Robust Indoor Localization for IoT using Machine Learning-Based Outlier Detection
| International Journal of Electronics and Communication Engineering |
| © 2026 by SSRG - IJECE Journal |
| Volume 13 Issue 3 |
| Year of Publication : 2026 |
| Authors : Mayank Gandhi, Nirali Shukla, Hiren Shukla |
10.14445/23488549/IJECE-V13I3P116
How to Cite?
Mayank Gandhi, Nirali Shukla, Hiren Shukla, "Robust Indoor Localization for IoT using Machine Learning-Based Outlier Detection," SSRG International Journal of Electronics and Communication Engineering, vol. 13, no. 3, pp. 193-202, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I3P116
Abstract:
Many of the Internet of Things (IoT) applications (e.g., smart buildings, health care, industrial monitoring) rely on indoor localization. Indoor positioning sensor-derived data is usually full of anomalies that negatively affect the localization accuracy. The current paper is a framework that integrates Machine Learning (ML) and Deep Learning (DL) localization models with an outlier-detecting preprocessing step based on the Isolation Forests. Experiments on one of the UCI Wi-Fi RSS datasets reveal that an outlier detection application can improve the accuracy of classifications by up to 2.7-10.1 percent and decrease localization error by up to 2.2 meters. A hybrid CNN-LSTM model performs best with an accuracy of 97.1 percent and a localization error of 1.52 meters.
Keywords:
Indoor localization, Internet of Things, Outlier detection, Isolation Forest, Machine learning, Deep learning, CNN- LSTM.
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