Reinforcement Machine Learning-based Improved Protocol for Energy Efficiency on Mobile Ad-Hoc Networks

Authors

  • Biradar Ashwini Vishwanathrao Research Scholar, Dr. A. P. J. Abdul Kalam University, Indore, Department of Computer Science and Engineering
  • Pradnya Ashish Vikhar Research Supervisor, Dr. A. P. J. Abdul Kalam University, Indore, Department of Computer Science and Engineering

Keywords:

Mobile ad-hoc network, Reinforcement learning, K-Means Clustering, Machine Learning, Clustering, Ad-hoc on demand distance vector etc

Abstract

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.

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Published

13.12.2023

How to Cite

Vishwanathrao , B. A. ., & Vikhar , P. A. . (2023). Reinforcement Machine Learning-based Improved Protocol for Energy Efficiency on Mobile Ad-Hoc Networks . International Journal of Intelligent Systems and Applications in Engineering, 12(8s), 654–670. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4259

Issue

Vol. 12 No. 8s (2024)

Section

Research Article

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