Open Access

ARTICLE

Enhanced Archimedes Optimization Algorithm for Clustered Wireless Sensor Networks

by E. Laxmi Lydia¹, T. M. Nithya², K. Vijayalakshmi³, Jeya Prakash Kadambaajan⁴, Gyanendra Prasad Joshi⁵, Sung Won Kim^6,*

1 Computer Science and Engineering, Vignan's Institute of Information Technology (Autonomous), Andhra Pradesh, 530049, India
2 Department of Computer Science and Engineering, K. Ramakrishnan College of Engineering, Tiruchirappalli, 621112, India
3 Department of Electronics & Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 600025, India
4 Department of Electronics & Communication Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, 626128, India
5 Department of Computer Science and Engineering, Sejong University, Seoul, 05006, Korea
6 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, Gyeongbuk-do, 38541, Korea

* Corresponding Author: Sung Won Kim. Email:

Computers, Materials & Continua 2022, 73(1), 477-492. https://doi.org/10.32604/cmc.2022.025939

Received 09 December 2021; Accepted 13 January 2022; Issue published 18 May 2022

Abstract

Wireless sensor networks (WSN) encompass a set of inexpensive and battery powered sensor nodes, commonly employed for data gathering and tracking applications. Optimal energy utilization of the nodes in WSN is essential to capture data effectively and transmit them to destination. The latest developments of energy efficient clustering techniques can be widely applied to accomplish energy efficiency in the network. In this aspect, this paper presents an enhanced Archimedes optimization based cluster head selection (EAOA-CHS) approach for WSN. The goal of the EAOA-CHS method is to optimally choose the CHs from the available nodes in WSN and then organize the nodes into a set of clusters. Besides, the EAOA is derived by the incorporation of the chaotic map and pseudo-random performance. Moreover, the EAOA-CHS technique determines a fitness function involving total energy consumption and lifetime of WSN. The design of EAOA for CH election in the WSN depicts the novelty of work. In order to exhibit the enhanced efficiency of EAOA-CHS technique, a set of simulations are applied on 3 distinct conditions dependent upon the place of base station (BS). The simulation results pointed out the better outcomes of the EAOA-CHS technique over the recent methods under all scenarios.

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Keywords

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