Lanyu Xu, PhD, is Assistant Professor in the Department of Computer Science and Engineering, Oakland University, Michigan, where she leads the Edge Intelligence System Laboratory. Her research intersects edge computing and deep learning, emphasizing the development of efficient edge intelligence systems. Her work explores optimization frameworks, intelligent systems, and AI applications to address challenges in efficiency and real-world applicability of edge systems across various domains. Weisong Shi, PhD, is an Alumni Distinguished Professor and Chair of the Department of Computer and Information Sciences at the University of Delaware, where he leads the Connected and Autonomous Research Laboratory. He is an internationally renowned expert in edge computing, autonomous driving, and connected health. His pioneer paper, “Edge Computing: Vision and Challenges,” has been cited more than 8000 times in eight years. He is an IEEE Fellow.

• About the Authors xiiiPreface xvAbout the Companion Website xvii1 Why Do We Need Edge Computing? 11.1 The Background of the Emergence 11.2 The Evolutionary History 61.2.1 Technology Preparation Period 71.2.2 Rapid Growth Period 121.2.3 Intelligence Integration Period 141.3 What Is Edge Computing? 151.4 Summary and Practice 181.4.1 Summary 181.4.2 Practice Questions 181.4.3 Course Projects 182 Fundamentals of Edge Computing 232.1 Distributed Computing 232.1.1 Distributed Computing Technologies 242.1.2 Distributed System Platforms 252.2 The Basic Concept and Key Characteristics of Edge Computing 262.2.1 The Basic Concept 272.2.2 The Key Characteristics 292.3 Edge Computing vs. Cloud Computing 332.3.1 The Concept of Cloud Computing 342.3.2 The Big Data Era 352.3.3 Edge Computing vs. Cloud Computing 362.3.4 Advantages and Challenges of Edge Computing 392.4 Summary and Practice 422.4.1 Summary 422.4.2 Practice Questions 422.4.3 Course Projects 423 Architecture and Components of Edge Computing 473.1 Edge Infrastructure 473.1.1 Introduction to Edge Computing Architecture 473.1.2 Different Grades/Layers of Edge 493.1.3 Capabilities of Edge Infrastructure 513.1.4 New Progress of Edge Computing Architecture 533.1.5 Open Questions 543.2 Edge Computing Models 553.2.1 Overview and Definitions 553.2.2 Collaborative Edge Computing Models 573.2.3 Choosing the Right Model 613.2.4 Open Questions 643.3 Networking in Edge Computing 653.3.1 Introduction and Development Process of Edge Computing-Network Integration 653.3.2 Edge Computing-Network Architectures 683.3.3 Current Progress and Future Trend 693.4 Summary and Practice 713.4.1 Summary 713.4.2 Practice Questions 713.4.3 Course Projects 714 Toward Edge Intelligence 774.1 What Is Edge Intelligence? 774.1.1 Formal Definition 784.2 Hardware and Software Support 804.2.1 Hardware 814.2.2 Software 864.2.3 Container 904.3 Technologies Enabling Edge Intelligence 914.3.1 Compression Techniques 914.3.2 Hardware-Software Codesign for Edge Optimization 1014.3.3 Applying Deep Learning Models on Resource-Constrained Edges 1024.4 Edge Intelligent System Design and Optimization 1044.4.1 Training on Edge 1044.4.2 Model Inference on Edge 1074.5 Summary and Practice 1114.5.1 Summary 1114.5.2 Practice Questions 1124.5.3 Course Projects 1125 Challenges and Solutions in Edge Computing 1235.1 Programmability and Data Management 1235.1.1 Programmability 1235.1.2 Automatic Program Partitioning 1255.1.3 Naming Conventions 1265.1.4 Data Abstraction 