UWB-BASED GEOFENCING: A ONE-CLASS CLASSIFICATION APPROACH WITH FINGERPRINTING AND TRILATERATION

Sandra Đošić, Milica Jovanović, Milijana Vejlković, Goran Lj Đorđević

DOI Number
https://doi.org/10.22190/FUACR250409004D
First page
047
Last page
061

Abstract


Ultra-wideband (UWB) technology has emerged as a powerful solution for indoor localization, offering high accuracy, low power consumption, and robust penetration through obstacles. Among its applications, geofencing enables the creation of virtual boundaries for monitoring and security purposes. This paper presents a novel one-class classification (OCC) approach for UWB-based geofencing, named the k-Nearest Neighbours with Residual Norm Threshold (kNN-RNT) algorithm. The proposed method utilizes fingerprinting and trilateration techniques, operating in two distinct phases: an offline phase for constructing a reference fingerprint database and an online phase for real-time classification of a mobile tag’s location. The kNN-RNT algorithm determines geofence violations by analysing the distribution of nearest fingerprints and computing a residual norm to classify locations. A filtering mechanism enhances detection stability, mitigating noise and transient errors. Experimental validation in a controlled indoor environment demonstrates the effectiveness of the method, achieving over 99% accuracy within the geofenced area and significantly reducing classification errors in proximity zones. The proposed approach provides a reliable and efficient solution for real-time UWB-based geofencing applications.

Keywords

Geofencing, ultra-wideband technology, one-class classification algorithm, fingerprinting, trilateration.

Full Text:

PDF

References


Y. Rahayu, T. A. Rahman, R. Ngah, P. S. Hall, “Ultra wideband technology and its applications,” 2008 5th IFIP International Conference on Wireless and Optical Communications Networks (WOCN'08), Surabaya, Indonesia, pp. 1-5, May 2009, doi: 10.1109/WOCN.2008.4542537

C. T. Li, J. C. Cheng, K. Chen, “Top 10 technologies for indoor positioning on construction sites,” Automation in Construction, vol. 118, 103309, 2020, doi: 10.1016/j.autcon.2020.103309

D. S. Jachowski, R. Slotow, J. J. Millspaugh, “Good virtual fences make good neighbors: opportunities for conservation,” Animal Conservation, vol. 17 no. 3, pp. 187-196, 2014, doi: 10.1111/acv.12082

W. Zhuo, K. H. Chiu, J. Chen, J. Tan, E. Sumpena, S. H. G. Chan, C. H. Lee, “Semi-supervised learning with network embedding on ambient rf signals for geofencing services,” arXiv preprint, 2025. [Online]. Available: https://arxiv.org/abs/2210.07889, doi: 10.48550/arXiv.2210.07889

Y. Yoo, J. Suh, J. Paek, S. Bahk, “Secure region detection using Wi-Fi CSI and one-class classification,” IEEE Access, vol. 9, pp. 65906-65913, 2021, doi: 10.1109/ACCESS.2021.3076176

K. Fu, C. Gong, Y. Qiao, J. Yang, I. Guy, “One-class SVM assisted accurate tracking,” 2012 Sixth International Conference on Distributed Smart Cameras (ICDSC), pp. 1-6, October 2012, doi: 10.1117/1.JEI.22.2.023002

C. Bellinger, S. Sharma, N. Japkowicz, “One-class versus binary classification: Which and when?,” 2012 11th international conference on machine learning and applications, vol. 2, pp. 102-106, December 2012, doi: 10.1109/ICMLA.2012.212

S. S. Khan, M. G. Madden, “One-class classification: taxonomy of study and review of techniques,” The Knowledge Engineering Review, vol. 29 no. 3, pp. 345-374, 2014, doi: 10.1017/S026988891300043X

A. Alarifi, A. Al-Salman, M. Alsaleh, A. Alnafessah, S. Al-Hadhrami, M. Al-Ammar, H. Al-Khalifa, “Ultra wideband indoor positioning technologies: Analysis and recent advances,” Sensors, vol. 16 no. 5, pp. 707, May 2016, doi: 10.3390/s16050707

F. Mazhar, M. G. Khan, B. Sällberg, “Precise indoor positioning using UWB: A review of methods, algorithms and implementations,” Wireless Pers. Commun., vol. 97 no. 3, pp. 4467–4491, Dec. 2017, doi: 10.1007/s11277-017-4734-x

