Power management and performance optimization of underwater wireless sensor networks based on MARL - Jingtao Guan (PLoS ONE, 2026)
In underwater wireless sensor network communication, communication performance degrades due to factors such as complex underwater channels and limited node resources. To reduce node redundancy energy consumption, improve transmission reliability, and extend the overall network lifetime, this study proposes an intelligent network performance optimization algorithm based on multi-agent reinforcement learning. By constructing an underwater wireless sensor network system model including fixed and mobile nodes, the network performance optimization problem is formalized as a partially observable Markov decision process. Then, multi-agent reinforcement learning is used to construct a comprehensive team reward function containing fair reuse rewards and survival time penalties, thereby establishing a distributed intelligent power management scheme. This solution enables each node to make transmission power decisions based on local observations, combined with the underlying media access control protocol, to collaboratively optimize higher-layer network performance indicators. The results show that in heterogeneous network scenarios, the proposed method achieves a network capacity of 245.68 kb and a fairness reuse index of 1.85. In imperfect networks with 5% node failures, the average communication latency is only 6.18 time slots, which is superior to the comparative algorithm. Under dynamic environments with a signal-to-interference-plus-noise ratio of 10–16 dB and a water flow velocity of 2.0 m/s, it can still maintain a network capacity of over 32,045 kb and an energy efficiency of 0.4 kb/J. These findings demonstrate that the proposed method significantly improves the robustness of underwater wireless sensor networks, providing communication support for ocean monitoring.