1285.2 Resource Allocation and Optimization 1305.2.1 Scheduling Strategies 1305.2.2 Data Offloading and Load Balancing 1315.2.3 Optimization Metrics 1335.3 Security, Privacy, and Service Management 1365.3.1 Privacy Protection and Security 1365.3.2 Edge Service Management 1405.4 Deployment Strategies and Integration 1425.4.1 Edge Nodes Deployment 1425.4.2 Deployment of AI Models on Resource-Constrained Edge Devices 1435.4.3 Integration with Vertical Industries 1455.4.4 Hardware and Software Selection 1465.5 Foundations and Business Models 1475.5.1 Theoretical Foundations 1475.5.2 Business Models 1485.6 Summary and Practice 1495.6.1 Summary 1495.6.2 Practice Questions 1515.6.3 Course Projects 1516 Future Trends and Emerging Technologies 1576.1 Edge Computing and New Paradigm 1576.1.1 Related New Paradigms 1576.1.2 What Is New for Edge Computing 1636.1.3 Future 1646.2 Integration with Artificial Intelligence 1646.2.1 Basic Overview and Why Need Edge Computing 1656.2.2 Integrating LLM with Edge Computing 1676.2.3 Integration with Generative AI 1716.2.4 Applications and Future 1726.3 6G and Edge Computing 1746.3.1 Basic Understanding for 6G 1746.3.2 Mutual Influence: 6G and Edge Computing 1756.3.3 Potential Applications and Challenges 1796.4 Edge Computing in Space Exploration 1806.4.1 Basic Concepts 1806.4.2 Advanced Concepts and Architecture 1826.4.3 Advanced Scenarios and Challenges 1846.5 Summary and Practice 1866.5.1 Summary 1866.5.2 Practice Questions 1866.5.3 Course Projects 1877 Case Studies and Practical Applications 1937.1 Manufacturing 1957.2 Telecommunications 1987.3 Healthcare 2007.4 Smart Cities 2037.5 Internet of Things 2107.6 Retail 2117.7 Autonomous Vehicles 2137.8 Summary and Practice 2177.8.1 Summary 2177.8.2 Practice Questions 2177.8.3 Course Projects 2188 Privacy and Bias in Edge Computing 2238.1 Privacy in Edge Computing 2248.1.1 Privacy Concerns at Edge Computing 2248.1.2 Various Forms of Privacy 2258.1.3 Introduction of Privacy-Preserving Techniques 2278.1.4 Open Research Problems 2358.2 Accessibility and Digital Divide 2368.2.1 What Is Bias? 2368.2.2 Types of Biases 2378.2.3 Causes of Biases? 2418.2.4 Bias Impact on Edge Computing Algorithms 2428.2.5 Bias Mitigation Techniques 2438.2.6 Open Research Problems 2458.3 Summary and Practice 2458.3.1 Summary 2458.3.2 Practice Questions 2468.3.3 Course Projects 246References 2479 Conclusion and Future Directions 2539.1 Key Insights and Conclusions 2539.2 So, What Is Next? 254Index 257

Lanyu Xu, PhD, is Assistant Professor in the Department of Computer Science and Engineering, Oakland University, Michigan, where she leads the Edge Intelligence System Laboratory. Her research intersects edge computing and deep learning, emphasizing the development of efficient edge intelligence systems. Her work explores optimization frameworks, intelligent systems, and AI applications to address challenges in efficiency and real-world applicability of edge systems across various domains. Weisong Shi, PhD, is an Alumni Distinguished Professor and Chair of the Department of Computer and Information Sciences at the University of Delaware, where he leads the Connected and Autonomous Research Laboratory. He is an internationally renowned expert in edge computing, autonomous driving, and connected health. His pioneer paper, “Edge Computing: Vision and Challenges,” has been cited more than 8000 times in eight years. He is an IEEE Fellow.

