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

arXiv: https://arxiv.org/abs/2506.00133 · HTML: https://mlsanity.com/paper/2506.00133/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 27 references that are in the map.
- First posted on arXiv 30 May 2025.

## Builds on (most cited first, 1 of 1)

- [RPLUW/M: Enabling RPL on the Internet of Underwater Things](https://mlsanity.com/paper/2408.08607/) (2024)

## Abstract (arXiv)

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.