Crowd-Resilient Wi-Fi Indoor Localization Framework Using Ensemble Regression Models
DOI:
https://doi.org/10.15837/ijccc.2026.2.7411Keywords:
indoor localization, wireless localization, human crowd, localization error correction, ensemble regression, Wi-Fi, smartphone, accelerometerAbstract
This paper presents a machine learning (ML)-based framework to predict performance degra- dation in Wi-Fi indoor localization systems (ILSs) under varying moving human crowd densities. While indoor localization can be performed in both mobile and fixed wireless settings, the majority of prior research emphasizes mobile devices in motion. In contrast, this study adopts a fixed-wireless configuration, where a smartphone node was held stationary while moving human density varied around it. This design particularly isolates the effect of human crowd-induced interference on re- ceived signal strength indicator (RSSI) fluctuations, enabling a controlled evaluation of ML-based error compensation, which is a perspective rarely explored in the literature. Accelerometer-derived motion features were integrated with RSSI measurements, and baseline localization errors were calculated using the conventional Weighted Least Squares (WLS) indoor localization algorithm. Three main ML regression models namely Random Forest, CatBoost, and XGBoost were trained and evaluated. Among them, CatBoost demonstrated the best performance, achieving a root mean squared error (RMSE) of 0.331 m compared to the WLS baseline error of 1.405 m, corresponding to a 76.47% improvement in localization accuracy. The evaluation was intentionally limited to a single indoor layout with a stationary device to isolate crowd-induced RSSI distortions, and multi- position validation and mobile-user scenarios are reserved for future work. The findings confirm that smartphone sensor-fused ML models can anticipate human crowd-induced localization errors and enhance the robustness of multilateration-based ILSs.
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