Fei Hu – författare

Fueled by ongoing and increasing consumer demand, the explosive growth in spectrum-based communications continues to tax the finite resources of the available spectrum. One possible solution, Cognitive Radio Network (CRN), allows unlicensed users opportunistic access to licensed bands without interfering with existing users. Although some initial study has been conducted in this field, researchers need a systematic reference book that presents clear definitions, functions, and current challenges of the CRNs. Cognitive Radio Networks presents state-of-the-art approaches and novel technologies for cognitive wireless radio networks and sheds light on future developments in these areas. Comprising the contributions of many prominent world-wide cognitive radio researchers, this book covers all CRN essentials including spectrum sensing, spectrum handoff, spectrum sharing, and CRN routing schemes. Divided into five parts, the book addresses the physical layer, medium access control, the routing layer, cross-layer considerations and advanced topics in cognitive radio networks. The chapters also review research, management, support, and cognitive techniques such as position and network awareness, infrastructure and physical and link layer concerns. The editors of this volume are noted experts in the field of wireless networks and security. Dr. Yang Xiao’s research has been supported by the U.S. National Science Foundation (NSF), U.S. Army Research, Fleet & Industrial Supply Center San Diego (FISCSD), and the University of Alabama’s Research Grants Committee. Dr. Fei Hu has worked with NSF, Cisco, Lockheed Martin, Sprint, and other organizations. By bringing together the combined input of international experts, these editors have advanced the field of this nascent technology and helped to forge new paths of discovery for progressive communications possibilities.

UAV swarm network has been used in many critical applications, such as disaster recovery, area surveillance, weather monitoring, and military communications. There are many challenging R&D issues in UAV network designs, such as the hardware/software integration for a large-scale UAV network management, long-distance data transmissions among UAVs, swarm shape/formation control, and intelligent UAV mobility/position prediction. This book will be the first one to cover the engineering designs (especially network protocol designs) for dynamic, large-scale UAV network. It has the technical models/algorithms and protocol specifications for practical UAV swarm network deployment.Features: Includes chapters written by professors, researchers, engineers, and experts in UAV networking fields Details network protocol descriptions for practical engineering designs Covers 7-layer protocols (particularly data routing layer) Presents novel AI models/algorithms for intelligent UAV swarming/networking control Highlights practical hardware/software implementations for advanced UAV networksThis book is suitable to a variety of audiences: (1) industry UAV R&D engineers, administrators, or technicians, who would like to grasp the latest trends in UAV communications; (2) college graduate students or researchers, who may want to pursue some advanced research on large-scale UAV swarming and networking technologies; (3) government agencies that determine the future society development in this exciting field; and (4) other interested readers with a strong desire to understand the challenges of designing a QoS-oriented UAV network.The book editors are: Dr. Fei Hu, Professor in Electrical and Computer Engineering at University of Alabama, Tuscaloosa, Alabama, USA; Dr. Xin-Lin Huang, Professor in Information and Communication Engineering, Tongji University, Shanghai, China; and Dr. DongXiu Ou, Professor in Transportation Information Institute at Tongji University, Shanghai, China.

UAV swarm network has been used in many critical applications, such as disaster recovery, area surveillance, weather monitoring, and military communications. There are many challenging R&D issues in UAV network designs, such as the hardware/software integration for a large-scale UAV network management, long-distance data transmissions among UAVs, swarm shape/formation control, and intelligent UAV mobility/position prediction. This book will be the first one to cover the engineering designs (especially network protocol designs) for dynamic, large-scale UAV network. It has the technical models/algorithms and protocol specifications for practical UAV swarm network deployment.Features: Includes chapters written by professors, researchers, engineers, and experts in UAV networking fields Details network protocol descriptions for practical engineering designs Covers 7-layer protocols (particularly data routing layer) Presents novel AI models/algorithms for intelligent UAV swarming/networking control Highlights practical hardware/software implementations for advanced UAV networksThis book is suitable to a variety of audiences: (1) industry UAV R&D engineers, administrators, or technicians, who would like to grasp the latest trends in UAV communications; (2) college graduate students or researchers, who may want to pursue some advanced research on large-scale UAV swarming and networking technologies; (3) government agencies that determine the future society development in this exciting field; and (4) other interested readers with a strong desire to understand the challenges of designing a QoS-oriented UAV network.The book editors are: Dr. Fei Hu, Professor in Electrical and Computer Engineering at University of Alabama, Tuscaloosa, Alabama, USA; Dr. Xin-Lin Huang, Professor in Information and Communication Engineering, Tongji University, Shanghai, China; and Dr. DongXiu Ou, Professor in Transportation Information Institute at Tongji University, Shanghai, China.

