions, results of diagnostic activities, etc.) can constitute an important source of information for increasing the accuracy of maintenance plans forecasts. The maintenance strategies used in the traditional Operation and Maintenance (O&M) management practice are, mainly, corrective maintenance and planned preventive maintenance (Talamo and Bonanomi 2015) (Fig. 7.2). In particular, corrective maintenance is performed only after the occurring of a fault. This strategy often involves high costs, long intervention times, as well as long systems downtime and service disruptions. In order to avoid these inconveniences, planned preventive maintenance is adopted. According to this strategy, interventions are performed at standard predetermined intervals, according to an established time schedule, in order to reduce the probability of degradation or failure of components. By applying planned preventive maintenance, failures are intercepted before they occur. This model reduces downtimes but it can lead to an over-utilization of economic and

Fig. 7.2 Condition-based and predictive maintenance strategies

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human resources. Indeed, the scheduled interventions usually have high frequencies, useful to intercept in time the forms of degradation. This often causes a large number of maintenance interventions, planned and programmed independently of the real conditions of the components. Therefore, it often happens that interventions are performed on components that are not actually affected by degradation or failure and which are still far from the end of their lifecycle. The IoT application to the maintenance management allows to outline new strategies (Fig. 7.2), such as condition-based maintenance and predictive maintenance, which help to reduce costs and resource waste, and to limit systems downtime and service disruptions. These benefits are achievable thanks to the continuous and dynamic remote monitoring and control of the building, enabled by IoT technologies. Through a distributed system of sensors and smart devices connected to the IoT platform (dynamic database and data analysis software), it is possible to collect and process data (Big Data) useful in order to outline the actual state of operation and use of building components and systems. In particular, through descriptive analyses of real-time data it is possible to effectively implement a real-time fault detection and the more advanced condition-based maintenance (Fig. 7.2). Descriptive analyses performed on relevant data are able to provide a description of the current behavior and operating status of building components. Hence, condition-based maintenance allows to promptly intervene when there are anomalous conditions—detected by the real-time monitoring system—which can lead to failures or performance drops. In this way, the periodic on-site inspections of components status by operators will no longer have reason for being, as the interventions will be planned and performed according to the actual operating profile of components, remotely detected by sensors. Thus, maintenance activities will be aligned to the actual symptoms of the building, preventing failures, avoiding unnecessary costs and limiting the use of resources. Another advanced maintenance strategy enabled by IoT is the predictive maintenance (Fig. 7.2). It introduces the possibility of managing preventive maintenance by knowing the real operating conditions of the building components. Thus, interventions will no longer be performed at regular and periodic time intervals, but the frequencies will be determined using appropriate mathematical models in order to estimate the remaining time before the failure. Therefore, in this case—in contrast to the traditional preventive maintenance—the maintenance program is not determined by a prescribed time line based on statistics. The maintenance program becomes a dynamic tool, where interventions are scheduled on the basis of events and times estimated through predictive analysis algorithms, fed by reliable datasets collected through IoT sensors and devices. Indeed, this innovative strategy is based on predictive analyses (Fig. 7.2) for the early detection of degradations, malfunctions or failures, thus allowing a timely planning of interventions, avoiding interferences with other ongoing activities, and allowing the necessary resources (e.g. spare parts, etc.) to be supplied in advance. Lastly, the most advanced strategy is prescriptive maintenance which is based on the use of prescriptive analyses. In particular, prescriptive analytics allows to: anticipates future events and phenomena; predict when they will happen; understand why they will happen by interpreting proper heterogeneous datasets; suggest and propose decision

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options (e.g. possible suitable maintenance interventions) useful to mitigate future risks showing also the implications of the suggested options. Moreover, thanks to the continuous real-time monitoring, prescriptive analytics can continually “re-predict”, thus improving the accuracy and reliability of predictions and forecasts over time and proposing more suitable decision options. In light of these innovative approaches to maintenance management, Table 7.2 shows a detail of main possible applicative actions, as result of the implementation of each strategy to the O&M management process, highlighting for each phase (i.e.: Service Monitoring, Service Planning and Programming, Service Delivery, Feedback and Adjusting) the main related improvements.

Table 7.2 Examples of application of the innovative IoT-based strategies to O&M management processes Operation and maintenance Phase

Service strategy

Examples of actions

Examples of improvements

Monitoring

Remote monitoring

– Equipment health and condition monitoring

1. Remote equipment identification and localization 2. Improvement of equipment health monitoring

– Monitoring of technical (e.g. walls, windows, doors) and spatial (e.g. rooms) elements usage

1. Increased knowledge of users’ behaviors 2. Definition of profiles of use of building components

– Security monitoring (services for access control, video surveillance, intrusion and emergency call systems, fire alarm monitoring, etc.)

1. Accurate determination of users’ location in case of emergency/fire and real-time guidance to the best escape route

Real-time fault detection and diagnosis

– Automatic requests for interventions after the detection of faults

1. Reduction of downtimes in case of faults 2. Improvement of the reliability of equipments 3. Reduction of iinspection and maintenance costs of non-critical systems

Condition-based maintenance

– Real-time management according to dynamic behaviors of systems – Descriptive analyses to describe present phenomena in the building, present state of degradation of elements, present state of use of the elements and their operating state – Condition-based maintenance allows to promptly intervene when there are abnormal conditions that can lead to failures or performance drops – Automatic generation of work orderd in case of detected abnormal conditions (values beyond the acceptable threshold)

1. Reduction of on-site periodic inspections of components 2. Interventions are carried out on component actually in need (e.g. the component is closed to the end of its life cycle) 3. Maintenance activity will be aligned with the actual symptoms of the building, avoiding unnecessary costs and limiting the use of resources 4. Reduction of downtimes. 5. Extended useful life of components (reduction of resources usage, increased environmental and economic sustainability)

Planning and programming

(continued)

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Table 7.2 (continued) Operation and maintenance Phase

Service strategy

Examples of actions

Examples of improvements

Predictive maintenance

– Prediction of residual useful life of components – Frequencies of interventions determined using mathematical models – Predictive analyzes are aimed at early recognition of degradation, malfunctions or failures in order to understand in advance the failure occurrence and automatically update the maintenance plans and programs – Equipment condition assessment and benchmarking (e.g. comparison with the performance of similar equipments) for developing data-driven recommendations on how it is possible to improve equipment performance

1. Reduction of on-site periodic inspectionschecks of components 2. Timely planning and programming of interventions, avoiding interference with ongoing activities 3. Reduction of downtimes 4. Prediction of future behaviors, supporting decision-making and mitigating risks 5. Extended useful life of components (reduced resources usage, increased environmental and economic sustainability)

Delivery

Advanced delivery

– When a maintenance intervention is required, the information platform will automatically select, among the several operating teams of the service provider, the closest one (in terms of physical distance on the urban territory) which has the required skills – Mobile apps provide access to information (e.g. localizations of technical elements; work procedures; needed spare parts; etc.) to maximize efficiency. For instance, operators can query the site map to identify the position of the component which needs the intervention and he can retrieve other useful information to speed up the intervention time (e.g. work procedure, needed equipments and/or tools) – Work orders can be opened and closed through the use of the mobile application

1. Automatic check and easier assessment on the operator’s work 2. Automatic and proper collection of feedback information from the operators (e.g. from work orders through mobile app) 3. Reduction of operating times

Feedback and adjusting

Advanced information management

– Unique, centralized and shared dynamic database, continuously updated with real-time data flows. – Real-time and historical data are analyzed and displayed on the information platform through visualization tools.

