Reinforcement Learning for Intelligent Wireless Networks
| Author | Mohammed, Najeeba | |
|---|---|---|
| metadata.dc.contributor.department | University of Bahrain, College of Engineering, Department of Electrical and Electronics Engineering | |
| metadata.dc.date.accessioned | 2026-09-20T15:45:53Z | |
| Publication date | 2024 | |
| Degree level | Master | |
| Abstract | Abstract : The growing presence of Internet of Things (IoT) devices requires efficient and reliable data transmission protocols. However, traditional routing protocols struggle in dynamic and resource constrained IoT networks due to factors like decentralization, limited energy, and frequent topology changes. This thesis proposes a novel routing protocol, modified Energy-Efficient Routing based on Reinforcement Learning (mEER-RL), specifically designed to address these challenges and enhance performance in both fixed and mobile IoT deployments. mEER-RL is built over the foundation of EER-RL, a cluster-based, RL-driven protocol focused on energy efficiency. mEER-RL contains a significant modification to address node mobility, a crucial factor often neglected in existing cluster-RL based protocols for IoT. By integrating mobility considerations into the RL agent's reward function, mEER-RL dynamically adapts its routing decisions to network conditions, improving Packet Delivery Ratio (PDR) and overall network reliability. This thesis presents a survey of most types of cluster-based routing protocols built for IoT networks. RL techniques are also reviewed to be applied for the routing protocol. The thesis then comprehensively evaluates mEER-RL's performance through simulations, comparing it against the established base protocol LEACH and EER-RL. The evaluation focuses on crucial metrics like network lifetime, energy consumption, packet delivery ratio, and the impact of node mobility. The results demonstrate that mEER-RL effectively balances energy efficiency, reliability, and adaptability, making it a promising solution for routing in complex and dynamic IoT networks. The key contributions of this work include enhanced reliability, increased PDR, enhanced adaptability, and improved energy efficiency for both fixed and mobile IoT environments by optimizing routing paths and minimizing data transmission through improved multi-hop communication techniques. For an advanced performance, compared to LEACH and EER-RL, mEERRL exhibits superior performance in terms of network lifetime, energy consumption, and PDR. | |
| metadata.dc.identifier.other | https://digitalrepository.uob.edu.bh/id/fe758562-a060-44ee-b59c-5ab642619586 | |
| metadata.dc.identifier.other | Drupal node 2710 | |
| metadata.dc.identifier.other | https://digitalrepository.uob.edu.bh/en/dar/reinforcement-learning-intelligent-wireless-networks | |
| metadata.dc.identifier.uri | http://165.227.56.122/handle/123456789/6390 | |
| Language | en | |
| Link | https://digitalrepository.uob.edu.bh/en/_flysystem/fedora/2025-02/reinforcement-learning-for-intelligent-wireless-networks.pdf | |
| Title | Reinforcement Learning for Intelligent Wireless Networks | |
| Type | Master's Thesis | |
| College | College of Engineering | |
| Department | Electrical and Electronics Engineering |
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