Energy-Efficient Routing Protocols in Mobile Ad-hoc Networks (MANETs) Using Machine Learning Optimization
Keywords:
MANET, Energy Efficiency, Reinforcement Learning, Routing Protocols, Optimization, Disaster Response.Abstract
Mobile Ad-hoc Networks (MANETs) play a crucial role in scenarios where fixed infrastructure is unavailable, such as disaster response and military operations. However, their resource-constrained nature makes energy efficiency a critical concern. Traditional routing protocols, while effective in stable networks, often fail to adapt dynamically to changing topology and traffic conditions. This research proposes the integration of reinforcement learning (RL) algorithms into MANET routing protocols to optimize energy consumption while maintaining network performance. By dynamically adjusting routing paths based on feedback from network states, RL-based approaches enhance both packet delivery ratio and latency performance. Simulation experiments demonstrate that the proposed RL-optimized protocol reduces energy consumption by 18–25% compared to traditional AODV and DSR protocols, with improved resilience in highly mobile and resource-constrained environments. The findings highlight the potential of AI-driven routing to support energy-efficient and reliable MANETs for mission-critical applications.
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