A Reinforcement Learning-Based Telematic Routing Protocol for the Internet of Underwater Things
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.
Keywords
Cite
@article{arxiv.2506.00133,
title = {A Reinforcement Learning-Based Telematic Routing Protocol for the Internet of Underwater Things},
author = {Mohammadhossein Homaei and Mehran Tarif and Agustin Di Bartolo and Victor Gonzalez Morales and Mar Avila Vegas},
journal= {arXiv preprint arXiv:2506.00133},
year = {2025}
}
Comments
8 Pages, 10 Figures, 2 Tables