Node WSN localisation based on adaptive crossover-mutation differential evolution
by Trong-The Nguyen; Thi-Kien Dao; Truong-Giang Ngo; Trinh-Dong Nguyen
International Journal of Sensor Networks (IJSNET), Vol. 44, No. 1, 2024

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.

Online publication date: Tue, 30-Jan-2024

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