This book covers comprehensively the theories and practical design of magnetic communications. It emphasizes the differences between it and RF communications. It first provides the models and signal propagation principles of magnetic communication systems. Then it describes the hardware architecture of the system, including transmitter, MODEM, inductors, coils, etc. Then, it discusses the corresponding communication software design principles and cases. Finally, it presents several types of practical implementations and applications.

This book focuses on the most critical technical aspects of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. It covers the smart city concept and architecture and explains how V2V and V2I fit into it. It describes the wireless communication protocols for V2V and V2I. It then explains the hardware design process for vehicle communication transceiver and antenna systems. It explains next-generation wireless technologies and their requirements for vehicle communication protocols. Case studies provide the latest V2V and V2I commercial design details. Finally, it describes how to implement vehicle communication protocol from practical hardware design angle.

This book presents state-of-the-art solutions to the theoretical and practical challenges stemming from the leverage of big data and its computational intelligence in supporting smart network operation, management, and optimization. In particular, the technical focus covers the comprehensive understanding of network big data, efficient collection and management of network big data, distributed and scalable online analytics for network big data, and emerging applications of network big data for computational intelligence.

Spectrum Sharing in Wireless Networks: Fairness, Efficiency, and Security provides a broad overview of wireless network spectrum sharing in seven distinct sections: The first section examines the big picture and basic principles, explaining the concepts of spectrum sharing, hardware/software function requirements for efficient sharing, and future trends of sharing strategies.The second section contains more than 10 chapters that discuss differing approaches to efficient spectrum sharing. The authors introduce a new coexistence and sharing scheme for multi-hop networks, describe the space-time sharing concept, introduce LTE-U, and examine sharing in broadcast and unicast environments. They then talk about different cooperation strategies to achieve mutual benefits for primary users (PU) and secondary users (SU), discuss protocols in a spectrum sharing context, and provide different game theory models between PUs and SUs.The third section explains how to model the interactions of PUs and SUs, using an efficient calculation method to determine spectrum availability. Additionally, this section explains how to use scheduling models to achieve efficient SU traffic delivery. The subject of the fourth section is MIMO-oriented design. It focuses on how directional antennas and MIMO antennas greatly enhance wireless network performance. The authors include a few chapters on capacity/rate calculations as well as beamforming issues under MIMO antennas.Power control is covered in the fifth section which also describes the interference-aware power allocation schemes among cognitive radio users and the power control schemes in cognitive radios. The sixth section provides a comprehensive look at security issues, including different types of spectrum sharing attacks and threats as well as corresponding countermeasure schemes. The seventh and final section covers issues pertaining to military applications and examines how the military task protects its data flows when sharing the spectrum with civilian applications.

Opportunities in 5G Networks: A Research and Development Perspective uniquely focuses on the R&D technical design of 5th-generation (5G) networks. It is written and edited by researchers and engineers who are world-renown experts in the design of 5G networks. The book consists of four sections: The first section explains what 5G is, what its real uses are, and the effects of 5G for mobile operators. It provides an overview of the evolution from 4G to 5G and discusses the services, visions, requirements, and key enabling technologies for 5G networks.The second section covers the nuts and bolts of 5G design, including cellular network deployment policies, directional antennas for cellular networks, and vertical sectoring. It discusses the development of quality-of-service management principles at the network level in the new Third Generation Partnership Project releases and their implementation in 5G networks. It covers massive multiple-in multiple-out systems—a key enabling technology for 5G, and looks at issues associated with channel estimation and channel feedback in massive multiple-in multiple-out. It also addresses converged management of radio and optical resources.The third section provides an overview of candidate physical layer technologies for 5G systems, nonorthogonal multiple access, and Nyquist signaling rates. The final section covers the centimeter-wave (cmWave) concept (below 30 GHz), the 5G cmWave concept for small cells, fundamental technology components such as optimized frame structure, dynamic scheduling of uplink/downlink transmission, interference suppression receivers, and rank adaptation. Finally, it examines millimeter-wave (mmWave) models along with medium access control design, 5G mmWave communications, and high-directional new medium access control mechanisms for directional mmWave wireless systems.

