Böcker i Wireless Networks-serien

This book investigates data security approaches in Heterogeneous Communications Networks (HCN). First, the book discusses the urgent need for a decentralized data management architecture in HCN. The book investigates preliminaries and related research to help readers obtain a comprehensive picture of the research topic. Second, the book presents three blockchain-based approaches for data management in HCN: data provenance, data query, and data marketing. Finally, based on the insights and experiences from the presented approaches, the book discusses future research directions.

This book presents and develops comprehensive knowledge of vehicular channel characteristics and proper vehicular channel models. The studied topics contain the propagation characteristics of vehicular communications, such as: a time-frequency non-stationary single-input single-output (SISO) vehicle-to-vehicle (V2V) non-geometry stochastic model (NGSM); a space-time non-stationary massive multiple-input multiple-output (MIMO) V2V regular-shaped geometry-based stochastic model (RS-GBSM); and a space-time non-stationary massive MIMO V2V irregular-shaped geometry-based stochastic model (IS-GBSM). Each is introduced, with characteristics then discussed in detail. Finally, this book discusses future research directions to inspire further investigation in the field of vehicular channels from three different perspectives.

To provide ubiquitous and various services, 6G networks tend to be more comprehensive and multidimensional by integrating current terrestrial networks with space-/air-based information networks and marine information networks; then, heterogeneous network resources, as well as different types of users and data, will be also integrated. Driven by the exponentially growing demands of multimedia data traffic and computation-heavy applications, 6G heterogenous networks are expected to achieve a high QoS with ultra-reliability and low latency. In response, resource allocation has been considered an important factor that can improve 6G performance directly by configuring heterogeneous communication, computing and caching resources effectively and efficiently. The book addresses a range of technical issues in cooperative resource allocation and information sharing for the future 6G heterogenous networks, from the terrestrial ultra-dense networks and space-based networks to the integrated satellite-terrestrial networks, as well as introducing the effects of cooperative behavior among mobile users on increasing capacity, trustworthiness and privacy. For the cooperative transmission in heterogeneous networks, the authors commence with the traffic offloading problems in terrestrial ultra-dense networks, and the cognitive and cooperative mechanisms in heterogeneous space-based networks, the stability analysis of which is also provided. Moreover, for the cooperative transmission in integrated satellite-terrestrial networks, the authors present a pair of dynamic and adaptive resource allocation strategies for traffic offloading, cooperative beamforming and traffic prediction based cooperative transmission. Later, the authors discuss the cooperative computation and caching resource allocation in heterogeneous networks, with the highlight of providing our current studies on the game theory, auction theory and deep reinforcement learning based approaches. Meanwhile, the authors introduce the cooperative resource and information sharing among users, in which capacity oriented-, trustworthiness oriented-, and privacy oriented cooperative mechanisms are investigated. Finally, the conclusion is drawn.

This book provides comprehensive insights into the theory, models, and unique characteristics of intelligent reflecting surface (IRS) and its appealing applications in wireless information and power transmission for B5G/6G. It is organized as follows:Chapter 1 gives an overview of the fundamentals of IRS, including the signal and channel models of IRS, its hardware architecture, advantages and practical constraints. Chapter 2, 3 and 4 focus on how to optimize the joint design of active and passive beamforming for three types of IRS-aided wireless information/energy transmission systems, namely IRS-aided power transfer (WPT), IRS-aided simultaneous wireless information and power transfer (SWIPT) and IRS-aided wireless powered communication networks (WPCN).Chapter 2 specifically presents a general framework design for IRS-aided WPT systems from the perspectives of energy beamforming design and channel estimation, while the dynamic IRS beamforming design is also discussed.Chapter 3 thoroughly studies the IRS-aided SWIPT for achieving better R-E trade-off, wherein two different designs based on weighted sum-power maximization at energy users (EUs) and under individual quality-of-service (QoS), requirements at both information users (IUs) and EUs are investigated. Chapter 4 presents three paradigms of IRS-aided WPCN, i.e., IRS-aided half-duplex (HD) WPCN, IRS-aided full-duplex (FD) WCPN and IRS-aided multiple-input-multiple-output (MIMO) FD WPCN. The authors present some related emerging topics, e.g., active IRS aided wireless communications and IRS-aided unmanned aerial vehicle (UAV) communications in chapter 5.This book targets postgraduate students studying electrical engineering and computer science, and can be used as a secondary textbook. Professionals, researchers and engineers working in wireless networks will also find this book useful as a reference.

