Work

A Q-Learning-Based Link-Aware Routing Protocol for Underwater Wireless Sensor Networks

Year: 2025

Type: article

Abstract: In Underwater Wireless Sensor Networks (UWSNs) with mobile nodes, the mobility of the nodes leads to dynamic changes in the network topology. Thus, pre-established routing paths may become invalid and next-hop nodes may be unavailable due to link disruptions. This implies that routing decisions for mobile UWSNs that do not account for changes in the connectivity state of communication links cannot guarantee reliable packet delivery. In this study, a Q-learning-based link-aware routing (QLAR) protocol designed for mobile UWSNs is proposed. The proposed QLAR protocol introduces the Link Expiration Time (LET) into the reward function of the Q-learning algorithm as a critical decision metric, thereby guiding the agent to prioritize more stable communication links with longer expected lifetime. In addition, multiple decision metrics are dynamically predicted and updated by actively perceiving and acquiring information from neighbor nodes through periodic control packet interactions. To achieve a balance among these metrics, the Entropy Weight Method (EWM) is employed to adaptively adjust their weights in response to real-time network conditions. Comprehensive simulation results demonstrate that QLAR outperforms existing routing protocols in terms of various performance metrics under different scenarios.

Source: Journal of Marine Science and Engineering

DOI: 10.3390/jmse13122374

Authors: Xinyang Li, Yanbo Wu, Min Zhu, Jie Ren

Institutions: Chinese Academy of Sciences, Institute of Acoustics, University of Chinese Academy of Sciences, State Key Laboratory of Acoustics

Language: English

Topic: Underwater Vehicles and Communication Systems

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137