IoT-enabled Unobtrusive Surveillance Systems for Smart Campus Safety E-Book
Theodoros Anagnostopoulos
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
IoT-enabled Unobtrusive Surveillance Systems for Smart Campus Safety Enables readers to understand a broad area of state-of-the-art research in physical IoT-enabled security IoT-enabled Unobtrusive Surveillance Systems for Smart Campus Safety describes new techniques in unobtrusive surveillance that enable people to act and communicate freely, while at the same time protecting them from malevolent behavior. It begins by characterizing the latest on surveillance systems deployed at smart campuses, miniatures of smart cities with more demanding frameworks that enable learning, social interaction, and creativity, and by performing a comparative assessment in the area of unobtrusive surveillance systems for smart campuses. A proposed taxonomy for IoT-enabled smart campus unfolds in five research dimensions: (1) physical infrastructure; (2) enabling technologies; (3) software analytics; (4) system security; and (5) research methodology. By applying this taxonomy and by adopting a weighted scoring model on the surveyed systems, the book presents the state of the art and then makes a comparative assessment to classify the systems. Finally, the book extracts valuable conclusions and inferences from this classification, providing insights and directions towards required services offered by unobtrusive surveillance systems for smart campuses. IoT-enabled Unobtrusive Surveillance Systems for Smart Campus Safety includes specific discussion of: * Smart campus's prior work taxonomies and classifications, a proposed taxonomy, and an adopted weight scoring model * Personal consumer benefits and potential social dilemmas encountered when adopting an unobtrusive surveillance system * Systems that focus on smart buildings, public spaces, smart lighting and smart traffic lights, smart labs, and smart campus ambient intelligence * A case study of a spatiotemporal authentication unobtrusive surveillance system for smart campus safety and emerging issues for further research directions IoT-enabled Unobtrusive Surveillance Systems for Smart Campus Safety is an essential resource for computer science and engineering academics, professionals, and every individual who is working and doing research in the area of unobtrusive surveillance systems and physical security to face malevolent behavior in smart campuses.
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Table of Contents
Cover
Series Page
Title Page
Copyright Page
Dedication Page
Author Biography
Preface
1 Introduction
1.1 Smart Cities Dimensions and Risks
1.2 Smart Campuses Components
1.3 Smart Campuses Unobtrusive Surveillance Systems
1.4 Smart Campus Safety Systems Survey
1.5 Smart Campuses Comparative Assessment
1.6 Smart Campus Systems Classification
1.7 Smart Campus Safety: A System Architecture
1.8 Human Factor as an Unobtrusive Surveillance System's Adoption Parameter for Smart Campus Safety
1.9 Smart Campus Surveillance Systems Future Trends and Directions
2 Smart City
2.1 Smart Cities Dimensions
2.2 Risks Related to Smart Cities
2.3 Mitigating Smart Cities Risks
2.4 Systems Beyond Smart Cities
3 Smart Campus
3.1 Smart Campus Components
3.2 Unobtrusive Surveillance Campus System
4 Unobtrusive Surveillance Systems
4.1 Geospatial Internet of Things
4.2 Smart Campus Unobtrusive Surveillance
4.3 Proposed Taxonomy
4.4 Adopted Weighted Scoring Model
5 Smart Campus Safety Systems Survey
5.1 Systems Not Classified
5.2 Systems That Focus on Public Spaces and Smart Parking
5.3 Systems That Focus on Smart Buildings, Smart Labs, Public Spaces, and Smart Lighting
5.4 Systems That Focus on Public Spaces and Smart Traffic Lights
5.5 Systems That Focus on Smart Buildings and Smart Classes
5.6 Systems That Focus on Smart Buildings, Public Spaces, Smart Lighting, and Smart Traffic Lights
5.7 Systems That Focus on Smart Buildings and Smart Labs
5.8 Systems That Focus on Smart Buildings and Public Spaces
5.9 Systems That Focus on Smart Campus Ambient Intelligence and User Context
5.10 Systems That Focus on Smart Campus Low‐Power Wide Area Networks Technology
6 Comparative Assessment
7 Classification and Proposed Solution
7.1 Weighting Process
7.2 Classification Process
8 Smart Campus Spatiotemporal Authentication Unobtrusive Surveillance System for Smart Campus Safety
8.1 Smart Campus Spatiotemporal Authentication Unobtrusive Surveillance System
8.2 Smart Campus Safety: A System Architecture
9 Human Factor as an Unobtrusive Surveillance System's Adoption Parameter for Smart Campus Safety
9.1 Ethical Dilemma of Adopting an Unobtrusive Surveillance System
9.2 Degree of Free Will Engagement and Negotiation with an Unobtrusive System
10 Smart Campus Surveillance Systems Future Trends and Directions
References
Index
End User License Agreement
List of Tables
Chapter 6
Table 6.1 Comparative assessment.
