One of the crucial challenges for future smart cities is to devise a citywide network infrastructure capable of effectively guaranteeing resource-efficient and reliable communications while managing the complexity of heterogeneous devices and access technologies. This edited book highlights and showcases state of the art research and innovations in 5G and beyond wireless communications technologies for connected smart cities. The main objectives of this work include the exploration of recent advances and application potentials of various communication technologies as promising enablers for future networked smart cities, the investigation of design-specific issues for the integration of different architectural components of smart cities, and addressing various challenges and identifying opportunities in terms of interoperability of potential solutions. The book is aimed at a core and interdisciplinary audience of engineers, researchers and professionals working on smart cities concepts and supporting the integration of nextgeneration information, communication, networking and sensing technologies. It will also be a very useful ancillary for advanced students and other professionals working on nextgeneration communication networks.
Inspec keywords: cloud computing; resource allocation; telecommunication network reliability; Internet of Things; smart cities
Other keywords: data privacy; smart cities; telecommunication network reliability; 5G mobile communication; cloud computing; learning (artificial intelligence); Internet of Things; carrier transmission on power lines; resource allocation; computer network security
Subjects: General electrical engineering topics; General and management topics; Computer communications; Mobile, ubiquitous and pervasive computing; Mobile radio systems; Education and training; Computer networks and techniques; Data security
The main objectives of the book are to explore the intersections and interfaces of various communication technologies under the umbrella of 5G and beyond networks as potential enablers for future networked smart cities, and to provide a uniform platform for researchers/industrial stakeholders to refer to design-specific issues for the integration of different architectural components of smart cities along with the potential future research perspectives. Future cities are expected to face several challenges of resource/energy shortage, traffic congestion, sustainability and safety with the ever-increasing trend of higher worldwide population being shifted to the city areas (United Nations predicts more than 68% of worldwide population to live in urban areas by 2050). To address these issues, recent advances in communication technologies, information and communication technology (ICT) infrastructures, sensing and communication devices, cloud and edge computing, data analytic, machine learning (ML) and artificial intelligence techniques will play a crucial role. To this end, the convergence of emerging communication technologies, the underlying ICT infrastructures and several Internet of Things (IoT) verticals, including connected cars, industrial automation, E-health and smart-grid, is indispensable to enable an integrated enabling platform for future smart and connected societies.
Internet of Things (IoT) has been rapidly gaining ground in recent years, due to its potential to revolutionize the way we live and work. The inclusion of the IoT technologies in the daily operations and services in a city spans a wide variety of applications, including transportation, smart parking systems, smart lighting, health care, smart buildings, etc. To deliver specific smart services in a city, heterogeneous IoT objects should be connected in a network, which then can process the collected data and take explicit actions, based on the required service. To this end, there exist a large number of IoT communication protocols, with distinct coverage range, capacity, operational cost, data rate, etc. In this chapter, we analyze the narrowband IoT (NB-IoT) protocol, as one of the key technologies expected to play a leading role in a smart city scenario. Compared to other protocols, the NB-IoT possess unique features that are treated and justified throughout the chapter. The physical (PHY) and medium access control (MAC) layer specifications are detailed, and several smart city applications through a terrestrial NB-IoT infrastructure are described. In addition, a satellite-based NB-IoT system is considered, together with some relevant applications, because of its ability to overcome the limitations of a terrestrial network. Last but not least, the challenges imposed by the satellite channel into the PHY and MAC layer procedures are characterized and supported by numerical simulations. To conclude the chapter, possible solutions and research directions are discussed.
