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Machine Learning-based Regression Analysis and Feature Ranking for Localization Error Prediction in Wireless Sensor Networks

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Publicado en:Informatica vol. 49, no. 20 (May 2025), p. 27-41
Autor principal: Wang, Peng
Otros Autores: Han, Qiuying, Zhang, Shaohui, Wu, Zhaodi
Publicado:
Slovenian Society Informatika / Slovensko drustvo Informatika
Materias:
Mean square errors
Accuracy
Datasets
Environmental monitoring
Optimization
Wireless sensor networks
Multiple regression models
Measurement techniques
Tracking
Ranking
Machine learning
Localization
Energy consumption
Decision trees
Efficiency
Position sensing
Artificial intelligence
Support vector machines
Root-mean-square errors
Decision making
Sensors
Regression analysis
Algorithms
Deep learning
Acceso en línea:Citation/Abstract
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Resumen:Wireless Sensor Networks (WSNs) localization is crucial for identifying the position of sensor nodes, as many applications, including environmental monitoring, target tracking, and disaster management, require accurate location information. The objective of this research is to conduct extensive data analytics using visualization techniques to explore key factors influencing localization error and to develop machine learning models for forecasting Average Localization Error (ALE) in WSNs. A dataset containing 107 records, sourced from Kaggle's online repository, was analyzed using eXtreme Gradient Boosting (XGB) for feature ranking to determine the most influential factors affecting ALE. Multiple regression models, including Support Vector Regression (SVR), Decision Tree (DT), K-Nearest Neighbors (KNN), and AdaBoost Regressor, were applied to predict ALE. The models were evaluated using R-squared (R2), Root Mean Square Error (RMSE), and computational efficiency. The results indicate that SVR achieved the highest accuracy with R2 = 0.99 and the lowest RMSE of 0.01, significantly outperforming the other models (KNN: R2 = 0.55, RMSE = 0.14; DT: R2 = 0.41, RMSE = 0.16; AdaBoost: R2 = 0.72, RMSE = 0.16). This study demonstrates that SVR is a highly effective model for ALE prediction, reinforcing the importance of feature ranking and selection in improving localization accuracy. The findings contribute to advancing machine learning-driven localization error prediction in WSNs and provide a foundation for further exploration of hybrid and deep learning-based models.
ISSN:0350-5596
1854-3871
DOI:10.31449/infv49120.8081
Fuente:Advanced Technologies & Aerospace Database