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

Abstract

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.