1. Reduction of information loss ( during the collection, the extraction and the processing of information) 2. Accessibility to reliable data, useful to support decision-making processes at the operational level (single data) and at the strategic level (aggregated data and indicators) 3. Greater efficiency in the processes of feedback information collection (continued)

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Table 7.2 (continued) Operation and maintenance Phase

Service strategy

Examples of actions

Examples of improvements

Knowledge bases to support decision-making

– Dynamic SLA and KPI system at the operational, tactical and strategic level with automatic reporting system – Identification of factors that affect asset performance in order to create knowledge bases, at different level of detail, that can be used to recommend adjustments to the adopted maintenance strategies; etc. – Growth and updating over time of the existing knowledge bases, useful to support continuous improvement processes

1. Identification of factors affecting performance 2. Optimization of maintenance plans/programs and resource allocation/management 3. Validation of the service provider compliance with contractual specifications in terms of performance of service provision (real-time updated dashboards of KPIs and SLAs)

7.2.2 Cleaning Management Exploiting IoT technology, the actual use of the rooms by users can be detected in real-time (e.g. motion sensors placed on doors, presence and occupancy sensors, etc.). This information can be used to dynamically plan and program cleaning activites, avoiding unnecessary interventions (e.g. cleaning of unused rooms). In order to perform a condition-based strategy, some metrics must be preliminarily defined, e.g. maximum number of users’ entrances in a room before requesting cleaning activites, residual levels of consumables, etc.). By monitoring in real-time these metrics, the IoT platform can detect needs in real time and automatically send a request to the nearest cleaning operator for prompt intervention (Table 7.3). Moreover, the detected data on usage patterns can be analyzed to accordingly adjust the cleaning routine, optimize cleaning schedules and supply consumable orders. Table 7.3 Examples of innovative IoT-based activities for cleaning management Cleaning management Activity

Description

Real-time monitoring of consumables levels in washrooms

Proximity sensors on dispensers can monitor in real time the consumable levels (e.g. hand towels, filling level of the soap dispensers, hand sanitizer, air fresheners, etc.,). In this way, the filling interventions are delivered only when needed, reducing the number of checks and refills carried out by the maintenance team. The system will automatically notify the need of consumable to the cleaning operators (e.g. through mobile applications)

Optimized scheduling of cleaning operations

Through motion sensors placed on rooms entrances, it is possible to identify which rooms have been used and automatically schedule the cleaning intervention. The cleaning schedule can be visualized by operators on mobile interfaces (e.g. smarphones)

Forecast of cleaning interventions based on weather data

Crossing weather data with occupancy data it is possible to forecast the need for cleaning interventions

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7.2.3 Waste Management IoT offers the possibility to optimize waste collection processes, reducing waiting times and maximizing service quality (Atta 2017). In particular, current experimental practices focus on the possibility of exploiting sensors to provide virtual identities and communication skills to waste bins. Waste bins with embedded sensors become “smart bins”, i.e. intelligent bins that can detect their level of filling (and also other contextual data, e.g. presence of fire, temperature, air quality, etc.) and automatically send a request for intervention. Hence, innovative IoT-based solutions (Table 7.4) have the potential to improve the quality of the waste management service by enabling the: optimization of collection routes, reduction of operating times, waste collection scheduling according to the actual filling conditions of containers.

Table 7.4 Examples of innovative IoT-based activities for waste management

Waste management Activity

Description

Real-time monitoring of the filling levels of waste bins and containers

Wireless ultrasonic sensors can continuously detect and monitor the filling level of waste containers. In order to optimize the collection service this information is made available through smart mobile interfaces (e.g. smatphones) to the waste collectors.

Planning and optimization of waste collection paths

Planning and optimization of waste collection paths: geo-localization data of the containers together with the data from sensors about their filling level allow to identify in real time the most efficient path for waste collection (e.g. if the container has not yet reached the filling threshold level, the collection vehicle will not pass from that point).

Feedback information collection

Collection vehicle drivers can use mobile apps to scan, confirm and report on any event (e.g. automatic billing for waste collection on demand, etc.).

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7.2.4 Space Management By integrating IoT technology, it is possible to implement strategies to support the space management service and optimize it in real time. Trough fixed and mobile interfaces it is possible, both for space managers and users, to access to relevant information and to a wide range of digital services (Table 7.5). For instance, interactive touch-screens in meeting rooms can display information in real time about free/occupied desks, rooms, toilets, etc. Moreover, sensor data can also be visualized on a wide range of end user touch-points, such as smartphones, web interfaces, etc.

Table 7.5 Examples of innovative IoT-based activities for space management

Space management Activity

Description

Real-time space occupancy monitoring Motion, occupancy or presence sensors can be installed within rooms, allowing people to remotely review whether there are available spaces Real-time monitoring of meeting rooms Motion, occupancy or presence sensors occupancy data can be correlated with room booking system information to determine: if people are using the meeting room without making a booking, conversely if people are making a booking without using a meeting room Advanced room booking system

Thanks to sensors data and information coming from the room booking system, it is possible to monitor in real-time if people are using the appropriate size meeting room for the number of participants and if the number and sizes of all the meeting rooms within the building meet the booking requirements. In this way, it is possible to be sure that all the meeting rooms are utilized to their full potential, by possibly (if needed) redirecting people to a meeting room with suitable size and requirements

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7.2.5 Energy and Utilities Management The availability of real-time data on energy demand offers different opportunities for optimizing energy management and reducing energy consumptions, enabling the implementation of innovative high-efficiency solutions. In particular, today the possibility offered by predictive analyses to achieve a proactive supply system can be realized through the implementation of a Smart Grid, an electrical network equipped with sensors. Smart grids enable to combine the distribution system with a communication and control network for the continuous monitoring of energy flows. Indeed, they are equipped with electronic meters, which allow to outline in real time the consumption profiles of each user. The knowledge of users’ usage models makes it possible to plan more accurately the production of energy on which a new range of more personalized and competitive products and services can be based (Table 7.6). Moreover, on the basis of these data, the smart grid can efficiently identify, manage and mitigate demand peaks (e.g. it can send in real time the energy surplus of a certain area to other areas that at the same time are in deficit, promptly reacting to imbalances).

