19 - Machine-to-machine (M2M) communications for smart cities

Internet of Things (IoT) and Big Data have played a significant role in the development and sustainability of Smart Cities. They are the backbones of Smart Cities' efforts to connect human resources, social capital, and ICT infrastructure to address public challenges, achieve sustainable development and improve people's standard of life. It helps Smart Cities improve public services, such as water management, healthcare, education, security issues, transportation, energy usage, and other community to achieve a better life. Big Data helps to analyze, store and back-ups a massive amount of data generated in smart cities in seconds day by day. For this matter, different analytical tools are employed, such as data analytics, machine learning, statistics, and data mining. Even though there are many benefits offered to smart cities; the Smart Cities encounters challenges that need to be addressed. Many of these challenges focus on security and privacy, infrastructures and operational and handling massive data. This paper describes the overview of IoT, Big Data, and Smart Cities. The Applications of IoT and Smart Cities were also discussed; the features of Smart Cities and Big Data; Different challenges of IoT, Big Data, and Smart Cities are described. Also, the proposed research work has presented the relationship between IoT and Big Data to Smart Cities; the role of IoT and Big Data in smart cities was identified; finally, the future directions for IoT and Big Data regarding Smart Cities are discussed.

This study examines how the application of the IoT in smart cities is discussed in the current academic literature. Based on bibliometric techniques, 1,802 articles were retrieved from the Scopus database and analyzed to identify the temporal nature of IoT research, the most relevant journals, authors, countries, keywords, and studies. The software tool VOSviewer was used to build the keyword co-occurrence network and to cluster the pertinent literature. Results show the significant growth of IoT research in recent years. The most productive authors, journals, and countries were also identified. The main findings from the keyword co-occurrence clustering and an in-depth qualitative analysis indicate that the IoT is used alongside other technologies including cloud computing, big data analytics, blockchain, artificial intelligence, and wireless telecommunication networks. The major applications of the IoT for smart cities include smart buildings, transportation, healthcare, smart parking, and smart grids. This review is one of the first attempts to map global IoT research in a smart city context and uses a comprehensive set of articles and bibliometric techniques to provide scholars and practitioners with an overview of what has been studied so far and to identify research gaps at the intersection of the IoT and the smart city.

Smart cities and behavioural change: (Un)sustainable mobilities in the neo-liberal city

In the quest for sustainable urban development, smart cities are increasingly harnessing cloud computing and big data analytics to optimize energy usage and drive policy initiatives. This review explores how leveraging these technologies can transform energy management within urban environments. Cloud computing provides the scalable infrastructure necessary for processing and storing vast amounts of data generated by smart sensors and IoT devices in real-time. By utilizing cloud-based platforms, cities can ensure the efficient aggregation, analysis, and dissemination of energy data, facilitating informed decision-making and policy formulation. Big data analytics plays a crucial role in this framework by enabling the analysis of complex and voluminous datasets to uncover patterns and insights that traditional methods may miss. Through advanced analytics, cities can gain a deeper understanding of energy consumption trends, predict peak demand periods, and identify opportunities for efficiency improvements. This data-driven approach allows policymakers to design and implement targeted energy policies, optimize resource allocation, and reduce operational costs. Key benefits of integrating cloud computing and big data analytics in energy optimization include enhanced real-time monitoring capabilities, improved accuracy in demand forecasting, and more effective management of energy resources. The ability to analyze and visualize large datasets supports the development of dynamic policies that adapt to changing conditions and promote energy efficiency across various sectors. Despite these advantages, challenges such as data privacy concerns, the need for robust cybersecurity measures, and the requirement for skilled personnel to interpret complex data must be addressed. Additionally, ensuring equitable access to these technologies across diverse urban areas is essential for maximizing their impact. In conclusion, the strategic application of cloud computing and big data analytics offers significant potential for optimizing energy use in smart cities. By embracing these technologies, urban centers can drive more effective and responsive energy policies, contributing to broader sustainability goals and enhancing overall quality of life.