Chapter 7
Table 7.1 Normalized weights and relative frequencies.
Table 7.2 Scoring of research efforts.
Table 7.3 Classification of research efforts.
List of Illustrations
Chapter 2
Figure 2.1 Smart city dimensions.
Chapter 3
Figure 3.1 Smart campus components.
Chapter 4
Figure 4.1 Conceptual map of physical infrastructure dimension.
Figure 4.2 Conceptual map of enabling technologies dimension.
Figure 4.3 Conceptual map of software analytics dimension.
Figure 4.4 Conceptual map of system security dimension.
Figure 4.5 Conceptual map of research methodology dimension.
Figure 4.6 Conceptual tree map of the proposed taxonomy.
Figure 4.7 Overview tree graph of the proposed taxonomy.
Figure 4.8 Work flow diagram of the proposed scoring model.
Chapter 5
Figure 5.1 Surveyed research efforts per year of publication.
Chapter 7
Figure 7.1 Scoring of research efforts visualization.
Figure 7.2 Classification of research efforts visualization.
Chapter 8
Figure 8.1 Method able to support spatiotemporal authentication not includin...
Figure 8.2 Tracking system to provide security in case of attempt to violate...
Figure 8.3 The method able to support spatiotemporal authentication includin...
Figure 8.4 Authentication method based on spatiotemporal context.
Figure 8.5 Authorization system based on spatiotemporal authentication metho...
Guide
Cover Page
Series Page
Title Page
Copyright Page
Dedication Page
Author Biography
Preface
Table of Contents
Begin Reading
References
Index
Wiley End User License Agreement
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Thomas Robertazzi
IoT‐enabled Unobtrusive Surveillance Systems for Smart Campus Safety
Theodoros Anagnostopoulos
Copyright © 2023 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
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To Georgia, Vasileios, and Davidia for their priceless presence in my life.
Author Biography
THEODOROS ANAGNOSTOPOULOS was born in Athens, Greece in 1976. He received the BEng degree from the University of West Attica, Greece, in 1997, the BSc and the MScIS degrees from the Athens University of Economics and Business, Greece, in 2001 and 2003, respectively. He received the PhD degree from the National and Kapodistrian University of Athens, Greece, in conjunction with the University of Geneva, Switzerland, in 2012. He also received the MScEd degree from the Hellenic Open University, Greece, in 2018. He was a postdoctoral researcher at the ITMO University, Russia, in 2015. He was a senior postdoctoral researcher at the University of Oulu, Finland, in 2016. He had been employed as a principal research scientist in smart cities at the Ordnance Survey: Great Britain’s Mapping Authority, UK, in 2017. Currently, he holds a lecturer (teaching) academic position in computer science & engineering at the DigiT.DSS.Lab: Digital Transformation & Decision Support Systems in Business and Education Laboratory, at the University of West Attica, Greece. He holds, an associate academic position in artificial intelligence at the Essence: Pervasive & Distributed Intelligence Laboratory, at the University of Glasgow, UK. He also holds, an associated lecturer position in internet of things at the International Research Laboratory in Modern Communications Technologies and Applications in Economics and Finances, at the ITMO University, Russia. Dr. Theodoros Anagnostopoulos holds two patents in the research area of cognitive aware spatiotemporal authentication for smart and sustainable environments, in the USA and EU, where he is the inventor while intellectual properties are with Ordnance Survey: Great Britain’s Mapping Authority, UK.
