用强化学习优化水下物联网路由，提升能效与网络寿命
A Reinforcement Learning-Based Telematic Routing Protocol for the Internet of Underwater Things
- 每个节点部署小型强化学习代理，基于链路质量等本地信息选择最优父节点
- 相比传统方法，包交付率提升9.2%，每包能耗降低14.8%，网络寿命延长80秒
- 适用于资源受限的水下传感器网络，适合关注能效与自适应路由的研究者
水下物联网（IoUT）面临带宽低、延迟高、设备移动性强和能量不足等问题，现有陆地网络路由协议如RPL难以适用。本文提出一种基于强化学习的路由协议RL-RPL-UA，使每个节点具备小型强化学习代理，根据链路质量、缓冲区状态、包交付率和剩余能量等局部信息动态选择最优父节点。该协议兼容标准RPL消息，并引入动态目标函数以支持实时决策。Aqua-Sim仿真表明，相较于先前方法，RL-RPL-UA可将包交付率提升最高9.2%，每包能耗降低14.8%，网络寿命延长80秒。结果证明，该方案在水下网络中具有良好的潜力与能效表现。
原文摘要 · Abstract (English)
The Internet of Underwater Things (IoUT) has a lot of problems, like low bandwidth, high latency, mobility, and not enough energy. Routing protocols that were made for land-based networks, like RPL, don't work well in these underwater settings. This paper talks about RL-RPL-UA, a new routing protocol that uses reinforcement learning to make things work better in underwater situations. Each node has a small RL agent that picks the best parent node depending on local data such the link quality, buffer level, packet delivery ratio, and remaining energy. RL-RPL-UA works with all standard RPL messages and adds a dynamic objective function to help people make decisions in real time. Aqua-Sim simulations demonstrate that RL-RPL-UA boosts packet delivery by up to 9.2%, uses 14.8% less energy per packet, and adds 80 seconds to the network's lifetime compared to previous approaches. These results show that RL-RPL-UA is a potential and energy-efficient way to route data in underwater networks.
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