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0 (hodnocen0 x )

EB

ONLINE

Cham : Springer International Publishing AG, 2021

1 online resource (123 pages)

ISBN 9783030839444 (electronic bk.)

ISBN 9783030839437

Simula SpringerBriefs on Computing Ser. ; v.9

Print version: Zhang, Yan Mobile Edge Computing Cham : Springer International Publishing AG,c2021 ISBN 9783030839437

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.

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(OCoLC)1314622113