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arXiv · 2506.00133

A Reinforcement Learning-Based Telematic Routing Protocol for the Internet of Underwater Things

Mohammadhossein Homaei·Mehran Tarif·Agustin Di Bartolo·Victor Gonzalez Morales·Mar Avila Vegas
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Abstract

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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Mohammadhossein Homaei, Mehran Tarif, Agustin Di Bartolo, Victor Gonzalez Morales, Mar Avila Vegas. 2025-11-21. A Reinforcement Learning-Based Telematic Routing Protocol for the Internet of Underwater Things. https://arxiv.org/abs/2506.00133

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