This book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal processing algorithms against adversarial attacks. Since data quality is crucial in signal processing, the adversary that can poison the data will be a significant threat to signal processing. Therefore, it is necessary and urgent to investigate the behavior of machine learning algorithms in signal processing under adversarial attacks. The authors in this book mainly examine the adversarial robustness of three commonly used machine learning algorithms in signal processing respectively: linear regression, LASSO-based feature selection, and principal component analysis (PCA). As to linear regression, the authors derive the optimal poisoning data sample and the optimal feature modifications, and also demonstrate the effectiveness of the attack against a wireless distributed learning system. The authors further extend the linear regression to LASSO-based feature selection and study the best strategy to mislead the learning system to select the wrong features. The authors find the optimal attack strategy by solving a bi-level optimization problem and also illustrate how this attack influences array signal processing and weather data analysis. In the end, the authors consider the adversarial robustness of the subspace learning problem. The authors examine the optimal modification strategy under the energy constraints to delude the PCA-based subspace learning algorithm. This book targets researchers working in machine learning, electronic information, and information theory as well as advanced-level students studying these subjects. R&D engineers who are working in machine learning, adversarial machine learning, robust machine learning, and technical consultants working on the security and robustness of machine learning are likely to purchase this book as a reference guide.

As the main trend and key enabling technology for next-generation wireless networks (i.e., 6G), integrated sensing and communication (ISAC) can effectively improve spectrum efficiency, hardware efficiency, and information processing efficiency. However, it faces several deficiencies, including limited coverage due to high-frequency signals and limited communication-sensing performance due to uncontrollable wireless environments. Reconfigurable intelligent surface (RIS) provides novel dimensions to address these deficiencies by intelligently manipulating the wireless propagation environment in an energy- and hardware-efficient manner. RIS-enabled ISAC is expected to comprehensively promote the multi-dimensional performance of 6G, such as communication capacity, sensing accuracy, and coverage. Nevertheless, to fully realize its potential, one needs to figure out the impacts of RIS on joint communication and sensing performance and tackle new technical challenges in beamforming design and signal processing. The goal of this book, therefore, is to deliver a thorough understanding of RIS-enabled ISAC from three perspectives: performance analysis, beamforming design, and signal processing. Specifically, the authors provide a brief introduction to RIS-enabled ISAC, including basic concepts, motivations, potential application scenarios, and an overview of the state-of-the-art research on RIS-enabled ISAC. The theoretical performance analytical frameworks of RIS-enabled ISAC and their corresponding results are also discussed. Based on this, several critical issues are identified and elaborated on, including signal processing technologies such as angle and Delay-Doppler information acquisition, and air interface technologies such as beamforming designs. Finally, the book concludes with future trends and open issues for further research.The book would be beneficial for researchers, graduate students, and industry professionals who wish to gain a comprehensive understanding of the latest developments and challenges in RIS-enabled ISAC. By providing insights into the potential of RIS-enabled ISAC and the technical challenges that need to be addressed, the book can aid in the development of practical solutions for next-generation wireless networks and contribute to the advancement of the field of wireless communications.

This book provides a comprehensive review and in-depth discussion of the state-of-the-art research literature and propose energy-efficient computation offloading and resources management for mobile edge computing (MEC), covering task offloading, channel allocation, frequency scaling and resource scheduling. Since the task arrival process and channel conditions are stochastic and dynamic, the authors first propose an energy efficient dynamic computing offloading scheme to minimize energy consumption and guarantee end devices' delay performance. To further improve energy efficiency combined with tail energy, the authors present a computation offloading and frequency scaling scheme to jointly deal with the stochastic task allocation and CPU-cycle frequency scaling for minimal energy consumption while guaranteeing the system stability. They also investigate delay-aware and energy-efficient computation offloading in a dynamic MEC system with multiple edge servers, and introduce an end-to-end deep reinforcement learning (DRL) approach to select the best edge server for offloading and allocate the optimal computational resource such that the expected long-term utility is maximized. Finally, the authors study the multi-task computation offloading in multi-access MEC via non-orthogonal multiple access (NOMA) and accounting for the time-varying channel conditions. An online algorithm based on DRL is proposed to efficiently learn the near-optimal offloading solutions.Researchers working in  mobile edge computing, task offloading and resource management, as well as advanced level students in electrical and computer engineering, telecommunications, computer science or other related disciplines will find this book useful as a reference. Professionals working within these related fields will also benefit from this book. 

