A better-performing Q-learning game-theoretic distributed routing for underwater wireless sensor networks

Abstract

Underwater sensor networks have recently emerged as a promising networking technique for various underwater applications. However, the acoustic routing of underwater sensor networks in the aquatic environment presents challenges in terms of dynamic structure, high rates of energy consumption, long propagation delay, and narrow bandwidth. Therefore, it is difficult to adapt traditional routing protocols, which are known to be reliable in terrestrial wireless networks. In this study, we focus on the development of novel routing algorithms to tackle acoustic transmission problems in underwater sensor networks. The proposed scheme is based on reinforcement learning and game theory and is designed as a routing game model to provide an effective packet-forwarding mechanism. In particular, our Q-learning game paradigm captures the dynamics of the underwater sensor networks system in a decentralized, distributed manner. The results of a performance simulation analysis show that the proposed scheme can outperform existing schemes while displaying balanced system performance in terms of energy efficiency and underwater sensor networks throughput.