This book provides a timely and comprehensive study of developing efficient network slicing frameworks in both 5G wireless and core networks. It also presents protocol stack layer perspectives, which includes virtual network topology design, end-to-end delay modeling, dynamic resource slicing, and link-layer and transport-layer protocol customization. This book provides basic principles, concepts and technologies for communication, computing and networking. Optimization and queueing analysis techniques are applied to solving different problems for network slicing illustrated in this book as well. Researchers working in the area of network slicing in 5G networks and beyond, and advanced-level students majoring in electrical engineering, computer engineering and computer science will find this book useful as a reference or secondary textbook. Professionals in industry seeking solutions to resource management for 5G networks and beyond will also want to purchase this book.

This book covers connectivity and edge computing solutions for representative Internet of Things (IoT) use cases, including industrial IoT, rural IoT, Internet of Vehicles (IoV), and mobile virtual reality (VR). Based on their unique characteristics and requirements, customized solutions are designed with targets such as supporting massive connections or seamless mobility and achieving low latency or high energy efficiency. Meanwhile, the book highlights the role of artificial intelligence (AI) in future IoT networks and showcases AI-based connectivity and edge computing solutions. The solutions presented in this book serve the overall purpose of facilitating an increasingly connected and intelligent world. The potential benefits of the solutions include increased productivity in factories, improved connectivity in rural areas, enhanced safety for vehicles, and enriched entertainment experiences for mobile users. Featuring state-of-the-art research in the IoT field, this book can help answer the question of how to connect billions of diverse devices and enable seamless data collection and processing in future IoT. The content also provides insights regarding the significance of customizing use case-specific solutions as well as approaches of using various AI methods to empower IoT. This book targets researchers and graduate students working in the areas of electrical engineering, computing engineering, and computer science as a secondary textbook or reference. Professionals in industry who work in the field of IoT will also find this book useful.

This book provides a timely and comprehensive study of dynamic resource management for network slicing in service-oriented fifth-generation (5G) and beyond core networks. This includes the perspective of developing efficient computation resource provisioning and scheduling solutions to guarantee consistent service performance in terms of end-to-end (E2E) data delivery delay. Network slicing is enabled by the software defined networking (SDN) and network function virtualization (NFV) paradigms. For a network slice with a target traffic load, the E2E service delivery is enabled by virtual network function (VNF) placement and traffic routing with static resource allocations. When data traffic enters the network, the traffic load is dynamic and can deviate from the target value, potentially leading to QoS performance degradation and network congestion. Data traffic has dynamics in different time granularities. For example, the traffic statistics (e.g., mean and variance) can be non-stationary and experience significant changes in a coarse time granularity, which are usually predictable. Within a long time duration with stationary traffic statistics, there are traffic dynamics in small timescales, which are usually highly bursty and unpredictable. To provide continuous QoS performance guarantee and ensure efficient and fair operation of the network slices over time, it is essential to develop dynamic resource management schemes for the embedded services in the presence of traffic dynamics during virtual network operation. Queueing theory is used in system modeling, and different techniques including optimization and machine learning are applied to solving the dynamic resource management problems. Based on a simplified M/M/1 queueing model with Poisson traffic arrivals, an optimization model for flow migration is presented to accommodate the large-timescale changes in the average traffic rates with average E2E delay guarantee, while addressing a trade-off between load balancing and flow migration overhead. To overcome the limitations of Poisson traffic model, the authors present a machine learning approach for dynamic VNF resource scaling and migration. The new solution captures the inherent traffic patterns in a real-world traffic trace with non-stationary traffic statistics in large timescale, predicts resource demands for VNF resource scaling, and triggers adaptive VNF migration decision making, to achieve load balancing, migration cost reduction, and resource overloading penalty suppression in the long run. Both supervised and unsupervised machine learning tools are investigated for dynamic resource management. To accommodate the traffic dynamics in small time granularities, the authors present a dynamic VNF scheduling scheme to coordinate the scheduling among VNFs of multiple services, which achieves network utility maximization with delay guarantee for each service. Researchers and graduate students working in the areas of electrical engineering, computing engineering and computer science will find this book useful as a reference or secondary text. Professionals in industry seeking solutions to dynamic resource management for 5G and beyond networks will also want to purchase this book.

