Open Access

ARTICLE

Enhanced Metaheuristics-Based Clustering Scheme for Wireless Multimedia Sensor Networks

R. Uma Mageswari¹, Sara A. Althubiti², Fayadh Alenezi³, E. Laxmi Lydia⁴, Gyanendra Prasad Joshi⁵, Woong Cho^6,*

1 Department of Computer Science and Engineering, Vardhaman College of Engineering (Autonomous), Hyderabad, Telangana, 501218, India
2 Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, 11952, Saudi Arabia
3 Department of Electrical Engineering, College of Engineering, Jouf University, Saudi Arabia
4 Department of Computer Science and Engineering, Vignan’s Institute of Information Technology, Visakhapatnam, 530049, India
5 Department of Computer Science and Engineering, Sejong University, Seoul, 05006, Korea
6 Department of Software Convergence, Daegu Catholic University, Gyeongsan, 38430, Korea

* Corresponding Author: Woong Cho. Email:

Computers, Materials & Continua 2022, 73(2), 4179-4192. https://doi.org/10.32604/cmc.2022.030806

Received 02 April 2022; Accepted 19 May 2022; Issue published 16 June 2022

Abstract

Traditional Wireless Sensor Networks (WSNs) comprise of cost-effective sensors that can send physical parameters of the target environment to an intended user. With the evolution of technology, multimedia sensor nodes have become the hot research topic since it can continue gathering multimedia content and scalar from the target domain. The existence of multimedia sensors, integrated with effective signal processing and multimedia source coding approaches, has led to the increased application of Wireless Multimedia Sensor Network (WMSN). This sort of network has the potential to capture, transmit, and receive multimedia content. Since energy is a major source in WMSN, novel clustering approaches are essential to deal with adaptive topologies of WMSN and prolonged network lifetime. With this motivation, the current study develops an Enhanced Spider Monkey Optimization-based Energy-Aware Clustering Scheme (ESMO-EACS) for WMSN. The proposed ESMO-EACS model derives ESMO algorithm by incorporating the concepts of SMO algorithm and quantum computing. The proposed ESMO-EACS model involves the design of fitness functions using distinct input parameters for effective construction of clusters. A comprehensive experimental analysis was conducted to validate the effectiveness of the proposed ESMO-EACS technique in terms of different performance measures. The simulation outcome established the superiority of the proposed ESMO-EACS technique to other methods under various measures.

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Keywords

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