AGV-Assisted Adaptive Cooperative Transmission for State Estimation in Industrial IoT Systems

• Published in: IEEE transactions on vehicular technology Vol. 74; no. 2; pp. 2390 - 2405
• Main Authors: Lyu, Ling, Qiao, Zexin, Dai, Yanpeng, Cheng, Nan, Chen, Cailian, Guan, Xinping, Shen, Xuemin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adaptive algorithms; Adaptive systems; AGV; Automated guided vehicles; Automation; Channel estimation; cooperative transmission; Data communication; Data transmission; Energy consumption; Energy costs; Error reduction; Estimation error; Industrial applications; Industrial Internet of Things; industrial IoT systems; Internet of Things; Iterative algorithms; Job shop scheduling; Performance evaluation; Performance indices; Prediction algorithms; Scheduling; sensor scheduling; Simulation; State estimation; Wireless sensor networks
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2024.3472030

The wide application of Internet of Things (IoT) technology in industrial automation promotes the emergence of industrial IoT systems, in which state estimation plays a crucial role in conjecturing system states with sensory data delivered over wireless channels. In this paper, we propose an automated guided vehicle (AGV)-assisted adaptive cooperative transmission scheme to minimize the mean square error of state estimation at a low energy cost. Specifically, a novel performance index, estimation gain, is introduced to evaluate the benefit of scheduling one sensor for estimation error reduction. Then, sensor scheduling and data transmission are jointly optimized to minimize the time-accumulated estimation error, which is challenging to directly solve due to the unclear impact of imperfect transmission on estimation performance. To this end, an estimation gain-based algorithm is designed to determine the scheduled sensors. Besides, an iterative algorithm is designed to solve the adaptive cooperative transmission problem. Simulation results show that the proposed scheme outperforms benchmark schemes in reducing estimation error and energy consumption.