Title: Node WSN localisation based on adaptive crossover-mutation differential evolution

Authors: Trong-The Nguyen; Thi-Kien Dao; Truong-Giang Ngo; Trinh-Dong Nguyen

Addresses: Fujian Provincial Key Laboratory of Big Data Mining and Applications, School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China; University of Information Technology, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City 700000, Vietnam ' Fujian Provincial Key Laboratory of Big Data Mining and Applications, School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China; University of Information Technology, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City 700000, Vietnam ' Faculty of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam ' University of Information Technology, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City 700000, Vietnam

Abstract: Accurate node positioning in wireless sensor networks (WSNs) is essential for optimising monitoring and tracking applications. This becomes increasingly challenging, especially in large-scale WSNs where precise location data is needed for unknown nodes. Traditional methods often struggle with computational complexity, particularly in enlarged network setups. To address this issue, we introduce an innovative adaptive optimisation approach called crossover mutation differential evolution (ACMDE) tailored for node localisation in WSNs. ACMDE rapidly localises unknown nodes by leveraging location data and employing adaptive optimisation strategies, including enhanced crossover, mutation, and reinitialisation techniques. The objective function is modelled for the WSNs node localisation to minimise localisation errors between actual and detected node positions that are obtained optimisation targets through ACMDE's superior capabilities. The ACMDE's effectiveness is evaluated in the test suits and node localisation through comprehensive comparisons with existing strategies using various metrics. Experimental results unequivocally demonstrate that ACMDE outperforms competing algorithms in node localisation within WSNs.

Keywords: node localisation; ACMDE algorithm; wireless sensor networks; differential evolution; DE; optimisation.

DOI: 10.1504/IJSNET.2024.136339

International Journal of Sensor Networks, 2024 Vol.44 No.1, pp.1 - 22

Received: 30 May 2023
Accepted: 30 Sep 2023
Published online: 30 Jan 2024 *

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