Reinforcement Learning Based MAC Protocol (UW-ALOHA-Q) for Underwater Acoustic Sensor Networks


The demand for regular monitoring of the marine environment and ocean exploration is rapidly increasing, yet the limited bandwidth and slow propagation speed of acoustic signals leads to low data throughput for underwater networks used for such purposes. This study describes a novel approach to medium access control that engenders efficient use of an acoustic channel. ALOHA-Q is a medium access protocol designed for terrestrial radio sensor networks and reinforcement learning is incorporated into the protocol to provide efficient channel access. In principle, it potentially offers opportunities for underwater network design, due to its adaptive capability and its responsiveness to environmental changes. However, preliminary work has shown that the achievable channel utilisation is much lower in underwater environments compared with the terrestrial environment. Three improvements are proposed in this paper to address key limitations and establish a new protocol (UW-ALOHA-Q). The new protocol includes asynchronous operation to eliminate the challenges associated with time synchronisation under water, offer an increase in channel utilisation through a reduction in the number of slots per frame, and achieve collision free scheduling by incorporating a new random back-off scheme. Simulations demonstrate that UW-ALOHA-Q provides considerable benefits in terms of achievable channel utilisation, particularly when used in large scale distributed networks.

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