This book presents the current research on safety message dissemination in vehicular networks, covering medium access control and relay selection for multi-hop safety message broadcast. Along with an overall overview of the architecture, characteristics, and applications of vehicular networks, the authors discuss the challenging issues in the research on performance improvement for safety applications, and provide a comprehensive review of the research literature.A cross layer broadcast protocol is included to support efficient safety message broadcast by jointly considering geographical location, physical-layer channel condition, and moving velocity of vehicles in the highway scenario. To further support multi-hop safety message broadcast in a complex road layout, the authors propose an urban multi-hop broadcast protocol that utilizes a novel forwarding node selection scheme. Additionally, a busy tone based medium access control scheme is designed to provide strict priority to safety applications in vehicle-to-infrastructure communications.This book offers useful insights into protocol design and inspires a new line of thinking in performance improvements for safety applications in vehicular networks. It is a valuable resource for professionals, researchers, or advanced-level students working in vehicular networks or quality of service.

This timely book provides broad coverage of vehicular ad-hoc network (VANET) issues, such as security, and network selection. Machine learning based methods are applied to solve these issues. This book also 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, and practitioners seeking solutions to VANET communication and security related issues. This book will also help readers understand how to use machine learning to address the security and communication challenges in VANETs. Vehicular ad-hoc networks (VANETs) support vehicle-to-vehicle communications and vehicle-to-infrastructure communications to improve the transmission security, help build unmanned-driving, and support booming applications of onboard units (OBUs). The high mobility of OBUs and the large-scale dynamic network with fixed roadside units (RSUs) make the VANET vulnerable to jamming. The anti-jamming communication of VANETs can be significantly improved by using unmanned aerial vehicles (UAVs) to relay the OBU message. UAVs help relay the OBU message to improve the signal-to-interference-plus-noise-ratio of the OBU signals, and thus reduce the bit-error-rate of the OBU message, especially if the serving RSUs are blocked by jammers and/or interference, which is also demonstrated in this book. This book serves as a useful reference for researchers, graduate students, and practitioners seeking solutions to VANET communication and security related issues.

The book presents the proceedings of the 2nd International Conference on 5G for Ubiquitous Connectivity (5GU 2018), which took place on December 4-5, 2018 in Nanjing, People's Republic of China. The aim of this conference is to bring together researchers and developers as well as regulators and policy makers to present their latest views on 5G, including new networking, new wireless communications, resource control & management, future access techniques, new emerging applications, and latest findings in key research activities on 5G. The book is applicable to researchers, academics, students, and professionals. Features practical, tested applications in 5G for ubiquitous connectivity; Includes discussion of 5G for ubiquitous connectivity in relation to wireless communications, resource control & management, and future access techniques; Applicable to researchers, academics, students, and professionals.

This book presents link-layer cooperative frameworks to improve transmission reliability and network throughput of distributed TDMA MAC protocols in vehicular ad hoc networks (VANETs). The main objective of this book is to present link-layer node cooperation schemes to enhance the link-layer performance of vehicular networks, in terms of reliability and system throughput. The authors present approaches proposed for the medium access control (MAC) and node cooperation in VANETs. The authors also cover cooperative ADHOC MAC for point-to-point communication between a pair of source and destination nodes, and cooperative relay broadcasting for broadcast services in this book. The performance of node cooperation frameworks is evaluated via mathematical analysis and computer simulations, in comparison with distributed TDMA MAC protocols without cooperation. The proposed node cooperation frameworks enhance the performance of distributed TDMA MAC and make it more robust to tackle the dynamic networking conditions in VANETs. Furthermore, with cooperation enabled transmission, the performance of distributed TMDA MAC is more suitable to support the wide range of mobile applications and their strict service requirements which is discussed in this book. The proposed node cooperation schemes and distributed cooperation decisions can be extended to wireless systems other than distributed TDMA MAC, such as cellular communication, for vehicular communications introduced in this book. This book is useful for researchers from academia and industry, as well as advanced level students interested in vehicular communication networks. It is also useful for professionals and engineers developing applications that use cooperative wireless communication systems.