In this chapter, radio frequency (RF) energy harvesting (EH) technologies like wireless power transfer (WPT) and simultaneous wireless information and power transfer (SWIPT) are discussed. Among the various wireless green technologies, SWIPT technique has gained a lot of attention among the researchers, and it is suitable for smart cities. The unique advantage of SWIPT gives communication nodes to harvest energy from the RF signal and also at the same time to decode information from the signal. Therefore, the transmitting nodes allocate resources for both EH and information decoding (ID). In SWIPT, the most famous resource allocation schemes are either based on power or time resource allocation, or in some cases hybrid power and time resource allocation schemes are implemented. If the power or time resource is not sufficient for the information symbols in the signal, then this can lead to performance degradations. Therefore, an alternative approach is allocating a specific set of symbols for EH, and remainder is used for ID. In this chapter, two novel methods are discussed based on the SWIPT techniques with symbol-wise resource allocation, they are modulation-based SWIPT (M-SWIPT) and frequency-splitting-based SWIPT (FS-SWIPT). In FS-SWIPT, the symbols belonging to specific frequency are used for EH without disturbing the remainder of the symbols in the signal. M-SWIPT allocates specific modulation for EH, preferably higher modulation and the power of modulated symbols are changed by using hybrid constellation shaping (HCS) and therefore, M-SWIPT is much more adaptable. The performances of both the SWIPT techniques are discussed and further in FS-SWIPT, the non-linear distortion (NLD) effects due to variation in transmit power of symbols at transmitter side are analysed. HCS is a novel constellation shaping and the approach of using this constellation shaping gives M-SWIPT to allocate resources in a flexible manner. Over performance of M-SWIPT is analysed in the presence of this shaping scheme.M-SWIPT and FS-SWIPT techniques are designed for improving the energy efficiency and improving the reliability of the low-power sensor devices at very low signal-to-noise regions.
The role of power line communication (PLC) in smart grids becomes more significant as the understanding of the channel improves. Since PLC reduces the additional infrastructure required to link regular devices onto the network, devices can hence be more easily linked to the Internet of Things (IoT) network to create smart buildings, vehicles and grids. The challenge with the harsh PLC channel has been how to mitigate noise in the channel, which is commonly man-made, hence difficult to predict. In this chapter, we evaluate the different indoor PLC channel models and discuss the memoryless channel and the channel with memory. The statistical representations that best describe the channel models are derived which can be used for offline analysis as well as inform appropriate modulation and coding techniques for error mitigation in the channel.
In this chapter, we provide an overview of non-orthogonal multiple access (NOMA)based visible light communication (VLC) techniques to ensure high data rates, reliable seamless connectivity and improved capacity for a connected smart city. Smart cities utilize information and communication technology (ICT) to monitor, analyze and manage the assets and resources as efficient as possible. With the advent of the Internet of Things (IoT) as an integral part of smart cities, the communication system of the smart city must provide improved quality of service (QoS) with high data rates. VLC is a promising technology to enhance the communication system of a smart city which enables high-speed data transmission simultaneously with illumination. The ability of integrating VLC in device-to-device (D2D) communication, motion detection and localization made it suit for indoor communication in the context of industrial 4.0. Also, VLC can be utilized in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications that are the key enabling technological components of intelligent transportation system (ITS). To provide a reliable multiple network access technique for the proposed VLC system, NOMA can be used. In addition to the spectral efficiency gain of NOMA, the research indicated that NOMA can effectively support massive connectivity which is paramount in smart city communication systems.
Over the last decade, increasing availability of Internet of Things (IoT) devices and broadband communications technologies has enabled a number of smart applications in many cities. Among various smart city applications, a smart street lighting application is considered as one of the most cost-effective choices for a smart city today, if relying on existing street lights that are already deployed and networked across a city. Piggy-bagging on a readily available networked street lighting infrastructure, several city-wide services can be offered rapidly and cost effectively. Examples of additional devices that can be deployed along with networked street lights are such as environmental sensors, traffic sensors, IP cameras, digital signage, and more. To enable connections among different sensing and control devices, communication technologies and networks are of crucial components. There are a wide variety of communication technologies that can be used, namely, Sigfox, long range (LoRa), Weightless, Symphony Link, random phase multiple access (RPMA), narrowband IoT (NB-IoT), long-term evolution category 0/1/M (LTE category 0/1/M), power line communications (PLC), ZigBee, IPv6 low-power wireless personal area network (6LoWPAN), wireless local area network (WLAN), wireless mesh, worldwide interoperability for microwave access (WiMAX), and Cellular (2G/3G/ 4G/5G). The chapter aims at providing a comprehensive discussion of various communication technologies that can serve smart street lighting and other add-on applications in a smart city. These technologies are evaluated on the basis of communication network requirements of different applications pertaining to their data rate, latency, reliability, and security.
