Abstract

his research work introduces an Ensemble Adaptive Reinforcement Learning (EARL) approach for efficient load balancing in Mobile Ad Hoc Networks (MANETs). Traditional methods often fail to adapt to the dynamic nature of MANETs, leading to congestion and inefficiency. EARL leverages multiple reinforcement learning agents, trained with Q-learning and Deep Q-Networks (DQN), to optimize routing decisions based on real-time network conditions. The ensemble mechanism combines the strengths of individual agents, enhancing adaptability and performance. Simulation results demonstrate that EARL significantly outperforms traditional methods like AODV and DSR, achieving higher packet delivery ratios, lower end-to-end delays, increased throughput, better energy efficiency, and reduced packet loss, thereby proving its effectiveness in dynamic network environments.

Authors

G. Rajiv Suresh Kumar¹, G. Arul Geetha²
Hindusthan College of Engineering and Technology, India¹, Bishop Appasamy College of Arts and Science, India²

Keywords

Ad Hoc Networks, Load Balancing, Adaptive, Learning, Efficient