Integrating Edge Intelligence and Blockchain: What, Why, and How

Description

This book examines whether the integration of edge intelligence (EI) and blockchain (BC) can open up new horizons for providing ubiquitous intelligent services. Accordingly, the authors conduct a summarization of the recent research efforts on the existing works for EI and BC, further painting a comprehensive picture of the limitation of EI and why BC could benefit EI. To examine how to integrate EI and BC, the authors discuss the BC-driven EI and tailoring BC to EI, including an overview, motivations, and integrated frameworks. Finally, some challenges and future directions are explored. The book explores the technologies associated with the integrated system between EI and BC, and further bridges the gap between immature BC and EI-amicable BC.Explores the integration of edge intelligence (EI) and blockchain (BC), including their integrated motivations, frameworks and challenges;Presents how BC-driven EI can realize computing-power management, data administration, and model optimization;Describes how to tailor BC to better support EI, including flexible consensus protocol, effective incentive mechanism, intellectuality smart contract, and scalable BC system tailoring;Presents some key research challenges and future directions for the integrated system. 1 Preface Acknowledgments Contents Acronyms 978-3-031-10186-1_1 1 Introduction 1.1 A Brief Introduction to EI 1.2 A Brief Introduction to BC 1.3 Our Focus 1.4 Our Contribution References 978-3-031-10186-1_2 2 Overview of Edge Intelligence and Blockchain 2.1 Overview of EI 2.1.1 Definition of EI 2.1.2 Division of EI 2.1.3 Deployment of EI 2.1.4 Applications of EI 2.2 Overview of BC 2.2.1 Definition of BC 2.2.2 Architecture of BC 2.2.3 Categories of BC 2.2.4 Application of BC References 978-3-031-10186-1_3 3 Motivations for Integrating Edge Intelligence with Blockchain 3.1 The Limitation of EI 3.2 The Benefit of BC 3.2.1 Computing-Power Management 3.2.2 Data Administration 3.2.3 Model Optimization References 978-3-031-10186-1_4 4 Blockchain Driven Edge Intelligence 4.1 BC-Driven Computing-Power Management in EI 4.1.1 Value-Driven Computing-Power Sharing 4.1.2 Performance-Driven Computing-Power Allocation 4.1.3 Service-Driven Computing Offloading 4.1.4 BC Implementation Tutorial for Computing-Power Management 4.2 BC-Driven Data Administration in EI 4.2.1 Incentive Data Trading 4.2.2 Data Caching Strategy 4.2.3 Reliable Data Collaboration 4.2.4 BC Implementation Tutorial for Data Administration 4.3 BC-Driven Model Optimization in EI 4.3.1 High Efficiency Training 4.3.2 Credible Inference 4.3.3 BC Implementation Tutorial for Model Optimization References 978-3-031-10186-1_5 5 Tailoring Blockchain to Edge Intelligence 5.1 Flexible Consensus Protocol Tailoring To EI 5.1.1 Multi-Functional Design 5.1.2 Compatibility Enhancement 5.1.3 Attack Defense 5.1.4 BC Implementation Tutorial for Consensus Protocol Tailoring 5.2 Effective Incentive Mechanism Tailoring to EI 5.2.1 Mining Strategy Optimization 5.2.2 Risk Prevention Against Cryptocurrency 5.2.3 BC Implementation Tutorial for Incentive Tailoring 5.3 Intellectuality Smart Contract Tailoring to EI 5.3.1 Performance Improvement for Smart Contract 5.3.2 Threat Detection Against Smart Contract 5.3.3 BC Implementation Tutorial for Smart Contract Tailoring 5.4 Scalability Tailoring to EI 5.4.1 Scalability Improvement 5.4.2 BC Implementation Tutorial for Scalability Tailoring References 978-3-031-10186-1_6 6 Research Challenges and Future Directions 6.1 Comprehensive Architecture 6.2 Quantization Intelligence 6.3 Trading Intelligence References 1 (1) Conclusions Index