S. Campaña-Bastidas, M. Espinilla-Estévez, J. Medina-Quero, “Review of Ultra Wide Band (UWB) for Indoor Positioning With Application to the elderly,” Hawaii International Conference on System Sciences, pp. 1-10, January 2022, doi: 10.24251/HICSS.2022.269

M. T. Hoang, B. Yuen, X. Dong, T. Lu, R. Westendorp, K. Reddy, “Recurrent neural networks for accurate rssi indoor localization,” IEEE Internet Things J., vol. 6 no. 6, pp. 10 639–10 651, 2019, doi: 10.1109/JIOT.2019.2940368

L. Li, X. Guo, N. Ansari, “Smartloc: Smart wireless indoor localization empowered by machine learning,” IEEE Transactions on Industrial Electronics, vol. 67 no. 8, pp. 6883-6893, Aug. 2020, doi: 10.1109/TIE.2019.2931261

R. Wang, H. Luo, Q. Wang, Z. Li, F. Zhao, J. Huang, “A spatial-temporal positioning algorithm using residual network and lstm,” IEEE Transactions on Instrumentation and Measurement, vol. 69 no. 11, pp. 9251-9261, Nov. 2020, doi: 10.1109/TIM.2020.2998645

M. Zhou, Y. Li, M. J. Tahir, X. Geng, Y. Wang, W. He, “Integrated statistical test of signal distributions and access point contributions for wi-fi indoor localization,” IEEE Transactions on Vehicular Technology, vol. 70 no. 5, pp. 5057-5070, May 2021, doi: 10.1109/TVT.2021.3076269

S. Fan, Y. Wu, C. Han, X. Wang, “Siabr: A structured intra-attention bidirectional recurrent deep learning method for ultra-accurate terahertz indoor localization,” IEEE Journal on Selected Areas in Communications, vol. 39 no. 7, pp. 2226-2240, July 2021, doi: 10.1109/JSAC.2021.3078491

X. Chen, H. Li, C. Zhou, X. Liu, D. Wu, G. Dudek, “Fidora: Robust wifi-based indoor localization via unsupervised domain adaptation,” IEEE Internet of Things Journal, vol. 9 no. 12, pp. 9872-9888, June, 2022, doi: 10.1109/JIOT.2022.3163391

S. Djosic, I. Stojanovic, M. Jovanovic, G. Lj. Djordjevic, “Multi-Algorithm UWB-based Localization Method for Mixed LOS/NLOS Environments,” Computer Communications, vol. 181, pp. 365-373, 2022, doi: 10.1016/j.comcom.2021.10.031

T. Ardoin, N. Pauli, B. Groß, M. Kholghi, K. Reaz, G. Wunder “Tracking UWB Devices Through Radio Frequency Fingerprinting Is Possible,” arXiv preprint, 2025. [Online]. Available: https://arxiv.org/abs/2501.04401, doi: 10.48550/arXiv.2501.04401

W. Q. Malik, B. Allen, “Wireless Sensor Positioning with Ultrawideband Fingerprinting,” The Second European Conference on Antennas and Propagation, EuCAP 2007, Edinburgh, pp. 1-5, 2007, doi: 10.1049/ic.2007.0891

P. Perera, O. Poojan, M. P. Vishal, “One-class classification: A survey,” arXiv preprint, 2021. [Online]. Available: https://arxiv.org/abs/2101.03064, doi: 10.48550/arXiv.2101.03064

C. Sanchez-Hernandez, S. B. Doreen, M. F. Giles, “One-class classification for mapping a specific land-cover class: SVDD classification of fenland,” IEEE Transactions on Geoscience and Remote Sensing, vol. 45 no. 4, pp. 1061-1073, 2007, doi: 10.1109/TGRS.2006.890414

QM33120W, Fully Integrated Impulse Radio Ultra-Wideband (UWB) Wireless Transceiver. [Online]. Available: https://www.qorvo.com/products/p/QM33120W

Murata Type 2AB UWB Evaluation Kit. [Online]. Available: https://www.murata.com/en-eu/products/connectivitymodule/ultra-wide-band/qorvo




DOI: https://doi.org/10.22190/FUACR250409004D

Refbacks

  • There are currently no refbacks.


Print ISSN: 1820-6417
Online ISSN: 1820-6425