Reliability of Location Detection in Intelligent Environments

  • Shumei Zhang
  • Paul J. McCullagh
  • Chris Nugent
  • Huiru Zheng
  • Norman Black
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 92)

Abstract

Radio Frequency Identification (RFID) technology has been used in Intelligent Environments to track objects and people, but the technology is subject to reliability issues of sensor malfunction, sensor range, interference and location coverage. This paper discusses the optimal deployment for a fixed RFID reader network in an indoor environment with the aim of achieving more accurate location whist minimizing the equipment costs. Given that data may be occasionally lost, a rule-based pre-processing algorithm was developed for missing data judgment and correction, to improve the robustness of the technique. The algorithms were evaluated using experiments of single mobile tag and multiple mobile tags. The average subarea location accuracy based on pre-processed and original data is 77.1% vs. 68.7% for fine-grained coverage, and 95.8% vs. 85.4% for the coarse-grained coverage.

Keywords

Reliability Location based systems RFID Missing data estimation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mao, G., Fidan, B., Anderson, B.: Wireless sensor network localization techniques. Computer Networks 51(10), 2529–2553 (2007)CrossRefMATHGoogle Scholar
  2. 2.
    Ni, L.M., Liu, Y., Lau, Y.C., Patil, A.P.: LANDMARC: indoor location sensing using active RFID. Wireless Networks 10(6), 701–710 (2004)CrossRefGoogle Scholar
  3. 3.
    Bahl, P., Padmanabhan, V.: RADAR: An in-building RF-based user location and tracking system. IEEE Infocom, 775 (2000)Google Scholar
  4. 4.
    Harter, A., Hopper, A., Steggles, P., Ward, A., Webster, P.: The anatomy of a context-aware application. Wireless Networks 8(2), 187–197 (2002)CrossRefMATHGoogle Scholar
  5. 5.
    Woodman, O., Harle, R.: Pedestrian localisation for indoor environments. In: Proceedings of the 10th International Conference on Ubiquitous Computing, p. 114. ACM, New York (2008)CrossRefGoogle Scholar
  6. 6.
    Ngai, E., Moon, K.K.L., Riggins, F.J., Yi, C.Y.: RFID research: An academic literature review (1995-2005) and future research directions. International Journal of Production Economics 112(2), 510–520 (2008)CrossRefGoogle Scholar
  7. 7.
    Satoh, I.: Location-aware communications in smart environments. Information Systems Frontiers 11(5), 501–512 (2009)CrossRefGoogle Scholar
  8. 8.
    Naisbitt, J.D.: Reliable location sensing through multi-sensor fusion, dynamic weighting, and confidence mapping, Thesis, University of Illinois at Urbana-Champaign (2010)Google Scholar
  9. 9.
    Elnahrawy, E., Li, X., Martin, R.P.: The limits of localization using signal strength: A comparative study. In: IEEE Conf. on Sensor and Ad Hoc Communications and Networks, pp. 406–414 (2004)Google Scholar
  10. 10.
    Hallberg, J., Nugent, C., Davies, R., Donnelly, M.: Localisation of Forgotten Items using RFID Technology. In: Proceedings of the 9th International Conference on Information Technology and Applications in Biomedicine, Larnaca, Cyprus (2009)Google Scholar
  11. 11.
    Nugent, C.D., Mulvenna, M.D., Hong, X., Devlin, S.: Experiences in the development of a smart lab. IJBET 2(4), 319–331 (2009)CrossRefGoogle Scholar
  12. 12.
  13. 13.
    Kim, T.H., Lee, S.J.: A hybrid hyper tag anti-collision algorithm in RFID system, In: 11th International Conference on IEEE Advanced Communication Technology, ICACT 2009, p. 1276 (2009)Google Scholar
  14. 14.
    Hahnel, D., Burgard, W., Fox, D., Fishkin, K., Philipose, M.: Mapping and localization with RFID technology. In: Proceedings of IEEE International Conference on Robotics and Automation, ICRA 2004, p. 1015 (2004)Google Scholar
  15. 15.
    Darcy, P., Stantic, B., Sattar, A.: A fusion of data analysis and non-monotonic reasoning to restore missed RFID readings. In: 5th International Conference on IEEE Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2009), p. 313 (2010)Google Scholar
  16. 16.
    Zhang, S., McCullagh, P., Nugent, C., Zheng, H.: A Theoretic Algorithm for Fall and Motionless Detection. In: Proceedings of the 3rd Annual IEEE International Conference on Pervasive Computing Technologies for Healthcare, pp. 1–6 (2009)Google Scholar
  17. 17.
    Zhang, S., McCullagh, P., Nugent, C., Zheng, H.: Activity Monitoring Using a Smart Phone’s Accelerometer with Hierarchical Classification. In: Proceedings of the 6th IEEE International Conference on Intelligent Environment 2010, pp. 158–163 (2010)Google Scholar
  18. 18.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Shumei Zhang
    • 1
  • Paul J. McCullagh
    • 1
  • Chris Nugent
    • 1
  • Huiru Zheng
    • 1
  • Norman Black
    • 1
  1. 1.University of UlsterUnited Kingdom

Personalised recommendations