V3I3P8

Hybrid Adaptive Clustering for Dynamic MANETs with Minimum Energy Consumption

Mrs. M. Sathiya^1*, Dr. D. Rajakumari²

Abstract

A mobile ad hoc network (MANET) is a wireless network with mobile nodes that roam at random and function without central control. The network structure of MANETs causes the nodes to use more energy, which reduces the network lifetime and has an impact on packet transmission.By dividing the network into virtual groups called clusters, the clustering process in MANETs (Mobile Ad-hoc Networks) can be accomplished. Data transmission inside each cluster is managed by the Cluster Head (CH).The Gravity Search Algorithm (GSA) is used to optimize parameters including node degree, neighborhood distance, battery power, and mobility in the first stage, which is based on the Weighted Clustering Algorithm (WCA) for choosing ideal CHs. To enhance network performance in MANETs, a two-stage Hybrid Adaptive Clustering Algorithm for Dynamics MANETs (HACADM) is suggested in this paper. The Gravity Search Algorithm (GSA) is used in the initial stage to optimize parameters including node degree, neighborhood distance, battery power, and mobility based on the Weighted Clustering Algorithm (WCA) for choosing the best CHs. Using one of the unsupervised learning techniques, the Enhanced Density Based Spatial Clustering of Applications with Noise (Enhanced-DBSCAN) algorithm, the clustering is carried out in the second phase by determining the member nodes and their functions of the chosen CHs. Additionally, By choosing gateway nodes for inter-cluster communication, this strategy lessens the strain on the CHs and improves cluster stability. This study improves the resilience of clustering processes in MANETs under dynamic network conditions, which is a major step towards increasing network lifetime and maximizing energy efficiency. By guaranteeing a more balanced load distribution than current clustering, the suggested HACADM approach has the potential to improve MANET performance.

The HACADM approach was contrasted with the EE-WCA, E-MAVMMF, TSDR, and MORS-ASO approaches based on important performance indicators such packet delivery, end-to-end delay, and remaining energy both throughput and ratio.

Keywords:

Adaptive clustering, energy efficiency, gravitational search algorithm, machine learning,MANET