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

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.