• About the Authors xiiiPreface xvAbout the Companion Website xvii1 Why Do We Need Edge Computing? 11.1 The Background of the Emergence 11.2 The Evolutionary History 61.2.1 Technology Preparation Period 71.2.2 Rapid Growth Period 121.2.3 Intelligence Integration Period 141.3 What Is Edge Computing? 151.4 Summary and Practice 181.4.1 Summary 181.4.2 Practice Questions 181.4.3 Course Projects 182 Fundamentals of Edge Computing 232.1 Distributed Computing 232.1.1 Distributed Computing Technologies 242.1.2 Distributed System Platforms 252.2 The Basic Concept and Key Characteristics of Edge Computing 262.2.1 The Basic Concept 272.2.2 The Key Characteristics 292.3 Edge Computing vs. Cloud Computing 332.3.1 The Concept of Cloud Computing 342.3.2 The Big Data Era 352.3.3 Edge Computing vs. Cloud Computing 362.3.4 Advantages and Challenges of Edge Computing 392.4 Summary and Practice 422.4.1 Summary 422.4.2 Practice Questions 422.4.3 Course Projects 423 Architecture and Components of Edge Computing 473.1 Edge Infrastructure 473.1.1 Introduction to Edge Computing Architecture 473.1.2 Different Grades/Layers of Edge 493.1.3 Capabilities of Edge Infrastructure 513.1.4 New Progress of Edge Computing Architecture 533.1.5 Open Questions 543.2 Edge Computing Models 553.2.1 Overview and Definitions 553.2.2 Collaborative Edge Computing Models 573.2.3 Choosing the Right Model 613.2.4 Open Questions 643.3 Networking in Edge Computing 653.3.1 Introduction and Development Process of Edge Computing-Network Integration 653.3.2 Edge Computing-Network Architectures 683.3.3 Current Progress and Future Trend 693.4 Summary and Practice 713.4.1 Summary 713.4.2 Practice Questions 713.4.3 Course Projects 714 Toward Edge Intelligence 774.1 What Is Edge Intelligence? 774.1.1 Formal Definition 784.2 Hardware and Software Support 804.2.1 Hardware 814.2.2 Software 864.2.3 Container 904.3 Technologies Enabling Edge Intelligence 914.3.1 Compression Techniques 914.3.2 Hardware-Software Codesign for Edge Optimization 1014.3.3 Applying Deep Learning Models on Resource-Constrained Edges 1024.4 Edge Intelligent System Design and Optimization 1044.4.1 Training on Edge 1044.4.2 Model Inference on Edge 1074.5 Summary and Practice 1114.5.1 Summary 1114.5.2 Practice Questions 1124.5.3 Course Projects 1125 Challenges and Solutions in Edge Computing 1235.1 Programmability and Data Management 1235.1.1 Programmability 1235.1.2 Automatic Program Partitioning 1255.1.3 Naming Conventions 1265.1.4 Data Abstraction 1285.2 Resource Allocation and Optimization 1305.2.1 Scheduling Strategies 1305.2.2 Data Offloading and Load Balancing 1315.2.3 Optimization Metrics 1335.3 Security, Privacy, and Service Management 1365.3.1 Privacy Protection and Security 1365.3.2 Edge Service Management 1405.4 Deployment Strategies and Integration 1425.4.1 Edge Nodes Deployment 1425.4.2 Deployment of AI Models on Resource-Constrained Edge Devices 1435.4.3 Integration with Vertical Industries 1455.4.4 Hardware and Software Selection 1465.5 Foundations and Business Models 1475.5.1 Theoretical Foundations 1475.5.2 Business Models 1485.6 Summary and Practice 1495.6.1 Summary 1495.6.2 Practice Questions 1515.6.3 Course Projects 1516 Future Trends and Emerging Technologies 1576.1 Edge Computing and New Paradigm 1576.1.1 Related New Paradigms 1576.1.2 What Is New for Edge Computing 1636.1.3 Future 1646.2 Integration with Artificial Intelligence 1646.2.1 Basic Overview and Why Need Edge Computing 1656.2.2 Integrating LLM with Edge Computing 1676.2.3 Integration with Generative AI 1716.2.4 Applications and Future 1726.3 6G and Edge Computing 1746.3.1 Basic Understanding