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.

The book presents the proceedings of the 2nd International Conference on 5G for Ubiquitous Connectivity (5GU 2018), which took place on December 4-5, 2018 in Nanjing, People's Republic of China. The aim of this conference is to bring together researchers and developers as well as regulators and policy makers to present their latest views on 5G, including new networking, new wireless communications, resource control & management, future access techniques, new emerging applications, and latest findings in key research activities on 5G. The book is applicable to researchers, academics, students, and professionals. Features practical, tested applications in 5G for ubiquitous connectivity; Includes discussion of 5G for ubiquitous connectivity in relation to wireless communications, resource control & management, and future access techniques; Applicable to researchers, academics, students, and professionals.

This book presents the current research on safety message dissemination in vehicular networks, covering medium access control and relay selection for multi-hop safety message broadcast. Along with an overall overview of the architecture, characteristics, and applications of vehicular networks, the authors discuss the challenging issues in the research on performance improvement for safety applications, and provide a comprehensive review of the research literature.A cross layer broadcast protocol is included to support efficient safety message broadcast by jointly considering geographical location, physical-layer channel condition, and moving velocity of vehicles in the highway scenario. To further support multi-hop safety message broadcast in a complex road layout, the authors propose an urban multi-hop broadcast protocol that utilizes a novel forwarding node selection scheme. Additionally, a busy tone based medium access control scheme is designed to provide strict priority to safety applications in vehicle-to-infrastructure communications.This book offers useful insights into protocol design and inspires a new line of thinking in performance improvements for safety applications in vehicular networks. It is a valuable resource for professionals, researchers, or advanced-level students working in vehicular networks or quality of service.

This brief presents a stochastic microscopic mobility model that describes the temporal changes of intervehicle distances. The model is consistent with simulated and empirical vehicle traffic patterns. Using stochastic lumpability methods, the proposed mobility model is mapped into an aggregated mobility model that describes the mobility of a group of vehicles. In addition, the proposed mobility model is used to analyze the spatiotemporal VANET topology. Two metrics are proposed to characterize the impact of vehicle mobility on VANET topology: the time period between successive changes in communication link state (connection and disconnection) and the time period between successive changes in node's one-hop neighborhood. Using the proposed lumped group mobility model, the two VANET topology metrics are probabilistically characterized for different vehicular traffic flow conditions. Furthermore, the limiting behavior of a system of two-hop vehicles and the overlap-state of their coverage ranges is modeled, and the steady-state number of common vehicle neighbors between the two vehicles is approximately derived. The proposed mobility model will facilitate mathematical analysis in VANETs. The spatiotemporal VANET topology analysis provides a useful tool for the development of mobility-aware vehicular network protocols. Mobility Modeling for Vehicular Communication Networks is designed for researchers, developers, and professionals involved with vehicular communications. It is also suitable for advanced-level students interested in communications, transport infrastructure, and infotainment applications.

This brief focuses on medium access control (MAC) in vehicular ad hoc networks (VANETs), and presents VeMAC, a novel MAC scheme based on distributed time division multiple access (TDMA) for VANETs. The performance of VeMAC is evaluated via mathematical analysis and computer simulations in comparison with other existing MAC protocols, including the IEEE 802.11p standard. This brief aims at proposing TDMA as a suitable MAC scheme for VANETs, which can support the quality-of-service requirements of high priority VANET applications.

