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High-precision indoor localization in multi-level buildings

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Języki publikacji
EN
Abstrakty
EN
Accurate localization in multi-floor indoor environments is essential for applications such as large-scale inventory management, healthcare, and security systems. However, achieving high-precision tracking with passive Radio Frequency Identification (RFID) tags in these complex settings presents significant challenges, including managing vertical spatial data, reducing signal interference between floors, and maintaining computational efficiency. This paper presents a novel approach that leverages holographic algorithms to enhance the localization accuracy of passive RFID tags in multi-floor buildings. By deploying multiple RFID readers across floors and constructing 3D holographic representations from signal phase data, our approach effectively distinguishes vertical positions, allowing for precise floor-specific tracking. The proposed method achieves an average localization error of approximately 5 cm, even in multifloor environments, through optimized reader placement and computational overhead reduction. This advancement has broad applications in sectors requiring highly accurate object tracking across large, multi-level indoor spaces, positioning holographic localization as a promising solution for modern multi-floor localization needs.
Twórcy
  • University of Monastir, Tunisia
autor
  • University of Monastir, Tunisia
  • University of Monastir, Tunisia
Bibliografia
  • [1] Tripicchio, P., D’Avella, S. Unetti, M. Efficient localization in warehouse logistics: a comparison of LMS approaches for 3D multilateration of passive UHF RFID tags. Int J Adv Manuf Technol 120, 4977-4988 (2022). https://doi.org/10.1007/s00170-022-09018-1
  • [2] T. Sanpechuda and L. Kovavisaruch, ”A review of RFID localization: Applications and techniques,” 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Krabi, Thailand, 2008, pp. 769-772, https://doi.org/10.1109/ECTICON.2008.4600544
  • [3] Moutaz Haddara, Anna Staaby,RFID Applications and Adoptions in Healthcare: A Review on Patient Safety,Procedia Computer Science, Volume 138,2018,Pages 80-88,ISSN 1877-0509, https://doi.org/10.1016/j.procs.2018.10.012
  • [4] K. Vidyasagar and Ssit and Sathupally and G. Naveen Balaji and Karthikeyan Reddy,RFID-GSM imparted School children Security System,Foundation of Computer Science FCS, New York, USA Volume 2 - No.2, June 2015, https://doi.org/10.5120/cae-1673
  • [5] Rácz-Szabó A, Ruppert T, Bántay L, Löcklin A, Jakab L, Abonyi J. Real-Time Locating System in Production Management. Sensors (Basel). 2020 Nov 26;20(23):6766. https://doi.org/10.3390/s20236766.PMID: 33256090; PMCID: PMC7730894.
  • [6] Rostamian, Majed ,Wang, Jing , Bolic, Miodrag. (2017). An Accurate Passive RFID Indoor Localization System Based on Sense-a-Tag and Zoning Algorithm. https://doi.org/10.1007/978-3-319-51204-422
  • [7] S. Subedi, E. Pauls and Y. D. Zhang, ”Accurate Localization and Tracking of a Passive RFID Reader Based on RSSI Measurements,” in IEEE Journal of Radio Frequency Identification, vol. 1, no. 2, pp. 144-154, June 2017, https://doi.org/10.1109/JRFID.2017.2765618.
  • [8] Shi W, Du J, Cao X, Yu Y, Cao Y, Yan S, Ni C. IKULDAS: An Improved kNN-Based UHF RFID Indoor Localization Al-gorithm for Directional Radiation Scenario. Sensors (Basel). 2019 Feb 25;19(4):968. https://doi.org/10.3390/s19040968. PMID: 30823553; PMCID: PMC6413016.
  • [9] D. A. Savochkin, ”Simple approach for passive RFID-based trilateration without offline training stage,” 2014 IEEE RFID Technology and Applications Conference (RFID-TA), Tampere, Finland, 2014, pp. 159-164, doi: 10.1109/RFID-TA.2014.6934220.
