A Reinforcement Learning-Based Telematic Routing Protocol for the Internet of Underwater Things
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911278561230848 |
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| author | Homaei, Mohammadhossein Tarif, Mehran Di Bartolo, Agustin Morales, Victor Gonzalez Vegas, Mar Avila |
| author_facet | Homaei, Mohammadhossein Tarif, Mehran Di Bartolo, Agustin Morales, Victor Gonzalez Vegas, Mar Avila |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00133 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Reinforcement Learning-Based Telematic Routing Protocol for the Internet of Underwater Things Homaei, Mohammadhossein Tarif, Mehran Di Bartolo, Agustin Morales, Victor Gonzalez Vegas, Mar Avila Networking and Internet Architecture Artificial Intelligence Machine Learning 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. |
| title | A Reinforcement Learning-Based Telematic Routing Protocol for the Internet of Underwater Things |
| topic | Networking and Internet Architecture Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2506.00133 |