Preface
Smart campus is a living area of academics and students and can be considered as a miniature of a smart city (SC), also known as City 2.0. Living and studying in a smart campus is a challenging issue for thirsty young minds who want to form the future of human kind starting with research challenges at present to be able to lead the society in the future. Concretely, the concept of a smart campus extends the potentiality of an SC with regards to sustainable living of persons willing to research and explore the unexpected in the era of Industry 4.0. However, in such environments of human activity, it is possible that risks are emerged and should be mitigated. For example, malicious third‐party entities might wish to cause inefficiencies in the physical infrastructure of the smart campus or even cause severe harm to individuals. To overcome such negative side effects, it is possible to apply a surveillance system to ensure physical safety of the personnel and the university campus infrastructure. The problem with surveillance systems is that humans do not wish to be monitored, thus such kind of systems are not actually used in practice. To overcome such an inefficiency, it is proposed that monitoring is performed unobtrusively, which means that surveillance systems respect user personality and protect fundamental human rights, such as privacy concerns, as defined by the general data protection regulation (GDPR) security policy making. In addition, it is given the option to campus users to negotiate their degree of free will to interact with certain services of the system, thus defining by their own the engagement they wish with the proposed system.
Specifically, what is fundamental in current research is the human factor as an unobtrusive surveillance system’s adoption parameter for smart campus safety. The ability of a person to accept or decline safety services provided by such a system depends on the personal benefit of adopting an unobtrusive surveillance system in the workplace. An ethical dilemma is emerged by the incorporation of such a system in smart campus, which is the benefit of safety that the user deserves in comparison to the privacy data sacrifice the user is intended to accept. In plain words, the more the system penetrates human privacy space, the more the security services are provided to the user and vice versa. However, the user should be able to express the degree of free will, which will dictate the engagement the user accepts to provide for an unobtrusive system adoption. Lesson learnt in this research is that the center of an unobtrusive surveillance system in a smart campus is human population and specifically every unique human being with their free personality, which differentiates one specific individual from another in the same spatiotemporal university campus context. No system should be applied to users if they do not wish to be monitored by any system even if they risk their safety in the smart campus. What is proposed is that every single user is free to decide the degree the system will affect personal safety. So, it holds that the system should provide a scalable engagement to users’ personality from no service provided, thus no surveillance service is activated, up to full surveillance provided, thus high‐quality protection is applied to the affirmative user.
Athens
Theodoros Anagnostopoulos
1Introduction
Smart cities (SC), also known as Cities 2.0, are embodiments of urban living in the digital age [1] . In the coming years, suburban and rural citizens are expected to move toward urban areas, forming a vast concentration of population in the inner city. It is anticipated that emerging paradigms such as Industry 4.0 will support the new needs of cities. A key component is the incorporation of the Internet of Things (IoT) paradigm as the backbone of society. IoT‐enabled services will produce a vast amount of data that can be used to support and optimize critical infrastructure and provide new insights and advances. However, the majority of these data will be sensitive and should be treated unobtrusively not to harm freedom and individual privacy [2] . The challenge today is to understand how to build and deploy massively interconnected systems such that they are both effective and trustworthy. An academic research and industrial innovation area for learning a significant concept of safety systems is the application of surveillance mechanisms in smart campuses. A college or university campus is a scaled‐down version of a city, which contains a somewhat closed community that is large enough to experience many of the technological, social, and human issues at a city scale [3] . In this study, a thorough and systematic survey on surveillance in smart campus systems is performed. This book is motivated by the lack of research that seeks to characterize the state of the art on smart campus surveillance.
