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:

This 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:

This 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.

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.

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:

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:

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:

This 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:

This 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.

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.

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:

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: