Open Access

ARTICLE

Genetic-Chicken Swarm Algorithm for Minimizing Energy in Wireless Sensor Network

A. Jameer Basha¹, S. Aswini¹, S. Aarthini¹, Yunyoung Nam^2,*, Mohamed Abouhawwash^3,4

1 Department of Computer Science and Engineering, Hindusthan Institute of Technology, Coimbatore, 641032, India
2 Department of Computer Science and Engineering, Soonchunhyang University, Asan, 31538, Korea
3 Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
4 Department of Computational Mathematics Science and Engineering (CMSE), Michigan State University East Lansing, MI, 48824, USA.

* Corresponding Author: Yunyoung Nam. Email:

Computer Systems Science and Engineering 2023, 44(2), 1451-1466. https://doi.org/10.32604/csse.2023.025503

Received 26 November 2021; Accepted 09 February 2022; Issue published 15 June 2022

Abstract

Wireless Sensor Network (WSN) technology is the real-time application that is growing rapidly as the result of smart environments. Battery power is one of the most significant resources in WSN. For enhancing a power factor, the clustering techniques are used. During the forward of data in WSN, more power is consumed. In the existing system, it works with Load Balanced Clustering Method (LBCM) and provides the lifespan of the network with scalability and reliability. In the existing system, it does not deal with end-to-end delay and delivery of packets. For overcoming these issues in WSN, the proposed Genetic Algorithm based on Chicken Swarm Optimization (GA-CSO) with Load Balanced Clustering Method (LBCM) is used. Genetic Algorithm generates chromosomes in an arbitrary method then the chromosomes values are calculated using Fitness Function. Chicken Swarm Optimization (CSO) helps to solve the complex optimization problems. Also, it consists of chickens, hens, and rooster. It divides the chicken into clusters. Load Balanced Clustering Method (LBCM) maintains the energy during communication among the sensor nodes and also it balances the load in the gateways. The proposed GA-CSO with LBCM improves the lifespan of the network. Moreover, it minimizes the energy consumption and also balances the load over the network. The proposed method outperforms by using the following metrics such as energy efficiency, ratio of packet delivery, throughput of the network, lifetime of the sensor nodes. Therefore, the evaluation result shows the energy efficiency that has achieved 83.56% and the delivery ratio of the packet has reached 99.12%. Also, it has attained linear standard deviation and reduced the end-to-end delay as 97.32 ms.

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