Power management and performance optimization of underwater wireless sensor networks based on MARL
Jingtao Guan
TL;DR
This paper introduces a new method using multi-agent reinforcement learning to optimize energy use and communication performance in underwater wireless sensor networks.
Contribution
A novel multi-agent reinforcement learning-based power management scheme for underwater wireless sensor networks is proposed.
Findings
The proposed method achieves a network capacity of 245.68 kb and a fairness reuse index of 1.85 in heterogeneous network scenarios.
In imperfect networks with 5% node failures, the average communication latency is only 6.18 time slots.
The method maintains a network capacity of over 32,045 kb and an energy efficiency of 0.4 kb/J in dynamic environments.
Abstract
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…
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Taxonomy
TopicsUnderwater Vehicles and Communication Systems · Energy Efficient Wireless Sensor Networks · Maritime Navigation and Safety