Tootfinder

A Reinforcement Learning-Based Telematic Routing Protocol for the Internet of Underwater Things
Mohammadhossein Homaei, Mehran Tarif, Agustin Di Bartolo, Oscar Mogollon Gutierrez, Mar Avila
https://arxiv.org/abs/2506.00133

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