An Adaptive and Energy-efficient Routing Protocol Based on Machine Learning for Underwater Delay Tolerant Networks

Underwater Sensor Network (UWSN) is emerging as a promising networking technique for aquatic environment monitoring and exploration. However, because of the adverse characteristics of underwater communications, underwater sensor networks may get partitioned temporarily, and hence call for techniques for Delay/Disruption Tolerant Networks (DTNs). In this paper, we propose an adaptive and energy-efficient routing protocol based on a machine learning technique, Q-learning, for underwater DTNs. Extensive simulations of the proposed protocol are carried out, and the results have shown that our protocol can cope with dynamic disconnections and disruptions in underwater DTNs well and achieves a good trade-off between energy efficiency and end-to-end delay.