This Springer Brief investigates the voice and elastic/interactive data service support over cognitive radio networks (CRNs), in terms of their delay requirements. The increased demand for wireless communication conflicts with the scarcity of the radio spectrum, but CRNS allow for more efficient use of the networks. The authors review packet level delay requirements of the voice service and session level delay requirements of the elastic/interactive data services, particularly constant-rate and on-off voice traffic capacities in CRNs with centralized and distributed network coordination. Some generic channel access schemes are considered as the coordination mechanism, and call admission control algorithms are developed for non-fully-connected CRNs. Other key topics include the advantages of supporting voice traffic flows with different delay requirements, the mean response time of the elastic data traffic over a centralized CRN, and effects of the traffic load at the base station and file length (service time requirement) distribution on the mean response time. The brief is designed for professionals and researchers working with wireless networks, cognitive radio, and communications. It is also a helpful reference for advanced-level students interested in efficient wireless communications.

This brief investigates distributed medium access control (MAC) with QoS provisioning for both single- and multi-hop wireless networks including wireless local area networks (WLANs), wireless ad hoc networks, and wireless mesh networks. For WLANs, an efficient MAC scheme and a call admission control algorithm are presented to provide guaranteed QoS for voice traffic and, at the same time, increase the voice capacity significantly compared with the current WLAN standard. In addition, a novel token-based scheduling scheme is proposed to provide great flexibility and facility to the network service provider for service class management. Also proposed is a novel busy-tone based distributed MAC scheme for wireless ad hoc networks and a collision-free MAC scheme for wireless mesh networks, respectively, taking the different network characteristics into consideration. The proposed schemes enhance the QoS provisioning capability to real-time traffic and, at the same time, significantly improve the system throughput and fairness performance for data traffic, as compared with the most popular IEEE 802.11 MAC scheme.

This brief focuses on radio resource allocation in a heterogeneous wireless medium. It presents radio resource allocation algorithms with decentralized implementation, which support both single-network and multi-homing services. The brief provides a set of cooperative networking algorithms, which rely on the concepts of short-term call traffic load prediction, network cooperation, convex optimization, and decomposition theory. In the proposed solutions, mobile terminals play an active role in the resource allocation operation, instead of their traditional role as passive service recipients in the networking environment.

This book provides a timely and comprehensive study of developing efficient network slicing frameworks in both 5G wireless and core networks. It also presents protocol stack layer perspectives, which includes virtual network topology design, end-to-end delay modeling, dynamic resource slicing, and link-layer and transport-layer protocol customization. This book provides basic principles, concepts and technologies for communication, computing and networking. Optimization and queueing analysis techniques are applied to solving different problems for network slicing illustrated in this book as well. Researchers working in the area of network slicing in 5G networks and beyond, and advanced-level students majoring in electrical engineering, computer engineering and computer science will find this book useful as a reference or secondary textbook. Professionals in industry seeking solutions to resource management for 5G networks and beyond will also want to purchase this book.

This book covers connectivity and edge computing solutions for representative Internet of Things (IoT) use cases, including industrial IoT, rural IoT, Internet of Vehicles (IoV), and mobile virtual reality (VR). Based on their unique characteristics and requirements, customized solutions are designed with targets such as supporting massive connections or seamless mobility and achieving low latency or high energy efficiency. Meanwhile, the book highlights the role of artificial intelligence (AI) in future IoT networks and showcases AI-based connectivity and edge computing solutions. The solutions presented in this book serve the overall purpose of facilitating an increasingly connected and intelligent world. The potential benefits of the solutions include increased productivity in factories, improved connectivity in rural areas, enhanced safety for vehicles, and enriched entertainment experiences for mobile users. Featuring state-of-the-art research in the IoT field, this book can help answer the question of how to connect billions of diverse devices and enable seamless data collection and processing in future IoT. The content also provides insights regarding the significance of customizing use case-specific solutions as well as approaches of using various AI methods to empower IoT. This book targets researchers and graduate students working in the areas of electrical engineering, computing engineering, and computer science as a secondary textbook or reference. Professionals in industry who work in the field of IoT will also find this book useful.

