Performance analysis of underwater wireless sensor networks using Reinforcement Learning

This article presents a novel routing protocol named DROR, specifically tailored for underwater wireless sensor networks (UWSNs) to tackle the challenge of void regions. DROR integrates Reinforcement Learning (RL) and Opportunistic Routing (OR) in a recipient-oriented approach, considering the energy limitations and the unique underwater setting. It incorporates a mechanism for void rehabilitation, allowing packets to circumvent void nodes and maintain continuous moving for dependable transmission. Furthermore, a dynamic scheduling strategy based on relative Q-values ensures proficient packet forwarding along the most efficient routing path. Simulation outcomes illustrate the efficacy of the suggested protocol concerning delay, PDR, and energy tax in UWSNs with varying Range, Depths, Packet sizes, and moving radius.