for 6G 1746.3.2 Mutual Influence: 6G and Edge Computing 1756.3.3 Potential Applications and Challenges 1796.4 Edge Computing in Space Exploration 1806.4.1 Basic Concepts 1806.4.2 Advanced Concepts and Architecture 1826.4.3 Advanced Scenarios and Challenges 1846.5 Summary and Practice 1866.5.1 Summary 1866.5.2 Practice Questions 1866.5.3 Course Projects 1877 Case Studies and Practical Applications 1937.1 Manufacturing 1957.2 Telecommunications 1987.3 Healthcare 2007.4 Smart Cities 2037.5 Internet of Things 2107.6 Retail 2117.7 Autonomous Vehicles 2137.8 Summary and Practice 2177.8.1 Summary 2177.8.2 Practice Questions 2177.8.3 Course Projects 2188 Privacy and Bias in Edge Computing 2238.1 Privacy in Edge Computing 2248.1.1 Privacy Concerns at Edge Computing 2248.1.2 Various Forms of Privacy 2258.1.3 Introduction of Privacy-Preserving Techniques 2278.1.4 Open Research Problems 2358.2 Accessibility and Digital Divide 2368.2.1 What Is Bias? 2368.2.2 Types of Biases 2378.2.3 Causes of Biases? 2418.2.4 Bias Impact on Edge Computing Algorithms 2428.2.5 Bias Mitigation Techniques 2438.2.6 Open Research Problems 2458.3 Summary and Practice 2458.3.1 Summary 2458.3.2 Practice Questions 2468.3.3 Course Projects 246References 2479 Conclusion and Future Directions 2539.1 Key Insights and Conclusions 2539.2 So, What Is Next? 254Index 257

Lanyu Xu, PhD, is Assistant Professor in the Department of Computer Science and Engineering, Oakland University, Michigan, where she leads the Edge Intelligence System Laboratory. Her research intersects edge computing and deep learning, emphasizing the development of efficient edge intelligence systems. Her work explores optimization frameworks, intelligent systems, and AI applications to address challenges in efficiency and real-world applicability of edge systems across various domains. Weisong Shi, PhD, is an Alumni Distinguished Professor and Chair of the Department of Computer and Information Sciences at the University of Delaware, where he leads the Connected and Autonomous Research Laboratory. He is an internationally renowned expert in edge computing, autonomous driving, and connected health. His pioneer paper, “Edge Computing: Vision and Challenges,” has been cited more than 8000 times in eight years. He is an IEEE Fellow.

• About the Authors xiiiPreface xvAbout the Companion Website xvii1 Why Do We Need Edge Computing? 11.1 The Background of the Emergence 11.2 The Evolutionary History 61.2.1 Technology Preparation Period 71.2.2 Rapid Growth Period 121.2.3 Intelligence Integration Period 141.3 What Is Edge Computing? 151.4 Summary and Practice 181.4.1 Summary 181.4.2 Practice Questions 181.4.3 Course Projects 182 Fundamentals of Edge Computing 232.1 Distributed Computing 232.1.1 Distributed Computing Technologies 242.1.2 Distributed System Platforms 252.2 The Basic Concept and Key Characteristics of Edge Computing 262.2.1 The Basic Concept 272.2.2 The Key Characteristics 292.3 Edge Computing vs. Cloud Computing 332.3.1 The Concept of Cloud Computing 342.3.2 The Big Data Era 352.3.3 Edge Computing vs. Cloud Computing 362.3.4 Advantages and Challenges of Edge Computing 392.4 Summary and Practice 422.4.1 Summary 422.4.2 Practice Questions 422.4.3 Course Projects 423 Architecture and Components of Edge Computing 473.1 Edge Infrastructure 473.1.1 Introduction to Edge Computing Architecture 473.1.2 Different Grades/Layers of Edge 493.1.3 Capabilities of Edge Infrastructure 513.1.4 New Progress of Edge Computing Architecture 533.1.5 Open Questions 543.2 Edge Computing Models 553.2.1 Overview and Definitions 553.2.2 Collaborative Edge Computing Models 573.2.3 Choosing the Right Model 613.2.4 Open Questions 643.3 Networking in Edge Computing 653.3.1 Introduction and Development Process of Edge Computing-Network Integration 653.3.2 Edge Computing-Network Architectures 683.3.3 Current Progress and Future Trend 693.4 Summary and Practice 713.4.1 Summary 713.4.2 Practice Questions 713.4.3 Course Projects 714 Toward Edge Intelligence 774.1 What Is Edge Intelligence? 