Intelligent IoT for the Digital World

YANG YANG, PhD, IEEE Fellow, is a Professor with ShanghaiTech University and an Adjunct Professor with Peng Cheng Laboratory, China.XU CHEN, PhD, IEEE Senior Member, is a Professor with Sun Yat-sen University, China.RUI TAN, PhD, IEEE Senior Member, is an Associate Professor with Nanyang Technological University, Singapore.YONG XIAO, PhD, IEEE Senior Member, is a Professor with Huazhong University of Science and Technology, China.

• Preface ixAcknowledgments xviiAcronyms xix1 IoT Technologies and Applications 11.1 Introduction 11.2 Traditional IoT Technologies 31.2.1 Traditional IoT System Architecture 31.2.2 IoT Connectivity Technologies and Protocols 71.3 Intelligent IoT Technologies 271.3.1 Data Collection Technologies 291.3.2 Computing Power Network 361.3.3 Intelligent Algorithms 391.4 Typical Applications 421.4.1 Environmental Monitoring 421.4.2 Public Safety Surveillance 421.4.3 Military Communication 441.4.4 Intelligent Manufacturing and Interactive Design 461.4.5 Autonomous Driving and Vehicular Networks 471.5 Requirements and Challenges for Intelligent IoT Services 481.5.1 A Generic and Flexible Multi-tier Intelligence IoT Architecture 481.5.2 Lightweight Data Privacy Management in IoT Networks 491.5.3 Cross-domain Resource Management for Intelligent IoT Services 501.5.4 Optimization of Service Function Placement, QoS, and Multi-operator Network Sharing for Intelligent IoT Services 501.5.5 Data Time stamping and Clock Synchronization Services for Wide-area IoT Systems 511.6 Conclusion 52References 522 Computing and Service Architecture for Intelligent IoT 612.1 Introduction 612.2 Multi-tier Computing Networks and Service Architecture 622.2.1 Multi-tier Computing Network Architecture 632.2.2 Cost Aware Task Scheduling Framework 652.2.3 Fog as a Service Technology 692.3 Edge-enabled Intelligence for Industrial IoT 742.3.1 Introduction and Background 742.3.2 Boomerang Framework 792.3.3 Performance Evaluation 832.4 Fog-enabled Collaborative SLAM of Robot Swarm 852.4.1 Introduction and Background 852.4.2 A Fog-enabled Solution 872.5 Conclusion 93References 943 Cross-Domain Resource Management Frameworks 973.1 Introduction 973.2 Joint Computation and Communication Resource Management for Delay-Sensitive Applications 993.2.1 2C Resource Management Framework 1013.2.2 Distributed Resource Management Algorithm 1043.2.3 Delay Reduction Performance 1073.3 Joint Computing, Communication, and Caching Resource Management for Energy-efficient Applications 1133.3.1 Fog-enabled 3C Resource Management Framework 1163.3.2 Fog-enabled 3C Resource Management Algorithm 1213.3.3 Energy Saving Performance 1273.4 Case Study: Energy-efficient Resource Management in Tactile Internet 1313.4.1 Fog-enabled Tactile Internet Architecture 1333.4.2 Response Time and Power Efficiency Trade-off 1353.4.3 Cooperative Fog Computing 1373.4.4 Distributed Optimization for Cooperative Fog Computing 1393.4.5 A City-wide Deployment of Fog Computing-supported Self-driving Bus System 1403.5 Conclusion 144References 1454 Dynamic Service Provisioning Frameworks 1494.1 Online Orchestration of Cross-edge Service Function Chaining 1494.1.1 Introduction 1494.1.2 Related Work 1514.1.3 System Model for Cross-edge SFC Deployment 1524.1.4 Online Optimization for Long-term Cost Minimization 1574.1.5 Performance Analysis 1624.1.6 Performance Evaluation 1654.1.7 Future Directions 1694.2 Dynamic Network Slicing for High-quality Services 1704.2.1 Service and User Requirements 1704.2.2 Related Work 1734.2.3 System Model and Problem Formulation 1744.2.4 Implementation and Numerical Results 1764.3 Collaboration of Multiple Network Operators 1804.3.1 Service and User Requirements 1814.3.2 System Model and Problem Formulation 1824.3.3 Performance Analysis 1874.4 Conclusion 189References 1905 Lightweight Privacy-Preserving Learning Schemes 1975.1 Introduction 1975.2 System Model and Problem Formulation 1995.3 Solutions and Results 2005.3.1 A Lightweight Privacy-preserving Collaborative Learning Scheme 2005.3.2 A Differentially Private Collaborative Learning Scheme 2135.3.3 A Lightweight and Unobtrusive Data Obfuscation Scheme for Remote Inference 2185.4 Conclusion 233References 2336 Clock Synchronization for Wide-area Applications 2396.1 Introduction 2396.2 System Model and Problem Formulation 2406.2.1 Natural Timestamping for Wireless IoT Devices 2406.2.2 Clock Synchronization forWearable IoT Devices 2416.3 Natural Timestamps in Powerline Electromagnetic Radiation 2436.3.1 Electrical Network Frequency Fluctuations and Powerline Electromagnetic Radiation 2436.3.2 Electromagnetic Radiation-based Natural Timestamping 2446.3.3 Implementation and Benchmark 2516.3.4 Evaluation in Office and Residential Environments 2546.3.5 Evaluation in a Factory Environment 2596.3.6 Applications 2616.4 Wearables Clock Synchronization Using Skin Electric Potentials 2696.4.1 Motivation 2696.4.2 Measurement Study 2716.4.3 TouchSync System Design 2766.4.4 TouchSync with Internal Periodic Signal 2856.4.5 Implementation 2886.4.6 Evaluation 2906.5 Conclusion 297References 2977 Conclusion 301Index 305