Personalized medical services using smart cities' infrastructures

A smart city is a city that uses information and communication technologies (ICT), smart sensors, actuators and video cameras to better support and optimize the delivery of urban services and to address challenges within city communities. A smart city collects and analyzes data from smart IoT sensors (IoT devices/nodes) and video cameras. The data collected by the sensor nodes can be used to develop applications and systems that benefit the city and the community. Smart city industry is projected to be a $400 billion market by 2021, with 600 cities around the globe expected to generate 60% of the world's GDP by 2025, according to McKinsey research. Many cities have adopted at least one smart city project. However, the majority of cities around the globe are not yet smart cities. The key hurdle is securing the huge fund needed to start a smart city project, and ensuring that there are sufficient resources to sustain the project over time. Because implementing new smart city infrastructure is a large and complex financial undertaking that poses a financial hurdle, specifically, for most of the under-developed countries, a viable and cost-effective strategy is needed to tackle this problem and conquer the digital divide. Building upon and leveraging existing infrastructure is the key to address this problem. This minimizes the cost of technology upgrades and allows for an easier integration process. In addition, leveraging existing infrastructure can give cities a better and quicker return on investment. The main objective of this work is to devise an innovative research and development initiative, which builds upon and leverages ongoing global deployment of smart city and outdoor lighting infrastructures trend, to explore the potential and assess the feasibility of gradually transitioning existing Egypt’s cities infrastructure into truly IoT-enabled smart cities. Specifically, we propose and develop an innovative cost-effective, future-proof, scalable and modular end-to-end device-to-cloud connected outdoor Lighting network infrastructure solution that is based on P2P 4G LTE cellular technology to provide the direct connectivity between lighting poles (IoT devices) and the cloud. The proposed intelligent Lighting solution includes three key building blocks:1) light pole-mounted smart control nodes; 2) cloud-based smart city software solution (NetServ) to remotely monitor, manage, and control the entire lighting infrastructure; and 3) P2P 4G LTE cellular network. In the proposed architecture, smart streetlights serve as the framework for a high- bandwidth, low-latency wireless sensor network, capable of transporting large amounts of data in real-time, while concurrently supporting deployment of a wide range of smart city services.

PurposeThe paper explores the emergence of smart city governance with a particular focus on the cognitive value of the new technologies and the different accountabilities emerging in the digital infrastructures attempting to visualize and rationalize urban dynamics.Design/methodology/approachDrawing on ethnographic, netnographic and interview data from an empirical case study of the Smart and Wise City Turku spearhead project, the study builds on the assumption that smart cities emerge from the interaction between the characteristics of technologies, constellations of actors and contextual conditions.FindingsThe results report smart city activities as an organizational process and a reconfiguration that incorporates new technology with old infrastructure. Through the lens of the empirical examples, we are able to show how smart city actors, boundaries and infrastructures are mobilized, become valuable and are rendered visible. The smart cities infrastructure traces, values and governs actors, identities, objects, ideas and relations to animate new desires and feats of imagination.Practical implicationsIn terms of implications to practice, the situated descriptions echo recent calls to leaders and managers to ask how much traceability is enough (Power, 2019) and limits of accountability (Messner, 2009).Originality/valueThe central theoretical concept of “thinking infrastructure” highlights how new accounting practices operate by disclosing (Kornberger et al., 2017) new worlds where the platforms and the users discover the nature of their responsibilities to the other. The contribution of this paper is that it examines what happens when smartness is understood as a thinking infrastructure. Different theorizations of infrastructure have implications for the study of smart cities. The lens helps us grasp possible tensions and consequences in terms of accountability that arise from new forms of participation in smart cities. It helps urban governance scholarship understand how smartness informs and shapes distributed and embodied cognition.

This paper aims to compare five smart city models selected based on a number of specific criteria. Following the comparison and assessment performed, we draw conclusions and further linkages identifying the components and characters found in resilient cities. The purpose of this analysis is to produce a new approach and concept: the “smart and resilient city.” Through in-depth literature study, this paper analyzes five conceptual smart city models deemed to have a background, point of view, and benchmark towards software group, as they focus on welfare, inclusion, social equality, and competitiveness. Analyzing the strategies, methods, and techniques of five smart city models, this paper concludes that there has been no inclusion of resilience concepts in the assessment, especially in the context of natural disasters. Basically, the models are also interrelated and there are some things that overlap. As a recommendation, there is a model that tries to combine the components and character of smart city and resilient city into one entity that is embedded as a whole in a conceptual picture towards the new concept, the “smart and resilient city”. The concept of smart city and resilient city go hand in hand with each other and thus are interrelated. Therefore, it is imperative to study that concept deeper, in this case primarily in the context of disaster.