  • [10] Liu F, Zhong D, Luo S. A three-dimensional localization algorithm for passive radio-frequency identification device tag. International Journal of Distributed Sensor Networks. 2017;13(10). https://doi.org/10.1177/1550147717736176
  • [11] Mondal S, Kumar D, Chahal P. Recent Advances and Applications of Passive Harmonic RFID Systems: A Review. Micromachines (Basel). 2021 Apr 12;12(4):420. https://doi.org/10.3390/mi12040420. PMID:33921474; PMCID: PMC8069358.
  • [12] Liu X, Cen J, Zhan Y, Tang C. An efficient crowd-sourcing-based approach for fingerprint database updating. International Journal of Distributed Sensor Networks. 2019;15(6). https://doi.org/10.1177/1550147719858512
  • [13] Khandker, S.; Torres-Sospedra, J.; Ristaniemi, T. Analysis of Received Signal Strength Quantization in Fingerprinting Localization. Sensors 2020, 20, 3203. https://doi.org/10.3390/s20113203
  • [14] Tian Sun, Lingxiang Zheng, Ao Peng, Biyu Tang, Gang Ou,Building information aided Wi-Fi fingerprinting positioning system, Computers and Electrical Engineering,Volume 71,2018,Pages 558-568,ISSN 0045-7906, https://doi.org/10.1016/j.compeleceng.2018.08.008
  • [15] Wen, Yutian and Tian, Xiaohua and Wang, Xinbing and Lu, Songwu. (2015). Fundamental limits of RSS fingerprinting based indoor localization. 2479-2487. https://doi.org/10.1109/INFOCOM.2015.7218637
  • [16] Yoo J. Multiple Fingerprinting Localization by an Artificial Neural Network. Sensors (Basel). 2022 Oct 3;22(19):7505. https://doi.org/10.3390/s22197505 PMID: 36236604; PMCID: PMC9573177.
  • [17] Paige Wenbin Tien, Shuangyu Wei, Jo Darkwa, Christopher Wood, John Kaiser Calautit, Machine Learning and Deep Learning Methods for Enhancing Building Energy Efficiency and Indoor Environmental Quality - A Review, Energy and AI,Volume 10,2022,100198,ISSN 2666-5468, https://doi.org/10.1016/j.egyai.2022.100198
  • [18] Bohr A, Memarzadeh K. The rise of artificial intelligence in health-care applications. Artificial Intelligence in Healthcare. 2020:25-60. https://doi.org/10.1016/B978-0-12-818438-7.00002-2 Epub 2020 Jun 26. PMCID: PMC7325854.
  • [19] Linardatos P, Papastefanopoulos V, Kotsiantis S. Explainable AI: A Review of Machine Learning Interpretability Methods. Entropy (Basel). 2020 Dec 25;23(1):18. https://doi.org/10.3390/e23010018 PMID: 33375658; PMCID: PMC7824368.
  • [20] Cuomo, S., Di Cola, V.S., Giampaolo, F. et al. Scientific Machine Learning Through Physics-Informed Neural Networks: Where we are and What’s Next. J Sci Comput 92, 88 (2022). https://doi.org/10.1007/s10915-022-01939-z
  • [21] Taye, M.M. Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers 2023, 12, 91. https://doi.org/10.3390/computers12050091
  • [22] Ajroud, C., Hattay, J. and Machhout, M. A novel holographic technique for RFID localization in indoor environments. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-16539-8
  • [23] Meng, X.; Jia, C.; Cai, C.; He, F.; Wang, Q. Indoor High-Precision 3D Positioning System Based on Visible-Light Communication Using Improved Whale Optimization Algorithm. Photonics 2022, 9, 93. https://doi.org/10.3390/photonics902009
  • [24] Bardareh H, Moselhi O (2022). An integrated RFID-UWB method for indoor localization of materials in construction, ITcon Vol. 27, pg. 642-661, https://doi.org/10.36680/j.itcon.2022.032
