文献类型：期刊文献

英文题名：Trust-AoI-Aware Codesign of Scheduling and Control for Edge-Enabled IIoT Systems

作者：Wang, Xiaolin[1,2];Zhang, Jinglong[2,3];Chen, Cailian[2,3];He, Jianping[2,3];Ma, Yehan[2,3];Guan, Xinping[2,3]

机构：[1]East China Univ Sci & Technol, Dept Math, Shanghai 200237, Peoples R China;[2]Minist Educ China, Key Lab Syst Control & Signal Proc, Shanghai 200240, Peoples R China;[3]Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China

年份：2024

卷号：20

期号：2

起止页码：2833

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20233214504913);WOS:【SCI-EXPANDED(收录号:WOS:001091060400002)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62025305 and Grant 61933009 and in part by the Shanghai Sailing Program under Grant 23YF1409500. Paper no. TII-21-5918.

语种：英文

外文关键词：Age of information (AoI); control and scheduling codesign; Industrial Internet of Things (IIoT) systems; learning-based trust model

摘要：The harsh industrial environment and the high exposure of wireless communication networks (WCNs) seriously degrade the control performance of edge-enabled Industrial Internet of Things systems. Recently, the codesign of control and scheduling has been studied as a promising method to improve system performance. However, due to the dynamic feature of multiple unreliable factors, the impact of communication randomness on data timeliness, and the difficulty to gather sensing data, it is challenging to jointly design the schedule and control policy to mitigate the adverse effects of WCNs. To address these issues, this article presents a trust-age of information (AoI)-aware codesign scheme (TACS). We first propose a learning-based trust model with the aid of a conditional generative adversarial network to handle the sparse industrial data and a deep-neural-network-based trust online prediction to comprehensively measure the WCNs' reliability. Then, we study the impact of AoI on control performance and design the optimal controller based on the separation principle. Moreover, we derive a trust-AoI-aware scheduling policy at the edge side to dynamically select the optimal data to participate in plant control, which maximizes the control system performance and the trust of WCNs. Simulation results reveal the effectiveness of the TACS in terms of improving the system performance significantly.