Blockchain and Semi-Distributed Learning-Based Secure and Low-Latency Computation Offloading in Space-Air-Ground-Integrated Power IoT

Authors

Haijun Liao, School of Electric and Electronic Engineering, North China Electric Power University, Hebei Key Laboratory of Power Internet of Things Technology, Hebei, Baoding, 071003, China
Zhao Wang, School of Electric and Electronic Engineering, North China Electric Power University, Hebei Key Laboratory of Power Internet of Things Technology, Hebei, Baoding, 071003, China
Zhenyu Zhou, School of Electric and Electronic Engineering, North China Electric Power University, Hebei Key Laboratory of Power Internet of Things Technology, Hebei, Baoding, 071003, China
Yang Wang, Electric Power Research Institute Company Ltd, State Grid Corporation of China, Institute of Information and Communication China, Beijing, 100192, China
Hui Zhang, Electric Power Research Institute Company Ltd, State Grid Corporation of China, Institute of Information and Communication China, Beijing, 100192, China
Shahid Mumtaz, Campus Universitario de Santiago, University of Aveiro, Instituto de Telecomunicacoes, Aveiro, 3810-193, Portugal
Mohsen Guizani, Mohamed Bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

IEEE Journal on Selected Topics in Signal Processing

Abstract

Power systems impose stringent security and delay requirements on computation offloading, which cannot be satisfied by existing power Internet of Things (PIoT) networks. In this paper, we tackle this challenge by combining blockchain, space-air-ground integrated PIoT (SAG-PIoT) and machine learning. Low earth orbit (LEO) satellites assist in broadcasting a consensus message to reduce the block creation delay, and unmanned aerial vehicles (UAVs) provide flexible coverage enhancement. Specifically, we propose a Blockchain and semi-distributed leaRning-based secure and low-latency electromAgnetic interferenCe-awarE computation offloading algorithm (BRACE) to minimize the total queuing delay under the long-term security constraint. First, the task offloading is decoupled from the computational resource allocation by Lyapunov optimization. Second, the task offloading problem is solved by the proposed federated deep actor-critic-based electromagnetic interference-aware task offloading algorithm (FDAC-EMI). Finally, the resource allocation problem is solved by smooth approximation and Lagrange optimization. Simulation results verify that BRACE achieves superior delay and security performance. © 2007-2012 IEEE.

First Page

381

Last Page

394

DOI

10.1109/JSTSP.2021.3135751

Publication Date

4-1-2022

Keywords

Antennas, Blockchain, Electromagnetic pulse, Internet of things, Learning systems, Network security, Orbits, Air grounds, Block-chain, Computation offloading, Computational modelling, Delay, Distributed learning, Electromagnetic interference awareness, Electromagnetics, Integrated power, Interference awareness, Security, Semi-distributed, Semi-distributed learning, Space-air-ground-integrated power IoT, Task analysis, Signal interference

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Recommended Citation

H. Liao, et al., "Blockchain and Semi-Distributed Learning-Based Secure and Low-Latency Computation Offloading in Space-Air-Ground-Integrated Power IoT", IEEE Journal on Selected Topics in Signal Processing, vol 16(3), pp. 381-394, Apr 2022. doi: 10.1109/JSTSP.2021.3135751