Title: A Reinforcement Learning-Based Telematic Routing Protocol for the Internet of Underwater Things Show Chinese title

Title: 基于强化学习的水下物联网路由协议

Abstract: The Internet of Underwater Things (IoUT) faces major challenges such as low bandwidth, high latency, mobility, and limited energy resources. Traditional routing protocols like RPL, which were designed for land-based networks, do not perform well in these underwater conditions. This paper introduces RL-RPL-UA, a new routing protocol that uses reinforcement learning to improve performance in underwater environments. Each node includes a lightweight RL agent that selects the best parent node based on local information such as packet delivery ratio, buffer level, link quality, and remaining energy. RL-RPL-UA keeps full compatibility with standard RPL messages and adds a dynamic objective function to support real-time decision-making. Simulations using Aqua-Sim show that RL-RPL-UA increases packet delivery by up to 9.2%, reduces energy use per packet by 14.8%, and extends network lifetime by 80 seconds compared to traditional methods. These results suggest that RL-RPL-UA is a promising and energy-efficient routing solution for underwater networks. Show Chinese abstract

Abstract: 水下物联网（IoUT）面临诸如带宽低、时延长、移动性强以及能量资源有限等主要挑战。传统路由协议（如为陆地网络设计的RPL）在这些水下条件下表现不佳。本文介绍了一种新的路由协议RL-RPL-UA，它利用强化学习来提升水下环境中的性能。每个节点都包含一个轻量级的RL代理，该代理基于数据包交付比率、缓冲区水平、链路质量和剩余能量等本地信息选择最佳的父节点。RL-RPL-UA完全兼容标准RPL消息，并增加了一个动态目标函数以支持实时决策。使用Aqua-Sim进行的仿真表明，与传统方法相比，RL-RPL-UA可将数据包交付率提高多达9.2%，每包能耗减少14.8%，网络寿命延长80秒。这些结果表明，RL-RPL-UA是一种有前景且节能的水下网络路由解决方案。