Uhf Rfid Localization with Machine Learning Approaches: Distance Estimation From Rssi Data





Indoor Localization, Received Signal Strength Indicator , RFID, Machine Learning


With the development of the Internet of Things (IoT), sensor technology and mobile internet, indoor localization has gained great importance. Indoor localization is the process of locating objects or people in an indoor environment. Unlike outdoor positioning, technologies such as GPS (Global Positioning System) cannot be used effectively indoors.  The reasons for this include attenuation of GPS signals by concrete structures such as walls, ceilings, etc. and inaccurate position calculations due to multiple signal reflection indoors.Indoor positioning is a solution to needs such as fixture tracking and inventory location control in warehouses and stores. RFID (Radio Frequency Identification) technology, which has been used in industry and academia in the past years, stands out in terms of cost, low power demand and easy applicability.

Indoor location systems designed with RFID technology usually use parameters such as received signal strength indicator (RSSI), time of arrival (ToA) and time difference of arrival (TDoA). Studies with RSSI values can find the estimated distance of the RFID tag to the antenna in response to the received signal strength. In this study, RSSI values of passive UHF (Ultra High Frequency) RFID tags were collected from the experimental environment. Time and frequency domain features are calculated from the RSSI values and these features and tag distance values are used to train machine learning models. The machine learning algorithms are discussed comparatively in terms of mean absolute error (MAE) values obtained from the test results.


Abdulghafor, R., Turaev, S., Almohamedh, H., Alabdan, R., Almutairi, B., Almutairi, A., and Almotairi, S. (2021). Recent advances in passive UHF-RFID tag antenna design for improved read range in product packaging applications: A comprehensive review. IEEE .

Baygin, M., Yaman, O., Baygin, N., and Karakose, M. (2022). A blockchain-based approach to smart cargo transportation using UHF RFID. Expert Systems with Applications, 188, 116030.

Friedman, J., Hastie, T., Tibshirani, R., Narasimhan, B., Tay, K., Simon, N., ... & Yang, J. (2023). glmnet: Lasso and elastic-net regularized generalized linear models. Astrophysics Source Code Library, ascl-2308.

Gomes, E. L., Fonseca, M. S. P., Lazzaretti, A. E., Munaretto, A., & Guerber, C. R. (2024). Sliding Window, Hierarchical Classification, Regression, and Genetic Algorithm for RFID Indoor Positioning Systems. Expert Systems with Applications, 238, 122298.

Hatem, E., Abou-Chakra, S., Colin, E., Laheurte, J. M., and El-Hassan, B. (2020). Performance, accuracy and generalization capability of RFID tags’ constellation for indoor localization. Sensors, 20(15), 4100.

Liashchynskyi, P., and Liashchynskyi, P. (2019). Grid search, random search, genetic algorithm: a big comparison for NAS. arXiv preprint arXiv:1912.06059.

Naser, R. S., Lam, M. C., Qamar, F., and Zaidan, B. B. (2023). Smartphone-Based Indoor Localization Systems: A Systematic Literature Review. Electronics, 12(8), 1814.

Ni, L. M., Liu, Y., Lau, Y. C., and Patil, A. P. (2003, March). LANDMARC: Indoor location sensing using active RFID. In Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003.(PerCom 2003). (pp. 407-415). IEEE.

Tondji, Y., Ghazi, G., and Mihaela Botez, R. (2024). Neural networks and support vector regression for the CRJ-700 longitudinal dynamics modeling. Journal of Aerospace Information Systems, 1-16.

Ulkir, O., and Akgun, G. (2023). Predicting and optimising the surface roughness of additive manufactured parts using an artificial neural network model and genetic algorithm. Science and Technology of Welding and Joining, 1-10.

Xu, H., Wu, M., Li, P., Zhu, F., and Wang, R. (2018). An RFID indoor positioning algorithm based on support vector regression. Sensors, 18(5), 1504.

Yao, Y. A., and Ma, Z. (2023). Toward a holistic perspective of congruence research with the polynomial regression model. Journal of Applied Psychology, 108(3), 446.

Zafari, F., Gkelias, A., and Leung, K. K. (2019). A survey of indoor localization systems and technologies. IEEE Communications Surveys & Tutorials, 21(3), 2568-2599.



How to Cite

MUSABEYOĞLU, A., TUĞ, N., DURMUŞ, Ömer C., AKGÜN, G. ., & AKÜNER, M. C. (2024). Uhf Rfid Localization with Machine Learning Approaches: Distance Estimation From Rssi Data. EJONS INTERNATIONAL JOURNAL, 8(1), 166–173. https://doi.org/10.5281/zenodo.10814462