Against this backdrop, the book surveys IoT‐based surveillance systems in smart campuses, as these environments, although similar to smart cities, have some unique requirements that call for additional security and privacy measures. The present book proposes a survey, which was carried out on 44 systems that were deployed for campus safety. The author developed a taxonomy for these systems along with a scoring model for each one of them. The functionality of the surveyed systems was sized up against five dimensions: (1) physical infrastructure, (2) enabling technologies, (3) software analytics, (4) system security, and (5) research methodology. The set of weights provided in the taxonomy enables a robust comparative assessment and classification model of state‐of‐the‐art systems. Furthermore, the proposed method facilitates the extraction of valuable conclusions and inferences and gives insights and directions toward required services offered by a surveillance system of a smart campus. In addition, the survey brings forth a set of research efforts for the development of future surveillance systems specific to smart campuses [4] . Moreover, benefits of adopting an unobtrusive surveillance system in smart campus are analyzed. Concretely, special focus is given to the ethical dilemma of adopting such a system, while the degree of user's free will engagement with unobtrusive surveillance systems is exploited.
The contributions of this book in the areas of unobtrusive monitoring safety systems are as follows.
1.1 Smart Cities Dimensions and Risks
A definition of smart cities is provided as a living environment of academic inhabitants, which maximizes research and innovation outcomes. Consequently, analyses of the six fundamental dimensions of smart cities are performed, which are (1) smart economy, (2) smart governance, (3) smart living, (4) smart mobility, (5) smart people, and (6) smart environment. Each dimension is analyzed in detail and provided examples explain how each dimension affects green and sustainable physical infrastructure. In addition, there are exploited risks that arose in smart cities due to certain human malevolent behavior by using harmful contemporary technological advancements. These risks are divided in two major categories, regarding technology involvement to malicious behavior, which are technical and nontechnical risks. Subsequently, certain solutions to face smart cities' risks are proposed to mitigate malevolent behavior. Next topic of the study is the transition beyond the smart cities' concept to more compact systems, which are easily managed. Such systems could be the case of smart campuses.
1.2 Smart Campuses Components
Smart campuses focus on the potentiality assessment of existing systems beyond the smart city. Specifically, university campuses are living areas where efficient development and upgrade of software applications are performed. Such progress is supported by the adoption of cloud storage, 5G and 6G networking technology as well as edge AI, data science, and deep learning analyses capabilities. In addition, IoT devices produce online and real‐time dynamic data sources, which are used to input intelligent systems and make early warnings and predictions of certain integrated application domains. To understand the operation background of a living smart campus, it is considered significant to separate five fundamental components, which describe university campus interoperability within both physical and digital worlds. Specifically, smart components are categorized as (1) smart grid, (2) smart community services, (3) smart management, (4) smart propagation services, and (5) smart prosperity. These components are derived from certain perspective of smart campus activity, such as social, environmental, and economic sustainability development processes. At this point, there is a transition from smart campus components and environment toward the adoption of a safe smart campus unobtrusive surveillance system to provide safety and privacy assurance to smart campus inhabitants.
1.3 Smart Campuses Unobtrusive Surveillance Systems
In this book, the concept of unobtrusive surveillance systems is defined as safety parameters in smart campus sustainable environment. Such concept is based on the five unique dimensions of the proposed taxonomy, which are also defined, namely (1) physical infrastructure, (2) enabling technologies, (3) software analytics, (4) system security, and (5) research methodology. These unique unobtrusive smart campus dimensions are used to deeply analyze the selected systems and exploit their potentiality in the area of smart campus safety. In addition, a taxonomy process is proposed that includes certain taxonomy components to assess the efficiency of the examined safety systems. Concretely, such taxonomy is based on the aforementioned defined five unobtrusive surveillance systems' dimensions, while it incorporates a weighted scoring system, which is specifically used for the classification and further assessment of the optimal quality of the adopted surveillance systems analyzed in this book.
