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

TL;DR

This paper introduces RL-RPL-UA, a reinforcement learning-based routing protocol for underwater networks that improves packet delivery, reduces energy consumption, and extends network lifetime.

Contribution

It presents a novel RL-based routing protocol tailored for underwater environments, enhancing performance over traditional methods.

Findings

Increases packet delivery by up to 9.2%.

Reduces energy consumption per packet by 14.8%.

Extends network lifetime by 80 seconds.

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.