Accurate Trilateration for Passive RFID Localization in Smart Homes

  • Kevin Bouchard
  • Dany Fortin-Simard
  • Sebastien Gaboury
  • Bruno Bouchard
  • Abdenour Bouzouane


The smart home as emerged in recent years as a new trend of research aiming to propose an alternative to postpone the institutionalization of cognitively-impaired people. These habitats are intended to provide security, guidance and direct support services to its resident. To fulfill this important mission, an algorithm first has to identify the ongoing activities of its user by tracking, in real time, the position of the main daily living objects. Many researchers addressed this issue by proposing systems based ultrasonic wave sensors, video cameras, and radio-frequency identification (RFID). However, the RFID technology, constitutes the most viable technology for smart homes. Recently, several RFID localization algorithms have been developed, mainly for commercial and industrial uses, but they are not precise enough to be used in an assistive context. Furthermore, the majority of them focuses on systems exploiting active RFID tags, which need batteries and are much more expensive. We present, in this paper, a new algorithmic approach for passive RFID localization in smart homes based on elliptical trilateration and fuzzy logic. This new algorithm has been implemented in a real smart home infrastructure. It has been rigorously tested and outperformed the comparable approaches.


Smart home Passive RFID Objects localization Fuzzy 



This work was supported by the Natural Sciences and Engineering Research Council of Canada, the Quebec Research Fund on Nature and Technologies and the Canadian Foundation for Innovation. The authors would like to thank their health regional center for providing us the Alzheimer participants and their neuropsychologist partner and her graduate students who indirectly worked on this project by supervising the clinical trials with patients.