With increasing urbanization, there has been huge emphasis on enhancing traffic efficiency and road safety. As the traditional cities are transiting to their digital versions, the classical transportation and relevant operations are expected to be implanted with more intelligence. Traditional traffic control systems and vehicle coordination are gradually substituted with smart systems, broadly known as intelligent transportation systems (ITS). On the similar lines, the advancement in communication and sensor technologies have equipped the vehicles with capabilities of understanding their surroundings, communicating with other vehicles, communicating with street furniture, communicating with road users and other external information sources. Vehicles of the smart cities will rely on various applications for its operation, trajectory planning, parking, maneuvering, etc. It goes without saying that such applications may be hosted within the vehicle or remotely, and availability of the right communication bit-pipes is between and among the communicating entities. The technological representative terms like vehicular network, internet of vehicles, vehicular cloud computing, and vehicular ad hoc networking advocate the importance of network technologies, when it comes to achieving the traffic safety and road efficiency objectives. Based on the type of ITS application, the service requirements of the network technologies vary for end-to-end delay, throughput, reliability, security, and adaptability. To ensure these requirements, research community and industry have contributed with solutions like edge computing, roadside units (RSUs), trust management frameworks, learningbased decision mechanisms for various decision-making instances, etc. The chapter discusses some of these solutions. To equip the readers with necessary background information, the chapter starts with introducing the basics of autonomous driving, which is followed by the types of communication models. Detailed discussion on trust management framework and application of edge computing for enabling unmanned aerial vehicles (UAVs) make major sections of the chapter.
With the advancement in technology, applications such as real-time object detection, route predictions, and infotainment required a significant amount of computing power. However, resource constraint onboard computing units installed in vehicles cannot provide the desired computing. Therefore, complex computation tasks are offloaded to nearby vehicles, connected roadside units (RSUs), or data centers. However, the main objective is to optimize the use of resources and to minimize the communication delay. Thus, various techniques have been proposed to support the computation of complex applications through near real-time resource sharing. In this chapter, we summarize the recent contributions proposed to support task distribution over vehicular networks. This work provides an overview of vehicular-assisted frameworks and their challenges. Further, explored a various resource selection methods proposed in the literature for efficient task offloading. To provide better understanding, the existing techniques are categorized as traditional, game-theory, fuzzy, and reward-based. This chapter serves as a guide for researchers to understand the challenges of vehicular-assisted networks and state-of-the-art contributions.
Intelligent transportation systems (ITSs) play an important role in emerging smart cities (SCs), improving the time and energy efficiency of transportation in the cities. A key enabler of the ITS is autonomous vehicle (AV) that is equipped with communication and computing capabilities. The AVs are also empowered by big data analytics and artificial intelligence (AI) and can quickly react and adapt to the changing road conditions of SCs. This chapter first describes the characteristics of big data in an SC, and vehicular mobility models based on big data analytics. Two examples of big-data-driven intelligent management of AVs are provided. Then, a network calculus (NC)-based fleet management method is presented to improve the energy efficiency of AVs and meanwhile offers passengers the best possible experience. At last, a federated learning (FL)-based autonomous driving framework is described to achieve privacy-preserving, intelligent management of the AVs in emerging SCs.
