Mobile Edge Computing

Preface
Acknowledgements
Contents
Acronyms
1 Introduction
	1.1 Mobile Cloud Computing (MCC)
	1.2 Overview of MEC
	1.3 Book Organization
2 Mobile Edge Computing
	2.1 A Hierarchical Architecture of Mobile Edge Computing (MEC)
	2.2 Computation Model
		2.2.1 Computation Model of Local Execution
		2.2.2 Computation Model of Full Offloading
		2.2.3 A Computation Model for Partial Offloading
	2.3 Offloading Policy
		2.3.1 Binary Offloading
		2.3.2 Partial Offloading
	2.4 Challenges and Future Directions
3 Mobile Edge Caching
	3.1 Introduction
	3.2 The Architecture of Mobile Edge Caching
	3.3 Caching Performance Metrics
		3.3.1 Hit Rate Ratio
		3.3.2 Content Acquisition Latency
		3.3.3 Quality of Experience (QoE)
		3.3.4 Caching System Utility
	3.4 Caching Service Design and Data Scheduling Mechanisms
		3.4.1 Edge Caching Based on Network Infrastructure Services
		3.4.2 Edge Caching Based on D2D Services
		3.4.3 Hybrid Service–Enabled Edge Caching
	3.5 Case Study: Deep Reinforcement Learning–Empowered …
		3.5.1 System Model
		3.5.2 Problem Formulation and a DDPG-Based Optimal Content Dispatch Scheme
		3.5.3 Numerical Results
4 Mobile Edge Computing for Beyond 5G/6G
	4.1 Fundamental Characteristics of 6G
	4.2 Integrating Mobile Edge Computing (MEC) …
		4.2.1 Use Cases of Integrating MEC into 6G
		4.2.2 Applications of Integrating MEC into 6G
		4.2.3 Challenges of Integrating MEC into 6G
	4.3 Case Study: MEC-Empowered Edge Model Sharing for 6G
		4.3.1 Sharing at the Edge: From Data to Model
		4.3.2 Architecture of Edge Model Sharing
		4.3.3 Processes of Edge Model Sharing
5 Mobile Edge Computing for the Internet of Vehicles
	5.1 Introduction
	5.2 Challenges in VEC
	5.3 Architecture of VEC
	5.4 Key Techniques of VEC
		5.4.1 Task Offloading
		5.4.2 Heterogeneous Edge Server Cooperation
		5.4.3 AI-Empowered VEC
	5.5 A Case Study
		5.5.1 Predictive Task Offloading for Fast-Moving Vehicles
		5.5.2 Deep Q-Learning for Vehicular Computation Offloading
6 Mobile Edge Computing for UAVs
	6.1 Unmanned Aerial Vehicle–Assisted Mobile Edge Computing (MEC) Networks
	6.2 Joint Trajectory and Resource Optimization in UAV-Assisted MEC Networks
		6.2.1 Resource Allocation and Optimization in the Scenario of a UAV Exploiting MEC Computing Capabilities
		6.2.2 Resource Allocation and Optimization in the Scenario of a UAV Serving as a Computing Server
		6.2.3 Resource Allocation and Optimization in the Scenario of a UAV Serving as a Relay for Computation Offloading
	6.3 Case Study: UAV Deployment and Resource Optimization for MEC at a Wind Farm
		6.3.1 UAV Deployment for MEC at a Wind Farm
		6.3.2 Joint Trajectory and Resource Optimization of UAV-Aided MEC at a Wind Farm
	6.4 Conclusions
7 The Future of Mobile Edge Computing
	7.1 The Integration of Blockchain and Mobile Edge Computing (MEC)
		7.1.1 The Blockchain Structure
		7.1.2 Blockchain Classification
		7.1.3 Integration of Blockchain and MEC
	7.2 Edge Intelligence: The Convergence of AI and MEC
		7.2.1 Federated Learning in MEC
		7.2.2 Transfer Learning in MEC
	7.3 MEC in Other Applications
		7.3.1 MEC in Pandemics
		7.3.2 MEC in the Industrial IoT (IIoT)
		7.3.3 MEC in Disaster Management
Appendix  References