With the rapid development of big data, it is necessary to transfer the massive data generated by end devices to the cloud under the traditional cloud computing model. However, the delays caused by massive data transmission no longer meet the requirements of various real-time mobile services. Therefore, the emergence of edge computing has been recently developed as a new computing paradigm that can collect and process data at the edge of the network, which brings significant convenience to solving problems such as delay, bandwidth, and off-loading in the traditional cloud computing paradigm. By extending the functions of the cloud to the edge of the network, edge computing provides effective data access control, computation, processing and storage for end devices. Furthermore, edge computing optimizes the seamless connection from the cloud to devices, which is considered the foundation for realizing the interconnection of everything. However, due to the open features of edge computing, such as content awareness, real-time computing and parallel processing, the existing problems of privacy in the edge computing environment have become more prominent. The access to multiple categories and large numbers of devices in edge computing also creates new privacy issues. In this book, we discuss on the research background and current research process of privacy protection in edge computing. In the first chapter, the state-of-the-art research of edge computing are reviewed. The second chapter discusses the data privacy issue and attack models in edge computing. Three categories of privacy preserving schemes will be further introduced in the following chapters. Chapter three introduces the context-aware privacy preserving scheme. Chapter four further introduces a location-aware differential privacy preserving scheme. Chapter five presents a new blockchain based decentralized privacy preserving in edge computing. Chapter six summarize this monograph and propose future research directions. In summary, this book introduces the following techniques in edge computing: 1) describe an MDP-based privacy-preserving model to solve context-aware data privacy in the hierarchical edge computing paradigm; 2) describe a SDN based clustering methods to solve the location-aware privacy problems in edge computing; 3) describe a novel blockchain based decentralized privacy-preserving scheme in edge computing. These techniques enable the rapid development of privacy-preserving in edge computing.

With the rapid development of big data, it is necessary to transfer the massive data generated by end devices to the cloud under the traditional cloud computing model. However, the delays caused by massive data transmission no longer meet the requirements of various real-time mobile services. Therefore, the emergence of edge computing has been recently developed as a new computing paradigm that can collect and process data at the edge of the network, which brings significant convenience to solving problems such as delay, bandwidth, and off-loading in the traditional cloud computing paradigm. By extending the functions of the cloud to the edge of the network, edge computing provides effective data access control, computation, processing and storage for end devices. Furthermore, edge computing optimizes the seamless connection from the cloud to devices, which is considered the foundation for realizing the interconnection of everything. However, due to the open features of edge computing, such as content awareness, real-time computing and parallel processing, the existing problems of privacy in the edge computing environment have become more prominent. The access to multiple categories and large numbers of devices in edge computing also creates new privacy issues. In this book, we discuss on the research background and current research process of privacy protection in edge computing. In the first chapter, the state-of-the-art research of edge computing are reviewed. The second chapter discusses the data privacy issue and attack models in edge computing. Three categories of privacy preserving schemes will be further introduced in the following chapters. Chapter three introduces the context-aware privacy preserving scheme. Chapter four further introduces a location-aware differential privacy preserving scheme. Chapter five presents a new blockchain based decentralized privacy preserving in edge computing. Chapter six summarize this monograph and propose future research directions. In summary, this book introduces the following techniques in edge computing: 1) describe an MDP-based privacy-preserving model to solve context-aware data privacy in the hierarchical edge computing paradigm; 2) describe a SDN based clustering methods to solve the location-aware privacy problems in edge computing; 3) describe a novel blockchain based decentralized privacy-preserving scheme in edge computing. These techniques enable the rapid development of privacy-preserving in edge computing.

This book aims to sort out the clear logic of the development of machine learning-driven privacy preservation in IoTs, including the advantages and disadvantages, as well as the future directions in this under-explored domain. In big data era, an increasingly massive volume of data is generated and transmitted in Internet of Things (IoTs), which poses great threats to privacy protection. Motivated by this, an emerging research topic, machine learning-driven privacy preservation, is fast booming to address various and diverse demands of IoTs. However, there is no existing literature discussion on this topic in a systematically manner. The issues of existing privacy protection methods (differential privacy, clustering, anonymity, etc.) for IoTs, such as low data utility, high communication overload, and unbalanced trade-off, are identified to the necessity of machine learning-driven privacy preservation. Besides, the leading and emerging attacks pose further threats to privacy protection in this scenario. To mitigate the negative impact, machine learning-driven privacy preservation methods for IoTs are discussed in detail on both the advantages and flaws, which is followed by potentially promising research directions. Readers may trace timely contributions on machine learning-driven privacy preservation in IoTs. The advances cover different applications, such as cyber-physical systems, fog computing, and location-based services. This book will be of interest to forthcoming scientists, policymakers, researchers, and postgraduates.

This book constitutes the refereed post-conference proceedings of the 13th International Conference on Ad Hoc Networks, ADHOCNETS 2021, held in December 2021, and the 16th International Conference on Tools for Design, Implementation and Verification of Emerging Information Technologies, TRIDENTCOM 2021, held in November 2021. Both conferences were held virtually due to COVID 19 pandemic. The 15 full papers of ADHOCNETS 2021 were selected from 29 submissions and cover a variety of network paradigms including ad hoc networks (MANETs), wireless sensor networks (WSNs), vehicular ad hoc networks (Vanets), airborne networks, underwater networks, underground networks, personal area networks, and home networks, etc. It promises a wide range of applications in civilian, commercial, and military areas. The 18 full papers were selected from 47 submissions and deal the emerging technologies such as Industry 4.0, blockchain, deep learning, cloud/edge/fog computing, cyber physical systems, cybersecurity and computer communications.

Since the 1980s, mobile communication has undergone major transitions from 1G to 4G, at a rate of roughly one generation per decade. And the next upgrade is set to come soon, with 5G heralding a new era of large-bandwidth Internet, and a multi-connection, low-latency Internet of Everything. 5G technology will be the standard for next-generation mobile Internet, and it will not only enhance the individual user's experience, but also provide technical support for artificial-intelligence-based applications, such as smart manufacturing, smart healthcare, smart government, smart cities and driverless cars. As a result, 5G is regarded as the "infrastructure" of the industrial Internet and artificial intelligence and both China and the United States are striving to become the 5G leader and spearhead this new generation of international mobile communication standards. Though trade tensions between China and the United States continue to escalate, with products ranging from soybeans to mobile phones and automobiles being affected, 5G technology may be the true cause of trade wars between the world's top two economies. In short, 5G will change not only society, but also international trade patterns. This book describes various 5G scenarios, changes and values; explains the standards, technologies and development directions behind 5G; and explores new models, new formats and new trends in 5G-based artificial intelligence.

This brief presents emerging and promising communication methods for network reliability via delay tolerant networks (DTNs). Different from traditional networks, DTNs possess unique features, such as long latency and unstable network topology. As a result, DTNs can be widely applied to critical applications, such as space communications, disaster rescue, and battlefield communications. The brief provides a complete investigation of DTNs and their current applications, from an overview to the latest development in the area. The core issue of data forward in DTNs is tackled, including the importance of social characteristics, which is an essential feature if the mobile devices are used for human communication. Security and privacy issues in DTNs are discussed, and future work is also discussed.