Table 7.6 Examples of innovative IoT-based activities for energy and utilities management

Energy and utilities management Activity

Description

Advanced monitoring and management of energy flows

Accumulation and storage of surplus energy and redirection in real time of the surplus energy to needing systems, thus mitigating the demand peaks

Intelligent scheduling of activities to reduce energy peaks

The real-time monitoring of energy consumption for each activity enables the creation of a database of energy consumed for activity and the system is able to manage the scheduling of activities according to their use of energy and avoiding energy peaks

Smart monitoring of performance

The use of intelligent smart grid systems enables the storage of key performance parameters in the database in order to create benchmarks useful to carry out analyses on the performance of the system. These data can be visualized on interactive dashboards displayed on web/mobile digital interfaces (e.g. through apps)

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References Atta N (2017) Information technology al servizio di una gestione dei rifiuti intelligente: smart waste management. In: Talamo C, Migliore M (2017) Le utilità dell’inutile. Economia circolare e strategie di riciclo dei rifiuti pre-consumo per il settore edilizio. Maggioli Editore, pp 247–264 Talamo C, Bonanomi M (2015) Knowledge management and information tools for building maintenance and facility management. Springer International Publishing

Chapter 8

IoT-Based FM Strategies Application: The Case of eFM Headquarter STATUTO 11

8.1 Smart and Digital Workplace: The Case of eFM Headquarter STATUTO 11 eFM (www.efmnet.com), co-funding company of the present Ph.D. research, is an Italian company (born in 2000) that provides consulting and outsourcing solutions for Real Estate and Facility Management. Today eFM counts about 220 employees and over 90 mln sqm of property being managed and over 2 bn e of undertaken service procurements, as well as about 32 mln sqm of provided service integration. In recent years, eFM has started to undertake a period of change towards the digitization of Real Estate management. The receivers of this change in conceiving the work environment have been the employees of eFM itself. Indeed, in the past few years eFM has changed its two main headquarters—first in Rome (2016) and then in Milan (2018)—transforming its workplaces in flexible IoT-based spaces configured according to employees needs. This change was possible through the implementation of IoT devices and an IoT platform which they developed in-house starting from ARCHIBUS Integrated Workplace Management System (IWMS). Together with technologies and working methods, also the physical workplace changes, becoming increasingly flexible, shared and dynamic. The concept of Digital Workplace implies a rethinking of spaces according to innovative digital ways of carrying out activities towards peer collaboration, networking and smart working, favoring new forms of creative work. Hence, according to eFM, the Digital Workplace is a work environment no longer divided into fixed and individual workstations, on the contrary it includes mobile and shared workstations, as well as diversified spaces in relation to the different forms of work, i.e.: focusing, collaboration, learning, networking (Fig. 8.1). The individual fixed workstations disappear and the areas for creative working methods, informal interaction and networking (such as meeting rooms, relax areas and break areas) multiply. The Digital Workplace implies a redefinition of the traditional space–time structure of work, since the methods and effectiveness of communication between © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Atta, Internet of Things for Facility Management, PoliMI SpringerBriefs, https://doi.org/10.1007/978-3-030-62594-8_8

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Fig. 8.1 Activity-based definition of work spaces. Source eFM

employees, as well as their involvement and productivity, are now increasingly independent from the physical perimeter of the office and increasingly dependent on the digital real-time remote interaction and on the ability of employees to collaborate and share knowledge. Hence, eFM ensures a synergistic cooperation between the physical environment and the digital environment in support of the transformation process towards a new work culture that opens up to sharing and inclusion. In particular, the Smart Digital Workplace of STATUTO 11 headquarter, is characterized by: – Variety of workspaces. The building layout is made up by different functional areas (Figs. 8.2, 8.3, 8.4 and 8.5) which constitute an environment capable of promoting forms of creative work, articulated into diversified spaces with mobile

Fig. 8.2 Scheme of eFM STATUTO 11 functional areas. Source eFM

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Fig. 8.3 Design Hive, ground floor—eFM STATUTO 11. Adapted from eFM

Fig. 8.4 Team Up, ground floor—eFM STATUTO 11. Adapted from eFM

workstations that guarantee maximum flexibility of the spaces themselves. The workplace also includes functions not conceived in the traditional office vision, such as relax areas, break areas, reception and networking areas, outdoor decompression areas, spaces suitable for carrying out physical activity, etc. It is up to the users to choose the most suitable space according to the activity they need to carry out at a given moment. – Efficient information management and adaptive responses. IoT solutions implemented by eFM enable the access, sharing and updating of real-time data flows coming from heterogeneous sources (e.g. sensors, cameras, smart-phones, etc.)

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Fig. 8.5 We Meet 4, first floor—eFM STATUTO 11. Adapted from eFM

useful for analyzing, predicting and efficiently managing flows of people, as well as material and energy flows, increasing the response capacity of the workplace. The latter, therefore, becomes a proactive sensing environment capable of self-management (e.g. by calibrating lights, activating the opening/closing of windows and doors, adjusting heating and air conditioning, etc.) in relation to the preferences and needs of the employees.

8.2 eFM Advanced Data Management: IoT Technology and Information Platform Different typologies of sensors have been installed within the different spaces of STATUTO 11 headquarter (Tables 8.1 and 8.2; Fig. 8.6) in order to detect in realtime significant parameters (e.g. presence of people, people flows, lightning, etc.) useful both: (i) for offering digital services to employees (e.g. reservation tool for booking desks or meeting rooms); and (ii) to monitor the effectiveness of the implemented solutions assessing the use of spaces by employees (e.g. occupancy rate of the different spaces of the workplace). The sensors are wireless connected, through the internet network, to the information platform ARCHIBUS, used by eFM for building management. Sensordata are stored and analyzed in order to deliver information and services to users through visualization tools and interface applications (e.g. web and mobile interfaces). In particular, ARCHIBUS is a web-based Integrated Workplace Management System (IWMS) platform—developed by ARCHIBUS Inc.—that enables to manage different aspects of the building management starting from its dynamic database that

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Table 8.1 Example of IoT sensors and devices implemented in STATUTO 11 workplace STATUTO 11 space

Sensors and devices

Objects on which Detected parameters sensors are installed and service functions

Design Hive

Presence sensors

Single desks of each workstation

Detection of the presence/absence of people seat in the desk position

Ergonomic sensors

Single desks

Information about highness of desks (variable highness) to achieve the best ergonomic condition

Camera

Ceiling—at the entrance

The camera is able to stream heat-maps for the analysis of occupation and crowding levels of spaces both in real time and periodically

Solar radiation sensors

Ceiling—in correspondence of windows and workstation

Sensors detect the natural solar radiation in order to automatically activate the window darkening system when necessary

Lightning sensors

Ceiling—in various Lightning domotics points of the space (automatic lights turn Above the entrance on/off according to the presence/absence of people) with a set timeframe of inactivity

Presence sensors

Single desks of each workstation

Detection of the presence/absence of people seat in the desk position

Lightning sensors

Ceiling (middle of the space) and above the entrance

Lightning domotics (automatic lights turn on/off according to the presence/absence of people)

Camera

Ceiling—at the entrance

The camera is able to stream heat-maps for the analysis of occupation and crowding levels of spaces both in real time and periodically

Team Up

We Meet 4

(continued)

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Table 8.1 (continued) STATUTO 11 space

Sensors and devices

Objects on which Detected parameters sensors are installed and service functions

Interacting tablet

Outside the front door (installed on the door)

Remote reservation of the space as well as Real-time information about the availability/not availability of the space are displayed on the tablet

Solar radiation sensors

Ceiling—in correspondence of windows and workstation

Sensors detect the natural solar radiation in order to automatically activate the window darkening system when necessary

Solar radiation sensors with dimmer

Ceiling—in correspondence of windows and workstation

Sensors detect the natural solar radiation and automatically they dim the artificial lights according to the preferences set by the user/s

Lightning sensors

Ceiling (middle of the space) and above the entrance

Lightning domotics (automatic lights turn on/off according to the presence/absence of people)

is bi-directionally integrated with BIM (Building Information Modeling) and CAD design software and it can be integrated with networks of mobile, fixed and wearable sensors and devices (e.g. WSN—Wireless Sensor Network), and with other information systems, software or customized extensions and plug-ins used for specific purposes. Moreover, ARCHIBUS information platform (Fig. 8.7) also allows to collect feedback information coming from employees that use smart interfaces, e.g. pc, smart-phone, tablet, wall-screens (Fig. 8.8). This information is gathered, managed and used in a centralized way, in order to enrich the knowledge base on the use of spaces towards a continuous service improvement. Indeed, in this way it is possible to create reliable series of historical data useful to perform descriptive and predictive analysis in order to increase the reliability of events forecasts (e.g. days in which one or more spaces will likely be over-crowded and related prediction of the influx of people) and to enhance the efficiency in managing these predictable events by planning and organizing on time appropriate measures.