This book systematically presents the wireless sensing technology, which has become a promising sensing paradigm in recent years. It includes the introduction of underlying sensing principles, wireless signals, sensing methodologies and enabled applications. Meanwhile, it provides case studies to demonstrate how wireless sensing is applied for representative human and object sensing applications.This book also provides a wireless sensing framework as a guidance to understand and design a wireless sensing system or prototype based on their needs. It also presents a critical investigation of the challenges in achieving wireless sensing in both signal-level and application-level contexts. Accordingly, it summarizes the typical solutions to tackle the related challenges.Researchers and advanced-level students in computer science or electrical engineering working on the design of a wireless system will find this book useful as a reference. Professionals working in the wireless sensing industry will also find this book valuable as a reference tool.

This book demonstrates that the reliable and secure communication performance of maritime communications can be significantly improved by using intelligent reflecting surface (IRS) aided communication, privacy-aware Internet of Things (IoT) communications, intelligent resource management and location privacy protection. In the IRS aided maritime communication system, the reflecting elements of IRS can be intelligently controlled to change the phase of signal, and finally enhance the received signal strength of maritime ships (or sensors) or jam maritime eavesdroppers illustrated in this book.The power and spectrum resource in maritime communications can be jointly optimized to guarantee the quality of service (i.e., security and reliability requirements), and reinforcement leaning is adopted to smartly choose the resource allocation strategy. Moreover, learning based privacy-aware offloading and location privacy protection are proposed to intelligently guarantee the privacy-preserving requirements of maritime ships or (sensors). Therefore, these communication schemes based on reinforcement learning algorithms can help maritime communication systems to improve the information security, especially in dynamic and complex maritime environments.This timely book also provides broad coverage of the maritime wireless communication issues, such as reliability, security, resource management, and privacy protection. Reinforcement learning based methods are applied to solve these issues. This book includes four rigorously refereed chapters from prominent international researchers working in this subject area. The material serves as a useful reference for researchers, graduate students. Practitioners seeking solutions to maritime wireless communication and security related issues will benefit from this book as well.

This book provides an overview of the Internet of Things Network and Machine Learning and introduces Internet of Things architecture. It designs a new intelligent IoT network architecture and introduces different machine learning approaches to investigate solutions. It discusses how machine learning can help network awareness and achieve network intelligent control. It also dicusses the emerging network techniques that can enable the development of intelligent IoT networks. This book applies several intelligent approaches for efficient resource scheduling in networks. It discusses Mobile Edge Computing aided intelligent IoT and focuses mainly on the resource sharing and edge computation offloading problems in mobile edge networks. The blockchain-based IoT (which allows fairly and securely renting resources and establishing contracts) is discussed as well.The Internet of Things refers to the billions of physical devices thatare now connected to and transfer data through the Internet without requiring human-to-human or human-to-computer interaction. According to Gartner's prediction, there will be more than 37 billion IoT connections in the future year of 2025. However, with large-scale IoT deployments, IoT networks are facing challenges in the aspects of scalability, privacy, and security. The ever-increasing complexity of the IoT makes effective monitoring, overall control, optimization, and auditing of the network difficult. Recently, artificial intelligence (AI) and machine learning (ML) approaches have emerged as a viable solution to address this challenge. Machine learning can automatically learn and optimize strategy directly from experience without following pre-defined rules. Therefore, it is promising to apply machine learning in IoT network control and management to leverage powerful machine learning adaptive abilities for higher network performance. This book targets researchers working in the Internet of Things networks as well as graduate students and undergraduate students focused on this field. Industry managers, and government research agencies in the fields of the IoT networks will also want to purchase this book.