In this developing paradigm, heterogeneous network infrastructure is combined with ubiquitous devices for sensing, smart control systems and big data analytics to create “smart cities.” Enhancing the overall quality of life is one of their main goals and they do so through smart services they offer in a broad range of areas such as transportation, healthcare, entertainment (including virtual reality), the environment (including pollution) and energy. To be able to offer those services, the job of big data and their analysis is complicated, since it allows for the extraction of useful insights from the enormous amounts of data that the smart cities produce. The current state-of-the-art research efforts aimed at big data analytics in a smart city setting are investigated and presented in this chapter. In particular, a comprehensive literature review on the big data analytic frameworks for smart cities is provided. Following that, a short talk on IoT and big data is briefed. Owing to different data that the smart cities collect, which frequently have contradictory processing needs, appropriate investigative methods based on the data type are also recommended, depending on the data type. The many smart city applications were investigated and a perspective on the difficulties associated with smart city development was developed. In this chapter, the methods addressing the smart city issues and development are examined, and the possibility for the employment of integrated IoT and big data analytics is presented. Finally, as a case study, a briefing on a call service application for the fire department in a smart city is provided to round up the presentation.

An exploration of smart city approaches by international ICT firms

Due to the incompleteness of planning ideas and construction methods, there are many problems in urban construction: the planning lacks prospectiveness, does not match the development needs of the city, and repeated construction is common; the community has insufficient functions and transportation. Therefore, it is of great significance to explore the path of smart city construction and development. This article mainly studies the application of intelligent logistics technology and big data in the construction and development of smart cities. Starting from the two main theories of theory and reality, this article first discusses the challenges of the big data era background to urban construction, analyzes the relationship between big data and smart cities, and then takes urban logistics big data as an object to study smart city construction. The smart logistics technology pointed out the convenience brought by the smart logistics technology and big data technology in the construction of smart cities. Finally, by drawing on the advanced experience of using big data in some cities at home and abroad to promote the construction of smart cities, the use of big data and smart logistics Technology assists the construction of smart cities, and has made concrete and detailed suggestions. The experimental results of this paper show that the group size is 60, the number of iterations is 30, and the minimum logistics cost through iteration is 1,284,100 yuan. In this paper, the logistics management method of intelligent logistics technology based on big data can minimize the logistics cost. This article provides some theoretical guidance for the key technical aspects of logistics network warehousing, service and resource management in the construction and development of smart cities, and provides guarantee for urban construction and stable development, which has certain reference significance for better and longer-term development.

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.

PropósitoEste artículo se enmarca dentro del estudio de los modelos de gobernanza colaborativa en las ciudades inteligentes y analiza aquéllas que son miembros de la red EUROCITIES y, además, participan en un grupo de trabajo sobre "ciudadanía creativa", buscando analizar el uso de las nuevas tecnologías por parte de los gobiernos de estas ciudades inteligentes con el objetivo de mejorar la e-participación pública.Diseño/metodología/enfoqueEsta investigación se basa en dos métodos de recopilación de datos. En primer lugar, este documento realiza una encuesta electrónica enviada a las principales ciudades inteligentes europeas sobre: a) la relevancia de la gobernanza colaborativa en las estrategias de las ciudades; b) los principales fundamentos y resultados de la gobernanza inteligente; y c) el modelo de participación en el desarrollo de una ciudad inteligente. En segundo lugar, este trabajo realiza, a fecha de mayo de 2017, un análisis de las nuevas tecnologías utilizadas por 47 gobiernos locales de estas ciudades inteligentes (aquéllas incluidas en el grupo de trabajo de "ciudadanía creativa" en la red EUROCITIES) para facilitar la participación electrónica de la ciudadanía.HallazgosLas ciudades inteligentes de muestra creen que la gobernanza colaborativa debe ser implementada en la gestión de la ciudad inteligente, pero menos del 50% han creado plataformas de e-participación ciudadana para promover la involucración de la ciudadanía en los asuntos públicos, y sólo unas pocas de estas ciudades promueven el desarrollo de consultas, discusiones y peticiones online. Además, un análisis realizado por culturas administrativas destaca la existencia de diferencias entre las percepciones obtenidas de los gestores de las ciudades inteligentes de la muestra y las herramientas que estas ciudades utilizan para fomentar la e-participación pública.Originalidad/valourEste documento contribuye a llenar el vacío en la investigación sobre el análisis de la relevancia de los modelos de gobernanza colaborativa en ciudades inteligentes y el uso de web de transparencia, de datos abiertos o de plataformas de participación electrónica para promover la participación ciudadana en los asuntos públicos dentro de estas ciudades.

