A Q-Learning-Based Distributed Energy-Efficient Routing Protocol in UASNs
Xuan Geng, Qingyuan Li, Xiaowei Pan, Fang Cao
TL;DR
This paper introduces a new routing protocol for underwater sensor networks that uses machine learning to save energy and improve performance.
Contribution
The novel contribution is a distributed Q-learning protocol that incorporates node energy and link quality for efficient routing in UASNs.
Findings
The QDER protocol outperforms existing methods in network lifetime and energy efficiency.
The protocol shows robustness and adaptability under varying signal-to-noise ratio conditions.
Distributed decision-making reduces communication overhead compared to centralized approaches.
Abstract
This paper proposes a Q-Learning-Based Distributed Energy-Efficient Routing (QDER) protocol for underwater acoustic sensor networks (UASNs). The routing problem is formulated as a Markov Decision Process (MDP) and a distributed Q-learning approach is proposed. Each sensor node is treated as an agent that independently selects its next-hop node based on a Q-table. The rewards function is designed that jointly considers node residual energy and depth information, enabling each node to learn an effective routing policy through distributed decision-making. Unlike centralized routing approaches that rely on extensive global information exchange, the proposed scheme allows nodes to make local decisions, thereby reducing communication overhead and energy consumption while maintaining efficient routing paths. In addition, link quality is designed in the reward to account for channel conditions,…
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Taxonomy
TopicsUnderwater Vehicles and Communication Systems · Energy Efficient Wireless Sensor Networks · Underwater Acoustics Research