This book provides a comprehensive overview of wireless technologies for industrial network systems. The authors first describe the concept of industrial network systems and their application to industrial automation. They then go on to cover the role of sensing and control in industrial network systems, and the challenge of sensing and control in the industrial wireless environment. Then, the existing techniques for resource efficiency information transmission are introduced and studied. Afterward, the authors introduce sensing and control-oriented transmission for industrial network systems, which take advantage of spatial diversity gain to overcome the interference and fading, which in turn improves the transmission reliability without expending extra spectrum resources and enlarging the transmission delay. Subsequently, edge assisted efficient transmission schemes are introduced, which integrate the capacities of communication, computing, and control to relieve the contradiction ofresource limitation and massive data. Finally, the authors discuss open research issues and future works about information transmission in industrial network systems.

This book provides a comprehensive investigation on new technologies for future vehicular networks. The authors propose different schemes to efficiently manage the multi-dimensional resources for supporting diversified applications. The authors answer the questions of why connected and automated vehicle technology should be considered; how the multi-access edge computing (MEC) and unmanned aerial vehicle (UAV) technologies can be helpful to vehicular networks; how to efficiently manage the multi-dimensional resources to support different vehicular applications with guaranteed quality-of-service (QoS) requirements; and how to adopt optimization and AI technologies to achieve resource management in vehicular networks. The book is pertinent to researchers, professionals, academics and students in vehicular technologies.

Relying on unmanned autonomous flight control programs, unmanned aerial vehicles (UAVs) equipped with radio communication devices have been actively developed around the world. Given their low cost, flexible maneuvering and unmanned operation, UAVs have been widely used in both civilian operations and military missions, including environmental monitoring, emergency communications, express distribution, even military surveillance and attacks, for example. Given that a range of standards and protocols used in terrestrial wireless networks are not applicable to UAV networks, and that some practical constraints such as battery power and no-fly zone hinder the maneuverability capability of a single UAV, we need to explore advanced communication and networking theories and methods for the sake of supporting future ultra-reliable and low-latency applications. Typically, the full potential of UAV network's functionalities can be tapped with the aid of the cooperation of multiple drones relying on their ad hoc networking, in-network communications and coordinated control. Furthermore, some swarm intelligence models and algorithms conceived for dynamic negotiation, path programming, formation flight and task assignment of multiple cooperative drones are also beneficial in terms of extending UAV's functionalities and coverage, as well as of increasing their efficiency. We call the networking and cooperation of multiple drones as the terminology 'flying ad hoc network (FANET)', and there indeed are numerous new challenges to be overcome before the idespread of so-called heterogeneous FANETs. In this book, we examine a range of technical issues in FANETs, from physical-layer channel modeling to MAC-layer resource allocation, while also introducing readers to UAV aided mobile edge computing techniques.

With the rapid proliferation of information and communications technology, industrial automation has undergone a sweeping transformation toward intelligent manufacturing. Wireless communication is widely considered to be one of the key technologies enabling intelligent manufacturing. On one hand, deterministic communication with high reliability and low latency is typically required in industrial automation applications. On the other hand, wireless communication in industrial settings is hindered by strictly limited communication resources and many other factors which mainly derive from the shared and error-prone nature of the wireless channels used. The limited communication resources and harsh channel conditions pose considerable challenges for reliable, real-time data transmission in industrial wireless networks. Resource optimization methods are vital to ensuring the deterministic performance of industrial wireless networks. Traditional resource optimization methods adopt the isolated resource optimization methods for each protocol layer, which is inherently local-optimal and leads performance uncontrollable. To focus on ¿Performance Controllable Industrial Wireless Networks¿, this book presents thejoint resource optimization methods across multiple protocol layers for industrial wireless networks; reviews recent, major advances; and discusses the practical implementations of the proposed methods. The joint resource optimization methods discussed here will greatly benefit scientists and researchers in the areas of industrial automation and Industrial Internet of Things. To gain the most from this book, readers should have a fundamental grasp of wireless communication, scheduling theory, and convex optimization.