774.1.1 Formal Definition 784.2 Hardware and Software Support 804.2.1 Hardware 814.2.2 Software 864.2.3 Container 904.3 Technologies Enabling Edge Intelligence 914.3.1 Compression Techniques 914.3.2 Hardware-Software Codesign for Edge Optimization 1014.3.3 Applying Deep Learning Models on Resource-Constrained Edges 1024.4 Edge Intelligent System Design and Optimization 1044.4.1 Training on Edge 1044.4.2 Model Inference on Edge 1074.5 Summary and Practice 1114.5.1 Summary 1114.5.2 Practice Questions 1124.5.3 Course Projects 1125 Challenges and Solutions in Edge Computing 1235.1 Programmability and Data Management 1235.1.1 Programmability 1235.1.2 Automatic Program Partitioning 1255.1.3 Naming Conventions 1265.1.4 Data Abstraction 1285.2 Resource Allocation and Optimization 1305.2.1 Scheduling Strategies 1305.2.2 Data Offloading and Load Balancing 1315.2.3 Optimization Metrics 1335.3 Security, Privacy, and Service Management 1365.3.1 Privacy Protection and Security 1365.3.2 Edge Service Management 1405.4 Deployment Strategies and Integration 1425.4.1 Edge Nodes Deployment 1425.4.2 Deployment of AI Models on Resource-Constrained Edge Devices 1435.4.3 Integration with Vertical Industries 1455.4.4 Hardware and Software Selection 1465.5 Foundations and Business Models 1475.5.1 Theoretical Foundations 1475.5.2 Business Models 1485.6 Summary and Practice 1495.6.1 Summary 1495.6.2 Practice Questions 1515.6.3 Course Projects 1516 Future Trends and Emerging Technologies 1576.1 Edge Computing and New Paradigm 1576.1.1 Related New Paradigms 1576.1.2 What Is New for Edge Computing 1636.1.3 Future 1646.2 Integration with Artificial Intelligence 1646.2.1 Basic Overview and Why Need Edge Computing 1656.2.2 Integrating LLM with Edge Computing 1676.2.3 Integration with Generative AI 1716.2.4 Applications and Future 1726.3 6G and Edge Computing 1746.3.1 Basic Understanding for 6G 1746.3.2 Mutual Influence: 6G and Edge Computing 1756.3.3 Potential Applications and Challenges 1796.4 Edge Computing in Space Exploration 1806.4.1 Basic Concepts 1806.4.2 Advanced Concepts and Architecture 1826.4.3 Advanced Scenarios and Challenges 1846.5 Summary and Practice 1866.5.1 Summary 1866.5.2 Practice Questions 1866.5.3 Course Projects 1877 Case Studies and Practical Applications 1937.1 Manufacturing 1957.2 Telecommunications 1987.3 Healthcare 2007.4 Smart Cities 2037.5 Internet of Things 2107.6 Retail 2117.7 Autonomous Vehicles 2137.8 Summary and Practice 2177.8.1 Summary 2177.8.2 Practice Questions 2177.8.3 Course Projects 2188 Privacy and Bias in Edge Computing 2238.1 Privacy in Edge Computing 2248.1.1 Privacy Concerns at Edge Computing 2248.1.2 Various Forms of Privacy 2258.1.3 Introduction of Privacy-Preserving Techniques 2278.1.4 Open Research Problems 2358.2 Accessibility and Digital Divide 2368.2.1 What Is Bias? 2368.2.2 Types of Biases 2378.2.3 Causes of Biases? 2418.2.4 Bias Impact on Edge Computing Algorithms 2428.2.5 Bias Mitigation Techniques 2438.2.6 Open Research Problems 2458.3 Summary and Practice 2458.3.1 Summary 2458.3.2 Practice Questions 2468.3.3 Course Projects 246References 2479 Conclusion and Future Directions 2539.1 Key Insights and Conclusions 2539.2 So, What Is Next? 254Index 257