  1. 1.
    K. Bouchard, B. Bouchard, and A. Bouzouane, Guideline to efficient smart home design for rapid AI prototyping: a case study, in Proceedings of the 5th ACM international conference on pervasive technologies related to assistive environments, Crete Island, Greece, 2012.Google Scholar
  2. 2.
    J. C. Augusto, and C. Nugent, Designing Smart Homes: The Role of Artificial Intelligence. Springer Verlag, London, 2006.CrossRefGoogle Scholar
  3. 3.
    P. Roy, B. Bouchard, and A. Bouzouane, Challenging issues of ambient activity recognition for cognitive assistance, in Handbook of Research on Ambient Intelligence and Smart Environments: Trends and Perspectives, pp. 320–345, 2011.Google Scholar
  4. 4.
    D. E. Riedel, S. Venkatesh, and W. Liu, Spatial activity recognition in a smart home environment using a chemotactic model, in Proceedings of the 2005 intelligent sensors, sensor networks and information processing conference, Melbourne, Australia, 2005.Google Scholar
  5. 5.
    Z. Junyi, and S. Jing, RFID localization algorithms and applications—a review, Journal of Intelligent Manufacturing, Vol. 20, pp. 695−707, 2009.Google Scholar
  6. 6.
    M. Addlesee, R. Curwen, S. Hodges, J. Newman, P. Steggles, A. Ward, and A. Hopper, Implementing a sentient computing system, IEEE Computer, Vol. 34, pp. 50–56, 2001.CrossRefGoogle Scholar
  7. 7.
    J. Hoey, M. J. Chantler, E. Trucco, and R. B. Fisher, Tracking using flocks of features, with application to assisted handwashing, BMVC, pp. 367–376, 2006.Google Scholar
  8. 8.
    C. Hekimian-Williams, B. Grant, and P. Kumar, Accurate localization of RFID tags using phase difference, in 2010 IEEE International Conference on RFID IEEE RFID 2010, pp. 89–96, 2010.Google Scholar
  9. 9.
    D. Hhnel, W. Burgard, D. Fox, K. Fishkin, and M. Philipose, Mapping and localization with RFID tags, in Proc of the IEEE International Conference on Robotics Automation ICRA, New Orleans, LA, pp. 1015–1020, 2004.Google Scholar
  10. 10.
    P. Vorst, S. Schneegans, Y. Bin, and A. Zell, Self-localization with RFID snapshots in densely tagged environments, in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Nice, France, pp. 1353–1358, 2008.Google Scholar
  11. 11.
    D. Joho, C. Plagemann, and W. Burgard, Modeling RFID signal strength and tag detection for localization and mapping, in Proceedings of the 2009 IEEE international conference on Robotics and Automation, Kobe, Japan, 2009.Google Scholar
  12. 12.
    K. Chawla, and G. Robins, An RFID-based object localisation framework, International Journal of Radio Frequency Identification Technology and Applications, Vol. 3, pp. 2–30, 2011.CrossRefGoogle Scholar
  13. 13.
    Y. Zhang, M. G. Amin, and S. Kaushik, Localization and tracking of passive RFID tags based on direction estimation, International Journal of Antennas and Propagation, Vol. 2007, p. 9, 2007.CrossRefGoogle Scholar
  14. 14.
    B.-S. Choi, and J.-J. Lee, Mobile robot localization in indoor environment using RFID and sonar fusion system, in Proceedings of the 2009 IEEE, RSJ international conference on Intelligent robots and systems, St. Louis, MO, 2009.Google Scholar
  15. 15.
    C. Heesung, and H. Kyuseo, Combination of RFID and vision for mobile robot localization, in Intelligent Sensors, Sensor Networks and Information Processing Conference, Melbourne, Australia, pp. 75–80, 2005.Google Scholar
  16. 16.
    A. Milella, D. Di Paola, G. Cicirelli, and T. D’Orazio, RFID tag bearing estimation for mobile robot localization, in International Conference on Advanced Robotics (ICAR), Munich, Germany, pp. 1–6, 2009.Google Scholar
  17. 17.
    A. P. Sample, C. Macomber, J. Liang-Ting, and J. R. Smith, Optical localization of passive UHF RFID tags with integrated LEDs, in IEEE International Conference on RFID (RFID), Orlando, FL, USA, pp. 116–123, 2012.Google Scholar
  18. 18.
    A. Parr, R. Miesen, F. Kirsch, and M. Vossiek, A novel method for UHF RFID tag tracking based on acceleration data, in IEEE International Conference on RFID (RFID), Orlando, FL, USA, pp. 110–115, 2012.Google Scholar
  19. 19.
    L. M. Ni, Y. Liu, Y. C. Lau, and A. P. Patil, LANDMARC: indoor location sensing using active RFID, Wireless Networks, Vol. 10, pp. 701–710, 2004.CrossRefGoogle Scholar
  20. 20.
    G.-y. Jin, X.-y. Lu, and M.-S. Park, An indoor localization mechanism using active RFID tag, in Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, Newport Beach, CA, USA, 2006.Google Scholar
  21. 21.
    A. Bekkali, H. Sanson, and M. Matsumoto, RFID indoor positioning based on probabilistic RFID map and Kalman filtering, in Third IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, New York, USA, 2007.Google Scholar
  22. 22.
    Y. Lei, C. Jiannong, Z. Weiping, and T. Shaojie, A hybrid method for achieving high accuracy and efficiency in object tracking using passive RFID, in 2012 IEEE International Conference on Pervasive Computing and Communications (PerCom), Lugano, Switzerland, pp. 109–115, 2012.Google Scholar
  23. 23.
    J. L. Brchan, Z. Lianlin, W. Jiaqing, R. E. Williams, and L. C. Perez, A real-time RFID localization experiment using propagation models, in IEEE International Conference on RFID (RFID), Orlando, FL, USA, pp. 141–148, 2012.Google Scholar
  24. 24.
    C. Wang, H. Wu, and N.-F. Tzeng, RFID-based 3-D positioning schemes, in 26th IEEE International Conference on Computer Communications, Anchorage, Alaska, USA, pp. 1235–1243, 2007.Google Scholar
  25. 25.
    J. Han, Y. Zhao, Y. S. Cheng, T. L. Wong, and C. H. Wong, Improving accuracy for 3D RFID localization, International Journal of Distributed Sensor Networks, Vol. 2012, p. 9, 2012.Google Scholar
  26. 26.
    Q. Fu, and R. Guenther, Active RFID trilateration and location fingerprinting based on RSSI for pedestrian navigation, Journal of Navigation, Vol. 62, pp. 323–340, 2009.CrossRefGoogle Scholar
  27. 27.
    K. Kim, and M. Kim, RFID-based location-sensing system for safety management, Personal and Ubiquitous Computing, Vol. 16, pp. 235–243, 2012.CrossRefGoogle Scholar
  28. 28.
    C. Y. Chen, J. P. Yang, G. J. Tseng, Y. H. Wu, and R. C. Hwang, An Indoor positioning technique based on fuzzy logic, in MultiConference of Engineers and Computer Scientists, Hong Kong, China, pp. 854–857, 2010.Google Scholar
  29. 29.
    K. P. Fishkin, B. Jiang, M. Philipose, and S. Roy, I sense a disturbance in the force: unobtrusive detection of interactions with RFID-tagged objects, Intellectual Property, Vol. 3205/2004, pp. 268–282, 2004.Google Scholar
  30. 30.
    J. R. Smith, K. P. Fishkin, B. Jiang, A. Mamishev, M. Philipose, A. D. Rea, S. Roy, and K. Sundara-Rajan, RFID-based techniques for human-activity detection, Communications of the ACM, Vol. 48, pp. 39–44, 2005.CrossRefGoogle Scholar
  31. 31.
    D. Patterson, H. Kautz, and D. Fox, Pervasive computing in the home and community, in Pervasive Computing in Healthcare, CRC Press, pp. 79–103, 2007.Google Scholar
  32. 32.
    K. Bouchard, B. Bouchard, and A. Bouzouane, Qualitative spatial activity recognition using a complete platform based on passive RFID tags: experimentations and results, in ICOST, Montreal, Canada, 2011.Google Scholar
  33. 33.
    J. Brusey, C. Floerkemeier, M. Fletcher, and M. Lane, Reasoning about uncertainty in location identification with RFID, in In Workshop on Reasoning with Uncertainty in Robotics at IJCAI-2003, pp. 23–30, 2003.Google Scholar
  34. 34.
    J. Katz, History of Mathematics, 3rd ed. Addison Wesley, Boston, 2008.Google Scholar
  35. 35.
    K. Chawla, G. Robins, and L. Zhang, Object localization using RFID, in Proceedings of the 5th IEEE international conference on Wireless pervasive computing, Mondena, Italy, 2010.Google Scholar
  36. 36.
    Y. Lei, C. Jiannong, Z. Weiping, and T. Shaojie, A hybrid method for achieving high accuracy and efficiency in object tracking using passive RFID, in Pervasive Computing and Communications (PerCom), 2012 IEEE International Conference on 2012, pp. 109-115, 2012.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Kevin Bouchard
    • 1
  • Dany Fortin-Simard
    • 1
  • Sebastien Gaboury
    • 1
  • Bruno Bouchard
    • 1
  • Abdenour Bouzouane
    • 1
  1. 1.LIARA Laboratory UQACChicoutimiCanada

Personalised recommendations