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Table 8.2 Legend of graphic symbols for sensors and devices represented in Fig. 8.6 Graphic symbol

Type of sensor Built-in People Counting Camera (by Onevo)

Outdoor People Counting Camera—wall/ceiling mounting (by Onevo)

Outdoor Heat Map Camera—wall/ceiling mounting (by Onevo)

Presence/movement/lightning sensor to switch on lights (by KNX)

Presence sensor for desks and workstations (by Onevo)

Fig. 8.6 Sensors location in STATUTO 11 Ground Floor. Source eFM

Picture of the sensor

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Fig. 8.7 ARCHIBUS information platform and IoT integration by eFM

Fig. 8.8 Example of web based interfaces for employees’ access to service applications (e.g. smart-phone and tablet). Source eFM

8.3 eFM IoT-Based Services and Monitoring Tools Below, a focus on IoT-based services and tools implemented by eFM for optimizing and innovating Workplace and Asset Management is provided.

8.3.1 Smart Reservation Tool The Smart Reservation Tool is a booking application to reserve desks or rooms. This tool is based on real-time analysis of occupancy of desks and rooms in order to verify the actual presence/absence of employees to optimize the occupancy rate of spaces. This tool manages all the STATUTO 11 working areas and the meeting rooms (e.g. Design Have, We Meet, We Focus, etc.) that are usable only if booked through this tool. The Smart Reservation Tool is interoperable with other systems and tools, such as Microsoft Outlook, Google Calendars, etc., increasing in this way the organizational efficiency. Figure 8.9 shows the procedure to use the Smart Reservation tool. In particular, before booking a space, the employee has to have a clear idea of the kind of activity he/she has to perform in order to choose the most suitable space to carry out the work. Once the employee has booked the suitable desk or room, the check-in before using the desk/room is required in order to validate the

8.3 eFM IoT-Based Services and Monitoring Tools

Fig. 8.9 Procedure for using the Smart Reservation Tool. Adapted from eFM

Fig. 8.10 Example of desk/room booking web interface. Source eFM

Fig. 8.11 Example of tablet to display the availability of a room in STATUTO 11. Source eFM

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related occupancy information and automatically update both the information platform (Fig. 8.10) and the tablet outside the rooms (Fig. 8.11). Once the employee has finished the activity, the check out is required in order to update again the information about the availability of the desk/room within the information platform and on the tablet. As mentioned above, the Smart Reservation tool exploits also data coming from sensors and from the smart interfaces of the employees. Indeed these data are useful to detect, for instance, unused reservations or spaces used without reservation. In fact, thanks to presence sensors, the tool enables to understand if the booked rooms or desks are actually occupied by people or not and, if not, to automatically cancel the reservation and update in real-time the information about the availability of the desk/room in the information platform, so the room/desk can be booked by another person. Moreover, it is possible to generate reports (Fig. 8.12) about the

Fig. 8.12 Example of report based on Smart Reservation Tool data. Source eFM

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use of spaces based on the data from the Smart Reservation Tool. These reports involve information, indexes and indicators—visualized also through a graphical representation—concerning (for a predefined time interval) for instance (Fig. 8.12): average utilization of the building; average utilization by team; average utilization by room; average utilization by section; average utilization by floor; average utilization by department.

8.3.2 People Counting Tool The People Counting system is a wireless sensor-camera network that creates an infrastructure capable of detecting and obtaining data and information such as: – – – –

total number of employees and/or visitors per day; flows of people during events, filtered by week, day, hours and minutes; preferences of desks/room by employees; preferential entry/exit routes for employees and/or visitors.

Sensor-cameras are installed in correspondence of accesses and gates and they count people who enter in and exit of a space (Fig. 8.13). The analysis of the entrances in a space in real time, especially for all those events with large numbers of people (e.g. conferences, workshops, software presentation,

Fig. 8.13 Streaming image detected by a People counting camera located in the main entrance of STATUTO 1. Source eFM powered by Onevo

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etc.), provides useful data for the sizing of the flows of people in order to optimize in real time the organization of the event but also to improve the preparation and management of similar future events.

8.3.3 Heat Map Tool The Heat Map Tool involves sensor-cameras based on heat detection. The sensorcameras are connected with the central information platform which automatically displays in real-time the detected data through a graphic representation (Figs. 8.14 and 8.15). The Heat Map Tool shows flows of people within spaces in a range of colors varying from blue (colder color), which indicates lower values, to yellow (medium) and red (warmer color) which indicate higher values. The video analysis system of the Heat Map Tool allows to understand the behavior and the way of living spaces of employees and visitors, in relation to: use of spaces; preferential routes; major points of interest; dead areas and cold points; crowding/stationing areas; areas of overcrowding, etc. The information platform allows to display data from the Heat Map Tool in realtime but also to retrieve heat maps of the past by choosing the time interval of interest. Indeed, the platform automatically stores all the recorded heat maps within its storage space or in the cloud.

Fig. 8.14 Example of web interface displaying data from the Heat Map Tool. Source eFM

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Fig. 8.15 Example of Heat Map concerning the Design Hive. Source eFM powered by Onevo

Chapter 9

IoT-Based FM Scenario: Supply Chain, Organizational Models and Contracts

9.1 IT Provider as a New Stakeholder of the FM Supply Chain Considering the IoT-based FM scenario, many aspects of current FM practices may be affected by profound mutations on both demand side and offer side. The possibility of seeing in the immediate future the actual maturation of the IoT potentials within the FM field strongly depends on the ability of Clients and Service Providers to successfully integrate their businesses, operating synergistically in a collaborative environment, sharing common objectives, policies and strategies. With reference to this innovative scenario, the IT provider is emerging as a new key player of the FM supply chain (Fig. 9.1). Therefore, the need for a review of the traditional supply chain structures arises, together with the re-definition of the roles, functions, profiles of competence, interactions and responsibilities of traditional FM stakeholders. In particular, according to this new network vision, the supply chain is no longer configured as a sum of suppliers, but as an unitary entity. All the stakeholders involved in the network should share objectives, policies, strategies, knowledge and know-how. Therefore, the supply chain becomes a higher level entity with its own functions that offers services to the involved stakeholders, who enter into a virtuous circle of growth where the sharing of knowledge and experiences is an opportunity of enhancement, training and innovation both at the overall level of the supply chain and also at the lower level of the individual stakeholders.