A smart city leverages the Internet of Things (IoT) and sensors to collect the available wealth of raw data from various urban surroundings. This huge volume of unstructured data gleaned in real time needs to be effectively analysed and utilised to spot trends, which can give city planners the information that is highly responsive to the needs of the citizens. The massive amount of information presented in this data is difficult to be viewed and processed by humans. Here, the information retrieval via machine learning (ML) helps in extracting knowledge or structured data from the unstructured form by recognising the underlying pattern. It produces a summarised tabular output in a relational database, which helps one to optimise the given set of services for enhanced functioning and sustainability of the city, such as predicting parking spots for drivers, helping first responders, and locating dangerous intersections. The factors responsible for surging interest in ML are powerful computational processing and cost-effective data storage options, which allow training models that gain experience by quickly and accurately analysing huge chunks of complex data. ML combined with the IoT helps to realise the vision of a more livable and resilient city that is capable of quickly responding to the critical challenges prompted by an outrageous urban population, encompassing traffic congestion, environment deterioration, sanitation issues, energy crises, thwart crime, healthcare, and many more. It can automate municipal operations and advance smart city initiatives at large. In this chapter, a comprehensive list of applications is curated to understand the nuts-and-bolts of ML in the domain of the smart city. The chapter walks through the recent applied examples alongside familiarising with the key research developments in the context of ML-assisted smart cities. Ultimately, the chapter concludes by mentioning the major challenges faced by the implication of ML as a smart city use case. On that account, we are focusing on various examples of ML in a smart city.
The smart city aims for analyzing big data collected from massive Internet of Things (IoT) devices deployed across the city, to get an understanding of connected society, and improve the services provided to the citizens. The rapid development of 5G and beyond communication technologies, IoT, cloud computing, mobile edge computing, and interconnected networks has led to a new digital urban environment (i.e., smart city). Despite several potential benefits, the smart city environment has a major challenge of information security and privacy. A comprehensive analysis of architecture and key elements of smart city along with security and privacy requirements is provided in this chapter. We argue that distributed ledger technology (blockchain) can be used for secure communication in the smart city. Blockchain-based security mechanisms applied to different aspects of the smart city are discussed in detail. Some case studies are presented for smart cities where blockchain has been applied. We also highlight some open issues and future research directions for blockchain-based smart city environments.
The increasing reliance on smart city operations has motivated the need for trusted Internet of Things (IoT) transactions due to the transformation of IoT devices from smart sensing to being active participants that share their data with fog/edge computing services. Existing security models such as centralized cloud-hosted security infrastructures cannot address IoT's security and privacy concerns because of the lack of resources and flexibility, which makes IoT devices susceptible to elevation of privileges, and distributed denial-of-service (DDoS). An attractive and more realistic alternative to address these challenges is the blockchain, which uses a decentralized infrastructure for fighting DDoS attacks and eliminate the risk of a single point of failure. Blockchain serves as the backbone for diverse IoT applications, such as transactive energy auctions, self-driving cars, and trusted health-care systems. Additionally, software-defined networking (SDN) allows the development of customized security policies and services in a dynamic, software-based fashion. Complementing SDN, network function virtualization (NFV) enables scaling IoT capabilities by allowing on-demand service orchestration and management. By combining blockchain and SDN/NFV, we can optimize the flow management in response to attacks by enabling sophisticated analysis of IoT transactions and improving security and privacy based on global network awareness given by centralized SDN controllers. To that end, in this chapter, we introduce an SDN architecture for enforcing security of IoT transaction in the blockchain. We also introduce a novel proof-of-authority (PoA) consensus algorithm to report suspected IoT smart devices and report them under smart contract. We then introduce a distributed intrusion detection system as a manifestation of virtualized network functions (VNFs) in the fog computing environment, i.e., a firewall-as-a-service in SDN network, which takes care of malicious flows and enables DDoS detection and mitigation on-demand.
Due to recent technical advances in both wireless communication and smart device capabilities, Internet of Things (IoT) emerges as the novel enabling paradigm for various smart city applications, including smart transportation, smart energy, smart water, and smart home. Because of the critical or even life-dependent nature of many such applications, security and trustworthiness have both become major concerns for the integration and utilization of both sensing and actuating capabilities in IoT. In addition, mobile crowdsensing has recently emerged as an additional means that complements the existing infrastructure-based sensing paradigm, which is also vulnerable to various cyberattacks. In this chapter, we will investigate the security and trust issues in IoT-based smart city applications and explore how to enhance their security and trustworthiness by using the emerging blockchain technology. More specifically, the threat model, attack surface, and the corresponding countermeasures for IoT-based smart cities will be first investigated. Then, we will explore how the blockchain technology could help one to enhance the security and trustworthiness in IoT. Finally, some existing challenges and future research directions will both be discussed.