Underwater Sensor Networks (UWSNs) are a desirable networking technique to facilitate various aquatic applications. However, the adverse characteristics of underwater communications and high cost of underwater sensor nodes limit UWSNs to sparse deployment, resulting in intermittent connectivity and therefore calling for techniques for Delay/Disruption Tolerant Networks (DTNs). To cope with disruptions, extra efforts have to be made in the routing protocol to provide transparent and robust end-to-end connections to upper-layer applications. In this paper, we propose a novel adaptive and energy-efficient routing protocol for underwater DTNs. By exploiting underwater node mobility patterns with adaptive filters, sensor nodes are able to estimate future contact events with other nodes in addition to the average contact probabilities over a prediction window. The proposed protocol is based on a distributed machine learning technique, Q-learning, which aims to select the most promising forwarders so as to minimize the end-to-end delay. Extensive simulations of the proposed protocol are carried out, and the results have shown that our protocol yields significantly better network performances and energy efficiency compared to other existing DTN routing protocols.
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