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Fig. 9.1 IT provider as a new FM stakeholder

9.2 IoT-Based FM: Scenarios of Application and Potential Organizational Models The effective implementation of the network approach to the supply chain is one of the variables that influence the assessment of the IoT-FM integration, both from the point of view of the economic feasibility (profitability) and from the one of the technological feasibility. Therefore, the Client together with the IT Provider and other involved key players should assess the IoT-FM integration solution considering the specificity of its organization, as well as the related internal and external context conditions, in order to find reliable answers to the following questions: – Is the IoT-based FM service viable for the Client in terms of available economic resources? – Is the IoT-based FM service profitable to the Client considering both the economic feasibility and the technological feasibility? Is the service characterized by a positive cost/benefit ratio? – Is the IoT-based FM service feasible in terms of value proposition both to the Client and to the end-users? – Is the global profitability (economic terms) fairly distributed on all the stakeholders (e.g. Real Estate Owner, FM company, IT Provider, FM suppliers, etc.) involved in the IoT-based FM service? – Is the IoT-based FM service feasible in terms of its future implementation at the operational level? Is it viable with respect to the available workforce (e.g. needed know-know and skills)? – Does the IoT-based FM service have a positive estimated end-suppliers and end-users acceptance (both in terms of service understanding and outcomes acceptance)?

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Fig. 9.2 Typologies of client approach to IoT-based FM service provision

In light of this premise, if the Client decides to undertake the implementation of the IoT-based FM scenario, seizing the opportunity to optimize its cognitive, managerial and decision-making processes, it is essential to clearly understand what are the configurations of the new possible organizational models with respect to the supply of IoT-based FM services. In particular, the identification of possible scenarios of activation of the IoT-based FM service by the Client1 and the definition of related organizational models depend mainly by a key factor: the ownership of the IoT Architecture. Starting from this premise, it is possible to identify the following two opposite broad cases (Fig. 9.2): – case 1. The Client (or its FM company) already owns the Information Platform, the technological infrastructure and tools, and it has the required know-how to manage them internally to its staff. In this first case, the information ownership depends by the type and terms of the FM contract and by the model of Command Center that the Client and the FM company decided to implement. The contracting parties, therefore, have to define and agree on who will be responsible for the information flows during the contract period and the Client must clearly state that it will own the information base at the end of the contract period;

1 Here the term “Client” refers to the demand organization, i.e. Real Estate owner or the FM company

that manages the Real Estate assets on its behalf.

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Fig. 9.3 Main features of the “as a Service” models

– case 2. The Client (or its FM company) does not own the Information Platform and the technological infrastructure and tools, and it does not intend to buy it.2 In this case, the Client can outsource the IT service to an IT provider. Given the still experimental nature of IoT adoption within current FM practice, the second case appears to be more viable. Hence, focusing on the second case (i.e. the case in which the Client prefers not to buy the IoT infrastructure but to outsource the IT service to an IT provider), it is possible to identify as solutions for IT service and infrastructure provision the so-called “as a Service” models (Fig. 9.3). In particular, three main models for the “as a service” provision of the IT infrastructure and data management capabilities can be outlined. In particular, sorted by increasing level of complexity, the models are (Fig. 9.3): Software as a Service (SaaS); Infrastructure as a Service (IaaS); Platform as a Service (Paas). According to these models, the Client (and its FM company) can develop, run and manage its own applications without the complexity of buying and maintaining the whole IT infrstructure. In this way, the IoT-based FM applications will be hosted in the “rented” digital infrastructure consisting of the network, servers, storage, platform or operating system and databases.

2 The Client choice not to buy the information platform may be due to various reasons, including by

way of example: the Client does not have the necessary financial resources; the Client is uncertain about the expected results and does not want to make a high risk investment; the Client does not have the necessary know-how to be able to manage and maintain the platform and the technological infrastructure over time.

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9.3 Information Responsibility and Ownership Within IoT-Based FM Invitations to Tender, Contracts and Service Delivery In the context of tendering processes for the provision of IoT-based FM services, the writing of a proper Invitation to Tender (ITT) is a crucial moment with respect to the effectiveness and the success of the whole tendering process (Talamo and Atta 2019). The writing of an ITT is a fundamental process but not always an easy task, especially in light of the increased complexity characterizing the novel IoT-based FM scenario. Indeed, the application of IoT and Big Data management solutions within the traditional FM practices is bringing out new issues (Table 9.1) that should not be overlooked and underestimated (Talamo and Atta 2019), especially in the context of ITTs and contracts drafting. These emerging issues can be articulated according to the reading keys highlighted in Table 9.1. In particular, for each reading key, Table 9.1 summarizes the main support lines for clients to draft clear and comprehensive ITTs and contracts for the provision of IoT-based FM services, reducing the risk of running into disputes with service providers (including IT providers) during the service delivery duration. Focusing on the last reading key of Table 9.1 about information responsibility and ownership in the context of the provision of IoT services for FM, it should be underlined that in case of IoT services outsourcing, the Client should include within the FM contract a specific policy for information security. This policy should clearly define the roles and responsibilities of the involved contracting parties. Within the context of a cloud-based environment as the one of IoT, the roles, functions, authorities and responsibilities can be allocated to a single contracting party (e.g. IT provider) or they can be shared between the contracting parties. In both cases, the role of the policy become crucial with respect to the topic of responsibility and ownership of data and information bases (BS ISO/IEC 27017:2015; BS EN ISO/IEC 27002:2017; ISO/IEC 27005:2018). Indeed, ambiguous information in this regard can give rise to misunderstandings that may translate into litigations and legal disputes during the service delivery. In Table 9.2 some support lines—for: (i) properly allocating information responsibilities, (ii) accurately express the information ownership, (iii) implement information security requirements for FM cloud-based services—are provided highlighting the duties of both Client and IT Provider in the context of FM agreements, focusing on technical requirements for information management within a cloud environment as the one of an IoT Platform.

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Table 9.1 Examples of support lines for ITTs and contracts for IoT-based FM service provision. Adapted from Talamo and Atta (2019) Reading key

Support lines for writing ITTs and contracts for IoT-based FM service provision

Existing information management software

The Client should include a description of the existing information system, specifying brand, included modules, functions, covered areas of interests and technical characteristics of each module, etc. Moreover, in case of adoption of a new platform, the Client has to request to the IT Provider an analysis of the already present modules in order to understand if and how to integrate (within the new platform) all the functions carried out by the existing software that will be substituted.

IP technical and functional characteristics

The Client must express its expectations, needs and requirements regarding the the architectural and functional characteristics of the IT platform, as well as requirements concerning the development methods and the gradual implementation and use of the platform (e.g. implementation step, data transfer, deliverables timetable, etc.). To support the formulation of this specifications and requirements, also in order to guarantee a level of quality in line with its requests, the Client can refer to reference standards regarding IoT platforms (such as, for example, ISO standards, PAS standards, etc.).

Allocation of roles and responsibilities to IT provider

The Client should specify: – who will be the purchaser of the IoT platform (if the Client itself or if it is a responsibility of the Provider to supply the information platform), – if the platform will be selected during the tender phase or in the mobilization phase (after the awarding phase) following cognitive meetings between the Client and the awarding IT supplier aimed at carrying out a detailed analysis of the internal and external conditions of the Real Estate and of the related provision of FM services. Moreover, with respect to the technical services connected with the maintenance and software update of the IoT platform, the Client should specify the roles and responsibilities of the IT Provider. In particular, it has to state if the IT Provider will have the accountability for only delivering the platforms or for delivering and maintaining it (including technical maintenance and software updating) during the entire contract period.