This article is single-source introduction to the emerging concept of smart cities. It can be used for familiarizing researchers with the vast scope of research possible in this application domain. The smart is primarily concept, and there is still not clear and consistent definition among practitioners and academia. As simplistic explanation, smart is place where traditional networks and services are made more flexible, efficient, and sustainable with the use of information, digital, and telecommunication technologies to improve the city's operations for the benefit of its inhabitants. Smart cities are greener, safer, faster, and friendlier. The different components of smart include smart infrastructure, smart transportation, smart energy, smart health care, and smart technology. These components are what make the cities smart and efficient. Information and communication technology (ICT) are enabling keys for transforming traditional cities into smart cities. Two closely related emerging technology frameworks, the Internet of Things (IoT) and big data (BD), make smart cities efficient and responsive. The technology has matured enough to allow smart cities to emerge. However, there is much needed in terms of physical infrastructure, smart city, the digital technologies translate into better public services for inhabitants and better use of resources while reducing environmental impacts. One of the formal definitions of the smart is the following: connecting the physical infrastructure, the information-technology infrastructure, the social infrastructure, and the business infrastructure to leverage the collective intelligence of the city [4]. Another formal and comprehensive definition is a smart sustainable is an innovative that uses information and communication technologies (ICTs) and other means to improve quality of life, efficiency of urban operations and services, and competitiveness, while ensuring that it meets the needs of present and future generations with respect to economic, social and environmental aspects [12]. A broad overview of various components needed in smart is depicted in Figure 1. Any combination of various smart components can make cities smart. A need not have all the components to be labeled as smart. The number of smart components depends on the cost and available technology.

Smart cities have brought significant improvements in quality of life and services to citizens and urban environments. They are fully enabled to control the physical objects in real time and provide intelligent information to citizens in terms of transport, healthcare, smart buildings, public safety, smart parking, and traffic system as well as smart agriculture, and so on. The applications of smart cities are able to collect sensitive information. However, various security and privacy issues may arise at different levels of the architecture. Therefore, it is important to be aware of these security and privacy issues while designing and implementing the applications. This chapter highlights main applications of smart cities and addresses the major privacy and security issues in the architecture of the smart cities’ applications. It also reviews some of the current solutions regarding the security and privacy of information-centric smart cities’ applications and presents future research challenges that still need to be considered for performance improvement.

The aim of the paper is to define what role can play smart city technology in the reconstruction of post-war cities for example in Syrian Arab Republic. The smart city concept offers different opportunities for different countries. The implementation of smart city technology concepts, especially in post-war cities, faces different challenges and if a given smart city solution can simply transplanted from one city situation to post-war cities. This paper will discuss some of these challenges. The immediate need for cities reconstruction in the post-war countries is to provide adequate urban infrastructure to meet the increasing pace of urbanization after war. In the process of meeting infrastructure demands, smart technology applications provide a way for such cities to achieve leapfrogging after war. Smart technology will able to plan better in the areas that affected by war, post-war cities with smart city's ability to analyse large amount of data. This will allow for proactive maintenance and a better planning for the future, having a smart-technology means that the post-war cities can move forward, and use data collected to make meaningful changes in city plans. Lack of finance and well-developed business models in post-war countries, make the challenge difficult and is often to maintain legacy infrastructure systems, which cannot be abandoned due to cost, space and other considerations unlike other countries that they respond to the sustainable development and needs of society. While technology in the form of smart city is an integral part of a smart city, it should only be seen as an enabler to meet the needs of the people of the city and the need to localize this technology. Smart technology should be chosen with a deep understanding of people’s needs, lifestyles, behaviours, and cultures. Ultimately, smart technology in planning post-war cities requires the participation of local communities for success.

Smart cities have expanded rapidly with the introduction of Internet of Things (IoT) devices and edge computing techniques. Such technologies have transformed how cities function through the ability to deliver smart services and analyze data in real time. But that advancement also adds to the demanding issues about privacy and data security. In smart city environments, privacy-preserving is essential to protect sensitive data collected from different channels against leakage, abuse and unauthorized access. This work is a review of data security and privacy-preserving models made for edge computing and smart cities. It examines the advantages and disadvantages of existing machine learning, blockchain, cryptography, hybrid architectures of optimizing resources, scalability and efficiency. The review discusses open challenges, including interoperability, resource constraints and governance issues, as well as future research directions such as quantum-secure security, federated learning and AI-based adaptive models. The concepts aim to help researchers and policymakers establish strong, durable smart city infrastructures.

