Title: Machine learning-based prediction of allocative localisation error in wireless sensor networks

Authors: Guo Li; Hongyu Sheng

Addresses: University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave. West Hi-Tech. Zone, Chengdu, 611731, China ' Beijing Union University, No. 97, Beisihuan East Road, Chaoyang District, Bei Jing Shi, 100101, China

Abstract: There may be a better way to deal with and write about any mistakes that happen in wireless sensor networks (WSNs) if they happen in a style called allocative localisation errors, or ALEs. Three important schemes are adaptive neuro-fuzzy inference systems (ANFIS), Stacking Regression, and AdaBoostRegressors. Multiple regression methods are used by the stacking regression model to guess wrong traits in more than one variable. As part of the study, Mayfly optimisation algorithm (MOA) was used to make some well-known ways of making predictions more correct. Someone came up with a brand-new ensemble model called STASA + MFO for this work. It takes the Stacking R model and adds ADA and MOA to it. With an R² value of 0.9913, this method did better than the others that were tried. The ADA + ANFIS model had an R² value of 0.9810, and the ANMF model (ADA with MO) had an R² value of 0.9824.

Keywords: WSNs; wireless sensor networks; ALEs; allocative localisation errors; machine learning algorithm; metaheuristic algorithms.

DOI: 10.1504/IJMNDI.2025.151004

International Journal of Mobile Network Design and Innovation, 2025 Vol.11 No.4, pp.190 - 204

Received: 17 Feb 2025
Accepted: 26 Mar 2025
Published online: 08 Jan 2026 *

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