A Novel RL-Driven Zone-Based Leader-Aware Energy-Efficient Routing Protocol for Dynamic MANET Environments

Abstract

Mobile Ad Hoc Networks (MANETs) face major routing problems because their network structure keeps changing, their energy levels are minimal, their nodes move between locations, and they lack any central network control systems. The traditional routing protocols Ad hoc On-Demand Distance Vector (AODV) and Ad hoc On-Demand Multipath Distance Vector (AOMDV) face major problems in dynamic environments because they use too much energy, their routes fail too often, and their network control demands become too high. The paper introduces RML-ZEREM to solve existing limitations, which functions as a Reinforcement Learning (RL) based Zone-Based Leader-Aware Energy-Efficient Routing Protocol for MANETs. The proposed approach partitions the network into multiple zones and employs energy-aware leader node selection to manage routing operations efficiently. The system uses Q-learning to create an adaptive routing system that chooses the best routing paths according to current network conditions, including residual energy levels, node movement, traffic intensity, and link reliability. The proposed protocol performance assessment uses the NS-2.35 simulator to test different simulation conditions, which include various simulation durations, node mobility rates, network capacity, and simulation area size. The simulation results show that RML-ZEREM achieves better performance than traditional AODV and AOMDV protocols through its ability to increase throughput while decreasing energy usage, improving packet delivery ratio, and reducing routing overhead. The zone-based hierarchical structure enhances network stability and scalability for MANET systems that operate in dynamic environments. The RML-ZEREM protocol functions as an intelligent routing system that adjusts its operations to achieve energy efficiency through its framework, which serves next-generation MANET applications.