  • [25] A. Tzitzis, S. Megalou, S. Siachalou, E. Tsardoulias, T. Yioultsis and A. G. Dimitriou, ”3D Localization of RFID Tags with a Single Antenna by a Moving Robot and ”Phase ReLock”,” 2019 IEEE International Conference on RFID Technology and Applications (RFID-TA), Pisa, Italy, 2019, pp. 273-278, https://doi.org/10.1109/RFID-TA.2019.8892256
  • [26] A. Motroni et al., ”SAR-Based Indoor Localization of UHF-RFID Tags via Mobile Robot,” 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Nantes, France, 2018, pp. 1-8, https://doi.org/10.1109/IPIN.2018.8533847
  • [27] Shi, W., Chen, Z., Zhao, K. et al. 3D target location based on RFID polarization phase model. J Wireless Com Network 2022, 17 (2022). https://doi.org/10.1186/s13638-022-02102-w
  • [28] Cheng, S.; Wang, S.; Guan, W.; Xu, H.; Li, P. 3DLRA: An RFID 3D Indoor Localization Method Based on Deep Learning. Sensors 2020, 20, 2731. https://doi.org/10.3390/s20092731
  • [29] M. Liu, H. Wang, Y. Yang, Y. Zhang, L. Ma and N. Wang, ”RFID 3-D In-door Localization for Tag and Tag-Free Target Based on Interference,” in IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 10, pp. 3718-3732, Oct. 2019, https://doi.org/10.1109/TIM.2018.2879678
  • [30] L. Qiu, Z. Huang, N. Wirstr¨om and T. Voigt, ”3DinSAR: Object 3D localization for indoor RFID applications,” 2016 IEEE International Conference on RFID (RFID), Orlando, FL, USA, 2016, pp. 1-8, https://doi.org/10.1109/RFID.2016.7488026
  • [31] Doan Perdana, I Made Arya Indra Tanaya, Abdul Aziz Marwan, Fityanul Akhyar, ”Evaluation of a High-Accuracy Indoor-Positioning System with Wi-Fi Time of Flight (ToF) and Deep Learning”, Journal of Computer Networks and Communications, vol. 2023, Article ID 6777759, 14 pages, 2023. https://doi.org/10.1155/2023/6777759
  • [32] P. Tripicchio, S. D’Avella, M. Unetti, Efficient localization in warehouse logistics: a comparison of LMS approaches for 3D multilateration of passive UHF RFID tags. Int J Adv Manuf Technol 120, 4977-4988 (2022). https://doi.org/10.1007/s00170-022-09018-1
  • [33] Nguyen-Huu Khanh, Lee Seon-Woo, A Multi-Floor Indoor Pedestrian Localization Method Using Landmarks Detection for Different Holding Styles, Mobile Information Systems, 2021, 6617417, 15 pages, 2021. https://doi.org/10.1155/2021/6617417
  • [34] P Tan, T.H. Tsinakwadi, Z. Xu, H. Xu, Sing-Ant: RFID Indoor Positioning System Using Single Antenna with Multiple Beams Based on LANDMARC Algorithm. Appl. Sci. 2022, 12, 6751. https://doi.org/10.3390/app12136751
  • [35] Ali Montaser, Osama Moselhi,RFID indoor location identification for construction projects, Automation in Construction, Volume 39,2014,Pages 167-179,ISSN 0926-5805, https://doi.org/10.1016/j.autcon.2013.06.012
  • [36] Mostafa Sherif, Harras Khaled, Youssef Moustafa. (2022). A Sur-vey of Indoor Localization Systems in Multi-Floor Environments. https://doi.org/10.36227/techrxiv.20439648.v1
  • [37] Nguyen-Huu Khanh, Lee Kyungho, Lee Seon-Woo. (2017). An indoor positioning system using pedestrian dead reckoning with WiFi and mapmatching aided. 1-8. https://doi.org/10.1109/IPIN.2017.8115898
  • [38] P. Tan, T.H. Tsinakwadi, Z. Xu, H. Xu, Sing-Ant: RFID Indoor Positioning System Using Single Antenna with Multiple Beams Based on LANDMARC Algorithm. Appl. Sci. 2022, 12, 6751. https://doi.org/10.3390/app12136751
  • [39] C. Ajroud, J. Hattay, and M. Machhout, A novel holographic technique for RFID localization in indoor environments. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-16539-8
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-272f7807-3ef4-4947-b433-8e99df5e904a
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