This book examines whether the integration of edge intelligence (EI) and blockchain (BC) can open up new horizons for providing ubiquitous intelligent services. Accordingly, the authors conduct a summarization of the recent research efforts on the existing works for EI and BC, further painting a comprehensive picture of the limitation of EI and why BC could benefit EI. To examine how to integrate EI and BC, the authors discuss the BC-driven EI and tailoring BC to EI, including an overview, motivations, and integrated frameworks. Finally, some challenges and future directions are explored. The book explores the technologies associated with the integrated system between EI and BC, and further bridges the gap between immature BC and EI-amicable BC.Explores the integration of edge intelligence (EI) and blockchain (BC), including their integrated motivations, frameworks and challenges;Presents how BC-driven EI can realize computing-power management, data administration, and model optimization;Describes how to tailor BC to better support EI, including flexible consensus protocol, effective incentive mechanism, intellectuality smart contract, and scalable BC system tailoring;Presents some key research challenges and future directions for the integrated system.

This book first presents a tutorial on Federated Learning (FL) and its role in enabling Edge Intelligence over wireless edge networks. This provides readers with a concise introduction to the challenges and state-of-the-art approaches towards implementing FL over the wireless edge network. Then, in consideration of resource heterogeneity at the network edge, the authors provide multifaceted solutions at the intersection of network economics, game theory, and machine learning towards improving the efficiency of resource allocation for FL over the wireless edge networks. A clear understanding of such issues and the presented theoretical studies will serve to guide practitioners and researchers in implementing resource-efficient FL systems and solving the open issues in FL respectively.

This book proposes secure schemes to address security challenges in secure automatic dependent surveillance-broadcast systems (ADS-B) from five different angles. First, the authors examine encryption schemes applied to the ADS-B environment for protecting messages confidentiality. Second, they propose an ADS-B broadcast authentication scheme with batch verification by employing an identity-based signature. Third, they present ADS-B broadcast authentication scheme based on a digital signature with message recovery, which provides a feature that the message is recoverable from the signature. Fourth, they propose a new cryptographic solution to ADS-B security. Finally, they propose an accurate and efficient cognitive aircraft location verification scheme preserving aircraft location privacy by utilizing a grid-based k-nearest neighbor algorithm. In summary, the authors show how ADS-B data links can greatly enhance flight safety by these proposed schemes without sacrificing data security.Proposes several secure schemes to address security challenges in secure automatic dependent surveillance-broadcast systems (ADS-B);Provides extensive experiments and evaluates the proposed schemes using real-world data;Shows how ADS-B data links can greatly enhance safety without sacrificing data security

This book reports on the latest advances from both industry and academia on ubiquitous intelligence and how it is enabled by 5G/6G communication technologies. The authors cover network protocol and architecture design, machine learning and artificial intelligence, coordinated control and digital twins technologies, and security and privacy enhancement for ubiquitous intelligence. The authors include recent studies of performance analysis and enhancement of the Internet of Things, cyber-physical systems, edge computing, and cyber twins, all of which provide importance guidance and theoretical tools for developing future ubiquitous intelligence. The content of the book will be of interest to students, educators, and researchers in academia, industry, and research laboratories.Provides comprehensive coverage of enabling communications, computing, and control technologies for ubiquitous intelligence;Presents a novel paradigm of ubiquitous intelligence powered by broadband communications, computing, and control;Includes a review of 5G/6G communication technologies, network protocol and architecture design, and ubiquitous computing.

This book proposes promising mmWave solutions to promoting safe and reliable vehicular communications. The authors include topics such as channel estimation, multi-user transceiver design, and advanced index modulation. For channel estimation, unique channel properties and hybrid structures are first introduced, followed by the development of a doubly-sparse doubly-selective channel estimator. For multi-user transceiver design, the concept of hybrid block diagonalization (HBD) is first introduced, followed by a generic HBD-based transceiver design to maximize the system capacity. For advanced index modulation, the generalized beamspace modulation for uplink multi-user scenarios are first introduced, followed by the precoded beamspace modulation for the downlink. Finally, this book discusses open problems and future research directions to inspire further studies in the field of mmWave vehicular communications.