The Internet of Things (IoT) has attracted strong interest from both academia and industry. Unfortunately, it has also attracted the attention of hackers. Security and Privacy in Internet of Things (IoTs): Models, Algorithms, and Implementations brings together some of the top IoT security experts from around the world who contribute their knowledge regarding different IoT security aspects. It answers the question "How do we use efficient algorithms, models, and implementations to cover the four important aspects of IoT security, i.e., confidentiality, authentication, integrity, and availability?"The book consists of five parts covering attacks and threats, privacy preservation, trust and authentication, IoT data security, and social awareness. The first part introduces all types of IoT attacks and threats and demonstrates the principle of countermeasures against those attacks. It provides detailed introductions to specific attacks such as malware propagation and Sybil attacks. The second part addresses privacy-preservation issues related to the collection and distribution of data, including medical records. The author uses smart buildings as an example to discuss privacy-protection solutions.The third part describes different types of trust models in the IoT infrastructure, discusses access control to IoT data, and provides a survey of IoT authentication issues. The fourth part emphasizes security issues during IoT data computation. It introduces computational security issues in IoT data processing, security design in time series data aggregation, key generation for data transmission, and concrete security protocols during data access. The fifth and final part considers policy and human behavioral features and covers social-context-based privacy and trust design in IoT platforms as well as policy-based informed consent in the IoT.

Directional antenna technologies have made significant advancements in the last decade. These advances have opened the door to many exciting new design opportunities for wireless networks to enhance quality of service (QoS), performance, and network capacity. In this book, experts from around the world present the latest research and development in wireless networks with directional antennas. Their contributed chapters provide detailed coverage of the models, algorithms, protocols, and applications of wireless networks with various types of directional antennas operating at different frequency bands.Wireless Network Performance Enhancement via Directional Antennas: Models, Protocols, and Systems identifies several interesting research problems in this important field, providing an opportunity to learn about solid solutions to these issues. It also looks at a number of practical hardware designs for the deployment of next-generation antennas, as well as efficient network protocols for exploitation of directional communications. The book is organized into six sections: Directional Antennas – covers the hardware design of different types of antennas Directional MAC – focuses on the principles of designing medium access control (MAC) protocols for directional networks Millimeter Wave – explores different design aspects of millimeter wave (mm-Wave) systems, which operate in higher-frequency bands (such as 60 GHz) MIMO – explains how to establish a multiple-input, multiple-output (MIMO) antenna system and describes how it operates in a cognitive radio network Advanced Topics – looks at additional topics such as beamforming in cognitive radio networks, multicast algorithm development, network topology management for connectivity, and sensor network lifetime issues Applications – illustrates some important applications, such as military networks and airborne networking, that benefit from directional networking designs With this book, researchers and engineers will be well-equipped to advance the research and development in this important field. If you’re new to this field, you will find this book to be a valuable reference on basic directional networking principles, engineering design, and challenges.

Although there are already some books published on Big Data, most of them only cover basic concepts and society impacts and ignore the internal implementation details—making them unsuitable to R&D people. To fill such a need, Big Data: Storage, Sharing, and Security examines Big Data management from an R&D perspective. It covers the 3S designs—storage, sharing, and security—through detailed descriptions of Big Data concepts and implementations.Written by well-recognized Big Data experts around the world, the book contains more than 450 pages of technical details on the most important implementation aspects regarding Big Data. After reading this book, you will understand how to: Aggregate heterogeneous types of data from numerous sources, and then use efficient database management technology to store the Big DataUse cloud computing to share the Big Data among large groups of peopleProtect the privacy of Big Data during network sharingWith the goal of facilitating the scientific research and engineering design of Big Data systems, the book consists of two parts. Part I, Big Data Management, addresses the important topics of spatial management, data transfer, and data processing. Part II, Security and Privacy Issues, provides technical details on security, privacy, and accountability.Examining the state of the art of Big Data over clouds, the book presents a novel architecture for achieving reliability, availability, and security for services running on the clouds. It supplies technical descriptions of Big Data models, algorithms, and implementations, and considers the emerging developments in Big Data applications. Each chapter includes references for further study.

Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and electronic devices, they are also vulnerable to various attacks and threats. AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, among many other attacks and threats. Such attacks can make AI products dangerous to use.While this discussion focuses on security issues in AI/ML/DL-based systems (i.e., securing the intelligent systems themselves), AI/ML/DL models and algorithms can actually also be used for cyber security (i.e., the use of AI to achieve security).Since AI/ML/DL security is a newly emergent field, many researchers and industry professionals cannot yet obtain a detailed, comprehensive understanding of this area. This book aims to provide a complete picture of the challenges and solutions to related security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks. Then, the book describes many sets of promising solutions to achieve AI security and privacy. The features of this book have seven aspects:This is the first book to explain various practical attacks and countermeasures to AI systemsBoth quantitative math models and practical security implementations are providedIt covers both "securing the AI system itself" and "using AI to achieve security"It covers all the advanced AI attacks and threats with detailed attack modelsIt provides multiple solution spaces to the security and privacy issues in AI toolsThe differences among ML and DL security and privacy issues are explainedMany practical security applications are covered

Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and electronic devices, they are also vulnerable to various attacks and threats. AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, among many other attacks and threats. Such attacks can make AI products dangerous to use.While this discussion focuses on security issues in AI/ML/DL-based systems (i.e., securing the intelligent systems themselves), AI/ML/DL models and algorithms can actually also be used for cyber security (i.e., the use of AI to achieve security).Since AI/ML/DL security is a newly emergent field, many researchers and industry professionals cannot yet obtain a detailed, comprehensive understanding of this area. This book aims to provide a complete picture of the challenges and solutions to related security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks. Then, the book describes many sets of promising solutions to achieve AI security and privacy. The features of this book have seven aspects:This is the first book to explain various practical attacks and countermeasures to AI systemsBoth quantitative math models and practical security implementations are providedIt covers both "securing the AI system itself" and "using AI to achieve security"It covers all the advanced AI attacks and threats with detailed attack modelsIt provides multiple solution spaces to the security and privacy issues in AI toolsThe differences among ML and DL security and privacy issues are explainedMany practical security applications are covered

Deep Learning (DL) is an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, location-prediction-based scheduling and routing, intelligent network congestion control mechanism, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart and efficient resource allocation and intelligent distributed resource allocation methods. This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: (I) DL for vehicle safety and security: This part covers the use of DL algorithms for vehicle safety or security.(II) DL for effective vehicle communications: Vehicle networks consist of vehicle-to-vehicle and vehicle-to-roadside communications. This part covers how Intelligent vehicle networks require a flexible selection of the best path across all vehicles, adaptive sending rate control based on bandwidth availability and timely data downloads from a roadside base-station. (III) DL for vehicle control: The myriad operations that require intelligent control for each individual vehicle are discussed in this part. This also includes emission control, which is based on the road traffic situation, the charging pile load is predicted through DL andvehicle speed adjustments based on the camera-captured image analysis. (IV) DL for information management: This part covers some intelligent information collection and understanding. We can use DL for energy-saving vehicle trajectory control based on the road traffic situation and given destination information; we can also natural language processing based on DL algorithm for automatic internet of things (IoT) search during driving. (V) Other applications. This part introduces the use of DL models for other vehicle controls. Autonomous vehicles are becoming more and more popular in society. The DL and its variants will play greater roles in cognitive vehicle communications and control. Other machine learning models such as deep reinforcement learning will also facilitate intelligent vehicle behavior understanding and adjustment. This book will become a valuable reference to your understanding of this critical field.

