Traffic-Aware Clustering Scheme for MANET

Using Modified Elephant Herding Optimization

Algorithm

Sreekanth Ramakrishnan, Latha Sevalaiappan, and Suganthe Ravichandran

Department of Computer Science and Engineering, Kongu Engineering College, India

Abstract: Clustering is the prevalent routing method in the large-scale Mobile Ad Hoc Network (MANET). The Cluster-Heads (CHs) play an important role in routing as it is transient through all communications of its associated nodes. To ensure fairness in the use of energy in all clusters, each CH has to deal with same amount of traffic. The previous clustering methods focused mainly on the distribution of equal member nodes in each cluster. They failed to consider every cluster's traffic generated. This paper introduces a novel technique for MANET clustering with Modified Elephant Herding Optimization based on the traffic generated within each cluster. This Traffic-Aware Clustering with Modified Elephants Herding Optimization (TAC-MEHO) produces optimized clusters for stable communication and is experimentally tested with well-known clustering techniques. Assessment metrics such as number of Cluster-Heads (CHs), lifetime of the network, and re-clustering rates are measured using various parameter values such as network size, network traffic and transmission distance. The results show that proposed TAC-MEHO improves the re-clustering rate by 91% and 58% when compared with Weighted Clustering Algorithm (WCA) and WCA-GA respectively. Further, it improves the network lifetime by 89% and 88 % over WCA and WCA-GA respectively.

Keywords: Clustering, MANET, traffic-aware, elephant heard optimization, cluster-head, large-scale network.

Received June 10, 2020; accepted February 25, 2021