AUV-Assisted Node Repair for IoUT Relying on Multiagent Reinforcement Learning
Ziyuan Wang, Zekai Zhang, Jingjing Wang, Chunxiao Jiang, Wei Wei, Yong Ren
Abstract
In recent years, the Internet of Underwater Things (IoUT) has garnered significant attention owing to its potential in ocean exploration and monitoring. However, environmental erosion and limited energy can cause node failures, leading to routing voids, communication congestion, and even IoUT breakdowns. Addressing these challenges, this work considers a node repair scheme for multiple autonomous underwater vehicles (AUVs) to search and repair faulty nodes to ensure the stable operation of the IoUT networks. Moreover, AUVs should adapt automatically to the unknown environment, working in cooperative or separative modes to balance repair efficiency and coverage. We propose a multiagent reinforcement learning-based AUV-assisted node repair (RANR) scheme, which considers limited underwater communication and scheduling between AUVs. To further enhance work efficiency, we introduce area information entropy to reduce redundant coverage among AUVs. Simulation results demonstrate that the RANR scheme is highly applicable to different working conditions.