Deep Learning (DL) is an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, location-prediction-based scheduling and routing, intelligent network congestion control mechanism, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart and efficient resource allocation and intelligent distributed resource allocation methods. This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: (I) DL for vehicle safety and security: This part covers the use of DL algorithms for vehicle safety or security.(II) DL for effective vehicle communications: Vehicle networks consist of vehicle-to-vehicle and vehicle-to-roadside communications. This part covers how Intelligent vehicle networks require a flexible selection of the best path across all vehicles, adaptive sending rate control based on bandwidth availability and timely data downloads from a roadside base-station. (III) DL for vehicle control: The myriad operations that require intelligent control for each individual vehicle are discussed in this part. This also includes emission control, which is based on the road traffic situation, the charging pile load is predicted through DL andvehicle speed adjustments based on the camera-captured image analysis. (IV) DL for information management: This part covers some intelligent information collection and understanding. We can use DL for energy-saving vehicle trajectory control based on the road traffic situation and given destination information; we can also natural language processing based on DL algorithm for automatic internet of things (IoT) search during driving. (V) Other applications. This part introduces the use of DL models for other vehicle controls. Autonomous vehicles are becoming more and more popular in society. The DL and its variants will play greater roles in cognitive vehicle communications and control. Other machine learning models such as deep reinforcement learning will also facilitate intelligent vehicle behavior understanding and adjustment. This book will become a valuable reference to your understanding of this critical field.

This book provides a comprehensive exploration of the engineering and scientific principles behind the Metaverse, a 3D digital world designed to replicate and enhance physical reality through some advanced technologies, such as extended reality (xR), artificial intelligence (AI), blockchain, 5G/6G, and computer vision. The book delves into the hardware (sensors, headsets, edge/cloud servers) and software (3D scene reconstruction, synchronization, AI processing) components that form the backbone of Metaverse platforms, addressing critical aspects such as security, privacy, and social implications.This book is organized into three parts and covers some foundational technologies, such as neural rendering and semantic communications, security challenges (involving attack models and countermeasures), and real-world applications in healthcare (such as mixed reality-based stroke rehabilitation and telehealth innovations). By linking virtual and augmented reality to the broader Metaverse ecosystem, this book serves as a crucial resource for engineers, scientists, and researchers in academia or industries working in the fields of robotics, smart cities, and intelligent transportation, offering valuable insights into the development of secure, efficient, and immersive digital 3D internet environments.

With nearly 7 billion mobile phone subscriptions worldwide, mobility and computing have become pervasive in our society and business. Moreover, new mobile multimedia communication services are challenging telecommunication operators. To support the significant increase in multimedia traffic—especially video—over wireless networks, new technological infrastructure must be created. Cognitive Radio Networks (CRNs) are widely regarded as one of the most promising technologies for future wireless communications. This book explains how to efficiently deliver video, audio, and other data over CRNs.Covering advanced algorithms, protocols, and hardware-/software-based experiments, this book describes how to encode video in a prioritized way to send to dynamic radio links. It discusses different FEC codes for video reliability and explains how different machine learning algorithms can be used for video quality control. It also explains how to use readily available software tools to build a CRN simulation model. This book explains both theoretical and experimental designs. It describes how universal software radio peripheral (USRP) boards can be used for real-time, high-resolution video transmission. It also discusses how a USRP board can sense the spectrum dynamics and how it can be controlled by GNU Radio software. A separate chapter discusses how the network simulator ns-2 can be used to build a simulated CRN platform.

Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning.Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, including compressive sensing and sampling, distributed signal processing, and intelligent signal learning. Presenting recent research results of world-renowned sensing experts, the book is organized into three parts:Machine Learning—describes the application of machine learning and other AI principles in sensor network intelligence—covering smart sensor/transducer architecture and data representation for intelligent sensorsSignal Processing—considers the optimization of sensor network performance based on digital signal processing techniques—including cross-layer integration of routing and application-specific signal processing as well as on-board image processing in wireless multimedia sensor networks for intelligent transportation systemsNetworking—focuses on network protocol design in order to achieve an intelligent sensor networking—covering energy-efficient opportunistic routing protocols for sensor networking and multi-agent-driven wireless sensor cooperationMaintaining a focus on "intelligent" designs, the book details signal processing principles in sensor networks. It elaborates on critical platforms for intelligent sensor networks and illustrates key applications—including target tracking, object identification, and structural health monitoring. It also includes a paradigm for validating the extent of spatiotemporal associations among data sources to enhance data cleaning in sensor networks, a sensor stream reduction application, and also considers the use of Kalman filters for attack detection in a water system sensor network that consists of water level sensors and velocity sensors.

