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