Performance Evaluation of Wi-Fi Indoor Positioning by Regression-Based Coordinate Estimation in High-Density and Multi-Device Environments

Serpil Üstebay

doi:10.29130/dubited.1819498

TR EN

Performance Evaluation of Wi-Fi Indoor Positioning by Regression-Based Coordinate Estimation in High-Density and Multi-Device Environments

Abstract

Determining the location of moving objects in indoor environments has become a crucial requirement for many application domains. While this could be vital in hospitals, it can also enhance cultural interaction in environments such as museums. It serves a variety of usage scenarios for shopping malls, museums, and airports. Fingerprinting-based location estimation, thanks to the widespread availability of Wi-Fi access points found in nearly every building, has become one of the most common solutions for indoor positioning. This study examines the performance of Wi-Fi RSSI–based regression methods for indoor localization. Using the TUJI1 multi-device dataset, six different regression models were evaluated under Euclidean distance–based performance metrics. Among these models, XGBoost regression determined the locations of moving objects with an average positioning error of 2.07 m on the test dataset and 2.04 m during training, outperforming other linear and nonlinear regression approaches. In addition, we investigated how noisy signal measurements originating from the hardware structures of different mobile devices in the test environment affect localization systems. To analyze device heterogeneity, multiple experimental scenarios were designed, including device-specific and unified models. The findings show that tree-based ensemble models provide robust and competitive performance without requiring complex deep learning architectures.

Keywords

Indoor positioning, Wi-Fi RSSI fingerprinting, Regression-based localization, Device heterogeneity

Supporting Institution

This research received no external funding.

Ethical Statement

This study does not involve human or animal participants. All procedures followed scientific and ethical principles, and all referenced studies are appropriately cited.

Thanks

The author does not wish to acknowledge any individual or institution.

References

Almutiri, T. M., Alomar, K. H., & Alganmi, N. A. (2024). Integrating multi-omics using Bayesian ridge regression with iterative similarity bagging. Applied Sciences, 14(13), Article 5660. https://doi.org/10.3390/app14135660
Attar, H., Saber Ismail, W., Hafez, M., Bahaa, S., Deif, M. A., Khosravi, M., & Youssry, H. (2025). Machine learning indoor localization prediction using received signal strength indicator and Wi-Fi network. International Journal of Intelligent Networks, 6, 233–243. https://doi.org/10.1016/j.ijin.2025.10.001
Azghadi, S. A. R., Mih, A. N., Kawnine, A., Wachowicz, M., Palma, F., & Cao, H. (2024). An adaptive indoor localization approach using WiFi RSSI fingerprinting with SLAM-enabled robotic platform and deep neural networks. In Proceedings of the 34th International Conference on Collaborative Advances in Software and Computing (CASCON). IEEE. https://doi.org/10.1109/CASCON62161.2024.10838149
Bedoui, A., & Lazar, N. A. (2020). Bayesian empirical likelihood for ridge and lasso regressions. Computational Statistics & Data Analysis, 145, Article 106917. https://doi.org/10.1016/j.csda.2020.106917
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). https://doi.org/10.1145/2939672.2939785
Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. https://doi.org/10.1109/TIT.1967.1053964
Duong, T. H., Trinh, A. V., & Hoang, M. K. (2024). Efficient and accurate indoor positioning system: A hybrid approach integrating PCA, WKNN, and linear regression. Journal of Communications, 19(1), 37–43. https://doi.org/10.12720/jcm.19.1.37-43
Friedman, J. H., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1–22. https://doi.org/10.18637/jss.v033.i01
Hamayat, F., Abbasi, H. F., Sohail, M., & Ahmad, R. F. (2025). DAELocNet: An ai-enabled indoor localization for accurate positioning in modern buildings using wireless communication technologies. In Proceedings of the 2025 International Conference on Innovation in Artificial Intelligence and Internet of Things (AIIT) (pp. 1-7). IEEE. https://doi.org/10.1109/AIIT63112.2025.11082899