While there are sporadic journal articles on socio-technical networks, there’s long been a need for an integrated resource that addresses concrete socio-technical network (STN) design issues from algorithmic and engineering perspectives. Filling this need, Socio-Technical Networks: Science and Engineering Design provides a complete introduction to the fundamentals of one of the hottest research areas across the social sciences, networking, and computer science—including its definition, historical background, and models.Covering basic STN architecture from a physical/technological perspective, the book considers the system design process in a typical STN, including inputs, processes/actions, and outputs/products. It covers current applications, including transportation networks, energy systems, tele-healthcare, financial networks, and the World Wide Web. A group of STN expert contributors addresses privacy and security topics in the interdependent context of critical infrastructure, which include risk models, trust models, and privacy preserving schemes. Covers the physical and technological designs in a typical STN Considers STN applications in popular fields, such as healthcare and the virtual community Details a method for mapping and measuring complexity, uncertainty, and interactions among STN components The book examines the most important STN models, including graph theory, inferring agent dynamics, decision theory, and information mining. It also explains structural studies, behavioral studies, and agent/actor system studies and policy studies in different STN contexts. Complete with in-depth case studies, this book supplies the practical insight needed to address contemporary STN design issues.

This book focuses on the most critical technical aspects of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. It covers the smart city concept and architecture and explains how V2V and V2I fit into it. It describes the wireless communication protocols for V2V and V2I. It then explains the hardware design process for vehicle communication transceiver and antenna systems. It explains next-generation wireless technologies and their requirements for vehicle communication protocols. Case studies provide the latest V2V and V2I commercial design details. Finally, it describes how to implement vehicle communication protocol from practical hardware design angle.

Fueled by ongoing and increasing consumer demand, the explosive growth in spectrum-based communications continues to tax the finite resources of the available spectrum. One possible solution, Cognitive Radio Network (CRN), allows unlicensed users opportunistic access to licensed bands without interfering with existing users. Although some initial study has been conducted in this field, researchers need a systematic reference book that presents clear definitions, functions, and current challenges of the CRNs. Cognitive Radio Networks presents state-of-the-art approaches and novel technologies for cognitive wireless radio networks and sheds light on future developments in these areas. Comprising the contributions of many prominent world-wide cognitive radio researchers, this book covers all CRN essentials including spectrum sensing, spectrum handoff, spectrum sharing, and CRN routing schemes. Divided into five parts, the book addresses the physical layer, medium access control, the routing layer, cross-layer considerations and advanced topics in cognitive radio networks. The chapters also review research, management, support, and cognitive techniques such as position and network awareness, infrastructure and physical and link layer concerns. The editors of this volume are noted experts in the field of wireless networks and security. Dr. Yang Xiao’s research has been supported by the U.S. National Science Foundation (NSF), U.S. Army Research, Fleet & Industrial Supply Center San Diego (FISCSD), and the University of Alabama’s Research Grants Committee. Dr. Fei Hu has worked with NSF, Cisco, Lockheed Martin, Sprint, and other organizations. By bringing together the combined input of international experts, these editors have advanced the field of this nascent technology and helped to forge new paths of discovery for progressive communications possibilities.

Written by award-winning engineers whose research has been sponsored by the U.S. National Science Foundation (NSF), IBM, and Cisco's University Research Program, Wireless Sensor Networks: Principles and Practice addresses everything product developers and technicians need to know to navigate the field. It provides an all-inclusive examination of the major wireless sensor network (WSN) technology, standards, and application topics.Using straightforward language, the text makes complex concepts and processes easy to understand. It covers hardware design, medium access control, routing schemes, transport protocols, OS support, middleware, data management, localization, synchronization, security, actuator/underwater/video sensor networking, power control, sensor simulations, and emerging research. This authoritative resource includes a wealth of exercises, end-of-chapter reviews, detailed case studies, as well as practical sensor network design cases that touch on medical applications.Complete with class labs that illustrate how to apply concepts to the development and application of WSNs, this book spells out the steps of design and implementation needed to address real-world challenges and optimization problems.A CD with instructional resources, including solutions to exercises and lab materials, is available upon qualified course adoption.