The rise of Internet of Things (IoT) and adoption of smart cities create opportunities for creative and efficient management and utilization of the available resources. One of the characteristics of smart cities is the interconnectivity of the city's infrastructure, which allows data to be transferred between vehicles, infrastructures and road side units. Future vehicles with powerful on-board computers, communication capabilities, and vast sensor arrays are perfect candidates in this hyper-connected environment to utilize into a fluid Vehicular Cloud (VC) capable of performing large-scale computations. In this paper, we propose a vehicle-to-infrastructure (V2I) communication protocol for enabling a dynamic Vehicular Cloud Computing system in smart cities.

Smart Cities have become one of the most interesting research topics for governments, businesses and researchers in the last few years. Being a Smart City implies a competitive edge compared to other cities in terms of economic growth, sustainability, human resources and governance. Therefore, more and more governments pursue this future vision and hop onto the Smart City ‘Bandwagon’. However, the development of Smart City solutions is faced with challenges since there is no clear definition for Smart Cities yet. This paper provides a review on what challenges arise when designing Smart City solutions to provide a complete overview of identified challenges. This paper carries out a systematic literature review of publications between 2008 and 2016 which will be compared with a field study on Smart City initiatives conducted by the municipality of Enschede to develop a requirements framework for these projects. This research is relevant to governments and businesses aiming to create Smart Cities and for researchers, who aim to enhance the concept of Smart Cities. This paper found eight sub-domains within the topic of Smart Cities and these are: Technical Infrastructure, Application Domains, System Integration, Data Processing, Governance & Management, Society & Citizens, Business Domain and Environmental Sustainability. The eight sub-domains are respectively divided in two types: technological and non-technological domains. By firstly identifying the challenges and their solutions within these sub-domains, this paper proposes a requirements framework in combination with a checklist of questions to provide guidance for promoters to conduct future initiatives.

In the last decade, the development and integration of the life of information technology society has affected the system of public governance. Key aspects of organizing modern urban development, management and planning that encompass different measurements: effective management, stability, environmental friendliness, innovation and understanding of the «Smart City» concept. «Smart City» is a digital toolkit (system), in which the city аdministration interact with the community and the city's infrastructure. As the basic tools of «Smart City» we can consider software and hardware complexes that are the part of the components (applications, software solutions, dedicated services, etc.), that perform specific typical operations for solving certain individual functions and form the overall architecture of the system. A significant barrier for the development of digital interference in Ukraine is a lack of clear classification of «Smart City» warehouse tools. One of the most important ways for systematization is the general classification of «Smart City» tools. The purpose of the article is the theoretical and methodological substantiation of the classification of «Smart City» tools. The article proposes to define the classification method as a set of rules for the creation of classification systems, as well as their coincidence. The proposed facet method most closely matches the system of classification of «Smart City» tools and allows to systematize a large set of objects. It is substantiated that using the facet method it is possible to construct a classification system of «Smart City» tools with flexible scaling at each level of such structure. The structure of classification of basic functions of public administration bodies is determined. The basic digital interactions of public intervention agencies (G - government) with the company (B - business) and citizens (C - citizen) are selected based on the levels of interaction of subjects and objects of electronic service provision. This will make it possible to formulate the first basic classification level depending on a certain initial position of objects in system. In order to determine the interaction levels, it is suggested to introduce index of the «Smart City» solutions, with the positioning of the system component by the classifiers. Based on the facet principle and defined types of fields, the basic system of classification «Smart City» tools was built. Having defined the tool of classification it becomes possible to describe and formaloze the inter-system interconnections at the level of data exchange between different components and subsystems. It expands the possibilities of implementation, cloning and scaling up successful technical, technological and managerial decisions. The proposed classification is a supersystem and does not affect internal architecture of specific systems and is appointmed, first of all, for the construction and standardization of inter-system interactions and systematization of «Smart City» tools. As a further development of issues we see the development of standardization and «Smart City» tools at the state level, the study of the world's experience in the implementation of «Smart City» to determine the recommendations for the development and construction of such systems.

