Rendering Secure and Trustworthy Edge Intelligence in 5G-Enabled IIoT using Proof of Learning Consensus Protocol

Qiu, Chao; Aujla, Gagangeet Singh; Jiang, Jing; Wen, Wu; Zhang, Peiying

Abstract

Industrial Internet of Things (IIoT) and fifth generation (5G) network have fueled the development of Industry 4.0 by providing an unparalleled connectivity and intelligence to ensure timely (or real time) and optimal decision making. Under this umbrella, the edge intelligence is ready to propel another ripple in the industrial growth by ensuring the next generation of connectivity and performance. With the recent proliferation of blockchain, edge intelligence enters a new era, where each edge trains the local learning model, then interconnecting the whole learning models in a distributed blockchain manner, known as blockchain-assisted federated learning. However, it is quiet challenging task to provide secure edge intelligence in 5G-enabled IIoT environment alongside ensuring latency and throughput. In this paper, we propose a Proof-of-Learning (PoL) consensus protocol that considers the reputation opinion for edge blockchain to ensure secure and trustworthy edge intelligence in IIoT. This protocol fetches each edge's reputation opinion by executing a smart contract, and partly adopts the winner's learning model according to its reputation opinion. By quantitative performance analysis and simulation experiments, the proposed scheme demonstrates the superior performance in contrast to the traditional counterparts.

Citation

Qiu, C., Aujla, G. S., Jiang, J., Wen, W., & Zhang, P. (2023). Rendering Secure and Trustworthy Edge Intelligence in 5G-Enabled IIoT using Proof of Learning Consensus Protocol. IEEE Transactions on Industrial Informatics, 19(1), 900-909. https://doi.org/10.1109/tii.2022.3179272

• Journal Article Type: Article
• Online Publication Date: Jun 6, 2022
• Publication Date: 2023-01
• Deposit Date: Sep 21, 2022
• Publicly Available Date: Sep 22, 2022
• Journal: IEEE Transactions on Industrial Informatics
• Print ISSN: 1551-3203
• Electronic ISSN: 1941-0050
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Issue: 1
• Pages: 900-909
• DOI: https://doi.org/10.1109/tii.2022.3179272
• Public URL: https://durham-repository.worktribe.com/output/1194079