While there are sporadic journal articles on socio-technical networks, there’s long been a need for an integrated resource that addresses concrete socio-technical network (STN) design issues from algorithmic and engineering perspectives. Filling this need, Socio-Technical Networks: Science and Engineering Design provides a complete introduction to the fundamentals of one of the hottest research areas across the social sciences, networking, and computer science—including its definition, historical background, and models.Covering basic STN architecture from a physical/technological perspective, the book considers the system design process in a typical STN, including inputs, processes/actions, and outputs/products. It covers current applications, including transportation networks, energy systems, tele-healthcare, financial networks, and the World Wide Web. A group of STN expert contributors addresses privacy and security topics in the interdependent context of critical infrastructure, which include risk models, trust models, and privacy preserving schemes. Covers the physical and technological designs in a typical STN Considers STN applications in popular fields, such as healthcare and the virtual community Details a method for mapping and measuring complexity, uncertainty, and interactions among STN components The book examines the most important STN models, including graph theory, inferring agent dynamics, decision theory, and information mining. It also explains structural studies, behavioral studies, and agent/actor system studies and policy studies in different STN contexts. Complete with in-depth case studies, this book supplies the practical insight needed to address contemporary STN design issues.

Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning.Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, including compressive sensing and sampling, distributed signal processing, and intelligent signal learning. Presenting recent research results of world-renowned sensing experts, the book is organized into three parts:Machine Learning—describes the application of machine learning and other AI principles in sensor network intelligence—covering smart sensor/transducer architecture and data representation for intelligent sensorsSignal Processing—considers the optimization of sensor network performance based on digital signal processing techniques—including cross-layer integration of routing and application-specific signal processing as well as on-board image processing in wireless multimedia sensor networks for intelligent transportation systemsNetworking—focuses on network protocol design in order to achieve an intelligent sensor networking—covering energy-efficient opportunistic routing protocols for sensor networking and multi-agent-driven wireless sensor cooperationMaintaining a focus on "intelligent" designs, the book details signal processing principles in sensor networks. It elaborates on critical platforms for intelligent sensor networks and illustrates key applications—including target tracking, object identification, and structural health monitoring. It also includes a paradigm for validating the extent of spatiotemporal associations among data sources to enhance data cleaning in sensor networks, a sensor stream reduction application, and also considers the use of Kalman filters for attack detection in a water system sensor network that consists of water level sensors and velocity sensors.

Software-defined networking (SDN) technologies powered by the OpenFlow protocol provide viable options to address the bandwidth needs of next-generation computer networks. And, since many large corporations already produce network devices that support the OpenFlow standard, there are opportunities for those who can manage complex and large-scale networks using these technologies.Network Innovation through OpenFlow and SDN: Principles and Design explains how you can use SDN and OpenFlow to build networks that are easy to design, less expensive to build and operate, and more agile and customizable. Among the first books to systematically address the design aspects in SDN/OpenFlow, it presents the insights of expert contributors from around the world. The book’s four sections break down basic concepts, engineering design, QoS (quality-of-service), and advanced topics.Introduces the basic principles of SDN/OpenFlow and its applications in network systemsIllustrates the entire design process of a practical OpenFlow/SDNAddresses the design issues that can arise when applying OpenFlow to cloud computing platforms Compares various solutions in QoS supportProvides an overview of efficient solutions to the integration of SDN with optical networksIdentifies the types of network attacks that could occur with OpenFlow and outlines possible solutions for overcoming themSupplying a cutting-edge look at SDN and OpenFlow, this book gives you the wide-ranging understanding required to build, deploy, and manage OpenFlow/SDN products and networks. The book’s comprehensive coverage includes system architectures, language and programming issues, switches, controllers, multimedia support, security, and network operating systems. After reading this book you will understand what it takes to make a smooth transition from conventional networks to SDN/OpenFlow networks.

