Digital Twin Empowered Industrial IoT Based on Credibility-weighted Swarm Learning

Xiang, Wei, Li, Jie, Zhou, Yuan, Cheng, Peng, Jin, Jiong, and Yu, Kan (2024) Digital Twin Empowered Industrial IoT Based on Credibility-weighted Swarm Learning. IEEE Transactions on Industrial Informatics, 20. pp. 775-784.

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Driven by digital twin (DT) technology, the industrial Internet of Things (IIoT) is expanding to open up new frontiers in industrial applications. However, traditional DT modeling approaches require synchronizing massive amounts of data, resulting in high communications overhead and privacy vulnerability. To address this problem, this paper proposes a novel DT architecture for IIoT, where the DT can showcase the real-time operating status of the industrial environment. Swarm learning (SL) is an emerging decentralized federated learning (FL) technique that eliminates the need of a centralized server. We present a novel credibility-weighted SL (CSL) scheme to construct the DT models, which improves data security while ensuring the fairness of participants as opposed to conventional FL. In addition, we develop a DT-assisted deep reinforcement learning (DRL) algorithm for simultaneously optimizing the system reliability and energy consumption of IIoT. Simulation comparisons demonstrate that the proposed scheme outperforms some state-of-the-art benchmarks in terms of both reliability and energy consumption.

Item ID: 79062
Item Type: Article (Research - C1)
ISSN: 1941-0050
Keywords: Biological system modeling, Data privacy, deep reinforcement learning (DRL), digital twin (DT), Energy consumption, Industrial Internet of Things, industrial Internet of Things (IIoT), Optimization, reliability, Reliability, Servers, swarm learning (SL)
Copyright Information: © 2023 IEEE.
Date Deposited: 13 Mar 2024 02:36
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4606 Distributed computing and systems software > 460603 Cyberphysical systems and internet of things @ 100%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220402 Applied computing @ 100%
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