1.4 Smart Campus Safety Systems Survey
Several university campus safety systems are analyzed extensively and surveyed based on certain group dimensions. The survey involves 42 papers and 2 patents, covering all the dimensions of the proposed taxonomy. Such dimensions are designed accordingly to provide an in‐depth exploitation of the potentiality of each surveyed system. Smart campus safety is examined by certain groups of interest containing unobtrusive surveillance systems that focus on (1) systems not classified; (2) public spaces and smart parking; (3) smart buildings, smart labs, public spaces, and smart lighting; (4) public spaces and smart traffic lights; (5) smart buildings and smart classes; (6) smart buildings, public spaces, smart lighting, and smart traffic lights; (7) smart buildings and smart labs; (8) smart buildings and public spaces; (9) smart campus ambient intelligence and user context; and (10) smart campus low‐power wide area networks and technology.
1.5 Smart Campuses Comparative Assessment
Smart campus systems' strengths and weaknesses are the basis of the analysis that is performed to define a concrete as well as equal comparative assessment. Such assessment aims to present each system's potentiality to face a malevolent behavior in smart campuses. Proposed systems' comparison is based on exploiting the potentiality of the certain five unique dimensions adopted by the proposed taxonomy during the performed safety systems' survey.
1.6 Smart Campus Systems Classification
While comparative assessment presents the efficiency of each surveyed system adopted, classification process separates the results to provide added value to observed outcomes. Concretely, classification is based on the adopted weighted scoring system. Intuitively, each system is classified in one of three proposed classes to provide an outcome of the surveyed monitoring systems. The first class contains the contemporary advanced surveillance systems, while the optimal system that is part of the first class will be addressed.
1.7 Smart Campus Safety: A System Architecture
Optimal smart campus safety is presented in this section to assess its potentiality. Concretely, smart campus safety system is disused, which incorporates an unobtrusive surveillance system with a compact architecture. Such system architecture presents the key components of the surveillance system, which is based on each campus users' spatiotemporal fingerprint. This fingerprint is unique for each university campus user, where the method of its creation and processing to provide unobtrusive monitoring and safety by malevolent individuals is presented. Such fingerprint is based on spatial and temporal data of each user captured during their daily activity in the smart campus. Surveillance mechanisms embedded in the campus infrastructure to feed the spatiotemporal fingerprint include a variety of components, such as closed‐circuit television (CCTV) camera networks, microphone networks, automated teller machine (ATM) networks, connected and autonomous vehicle (CAV) networks, unmanned aerial vehicle (UAV) networks, surveillance humanoid robot networks for systems' safety, as well as other emerging monitoring devices.
An authorization system is also proposed, which exploits spatiotemporal fingerprint authentication to provide an early warning and prediction when an unauthorized individual tries to enter the system without adequate permission. Concretely, the system is able to distinguish between a certain, not authorized, malevolent individual who wish to harm the system and a smart campus user who might seem as a malicious user but actually needs to invoke system's data updating processes to update their spatiotemporal fingerprint. In the latter case, the user is part of the system, but there is a need to enable system's processes to be re‐recognized by the system access module to be able to gain access in a certain area and/or asset of the campus. Such a university campus system is proposed to be used for designing future surveillance systems aiming at smart campuses' safety.
1.8 Human Factor as an Unobtrusive Surveillance System's Adoption Parameter for Smart Campus Safety
On acceptance of an unobtrusive surveillance system by university campus population, human factor is a key point where it should be treated gently. This is because every technical system is able to be adopted by human population only if it respects fundamental human rights, such as individual's freedom to adopt a proposed technology or not. Concretely, proposed system deals with the ethical dilemma of adopting an unobtrusive surveillance system based on certain issues to be considered, such as (1) privacy, (2) ethical, and (3) social implications of a monitoring smart campus system. Intuitively, university campus actors should be provided the option to evaluate their degree of free will engagement and negotiation with the proposed unobtrusive system. Monitoring system provides end users the feasibility to share their private data with the system in the degree they wish to acquire proposed system's safety services.
1.9 Smart Campus Surveillance Systems Future Trends and Directions
The book concludes by summarizing the analytical survey performed that focused on smart campus as a socially acceptable solution, since contemporary universities are open to experiment with emerging management regulations, as well as to try applying intuitively new safety solutions. Specifically, there are some real implications, which make these systems acceptable by the scientific community, such as the prevention and repression of delinquent behavior as well as studying the motivation and the development behind this behavior. The findings in this research focus on important aspects in future research directions, such as to verify the impact of scientific invention in the area of IoT‐enabled smart campus monitoring systems toward an industrial innovation for the well‐being of humans.
