Adaptive MAC Scheduling Strategy Based on Channel Sensing and Reinforcement Decision in Distributed Internet of Underwater Things

Distributed Internet of Underwater Things (D-IoUT) has broad application prospects in marine monitoring, resource development, and security protection. However, the spatiotemporal uncertainty of underwater acoustic networks and the presence of multiple collision domains pose severe challenges to media access control (MAC)-layer scheduling and interference management. To address these issues, this article proposes an adaptive scheduling MAC protocol based on channel sensing and reinforcement decision (CSRD-ASMAC), aiming to provide an efficient and adaptive transmission scheduling scheme for D-IoUT. The protocol first introduces a hierarchical channel-state classification framework to accurately characterize the channel environment for each slot. Building on predivided basic slots, it integrates channel sensing and slot prediction into a reinforcement learning (RL) framework and employs a mixed-Action exploration strategy to update Q values, enabling each node to adaptively select the optimal transmission slot. The simulation results show that CSRD-ASMAC effectively improves overall network throughput in multicollision domain environments.