International Journal of Computer Networks and Applications (IJCNA)

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International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Cluster Head Selection Algorithm for MANETs Using Hybrid Particle Swarm Optimization-Genetic Algorithm

Author NameAuthor Details

Fouziah Hamza, S. Maria Celestin Vigila

Fouziah Hamza[1]

S. Maria Celestin Vigila[2]

[1]Department of Computer Applications, Noorul Islam Centre for Higher Education, Kumarakovil, Tamil Nadu, India

[2]Department of Information Technology, Noorul Islam Centre for Higher Education, Kumarakovil, Tamil Nadu, India

Cite this Article

DOI: 10.22247/ijcna/2021/208892

Pages : 119 - 129

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Abstract

The Mobile Ad-hoc Network (MANET) is a decentralized system that consists of mobile nodes. Wireless connections are used to connect these nodes. The primary issues of concern of MANETs are mobility and limited battery lifetime. Advanced techniques for improving MANET energy efficiency and extending network lifespan are critical. Clustering is one of the tried-and-true methods for increasing network lifetime by lowering and balancing energy consumption. Choosing a suitable cluster head from the cluster improves the network’s energy efficiency even further. Because of the additional workloads, the cluster heads (CHs) utilize more energy than non-cluster heads. A novel algorithm for CH selection with a Hybrid Particle Swarm Optimization-Genetic Algorithm (PSO-GA) is proposed to improve the MANET network’s energy efficiency and lifetime. The proposed method is implemented using the NS-2 platform for the analysis. The proposed model outperforms the existing OSCA, EP-MBO, GBTC, SM-WCA, CM-BCA, and FCO methods in terms of network performance. The model’s performance has achieved a low Bit Error Rate (BER) of 7% for 100 nodes with 99.38% Packet Delivery Ratio (PDR) with minimized delay in the range 2.01sec with the energy efficiency of 99.03%. The validation indicates that the Hybrid PSO-GA approach is more efficient than the other methods.

Index Terms

Mobile Ad-hoc Network

Clustering

Soft k-Means

Cluster Head Selection

PSO-GA Optimization Algorithm

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