Optimization of energy consumption and routing in MANET using Artificial Neural Network

Abstract

Mobile Ad Hoc Networks (MANETs) face challenges in optimizing energy consumption and routing due to their dynamic and decentralized nature. This paper presents a novel approach utilizing artificial neural networks (ANNs) to address these limitations. The study explores the potential of ANNs in making intelligent energy consumption decisions, considering factors such as node mobility, transmission power, and network traffic. Additionally, ANNs are employed for dynamic routing decisions based on node energy levels, link quality, and network congestion. To train the ANNs, relevant data is used to capture the complex relationships between the network parameters. The experimental evaluation demonstrates the superiority of ANNs compared to conventional methods, showcasing improved network efficiency, reduced energy consumption, and enhanced overall performance. By leveraging ANNs, MANETs can achieve optimized energy utilization, leading to prolonged network lifetime and reduced instances of service disruptions caused by node power exhaustion. The findings of this research contribute to the advancement of power-aware protocols for wireless networks by addressing the challenges specific to MANETs and improving their functionality in practical scenarios.

URN:NBN:sciencein.jist.2024.v12.718