边缘计算 雾计算研究与应用 英文版 作者： 林福宏等著 出版时间：2018年版 内容简介 The main goalof this book is sharing the recent achievements of Edge & Fog Computing inour lab. It contains three parts. In the first part， we focus on the resourcemanagement in Edge & Fog Computing including Resource Caching Scheme in FogComputing， Radio Resource Management in 5GFog Cell， Transmission of Malware in Fog Computing， Incentive to ContributeResource-based Crowd funding in Fog Computing， Resource Scheduling Scheme inFog Computing， Resource sharing Model in Fog Computing， and Fair ResourceAllocation in IDS for Edge Computing. In the second part， we introduce thesecurity management in Edge & Fog Computing including Security Model in FogComputing， Node State Monitoring Scheme in Fog Computing， IDS Model in FogComputing， Key Management Scheme in Fog Computing， Intrusion Response Strategyin Fog Computing， Intrusion Detection in Fog Computing， and Security Mechanismin Fog Computing. In the third part， we propose some applications of Edge &Fog Computing. They are Real-time Fast Bi-dimensional Empirical ModeDecomposition， Resource Management Scheme in Vehicular Social Edge Computing，and Real-time Image Restoration in Edge Computing. 目录 Contents PART Ⅰ： Resource Management in Edge & Fog Computing 1 SteinerTree based Optimal Resource Caching Scheme in Fog Computing 1．1 Introduction 1．2 Related work 1．3 Problem formulation 1．4 Algorithm design 1．5 Running illustration 1．6 Numerical simulation 1．7 Conclusion References 2 HypergraphBased Radio Resource Management in 5G Fog Cell 2．1 Introduction 2．2 Related work 2．3 Network architecture of fogcomputing in 5G 2．4 Radio resource management ofhypergraph partitioning in 5G Fog Cell 2．4．1 Task model 2．4．2 Hypergraph model of 5G FogCell resource pool 2．4．3 Hypergraph cluster andresource allocation 2．5 Numerical simulation 2．6 Conclusion References …… 18．1 Introduction 18．2 Background materials 18．2．1 Fruitfly optimization algorithm （FOA） 18．2．2 Support vector machine （SVM） 18．3 Theproposed methodology 18．3．1 Process of image restoration processing 18．3．2 Optimization algorithm of TFOA based on LSSVR 18．4 Experiment and application 18．4．1 Parameter optimization analysis of TFOA 18．4．2 Imagerestoration analysis of LSSVM- TFOA 18．5 Conclusion References
