A Reinforcement Learning-Based Telematic Routing Protocol for the Internet of Underwater Things
This addresses routing inefficiencies for underwater networks, which face challenges like low bandwidth and limited energy, but it is an incremental improvement over existing protocols.
The paper tackles the problem of inefficient routing in the Internet of Underwater Things (IoUT) by introducing RL-RPL-UA, a reinforcement learning-based protocol that improves packet delivery by up to 9.2%, reduces energy use per packet by 14.8%, and extends network lifetime by 80 seconds compared to traditional methods.
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 on local information such as packet delivery ratio, buffer level, link quality, and remaining energy. RL-RPL-UA keeps full compatibility with standard RPL messages and adds a dynamic objective function to support real-time decision-making. Simulations using Aqua-Sim show that RL-RPL-UA increases packet delivery by up to 9.2%, reduces energy use per packet by 14.8%, and extends network lifetime by 80 seconds compared to traditional methods. These results suggest that RL-RPL-UA is a promising and energy-efficient routing solution for underwater networks.