Machine learning-based efficient clustering and improve quality of service in manet

Abstract

A Mobile Ad hoc Network (MANET) is a self-governing system in that numerous nodes are connected with each other, applying multi-hop wireless links. MANET has overturned society owing to their infrastructure-less decentralized modes of transaction. As a result, researchers have concentrated on discovering better directions to operate the probability of MANETs completely. The current initiation of machine learning methods has prepared it to concern artificial intelligence to increase the best approaches for this function. In this work, Machine Learning-based Efficient Clustering and Improve Quality of Service in MANET (MLEC) is proposed. In this work, we improve the clustering technique that applied Quality of Service (QoS) parameters to choose Cluster Heads (CHs). The cluster formation and CH selection is accessible via a multi-objective fitness function applying Particle Swarm Optimization (PSO) algorithm. This approach gives three inputs: node mobility, energy utilization rate, and bandwidth. The algorithm establishes a higher rate of achievement in forecasting output values also could correctly recognize better CH that should offer the well-organized routing. In addition, it reduces the intra-cluster distance among nodes and their relevant CHs. The proposed work is experimented with broadly in the network simulator and compared with the other baseline protocols.