Recently, the notion of a smart city, which includes smart well‐being, smart transit, and smart society, has attracted much attention due to its impact on people's quality of living. Data in smart cities are characterized by variety, velocity, volume, value, and veracity that are the well‐known characteristics of big data. The fast pace expanding of IoT devices and sensors in smart cities generates a huge volume of data that can help decision‐makers and managers in city management. The aim of this article is to wholly and systematically review big data handling approaches in smart cities, in which we analyze research efforts published between 2013 and February 2021, where these techniques are categorized based on their algorithms and architectures. Further, the main ideas, evaluation techniques, tools, evaluation metrics, algorithm types, advantages, and disadvantages are explored. Additionally, essential evaluation factors are introduced in which scalability and availability by 16%, time by 15% and accuracy by 11% are more in focus, and finally, some of the challenges, open issues, and future trends that are valuable for further research are suggested in big data handling approaches in smart cities.

Respiratory viruses, such as COVID-19, are spread over time and space based on human-to-human interactions. Human mobility plays a key role in the propagation of the virus. Different types of sensors in smart cities are able to continuously monitor traffic-related human mobility, showing the impact of COVID-19 on traffic volumes and patterns. In a similar way, traffic volumes measured by smart traffic sensors provide a proxy variable to capture human mobility, which is expected to have an impact on new COVID-19 infections. Adding traffic data from smart city sensors to machine learning models designed to estimate upcoming COVID-19 incidence values should provide optimized results compared to models based on COVID-19 data alone. This paper proposes a novel model to extract spatio-temporal patterns in the spread of the COVID-19 virus for short-term predictions by organizing COVID-19 incidence and traffic data as interrelated temporal sequences of spatial images. The model is trained and validated with real data from the city of Madrid in Spain for 84 weeks, combining information from 4372 traffic measuring points and 143 COVID-19 PCR test centers. The results are compared with a baseline model designed for the extraction of spatio-temporal patterns from COVID-19-only sequences of images, showing that using traffic information enhances the results when forecasting a new wave of infections (MSE values are reduced by a 70% factor). The information that traffic data has on the spread of the COVID-19 virus is also analyzed, showing that traffic data alone is not sufficient for accurate COVID-19 forecasting.

The concept of smart cities is getting popularity day by day. Many countries have started adopting the idea of smart city to improve the quality of life by achieving the recommended level of sustainable development. The smart city is a technologically advanced area which could understand the world by analyzing the data in order to improve living conditions. The underlying technology infrastructure of smart cities are wireless sensor network (WSN), Internet of thing (IoT), RFID, and 6G are among others. Along with the technology, the role of machine learning and data analytics can’t be ignored. The smart cities generate massive amount of data from the monitoring equipments and sensors. Big data analytics is one of the important technologies which is capable of improving intelligent urban facilities. In smart cities large amount of data is continuously received from many sensors, autonomous machines or intelligent IoT devices. The accurate prediction depends on the approaches of data analytics and machine learning techniques. This chapter presents fundamental of smart cities, vertical in smart cities and data analytics approaches.

As urbanization continues to accelerate, the concept of smart cities has emerged as a promising solution to address the complex challenges faced by rapidly growing urban areas. Smart cities leverage advanced technologies to enhance the quality of life for residents, optimize resource utilization, and promote sustainable development. However, the successful implementation of smart cities heavily relies on the engineering of resilient and sustainable infrastructure. This chapter highlights the key aspects of engineering resilient and sustainable infrastructure for smart cities, focusing on the integration of technological innovations, resilience planning, and sustainable practices. The engineering of resilient infrastructure plays a crucial role in smart cities' ability to withstand and recover from various shocks and stresses, including natural disasters, climate change impacts, and cyber-attacks. Resilient infrastructure emphasizes the integration of robust design principles, redundancy, adaptability, and effective risk management strategies.

