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.
Author(s): Xiaofei Wang, Chao Qiu, Xiaoxu Ren, Zehui Xiong, Victor C. M. Leung, Dusit Niyato
Series: Wireless Networks
Publisher: Springer
Year: 2022
Language: English
Pages: 117
City: Cham
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
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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
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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
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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
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6 Research Challenges and Future Directions
6.1 Comprehensive Architecture
6.2 Quantization Intelligence
6.3 Trading Intelligence
References
1 (1)
Conclusions
Index