The rest of the book is structured as follows. Chapter 2 presents the fundamental sociotechnical paradigm of future habitation, which is the concept of a smart city. Chapter 3 focuses on smart campus as an area of human engagement, which is considered as a miniature of smart city. Chapter 4 specifies the adoption of unobtrusive surveillance systems by smart campus. Chapter 5 performs survey analyses to state‐of‐the‐art systems for smart campus safety. Chapter 6 conducts comparative assessment on the surveyed systems. Chapter 7 performs classification of the analyzed systems and proposes optimal solutions based on the assessment of the systems. Chapter 8 examines a case study of the optimal spatiotemporal authentication unobtrusive surveillance system architecture for smart campus safety. Chapter 9 describes human factor as an unobtrusive surveillance system's adoption parameter for smart campus safety. Chapter 10 finally concludes the findings of the book and discusses smart campus surveillance systems' future trends and directions.
2Smart City
The concept of smart cities' (SCs) is analyzed and designed in different regions worldwide. There is an actual need for exploitation of information and communications technology (ICT) potentiality in SCs. Different dimensions of human activity should be considered to the development of SC planning and implementation [5] . This inherent complexity exists within each dimension of the city. Such complexity needs treatment based on incorporated technologies and their integration, which brings the risk uncertainty parameter as part of the implementation phase of the SC concept. In case these risks are not effectively understood and faced, it is possible they could create critical issues in terms of citizens' privacy and security, which might have severe effects in the SCs' functionality. In this chapter, dimensions are identified, available technologies are addressed, technical and nontechnical risks are presented, and risk management to mitigate SCs' risks are discussed to support SCs implementation.
2.1 Smart Cities Dimensions
Smart cities are the future of human habitation since 67% of human population will live in such cities by 2050. Certain infrastructure should be adopted to ensure a viable green and sustainable ecosystem to citizens. Living in an SC is challenging since new problems have emerged and they infrastructure, there are certain risks need to be faced by the local authorities, such as fresh water provision, traffic handling in rush hours during the day to provide green transportation, and smart home architectural design to provide citizens a promising well‐being. SCs are composed by six fundamental dimensions, which are (1) smart economy, (2) smart governance, (3) smart living, (4) smart mobility, (5) smart people, and (6) smart environment [6] . Fulfillment of all SC dimensions assures the quality of living in such a city. It holds that a successful city should have high level of engagement and social activity with the SC dimensions. In addition, since SCs are a living environment, which includes the collaboration of citizens and technical infrastructure, there are certain risks that need to be addressed. Such risks are either technical or nontechnical. To face different kind of risks, certain risk analysis and assessment tools should be considered by the SC management personnel. SC dimensions are presented in Figure 2.1.
Figure 2.1 Smart city dimensions.
2.1.1 Smart Economy
Smart economy is well aligned with legislation and policies relevant to business innovation and industrial creativity. Economic innovation engages scientific research in upcoming technological progress as well as enables sustainability toward a green ecosystem. Smart economy might conceive certain areas of ICT as well as industrial innovation and competitiveness as part of the contemporary economic trends aiming for the efficient use of socially responsible resources. Conventional theories in the field of economic research imply that smart economy in the context of SC habitation promotes at‐hand experience and valuable knowledge, which is based on state‐of‐the‐art academic innovation [7] . Such innovation is applied horizontally in many research areas of human activity including science, industry, social cultural heritage, logistics, and planning, as well as business research and development. There are many applications and directions of smart economy activities within SC development. Every research direction has its unique economic characteristics well aligned with upcoming challenges and proposed solutions. Emerging areas of smart economy dimension for SCs' sustainability are extended to certain application domains, such as (1) entrepreneurship and innovation, (2) productivity and employment, and (3) international embeddedness. These areas are promising in enabling citizen well‐being in SCs. This in fact holds because such domains provide the pillar of smart economy ongoing progress, which results in every single citizen's daily quality of life and enhanced social activity.
