Reinforcement Machine Learning-based Improved Protocol for Energy Efficiency on Mobile Ad-Hoc Networks - Vishwanathrao , Biradar Ashwini; Vikhar , Pradnya Ashish (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Mobile Ad-Hoc Networks (MANETs) are crucial in environments lacking permanent infrastructure, with energy efficiency being a primary concern due to the reliance on battery-powered devices. This study presents an innovative solution: the Reinforcement Machine Learning-enhanced Energy Efficient AODV (Ad-Hoc On-Demand Distance Vector) Protocol (RML-EEAODV). This novel approach integrates the adaptive capabilities of reinforcement machine learning with the AODV routing protocol to forge a smart, energy-conserving routing mechanism. The core challenge in MANETs is minimizing energy use and operational overhead while ensuring optimal packet delivery. RML-EEAODV addresses this by enhancing the AODV protocol’s routing decisions. It employs machine learning to enable nodes to maintain and utilize a dynamic database of state information for intermediate nodes along potential routes. This database informs decision-making for forwarding packets, ensuring routes with guaranteed Quality of Service (QoS). The RML-EEAODV protocol significantly improves energy efficiency and reduces network overhead, while maintaining a satisfactory packet delivery ratio.