Abstract: As urban populations grow and cities face increasing challenges in traffic congestion, energy management, and environmental sustainability, Quantum Artificial Intelligence (Quantum AI) is emerging as a transformative technology to optimize urban systems. This book, "Quantum AI for Smart Cities: Optimizing Traffic and Urban Sustainability," explores how the convergence of quantum computing and AI-driven analytics can revolutionize traffic flow management, smart grids, and sustainable urban planning. It delves into quantum-enhanced predictive modeling, real-time optimization algorithms, and autonomous mobility solutions, enabling cities to reduce congestion, lower emissions, and improve resource allocation. The book also examines Quantum AI’s role in energy-efficient infrastructure, climate adaptation strategies, and smart governance models, demonstrating its potential to create data-driven, intelligent, and eco-friendly urban environments. Additionally, it addresses ethical, security, and regulatory considerations, providing insights into quantum cybersecurity, AI governance frameworks, and policy recommendations for responsible adoption. Designed for urban planners, AI researchers, policymakers, and technology leaders, this book serves as a comprehensive guide to leveraging Quantum AI for the next-generation evolution of smart, sustainable, and efficient cities. Keywords: Quantum AI, smart cities, traffic optimization, urban sustainability, quantum computing, artificial intelligence, machine learning, predictive analytics, smart mobility, autonomous vehicles, intelligent transportation, energy management, smart grids, renewable energy, climate adaptation, IoT, big data analytics, sustainable urban planning, ethical AI, AI governance, cybersecurity, quantum encryption, urban infrastructure, smart governance, environmental sustainability, digital transformation.

Chapter 17 - Smart city vision and practices across the Kingdom of Saudi Arabia—a review

During the latest years, smart city projects have been more and more popular and widespread all over the world. The continuous increasing of city’s population and the complexity of city management drive local governments towards the strong use of technologies to support a higher quality of urban spaces and a better offering of public services. The fascination of smart cities, able to link high technology, green environment and well-being for citizens, interests all the municipalities, independently on their dimensions, geographical area or culture. However, the concept of smart city is far from to be unambiguous. Several experiences all over the world show that cities define themselves as smart, but the meaning attributed to this word is different each time. Smart city concept has been growing from empirical experience, therefore a systemic theoretical study about this phenomenon still lacks. In this paper, the author aims to propose a comprehensive and verified definition of smart city, based on both a deep literature investigation about smart city studies and a large survey of smart city projects in the international panorama. The goal of this work is not only to provide a clear framework about this interesting and current topic, but also to support local governments and public administrations in effective smart city implementation, able to create public value and well being for citizens and environmental sustainability in the urban space.Â

Building a smart city that follows sustainability goals enhances the quality of life and preserves environmental, human, and social capital. Yet, existing smart sustainable city projects have concentrated on the technological dimensions of smart cities such as using big data or smart devices to follow sustainability goals. Currently, there is no comprehensive category of smart sustainable city indicators in the literature. This paper aims to discover these indicators by considering the common features of sustainability and smart city concepts. Two rounds of the content analysis technique were employed to investigate semantic, lexical, and conceptual relationships between smart city and sustainability indicators. This paper employed the Sustainable Development Indicators suggested by OECD and the Smart City Index Master by Cohen as the two main groups of indicators. The findings offer a novel set of indicators that enables policymakers and researchers to consider the smartness and sustainability of their projects simultaneously. This includes socio-cultural, economic, environmental, and governance categories with 28 associated indicators. The outcome of this paper offers a unique combined category of smart sustainable city indicators by considering the key elements of sustainability and smart city concepts. Academics and policymakers can also employ this set of indicators as a guideline to build a smart sustainable community.

For highly populated Asian countries like Pakistan, the smart cities may play numerous positive roles resulting in efficient utilization of available infrastructure. This can be achieved by transforming existing infrastructures into smart ones. To start with such transformation, the main emphasis would be given to energy. For this purpose, the model of a framework capable of analyzing emerging trends in upcoming power system to make it more reliable. This research work focused on the significance of implementing advanced systems like smart grids. Therefore the proposed framework addresses the provision of an integrated simulation tool for in-depth study of system to determine susceptibility of failure, critical situations and restoration scheme by adding renewable energy resources. The planned framework will be used to analyze the impact of emerging trends in power grid operation of intelligent grid which is more commonly known as the smart grid. The framework may also be used to develop contingency plans to optimize the generation, transmission and distribution schemes thus providing the more flexible solutions. The proposed framework may also be used to enhance the security features of supervisory control and data acquisition system which is essential part of such smart transformations.