Hodson, T. O. (2022). Root mean square error (RMSE) or mean absolute error (MAE): When to use them or not. Geoscientific Model Development, 15(14), 5481–5487. https://doi.org/10.5194/gmd-15-5481-2022
Jang, B., Kim, H., & Kim, J. W. (2023). Survey of landmark-based indoor positioning technologies. Information Fusion, 89, 166–188. https://doi.org/10.1016/j.inffus.2022.08.013
King, T., Kopf, S., Haenselmann, T., Lubberger, C., & Effelsberg, W. (2008). CRAWDAD dataset mannheim/compass (v. 2008-04-11) [Data set]. CRAWDAD. https://doi.org/10.15783/c7js3p
Klus, L., Klus, R., Lohan, E. S., Nurmi, J., Granell, C., Valkama, M., Talvitie, J., Casteleyn, S., & Torres-Sospedra, J. (2024a). TUJI1 dataset: Multi-device dataset for indoor localization with high measurement density. Data in Brief, 54, Article 110356. https://doi.org/10.1016/j.dib.2024.110356
Klus, L., Klus, R., Torres-Sospedra, J., Lohan, E. S., Silva, I., Pendao, C., & Valkama, M. (2024b). Enabling dynamic indoor localization by employing intersection over union as a metric. In Proceedings of the 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall) (pp. 1-7). IEEE. https://doi.org/10.1109/VTC2024-Fall63153.2024.10758013
Loh, W.-Y. (2014). Fifty years of classification and regression trees. International Statistical Review, 82(3), 329–348. https://doi.org/10.1111/insr.12016
Mendoza-Silva, G. M., Costa, A. C., Torres-Sospedra, J., Painho, M., & Huerta, J. (2021). Environment-aware regression for indoor localization based on WiFi fingerprinting. IEEE Sensors Journal, 22(6), 4978–4988. https://doi.org/10.1109/JSEN.2021.3073878
Mendoza-Silva, G. M., Richter, P., Torres-Sospedra, J., Lohan, E. S., & Huerta, J. (2018). Long-term WiFi fingerprinting dataset for research on robust indoor positioning. Data, 3(1), Article 3. https://doi.org/10.3390/data3010003
Obeidat, H., Shuaieb, W., Obeidat, O., & Abd-Alhameed, R. (2021). A review of indoor localization techniques and wireless technologies. Wireless Personal Communications, 119(1), 289–327. https://doi.org/10.1007/s11277-021-08209-5
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., VanderPlas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Qin, F., Zuo, T., & Wang, X. (2021). CCpos: WiFi fingerprint indoor positioning system based on CDAE-CNN. Sensors, 21(4), Article 1114. https://doi.org/10.3390/s21041114
Saeed, M. T. M., Yousif, M. A. A., & Ozturk, I. (2025). Mitigating device heterogeneity for enhanced indoor positioning system performance using deep feature learning. IEEE Access, 13, 180203–180217. https://doi.org/10.1109/ACCESS.2025.3621505
Sheikholeslami, N., & Valaee, S. (2021). Learning K-nearest neighbour regression for noisy dataset with application in indoor localization. In Proceedings of the 2021 IEEE Global Communications Conference (GLOBECOM) (pp. 1–6). IEEE. https://doi.org/10.1109/GLOBECOM46510.2021.9685151
Singh, N., Choe, S., & Punmiya, R. (2021). Machine learning based indoor localization using Wi-Fi RSSI fingerprints: An overview. IEEE Access, 9, 127150–127174. https://doi.org/10.1109/ACCESS.2021.3111083
Sze, V., Chen, Y.-H., Yang, T.-J., & Emer, J. S. (2017). Efficient processing of deep neural networks: A tutorial and survey. Proceedings of the IEEE, 105(12), 2295–2329. https://doi.org/10.1109/JPROC.2017.2761740
Tilwari, V., Pack, S., Maduranga, M., & Lakmal, H. K. I. S. (2024). An improved Wi-Fi RSSI-based indoor localization approach using deep randomized neural network. IEEE Transactions on Vehicular Technology, 73(12), 18593–18604. https://doi.org/10.1109/TVT.2024.3437640
Torres-Sospedra, J., Montoliu, R., Martínez-Úso, A., Avariento, J. P., Arnau, T. J., Benedito-Bordonau, M., & Huerta, J. (2014). UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems. In Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 261–270). IEEE. https://doi.org/10.1109/IPIN.2014.7275492
Turgut, Z. (2025). XDOcc: An explainable artificial intelligence empowered deep framework for occupancy detection and occupant count estimation. IEEE Access, 13, 175386–175409. https://doi.org/10.1109/ACCESS.2025.3619449
Ustebay, S., Turgut, Z., Durukan Odabaşı, Ş., Aydın, M. A., & Sertbaş, A. (2024). A machine learning approach based on indoor target positioning by using sensor data fusion and improved cosine similarity. Electrica, 24(1), 218–227. https://doi.org/10.5152/electrica.2023.23080
Ustebay, S., Turgut Akgün, Z., Turna, Ö. C., Aydın, M. A., & Berber Atmaca, T. (2019). Analysis of device-free and device-dependent signal filtering approaches for indoor localization based on Earth's magnetic field system. In Proceedings of the 1st International Workshop on Digital Content & Smart Multimedia (DCSMart 2019) (pp. 1–13).
Yaman, Ş., Gündoğdu, K., & Çalhan, A. (2021). A New Indoor Positioning System. Düzce University Journal of Science and Technology, 9(2), 636–645. https://doi.org/10.29130/dubited.747445
Zafari, F., Gkelias, A., & Leung, K. K. (2019). A survey of indoor localization systems and technologies. IEEE Communications Surveys & Tutorials, 21(3), 2568–2599. https://doi.org/10.1109/COMST.2019.2911558
Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B: Statistical Methodology, 67(2), 301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x

