In this paper, we propose a reinforcement learning (RL) based Medium Access Control (MAC) protocol with dynamic transmission range control (TRC). This protocol provides an adaptive, multi-hop, energy-efficient solution for communication in underwater sensors networks. It features a contention-based TRC scheme with a reactive multi-hop transmission. The protocol has the ability to adjust to network conditions using RL-based learning algorithm. The combination of TRC and RL algorithms can hit a balance between the energy consumption and network performance. Moreover, the proposed adaptive mechanism for relay-selection provides better network utilization and energy-efficiency over time, comparing to existing solutions. Using a straightforward ALOHA-based channel access alongside “helper-relays” (intermediate nodes), the protocol is able to obtain a substantial amount of energy savings, achieving up to 90% of the theoretical “best possible” energy efficiency. In addition, the protocol shows a significant advantage in MAC layer performance, such as network throughput and end-to-end delay.

中文翻译：

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水下无线传感器网络媒体访问控制中基于强化学习的动态传输范围调整

在本文中，我们提出了一种基于强化学习（RL）的具有动态传输范围控制（TRC）的媒体访问控制（MAC）协议。该协议为水下传感器网络中的通信提供了一种自适应的，多跳的，节能的解决方案。它具有基于反应的TRC方案和无功多跳传输。该协议具有使用基于RL的学习算法来适应网络条件的能力。TRC和RL算法的结合可以在能耗和网络性能之间达到平衡。而且，与现有解决方案相比，所提出的用于中继选择的自适应机制随着时间的推移提供了更好的网络利用率和能效。通过直接使用基于ALOHA的渠道访问以及“辅助中继”（中间节点），该协议能够节省大量能源，达到理论“最佳可能”能源效率的90％。此外，该协议在MAC层性能（例如网络吞吐量和端到端延迟）方面显示出显着优势。