2.1.2 Smart Governance
Smart governance refers to the study of SC structural dimensions along with challenges and proposed solutions provided to support the legal authorities of the city. A key concept of smart governance is the sufficient contribution in decision‐making processes aiming to provide solutions to everyday problems that might arise in the sustainable environment. Certain SC digital infrastructures, such as social services and transparent governance, should be provided to citizens to assess the effectiveness level of municipality's smart governance. Local authorities are responsible to apply specific policies and strategies adding the value of citizens' well‐being. It is obvious that smart governance is the outcome of decent collaboration between citizens, local authorities, and administrative institutions to provide efficient services to human population [8] . Such outcomes can further provide maximal sociotechnical benefits to SC infrastructure by enabling reliability, efficiency, and effectiveness of citizens' assistance, which in turn focus to the integration of public, private, and civil operations. In addition, technical governance is a critical parameter of smart governance because it is able to provide SC state‐of‐the‐art solutions due to sustainable and technological maturity. In this research, smart governance is further divided to certain subdomains, such as (1) non‐ICT infrastructure, (2) online services, and (3) open governance. These domains address all SC services provided to citizens while assuring the ability of the city to research and innovate in the area of governance. Actually, governance is considered the basic building block of all collective efforts incorporated to provide effective interactions with all stakeholders in SCs. Future of smart governance should be interactive with the actors of the city to promote research invention and open industrial innovation. This kind of social interaction could be enhanced with the proliferation of e‐governance, which might progress in the direction of building social engagement and transparency in municipality decision‐making processes. E‐governance is possible to be applied in SC policies with the advancements in the areas of 5G and 6G technologies, as well as the optimizations performed in present in the research field of edge artificial intelligence (AI) and Internet of Things (IoT). Cloud‐based architecture as well as big data analytics are able to provide the technical test bed, which is fundamental to evaluate e‐governance participation engagement as well as effective validation of information sharing and smart governance in further collaboration.
2.1.3 Smart Living
Smart living is the concept of considering the development and preservation of certain elements, such as nature green ecosystem quality, economic growth, and human capital management. Smart buildings, smart public spaces, society's education level, and health care infrastructure are forming the notion of social context awareness as a principal parameter of smart living. Online real‐time health care monitoring can save lives of elderly and impairs citizens. In addition, special care and medical support with IoT technology can assist medical professionals in health emergency situations, like the case of covid‐19 pandemic. By another point of view, smart living could also be considered as the social result of smart economy is SCs. Specifically, the use of ICT aims to provide advanced services in the areas of digital networking, IoT‐enabled smart public spaces, SC lighting, as well as autonomous and connected safety systems. Smart homes provide smart assistance to citizens' daily activities by exploiting user‐generated private data, which should be protected under the general data protection regulation (GDPR) safety and privacy assurance legislation. Insight of SC's data sources emerges the need of transparency and open‐data policies to provide high‐quality services for users' smart living [9] . Toward these directions, standards and data specifications should be examined to enhance smart applications, which will be able to perform detection and management actions related with certain risk assessment methods. In addition, such challenging smart applications, which support quality of living in the SC, incorporate state‐of‐the‐art available technologies including but not limited to data science, AI, machine learning, user profiling, cloud and edge storage and computing, as well as networking technologies and wireless sensor networks architectures. Smart living should exploit the potentiality of such technologies to provide quality of life the SC population.
2.1.4 Smart Mobility
Smart mobility focuses on intelligent transport systems (ITS) and IoT‐based transportation infrastructure. Specifically, there are many open issues in the area of smart mobility including vehicle congestions in central roadways as well as long queues of traffic bottlenecks and significant time of arrival delays to reach certain destinations on time. Vehicle ride sharing systems and carpooling architectures could assist user movements and commuting in the SC. Real data are possible to be gathered by existing IoT infrastructure in the road network aiming in performing routing analytics [10]