Inventory process and registry system

The Client should specify how the inventory process could be improved through the implementation of IoT technologies focusing on: devices and sensors that must be installed, information flow management modes (e.g. storage and processing of data), categories of information to be adopted for the registry system, quantities and types of technical elements to be recorded, level of informational detail to be guaranteed, etc. If the Client does not have the required know-how to formalize these requests, requirements and specifications, it could decide to rely on the competence of IT providers, formalizing a request for proposals/suggestions in this regard. (continued)

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Table 9.1 (continued) Reading key

Support lines for writing ITTs and contracts for IoT-based FM service provision

Feedback data and information

Regarding the process of acquisition of feedback information—both at the strategic level (e.g. from report and from users satisfaction surveys) and at the operational level (e.g. from the closure of the work orders, thus from the operators after the execution of interventions), the Client should formalize and specify: – which feedback information is useful to collect; – procedures and techniques for improving the historical and statistical information base, by means of the IoT-based data collection tools and the analytical capabilities of Big Data management; – objectives of the collection and analysis of feedback information (e.g. identification of possible current and future trends; event forecasting for supporting risk management processes; etc.).

Service monitoring and control (SLAs and KPIs)

The Client should define the SLAs and the related KPIs—which will now be dynamic, both in terms of updating in real time their calculation and evaluation, and in terms of modification of the benchmarks according to possible changes in the requests or needs of users). The Client should also include new requirements and SLAs concerning the main features of the IoT systems and the new related KPIs for their monitoring and assessment overtime. If the Client does not have the skills and knowledge required for formulating the IoT-related SLAs and KPIs, it should formalize a request for proposal to the providers, in order to jointly agree the system during the pre-contractual negotiation phase in which the two contracting parties can discuss and make an agreement in this regard.

Risks identification and assessment

Risk identification and assessment to “determine the practical implications of managing innovation and transformation in service delivery against the anticipated benefits” (BS 8572:2018). As the issue is completely new and still unexplored on the market, the Client could propose methods to perform this activity with the help of the IT provider in order to effectively identify and manage possible implication deriving from the IoT-based FM innovation. Moreover, in conducting this activity, the Client should “ensure that appropriate provisions are incorporated in the service level agreement to accommodate such an arrangement and the changes that might be necessary to the associated service specification” (BS 8572:2018).

Possible ethical and social implications

The Client should perform an analysis of ethical and social implications which may negatively affect the quality of the FM service provision. These implications may include, among others (BS 8572:2018): (i) social and ethical issues arising from the choice to employ IoT devices, platforms and infrastructure; (ii) restrictions in interoperability required to assure security of data; (iii) individual rights of sensor-gathered data and information about staff, users, etc. (data privacy); (iv) arrangements for protecting intellectual property between the two contracting parties; (v) measures to avoid and prevent unauthorized access to data.

Information responsibility and ownership

The client should define and describe: – responsibility allocations for the accuracy, reliability and security of data from sensors and devices; – ownership of data generated during service delivery; – nature of the interface between the IoT platform/devices and operative staff; – information security and privacy measures.

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Table 9.2 Support lines useful for information security management requirements for FM cloudbased services. Adapted from: BS ISO/IEC 27017:2015 Client

IT Provider

Inventory of assets

The Client should list the assets and the associated The IT Provider should implement and information (e.g. locations, identification of the update the inventory of assets of the specific cloud service, etc.) that need to be stored in Client. the cloud environment and validated/updated over time.

Coding of assets

The Client should encode assets (and associated information) maintained in the cloud environment in accordance with the agreed and adopted coding procedures.

The IT Provider should adopt the coding system of assets proposed by the Client.

Administrator and operator logs and user access provisioning, registration and deregistration

If one or more IT operations are managed by the Client, the operations and the related performance should be logged and recorded. Thus, the Client should determine whether logging capabilities provided by the IT Provider are appropriate or whether the IT Provider should implement additional logging and recording capabilities.

The IT Provided, if requested, should provide to the Client: administrator and operator logs, as well as (if requested) functions for user de/registration and for managing the access rights, along with the related use specifications.

Management of privileged access rights

The Client should use sufficient authentication techniques (e.g., multi-factor authentication), according to the identified risks, for authenticating the cloud service administrators (of the Client itself) who have access to the administrative capabilities of one or more cloud service.

The IT Provider should provide the requested authentication techniques for authenticating the cloud service administrators of the Client to the administrative capabilities of a cloud service. It may also propose to the Client proper options (according to the identified risks) for managing privileged access rights.

Management of secret authentication information of users

The Client should verify that the management procedures for allocating secret authentication information (e.g. passwords, etc.) implemented by the IT Provider meet the requirements (set by the Client itself within the agreement).

The IT Provider should provide information on procedures for the management of the secret authentication information of the Client, including procedures for allocating such information and for user authentication.

Information access restriction

The Client should ensure that access to information in the cloud service can be restricted in accordance with its access control policy and that such restrictions are realized.

The IT Provider should provide access controls that allow the Client to restrict access to its cloud services, to its cloud service functions and to the data and information stored and maintained.

Use of privileged utility programs

The Client should identify the utility programs to be used in its cloud computing environment, ensuring that they do not interfere with the controls of the cloud service.

The IT Provider should: (i) identify the requirements for any utility programs used within the cloud service; (ii) ensure that any use of utility programs capable of bypassing normal operating or security procedures is strictly limited to authorized personnel; (iii) ensure that the use of such programs is periodically reviewed and audited. (continued)

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Table 9.2 (continued) Client

IT Provider

Policy on the use of cryptographic controls

The Client should request the IT Provider to: The IT Provider should assist the Client implement cryptographic controls for its use of in applying cryptographic protection. cloud services (if needed according to the results of the risk analysis). The controls should be of sufficient strength to mitigate the identified risks. In the case in which the Client requested to the IT Provider to provide cryptography and related cryptographic controls, the Client should periodically review any information supplied by the IT Provider in order to confirm whether the cryptographic capabilities: (i) meet its information security policy requirements; (ii) are compatible with any other cryptographic protection used by the Client itself; (iii) are applied to the requested static, dynamic and real-time data transiting to/from and within the cloud service.

Key management

The Client should identify the cryptographic keys If requested, the IT Provider should for each cloud service, and implement procedures assist the Client in the key for key management. In particular, with respect to management. key management functionalities, the Client should provide/request to the IT Provider the following information about the procedures used to manage keys related to the cloud service: (i) type of keys; (ii) specifications of the key management system, including procedures for each stage of the key life-cycle (e.g. generating, changing or updating, storing, retiring, retrieving, retaining and destroying, etc.); (iii) specification of recommended key management procedures. The Client should not allow the IT Provider to store and manage the encryption keys for cryptographic operations when the cloud service customer employs its own key management or a separate and distinct key management service.

Capacity management

The Client should ensure that the agreed capacity provided by the cloud service meets its requirements. The Client should also monitor the use of cloud services and forecast their capacity needs in order to ensure the needed performance of the cloud services over time.

The IT Provider should monitor the total resource capacity to prevent information security incidents caused by resource shortages.

Information backup

If the IT Provider provides backup capability as part of the cloud service, the Client should request the related specifications. The Client should also verify that the specifications provided by the IT Provider meet the request.

