Reseach Article
Reinforcement Learning-based Optimization of AODV Routing Protocol (RL_AODV) for Mobile Adhoc Networks
| Journal of Advanced Artificial Intelligence |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 1 - Number 5 |
| Year of Publication: 2025 |
| Authors: Anu Mangal, Anjali Potnis, M.A. Rizvi |
10.5120/jaai202421
|
Anu Mangal, Anjali Potnis, M.A. Rizvi . Reinforcement Learning-based Optimization of AODV Routing Protocol (RL_AODV) for Mobile Adhoc Networks. Journal of Advanced Artificial Intelligence. 1, 5 ( Feb 2025), 1-14. DOI=10.5120/jaai202421
Abstract
In recent years, mobile ad hoc networks (MANETs) have garnered attention for their adaptability in diverse scenarios, particularly in emergency situations. Despite their potential, achieving efficient and reliable communication in MANETs remains a persistent challenge due to their dynamic and decentralized nature. This paper introduces a reinforcement learning- based optimization approach for the widely-used Ad-hoc On-demand Distance Vector (AODV) routing protocol within MANETs. Leveraging ns2 simulations, we define a comprehensive state space, action space, and reward function for the reinforcement learning agent. This study not only showcases the effectiveness of reinforcement learning in optimizing MANETs routing but also establishes a foundation for future research in this domain. The paper addresses critical challenges in MANETs and outlines a promising avenue for further exploration.
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