Blockchain and Federated Deep Reinforcement Learning Based Secure Cloud-Edge-End Collaboration in Power IoT

Authors

Sunxuan Zhang, North China Elect Power Univ, Sch Elect & Elect Engn, State Key Lab Alternate Elect Power Syst Renewabl, Beijing, Peoples R China
Zhao Wang, North China Elect Power Univ, Sch Elect & Elect Engn, State Key Lab Alternate Elect Power Syst Renewabl, Beijing, Peoples R China
Zhenyu Zhou, North China Elect Power Univ, Sch Elect & Elect Engn, State Key Lab Alternate Elect Power Syst Renewabl, Beijing, Peoples R China
Yang Wang, China Elect Power Res Inst, Beijing, Peoples R China
Hui Zhang, China Elect Power Res Inst, Beijing, Peoples R China
Geng Zhang, China Elect Power Res Inst, Beijing, Peoples R China
Huixia Ding, China Elect Power Res Inst, Beijing, Peoples R China
Shahid Mumtaz, Inst Telecomunicacoes, Aveiro, Portugal
Mohsen Guizani, Mohamed bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

IEEE Wireless Communications

Abstract

Cloud-edge-end collaboration enables harmonious and efficient resource allocation for the Power Internet of Things (PIoT). However, the security and complexity issues of computation offloading evolve into the main obstacles. In this article, we first propose a blockchain and AI-based secure cloud-edge-end collaboration PIoT (BASE-PIoT) architecture to ensure data security and intelligent computation offloading. Its advantages in flexible resource allocation, secure data sharing, and differentiated service guarantee are elaborated. Then the adaptability of three typical blockchains with PIoT is analyzed, and some typical application scenes of BASE-PIoT including computation offloading, energy scheduling, and access authentication are illustrated. Finally, we propose a blockchain-empowered federated deep actor-critic-based task offloading algorithm to address the secure and low-latency computation offloading problem. The coupling between the long-term security constraint and short-term queuing delay optimization is decoupled by using Lyapunov optimization. Numerical results verify its excellent performance in total queuing delay and consensus delay.

First Page

84

Last Page

91

DOI

10.1109/MWC.010.2100491

Publication Date

4-2022

Keywords

Cloud computing, Simulation, Collaboration, Computer architecture, Reinforcement learning, Delays, Blockchains

Comments

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Recommended Citation

S. Zhang et al., "Blockchain and Federated Deep Reinforcement Learning Based Secure Cloud-Edge-End Collaboration in Power IoT," in IEEE Wireless Communications, vol. 29, no. 2, pp. 84-91, April 2022, doi: 10.1109/MWC.010.2100491.