Mobile Edge Computing.

• Superior document: Simula SpringerBriefs on Computing Series ; v.9
• Author: Zhang, Yan.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2021.; ©2022.
• Year of Publication: 2021
• Edition: 1st ed.
• Language: English
• Series: Simula SpringerBriefs on Computing Series
• Physical Description: 1 online resource (123 pages)

Table of Contents

• Intro
• 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.