The IT Provider should provide the specifications of backup capabilities to the Client. The specifications should include information regarding: scope and schedule of backups; backup methods and data formats, including encryption, if relevant; retention periods for backup data; procedures for verifying integrity of backup data; procedures and timescales involved in restoring data from backup; procedures to test the backup capabilities; storage location of backups. Moreover, the IT Provider should provide to the Client secure and segregated access to backups (e.g. virtual snapshots, etc.). (continued)

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Table 9.2 (continued) Client

IT Provider

Management of technical vulnerabilities

The Client should request information from the IT Provider about the management of technical vulnerabilities that can affect the cloud services provided. The Client should identify the technical vulnerabilities it will be responsible to manage, and clearly define a process for managing them.

The IT Provider should make available to the Client information about the management of technical vulnerabilities that can affect the cloud services provided.

Segregation in networks

The Client should define its requirements for segregating networks to achieve tenant isolation in the shared environment of a cloud service and verify that the IT Provider meets those requirements.

The IT Provider should enforce segregation of network access for the following cases: (i) segregation between tenants in a multi-tenant environment; (ii) segregation between its internal administration environment and the cloud environment of the Client. Moreover, the IT Provider should support the Client in verifying the implemented segregation.

Reporting information security events

The Client should implement or request to the IT Provider a reporting tool useful for: (i) the Client itself to report an information security event it has detected to the IT Provider; (ii) the IT Provider to receive reports regarding detected events (e.g. information security events); (iii) the Client itself to track the status of reported events (e.g. information security events).

The IT Provider, if requested, should provide a tool (and related procedures) for reporting possible significant events.

Identification of applicable legislation and contractual requirements

The Client should request evidence of the IT Provider compliance with relevant regulations and standards required for the business of the Client (e.g. certifications by third-parties, etc.).

The IT Provider should inform the Client of the legal jurisdictions governing the cloud service. The IT Provider should also identify its own relevant legal requirements (concerning, for instance, encryption to protect Personally Identifiable Information-PII, etc.) and communicate them to the Client. The IT Provider should provide to the Client the evidence of its current compliance with both applicable legislation and contractual requirements.

Protection of records

The Client should request to the IT Provider information about the protection of records—gathered and stored by the IT Provider—that are relevant to the use of cloud services by the Client itself.

The IT Provider should provide information to the Client about the protection of gathered and stored records.

monitoring of cloud services

The Client should request information from the IT Provider about the monitoring capabilities available for each cloud service. Moreover, the Client should specify—and communicate to the IT Provider—what are the relevant and significant aspects of the operation of the cloud services that it wants to regularly monitor.

The IT Provider should provide tools that enable the Client to monitor specified aspects (relevant to the Client) of the operation of the cloud services. The IT Provider should provide documentation of the service monitoring capabilities to the Client. The monitoring should provide data consistent with the event logs and the related requirements defined by the Client and it has also to be aligned with the SLA and KPI system, supporting the evaluation of service performance over time. (continued)

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Table 9.2 (continued) Removal of cloud service client assets

Client

IT Provider

The Client should request a documented description of the termination of service process that covers return and removal of the assets of the Client followed by the deletion of all copies of those assets from the systems of the IT Provider. The description should list all the assets and document to be released/removed, as well as the related schedule.

The IT Provider should provide information about the arrangements for the return and removal of any assets of the Client upon termination of the agreement for the use of a cloud service. The asset return and removal arrangements should be documented in the agreement and should be performed in a timely manner. The arrangements should specify the assets to be returned/removed.

9.4 New Profiles of IoT-Related Skills and Competencies for FM Stakeholders The FM supply chain is characterized by a multiplicity of know-how and backgrounds. Each stakeholder is a holder of specific knowledge, skills, experiences, and expertise. In this regard, the network approach to the FM supply chain and the integration of the IT provider as a new FM stakeholder lead to the rise of some “interface areas”, i.e. areas of expertise that overlap with respect to the different skills of the involved stakeholders. These overlapping areas consequently generate the need for new cross-sectoral profiles of expertise and skills, required to the traditional FM stakeholders in order to guarantee: a good communication along the supply chain; a common understanding of services and related specifications; good quality and performance of the service provision; and the absence of misunderstandings and litigation during the contract period. In particular, the diffusion of most of the IoTbased FM innovations—fundamental for increasing the efficiency of management services—may depend in the next future on the presence of professionals with the knowledge and skills necessary for the operational implementation of technological innovations to the Real Estate management field. Hence, the training of traditional FM stakeholders, especially facility managers, on subjects related to IoT management (e.g. sensing devices, computing devices, cloud computing, network environment, etc.) is crucial for the establishment of long-term win–win relationships with IT providers, as well as for the success of the overall FM service provision, ensuring high service performance and quality. In this regard, Table 9.3 proposes a set of IoT-related competency profiles for facility managers, articulated in four increasing maturity levels.

– Knowledge of basic notions regarding digital dynamic database – Basic notions about API-based access to raw data – IoT system interface – Basic notions about real-time data collection – Basic notions regarding big data collection, extraction and analysis – Knowledge about relevant IoT-related national and international standards

Category of competence

Knowledge

Application of interfacing techniques and visualization tools (to be connected with computer systems) for: – Using dynamic interfaces (e.g. dashboards, smart panels, etc.); – Reading and properly interpreting data, information and indicators included in smart interfaces; – Data and information communication, visualization, networking, sharing, etc. Keywords: Interface techniques; big data dashboards; data integration; big data collection, analysis and interpretation

Proficiency description

Level 1

Levels of maturity

– All notions listed in Level 1 – Basic notions regarding Big Data collection, extraction and analysis through different techniques of data analytics (e.g. classification, clustering, descriptive analyses) – Notions about storage methods – Knowledge of data integration and data export techniques for the integration of internal and external data sources

Proficiency described in Level 1 plus: – Management of data storage within the platform, use of methods of storing the data received from different systems and software; – Big data modeling and data model management, allowing the creation and correlation of additional parameters (e.g. external, calculated, virtual/simulated etc.) – Use of the dynamic information base for Services Management (e.g. planning and programming activities, etc.) Keywords: Data modeling; data storage; service management

Level 2

Table 9.3 Maturity levels of IoT-related competency profiles for facility managers Level 3

– All notions listed in Level 2 – Knowledge of descriptive analyses – Notions regarding automated systems, control models and process control algorithms – Knowledge of procedure for real-time service monitoring and control techniques and procedures – Notions regarding cloud computing – Knowledge of S-R models and related techniques

Proficiency described in Level 2 plus: – Implementation of procedures for real-time service monitoring and control, including: use of real-time data to measure KPIs; carry out performance benchmarking – Performing descriptive analysis for detecting faults and anomalies, and for revealing medium-term trends and patterns – Perform big data analysis and cloud computing to improve decision-making – Implementation of S-R models Keywords: Service monitoring and control; descriptive analysis; cloud computing; sensing and responding

Level 4

(continued)

– All notions listed in Level 3 – Knowledge of predictive and prescriptive analyses – Knowledge of predicting and responding models and related techniques – Notions regarding resilience of buildings

Proficiency described in Level 3 plus: – Performing predictive and prescriptive analysis for: revealing long term trends and patterns and accordingly act; making predictions of failures or anomalies and consequently take intelligent actions – Implementation Predicting and Responding models – Design of IoT models and solutions for improve building resilience Keywords: Predictive analysis; predict and responding; Prescriptive analysis; building resilience

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Abilities

Table 9.3 (continued)

– Ability to query, filter and select data and formats – Ability to monitor in real-time key parameters – Ability to use and manage big data dashboard – Analysis and interpretation of Big Data for task optimization – Ability to share data (invoking external systems interfaces for data sharing, etc.) – Ability to perform tasks to interact with IoT devices

