ZigBee Based Indoor Localization via RSSI-Based Fingerprinting with Adaptive Path Loss Estimation
Bolatbek A. Orynbassar S. Saymbetov A. Nurgaliyev M. Zholamanov B. Dosymbetova G. Kuttybay N. Kapparova A.
2025Institute of Electrical and Electronics Engineers Inc.
International Conference on Computer Science and Engineering, UBMK
2025Issue 20251449 - 1454 pp.
Indoor localization remains an important task in wireless sensor networks (WSNs), especially in complex multi-room environments. This study proposes a three-dimensional (3D) localization method based on received signal strength indicator (RSSI) fingerprinting and adaptive path loss modeling. The method provides accurate RSSI estimation at unmeasured locations, thereby reducing the need for exhaustive manual data collection. Experimental results on ZigBee-based WSNs show that machine learning models trained on synthetically generated radio maps achieve accuracy levels comparable to those using traditional fingerprints. The multilayer perceptron (MLP) achieved the best result on real data with a root mean square error (RMSE) of 0.83 m, while the Extreme Gradient Boosting (XGBoost) model achieved the best performance on synthetic fingerprints with a RMSE of 1.03 m. These results demonstrate the effectiveness of the proposed method and highlight the generalization capabilities of state-of-the-art machine learning models, especially in scenarios with limited empirical data.
Indoor localization , machine learning , path loss model , RSSI , wireless sensor networks , ZigBee
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Al-Farabi Kazakh National University, Faculty of Physics and Technology, 71 Al-Farabi, Almaty, 050040, Kazakhstan
Al-Farabi Kazakh National University
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026