Introduction Today, the rapid acceleration of urbanization has made it necessary to reconsider the balance among energy consumption, environmental sustainability, and quality of life. Buildings, which account for a significant share of cities’ carbon footprint, play a critical role in efforts to improve energy efficiency and ensure user well-being. In this context, advances in digitalization and Internet of Things (IoT) technologies have enabled buildings to evolve beyond mere physical structures into dynamic, data-driven, and user-interactive systems. Within this framework, the present study constitutes a systematic literature review addressing the effects of indoor environment design in smart cities on energy efficiency (E), user comfort (C), and environmental sustainability (S). In recent years, IoT-based sensor and control technologies have reconfigured approaches to energy management in buildings by enabling the continuous monitoring of environmental parameters such as temperature, humidity, carbon dioxide levels, lighting, and movement, while also strengthening user experience through a holistic perspective. Methods In this regard, the study examined 76 different works in the literature, including field applications, experimental research, and conceptual models. These studies were evaluated through an inductive thematic analysis approach based on content and classified according to recurring conceptual clusters in the literature. Results An examination of these sources reveals that the contributions of IoT technologies to smart buildings and cities are multidimensional in nature. This extensive body of knowledge in the literature demonstrates that IoT is not merely a technical infrastructure but also an ecosystem that transforms energy, health, the environment, transportation, and social life. It is evident that the data derived from all reviewed studies were synthesized under the headings of energy efficiency, indoor environmental quality and comfort, smart city infrastructure, user interaction, security/facility management, and industrial applications. Accordingly, the present study evaluates the contributions of IoT-based solutions to reducing energy consumption, improving environmental conditions, and supporting user-centered indoor design. The reviewed studies show that these technologies not only enhance energy efficiency (maximum savings: smart parking [92.6%] and smart lighting [73.2%]; average building savings: 20–30% through BEMS and IoT systems; HVAC optimization: 30–70% through artificial intelligence), but also support user health and comfort (the use of smart systems is generally expected to produce an improvement of more than 20% in comfort levels, while this rate can reach the 70–90% range with advanced personalized models). Furthermore, they demonstrate that IoT-based systems play a strategic role in achieving environmental sustainability goals, reducing carbon emissions, and implementing smart city policies. Discussion The original contribution of this study lies in its systematic synthesis of energy, comfort, and sustainability within an integrated thematic classification framework, thereby revealing trends in the field, research gaps, and potential future directions.

PurposeThe purpose of this paper is to advance knowledge of the transformative potential of big data on city-based transport models. The central question guiding this paper is: how could big data transform smart city transport operations? In answering this question the authors present initial results from a Markov study. However the authors also suggest caution in the transformation potential of big data and highlight the risks of city and organizational adoption. A theoretical framework is presented together with an associated scenario which guides the development of a Markov model.Design/methodology/approachA model with several scenarios is developed to explore a theoretical framework focussed on matching the transport demands (of people and freight mobility) with city transport service provision using big data. This model was designed to illustrate how sharing transport load (and capacity) in a smart city can improve efficiencies in meeting demand for city services.FindingsThis modelling study is an initial preliminary stage of the investigation in how big data could be used to redefine and enable new operational models. The study provides new understanding about load sharing and optimization in a smart city context. Basically the authors demonstrate how big data could be used to improve transport efficiency and lower externalities in a smart city. Further how improvement could take place by having a car free city environment, autonomous vehicles and shared resource capacity among providers.Research limitations/implicationsThe research relied on a Markov model and the numerical solution of its steady state probabilities vector to illustrate the transformation of transport operations management (OM) in the future city context. More in depth analysis and more discrete modelling are clearly needed to assist in the implementation of big data initiatives and facilitate new innovations in OM. The work complements and extends that of Setia and Patel (2013), who theoretically link together information system design to operation absorptive capacity capabilities.Practical implicationsThe study implies that transport operations would actually need to be re-organized so as to deal with lowering CO2 footprint. The logistic aspects could be seen as a move from individual firms optimizing their own transportation supply to a shared collaborative load and resourced system. Such ideas are radical changes driven by, or leading to more decentralized rather than having centralized transport solutions (Caplice, 2013).Social implicationsThe growth of cities and urban areas in the twenty-first century has put more pressure on resources and conditions of urban life. This paper is an initial first step in building theory, knowledge and critical understanding of the social implications being posed by the growth in cities and the role that big data and smart cities could play in developing a resilient and sustainable transport city system.Originality/valueDespite the importance of OM to big data implementation, for both practitioners and researchers, we have yet to see a systematic analysis of its implementation and its absorptive capacity contribution to building capabilities, at either city system or organizational levels. As such the Markov model makes a preliminary contribution to the literature integrating big data capabilities with OM capabilities and the resulting improvements in system absorptive capacity.