This book provides a timely and comprehensive study of dynamic resource management for network slicing in service-oriented fifth-generation (5G) and beyond core networks. This includes the perspective of developing efficient computation resource provisioning and scheduling solutions to guarantee consistent service performance in terms of end-to-end (E2E) data delivery delay. Network slicing is enabled by the software defined networking (SDN) and network function virtualization (NFV) paradigms. For a network slice with a target traffic load, the E2E service delivery is enabled by virtual network function (VNF) placement and traffic routing with static resource allocations. When data traffic enters the network, the traffic load is dynamic and can deviate from the target value, potentially leading to QoS performance degradation and network congestion. Data traffic has dynamics in different time granularities. For example, the traffic statistics (e.g., mean and variance) can be non-stationary and experience significant changes in a coarse time granularity, which are usually predictable. Within a long time duration with stationary traffic statistics, there are traffic dynamics in small timescales, which are usually highly bursty and unpredictable. To provide continuous QoS performance guarantee and ensure efficient and fair operation of the network slices over time, it is essential to develop dynamic resource management schemes for the embedded services in the presence of traffic dynamics during virtual network operation. Queueing theory is used in system modeling, and different techniques including optimization and machine learning are applied to solving the dynamic resource management problems. Based on a simplified M/M/1 queueing model with Poisson traffic arrivals, an optimization model for flow migration is presented to accommodate the large-timescale changes in the average traffic rates with average E2E delay guarantee, while addressing a trade-off between load balancing and flow migration overhead. To overcome the limitations of Poisson traffic model, the authors present a machine learning approach for dynamic VNF resource scaling and migration. The new solution captures the inherent traffic patterns in a real-world traffic trace with non-stationary traffic statistics in large timescale, predicts resource demands for VNF resource scaling, and triggers adaptive VNF migration decision making, to achieve load balancing, migration cost reduction, and resource overloading penalty suppression in the long run. Both supervised and unsupervised machine learning tools are investigated for dynamic resource management. To accommodate the traffic dynamics in small time granularities, the authors present a dynamic VNF scheduling scheme to coordinate the scheduling among VNFs of multiple services, which achieves network utility maximization with delay guarantee for each service. Researchers and graduate students working in the areas of electrical engineering, computing engineering and computer science will find this book useful as a reference or secondary text. Professionals in industry seeking solutions to dynamic resource management for 5G and beyond networks will also want to purchase this book.

This timely book provides broad coverage of vehicular ad-hoc network (VANET) issues, such as security, and network selection. Machine learning based methods are applied to solve these issues. This book also 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, and practitioners seeking solutions to VANET communication and security related issues. This book will also help readers understand how to use machine learning to address the security and communication challenges in VANETs. Vehicular ad-hoc networks (VANETs) support vehicle-to-vehicle communications and vehicle-to-infrastructure communications to improve the transmission security, help build unmanned-driving, and support booming applications of onboard units (OBUs). The high mobility of OBUs and the large-scale dynamic network with fixed roadside units (RSUs) make the VANET vulnerable to jamming. The anti-jamming communication of VANETs can be significantly improved by using unmanned aerial vehicles (UAVs) to relay the OBU message. UAVs help relay the OBU message to improve the signal-to-interference-plus-noise-ratio of the OBU signals, and thus reduce the bit-error-rate of the OBU message, especially if the serving RSUs are blocked by jammers and/or interference, which is also demonstrated in this book. This book serves as a useful reference for researchers, graduate students, and practitioners seeking solutions to VANET communication and security related issues.

The next-generation of wireless communications are envisioned to be supported by heterogeneous networks by using various wireless access technologies. The popular cellular networks and wireless local area networks (WLANs) present perfectly complementary characteristics in terms of service capacity, mobility support, and quality-of-service (QoS) provisioning. The cellular/WLAN interworking is an effective way to promote the evolution of wireless networks. Interworking of Wireless LANs and Cellular Networks focuses on three aspects, namely access selection, call admission control and load sharing to investigate heterogeneous interworking for cellular/WLAN integrated networks. It not only reveals important observations but also offers useful tools for performance evaluation. The unique traffic and network characteristics are exploited to enhance interworking effectiveness. Theoretical analysis and simulation validation demonstrate benefits of cellular/WLAN interworking in real networks. Last but not the least, this brief highlights promising future research directions to guide interested readers.