Cyber-physical systems (CPSs) have quickly become one of the hottest computer applications today. With their tight integration of cyber and physical objects, it is believed CPSs will transform how we interact with the physical world, just like the Internet transformed how we interact with one another. A CPS could be a system at multiple scales, from large smart bridges with fluctuation detection and responding functions, to autonomous cars and tiny implanted medical devices.Cyber-Physical Systems: Integrated Computing and Engineering Design supplies comprehensive coverage of the principles and design of CPSs. It addresses the many challenges that must be overcome and outlines a roadmap of how to get there.Emphasizes the integration of cyber computing and physical objects controlCovers important CPS theory foundations and modelsIncludes interesting case studies of several important civilian and health care applications that illustrate the CPS design processAddresses the collaboration of the sensing and controlling of a physical system with robust software architecture Explains how to account for random failure events that can occur in a real CPS environmentPresented in a systematic manner, the book begins by discussing the basic concept underlying CPSs and examining some challenging design issues. It then covers the most important design theories and modeling methods for a practical CPS. Next, it moves on to sensor-based CPSs, which use embedded sensors and actuators to interact with the physical world.The text presents concrete CPS designs for popular civilian applications, including building and energy management. Reflecting the importance of human health care in society, it includes CPS examples of rehabilitation applications such as virtual reality-based disability recovery platforms.

With nearly 7 billion mobile phone subscriptions worldwide, mobility and computing have become pervasive in our society and business. Moreover, new mobile multimedia communication services are challenging telecommunication operators. To support the significant increase in multimedia traffic—especially video—over wireless networks, new technological infrastructure must be created. Cognitive Radio Networks (CRNs) are widely regarded as one of the most promising technologies for future wireless communications. This book explains how to efficiently deliver video, audio, and other data over CRNs.Covering advanced algorithms, protocols, and hardware-/software-based experiments, this book describes how to encode video in a prioritized way to send to dynamic radio links. It discusses different FEC codes for video reliability and explains how different machine learning algorithms can be used for video quality control. It also explains how to use readily available software tools to build a CRN simulation model. This book explains both theoretical and experimental designs. It describes how universal software radio peripheral (USRP) boards can be used for real-time, high-resolution video transmission. It also discusses how a USRP board can sense the spectrum dynamics and how it can be controlled by GNU Radio software. A separate chapter discusses how the network simulator ns-2 can be used to build a simulated CRN platform.

Directional antenna technologies have made significant advancements in the last decade. These advances have opened the door to many exciting new design opportunities for wireless networks to enhance quality of service (QoS), performance, and network capacity. In this book, experts from around the world present the latest research and development in wireless networks with directional antennas. Their contributed chapters provide detailed coverage of the models, algorithms, protocols, and applications of wireless networks with various types of directional antennas operating at different frequency bands.Wireless Network Performance Enhancement via Directional Antennas: Models, Protocols, and Systems identifies several interesting research problems in this important field, providing an opportunity to learn about solid solutions to these issues. It also looks at a number of practical hardware designs for the deployment of next-generation antennas, as well as efficient network protocols for exploitation of directional communications. The book is organized into six sections: Directional Antennas – covers the hardware design of different types of antennas Directional MAC – focuses on the principles of designing medium access control (MAC) protocols for directional networks Millimeter Wave – explores different design aspects of millimeter wave (mm-Wave) systems, which operate in higher-frequency bands (such as 60 GHz) MIMO – explains how to establish a multiple-input, multiple-output (MIMO) antenna system and describes how it operates in a cognitive radio network Advanced Topics – looks at additional topics such as beamforming in cognitive radio networks, multicast algorithm development, network topology management for connectivity, and sensor network lifetime issues Applications – illustrates some important applications, such as military networks and airborne networking, that benefit from directional networking designsWith this book, researchers and engineers will be well-equipped to advance the research and development in this important field. If you’re new to this field, you will find this book to be a valuable reference on basic directional networking principles, engineering design, and challenges.