Level 1

Levels of maturity Level 2 – All abilities listed in Level 1 – Ability to operate system information integration (e.g. manage the integration of data and information coming from different software and information system, as BMS, CAFM, CMMS, etc. within the dynamic database of the platform) – Ability to configure and optimize storage methods (e.g. configuration of data storage engines/plug-ins, etc.) – Ability to interpret network information to monitor performance and/or schedule service activities

Level 3 – All abilities listed in Level 2 – Ability to perform different techniques of data analytics (classification, clustering, descriptive/prescriptive analyses) – Ability to use real-time data to measure KPIs and performing internal/external benchmarking – Ability to perform descriptive analyses and cloud computing – Ability to interpret results of descriptive analyses in order to detect faults/anomalies, and reveal medium-term trends and patterns – Ability to implement S-R models

Level 4 – All abilities listed in Level 3 – Ability to perform predictive and prescriptive analyses (e.g. machine learning over historical and situational data) – Ability to interpret results of predictive and prescriptive analyses in order to predict faults and anomalies and consequently triggering proper actions; and to reveal long-term trends and patterns – Ability to implement predicting and responding models

9.4 New Profiles of IoT-Related Skills and Competencies for FM Stakeholders 115

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Reference Talamo C, Atta N (2019) Invitations to tender for facility management services: process mapping, service specifications and innovative scenarios. Springer

Standards and Laws BS EN ISO/IEC 27002:2017 Information technology. Security techniques. Code of practice for information security controls (ISO/IEC 27002:2013) BS ISO/IEC 27017:2015 Information technology. Security techniques. Code of practice for information security controls based on ISO/IEC 27002 for cloud services ISO/IEC 27005:2018 Information technology. Security techniques. Information security management systems. Overview and vocabulary BS 8572:2018 Procurement of facility-related services. Code of practice

Conclusions

Today FM stakeholders express a growing demand of advanced information management tools able to reduce process inefficiencies and data loss in order to improve building maintenance and management. Indeed, current FM practices are often affected by the following criticalities: slow and expensive manual processes of data collection and management; not-shared procedures for data gathering and processing; static and not-updated information; lack of information sharing among stakeholders; lack of a systematic approach to feedback information management. These above-highlighted inefficiencies are related to three main topics that today represent emerging needs for innovation of the FM field, namely: real-time data availability and accessibility; network approach to information management; data analytics to improve the decision-making. In this regards, nowadays the paradigms of Internet of Things (IoT) and Big Data management represent an opportunity for improving FM processes, providing advanced technologies and tools for increasing the efficiency of data collection and management. Indeed, IoT technologies make it possible to collect real-time data and information on actual conditions and behaviors of buildings and their components. This additional information base, if properly organized and shared among all the involved stakeholders, can support and optimize FM cognitive and decision-making processes. In particular, the new information management capabilities offered by IoT technologies allow to shift: – from static data to real-time data flows. The development of a single database, shared by all stakeholders, which follows the building throughout its life cycle by dynamically updating data, even in real time, constitutes a reliable information base which is currently required by FM operators in order to accurately weigh strategies, decisions and investments. – From linear and not-integrated to centralized and integrated data management processes. The IoT-based approach allows to streamline the processes for © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 N. Atta, Internet of Things for Facility Management, PoliMI SpringerBriefs, https://doi.org/10.1007/978-3-030-62594-8

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Conclusions

exchanging and sharing information among all the involved actors at all the levels of the management (strategic, tactical and operational), reshaping traditional siloed and vertical FM information management processes in a logic of horizontal integration. – From unstructured to structured data-driven decision making processes. The new data availability offered by IoT makes it possible to introduce new service strategies and optimize decision-making processes. In particular, one of the most innovative aspect lies in the possibility of optimizing the planning and scheduling of interventions in a dynamic way according to the real conditions and needs (detected in real time or predicted) of the building, its components and its users. This improvement allows to overcome the inefficiencies of the traditional practice, reducing in this way operating costs and waste of resources and minimizing downtimes. Indeed, the traditional FM practice is often characterized by interventions and service activities planned and programmed regardless of the real behaviors and conditions of use of the building components, relying on statistical data that often lead to perform interventions on components which, however, are not in need and neglecting components that instead have a real need for intervention. – From siloed and linear to inclusive and network-based supply chain management. The network approach to the supply chain promotes an inclusive and collaborative environment by facilitating communication and cooperation among FM stakeholders. The supply chain is no more configured as a sum of suppliers, but as an unitary entity. All the stakeholders involved in the network share objectives, policies, strategies, tools, knowledge, know-how and information. Hence, the supply chain becomes a higher level entity that offers services to the involved stakeholders, who enter into a virtuous circle of growth where the sharing of knowledge and experiences is an opportunity of enhancement, training and innovation. These paradigm shifts can mark the transition from a work-intensive to an information-intensive scenario for FM, while also allowing to optimize the traditional FM service delivery and expand the range of FM services (also by introducing new digital ones), improving in this way the ability of Facility Managers and Service Providers to meet the increasing Clients requests and expectations. Despite the growing interest expressed by FM stakeholders in achieving these expected benefits, the transition of the FM sector towards innovative solutions is still limited at the experimental stage and it requires harmonization efforts. Indeed, the IoT-based FM is rapidly spreading but the basic references and tools are not yet completely consolidated and shared among all the engaged stakeholders. The IoTbased FM—as a new management discipline—requires studies and accurate experimentations in various areas such as (among others): technological infrastructure, information management, organizational models, stakeholder relationships, supply chain management, FM agreements and contracts development, multidisciplinary training for FM stakeholders, etc. Within this challenging scenario, the present Thesis proposes strategies for IoT adoption within FM able to assist FM stakeholders (Real Estate owners, facility managers, FM consulting companies, FM providers and suppliers as well as IT

Conclusions

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providers) in consciously exploiting the opportunity to create value from the implementation of IoT technologies within their business, supporting them in: – handling the highly-variegated technological offer of sensors and devices on the market, which is often difficult to understand and compare. The Thesis proposes a classification of detectable FM-related parameters and IoT sensors and devices useful to express the FM information need with respect to IoT requirements (Chap. 5); – enhancing the responsiveness of the building and its components as well as defining new strategies for improving FM services planning and programming. The Thesis proposes Sensing and Responding (S-R) models and a functional structure of the multi-layered IoT Architecture (Chap. 6) for an advance FM information management, as well as innovative IoT-based strategies for the integrated management of FM services, including Maintenance, Cleaning, Waste, Space and Energy (Chap. 7) and focusing on smart workplace management (eFM Headquarter STATUTO 11 case study) (Chap. 8); – assessing the IoT-based FM scenario, orienting FM stakeholders in defining new possible organizational models. The Thesis defines possible scenarios of application of IoT-based FM services as well as potential sourcing strategies and organizational models, analyzing the related concerns and risks with respect to information security, responsibility and ownership within the context of agreements between Client and IT Provider (Chap. 9); – reshaping the FM supply chain. The Thesis proposes a network approach to the management of the FM supply chain for setting the stage to win-win relationships among FM stakeholders and it outlines a set of new required cross-sectoral (IoT— FM) profiles of competences and skills for facility managers, articulated into four increasing levels of maturity (Chap. 9).