Abstract
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.
Highlights
The Earth’s surface comprises 71% water [1] and the market value of coastal resources is estimated to be 3 trillion USD per year [2], with our oceans contributing 1.5 trillion USD annually in value-added to the global economy [3]
Park et al.: Reinforcement Learning Based Medium Access Control (MAC) Protocol (UW-ALOHA-Q) for Underwater Acoustic Sensor Networks acoustic signals in water compared to radio signals in the air (≈ 3 × 108 m/s) leads to poor channel utilisation in underwater networks, and the limited and distance dependent bandwidth brings about low fundamental capacity based on Shannon’s channel capacity theory [5]
To address these problems limiting the efficient use of acoustic networks for underwater monitoring, we describe a novel reinforcement learning based Medium Access Control (MAC) protocol, UW-ALOHA-Q
Summary
The Earth’s surface comprises 71% water [1] and the market value of coastal resources is estimated to be 3 trillion USD per year [2], with our oceans contributing 1.5 trillion USD annually in value-added to the global economy [3]. Park et al.: Reinforcement Learning Based MAC Protocol (UW-ALOHA-Q) for Underwater Acoustic Sensor Networks acoustic signals in water compared to radio signals in the air (≈ 3 × 108 m/s) leads to poor channel utilisation in underwater networks, and the limited and distance dependent bandwidth brings about low fundamental capacity based on Shannon’s channel capacity theory [5]. To address these problems limiting the efficient use of acoustic networks for underwater monitoring, we describe a novel reinforcement learning based Medium Access Control (MAC) protocol, UW-ALOHA-Q.
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