Details

Primary Language

English

Subjects

Machine Learning Algorithms

Journal Section

Research Article

Authors

Serpil Üstebay ^*
0000-0003-0541-0765
Türkiye

Publication Date

April 19, 2026

Submission Date

November 11, 2025

Acceptance Date

March 13, 2026

Published in Issue

Year 2026 Volume: 14 Number: 2

DOI

https://doi.org/10.29130/dubited.1819498

IZ

https://izlik.org/JA74UP85FA

APA

Üstebay, S. (2026). Performance Evaluation of Wi-Fi Indoor Positioning by Regression-Based Coordinate Estimation in High-Density and Multi-Device Environments. Duzce University Journal of Science and Technology, 14(2), 605-616. https://doi.org/10.29130/dubited.1819498

AMA

1.Üstebay S. Performance Evaluation of Wi-Fi Indoor Positioning by Regression-Based Coordinate Estimation in High-Density and Multi-Device Environments. DUBİTED. 2026;14(2):605-616. doi:10.29130/dubited.1819498

Chicago

Üstebay, Serpil. 2026. “Performance Evaluation of Wi-Fi Indoor Positioning by Regression-Based Coordinate Estimation in High-Density and Multi-Device Environments”. Duzce University Journal of Science and Technology 14 (2): 605-16. https://doi.org/10.29130/dubited.1819498.

EndNote

Üstebay S (April 1, 2026) Performance Evaluation of Wi-Fi Indoor Positioning by Regression-Based Coordinate Estimation in High-Density and Multi-Device Environments. Duzce University Journal of Science and Technology 14 2 605–616.

IEEE

[1]S. Üstebay, “Performance Evaluation of Wi-Fi Indoor Positioning by Regression-Based Coordinate Estimation in High-Density and Multi-Device Environments”, DUBİTED, vol. 14, no. 2, pp. 605–616, Apr. 2026, doi: 10.29130/dubited.1819498.

ISNAD

Üstebay, Serpil. “Performance Evaluation of Wi-Fi Indoor Positioning by Regression-Based Coordinate Estimation in High-Density and Multi-Device Environments”. Duzce University Journal of Science and Technology 14/2 (April 1, 2026): 605-616. https://doi.org/10.29130/dubited.1819498.

JAMA

1.Üstebay S. Performance Evaluation of Wi-Fi Indoor Positioning by Regression-Based Coordinate Estimation in High-Density and Multi-Device Environments. DUBİTED. 2026;14:605–616.

MLA

Üstebay, Serpil. “Performance Evaluation of Wi-Fi Indoor Positioning by Regression-Based Coordinate Estimation in High-Density and Multi-Device Environments”. Duzce University Journal of Science and Technology, vol. 14, no. 2, Apr. 2026, pp. 605-16, doi:10.29130/dubited.1819498.

Vancouver

1.Serpil Üstebay. Performance Evaluation of Wi-Fi Indoor Positioning by Regression-Based Coordinate Estimation in High-Density and Multi-Device Environments. DUBİTED. 2026 Apr. 1;14(2):605-16. doi:10.29130/dubited.1819498

Performance Evaluation of Wi-Fi Indoor Positioning by Regression-Based Coordinate Estimation in High-Density and Multi-Device Environments

Yüksek Yoğunluklu ve Çoklu Cihaz Ortamlarında Regresyon Tabanlı Koordinat Tahmini ile Wi-Fi İç Mekan Konumlandırmasının Performans Değerlendirmesi

Abstract

Keywords

Performance Evaluation of Wi-Fi Indoor Positioning by Regression-Based Coordinate Estimation in High-Density and Multi-Device Environments

Abstract

Keywords

Supporting Institution

Ethical Statement

Thanks

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

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