Open Access

ARTICLE

An Optimal Node Localization in WSN Based on Siege Whale Optimization Algorithm

Thi-Kien Dao¹, Trong-The Nguyen^1,2,*

1 Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, 350118, China
2 Multimedia Communication Lab, University of Information Technology, Ho Chi Minh City, Vietnam

* Corresponding Author: Trong-The Nguyen. Email:

Computer Modeling in Engineering & Sciences 2024, 138(3), 2201-2237. https://doi.org/10.32604/cmes.2023.029880

Received 13 March 2023; Accepted 27 July 2023; Issue published 15 December 2023

Abstract

Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging and fundamental operations in various monitoring or tracking applications because the network deploys a large area and allocates the acquired location information to unknown devices. The metaheuristic approach is one of the most advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditional methods that often suffer from computational time problems and small network deployment scale. This study proposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on the siege mechanism (SWOA) for node localization in WSN. The objective function is modeled while communicating on localized nodes, considering variables like delay, path loss, energy, and received signal strength. The localization approach also assigns the discovered location data to unidentified devices with the modeled objective function by applying the SWOA algorithm. The experimental analysis is carried out to demonstrate the efficiency of the designed localization scheme in terms of various metrics, e.g., localization errors rate, converges rate, and executed time. Compared experimental-result shows that the SWOA offers the applicability of the developed model for WSN to perform the localization scheme with excellent quality. Significantly, the error and convergence values achieved by the SWOA are less location error, faster in convergence and executed time than the others compared to at least a reduced 1.5% to 4.7% error rate, and quicker by at least 4% and 2% in convergence and executed time, respectively for the experimental scenarios.

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Keywords

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