Advertisement

ERFS: Enhanced RSSI value Filtering Schema for Localization in Wireless Sensor Networks

  • Seung-chan Shin
  • Byung-rak Son
  • Won-geun Kim
  • Jung-gyu Kim
Part of the IFIP – The International Federation for Information Processing book series (IFIPAICT, volume 264)

Abstract

In this research, we have suggested the Localization Algorithm using Probable Filtering Schema of RSSI without additional hardwares. The existing method has been filtering with only average and feedback of received RSSI values. This method was not considering about the variation of RSSI when obstacles are moving at indoor environment. In this research, we have suggested the probable filtering algorithm which is considered factors of errors at indoor environment and we have demonstrated the superiority of this algorithm through the examination. It presents 14.66% and 11.65% improved accuracy than the existing filtering algorithm.

Keywords

Sensor Node Wireless Sensor Network Location Information Indoor Environment Distance Estimation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Lee, Yang, Lee, Cha.: Localization Technology in Unbiquitous Environment. Korean Society for Internet Information, Vol 7. No 2, pp.30–37 (2006)Google Scholar
  2. 2.
    Hakyoung Kim: Location Information Service basedWireless Lan. Telecommunications Review, Vol 16, pp.188–202, (2006)Google Scholar
  3. 3.
    P. Bahl, V.N. Padmanabhan,: RADAR : An In Building RF-Based User Location and Tracking System. In Proceeding of IEEE infocom 2000 Conference on Cmputer Communication, Vol.2, pp.775–784 (2000)Google Scholar
  4. 4.
    Ekahau, Inc., http://www.ekahau.comGoogle Scholar
  5. 5.
    Place Lab at Intel Corporation, http://www.placelab.orgGoogle Scholar
  6. 6.
    R. Want, A. Hopper, V. Falcao, and J. Gibbons.: The Active Badge Location System. ACM Trans. on Information Systems, Vol.10, pp.91–102 (1992)CrossRefGoogle Scholar
  7. 7.
    N. Priyantha, A. Chakraborty, and H. Balakrishnan: The Cricket Location-Support System. Proc. of the ACM Int’l Conf. on MobICom, (2000)Google Scholar
  8. 8.
    J. Hightower, R. Wand, and G. Borriello: Spoton: An Indoor 3d Location Sensing Technology Based on RF Signal Strength. Technical Report 00-02-02. University of Washington (2000)Google Scholar
  9. 9.
    SpotON: Ad-hoc Location Sensing, http://portolano.cs.washington.edu/projects/spotonGoogle Scholar
  10. 10.
    Portolano: An Expedition into Invisible Computing, http://portolano.cs.washington.eduGoogle Scholar
  11. 11.
    Hakyoung Kim, Sungduk Kim, Donggil Sue, Jungkang Ji, Hyuntae Jang: A close distance localization Technology tendency. IITA weekly technology tendency, No. 1322, pp.1–12 (2007)Google Scholar
  12. 12.
    Wonhee Lee, Wooyoung Lee, Minkyu Kim, Duseup Eum, Jinwon Kim: Location Measurement System Technology tendency for Ubiquitous Environment. The Korean Institute of Information Scientists and Engineers, Vol 22. No 12, pp.41–50 (2004)Google Scholar
  13. 13.
    K. Aamodt: CC2431 Location Engine. Applications Note AN042. Texas Instrument Incorporated (2006)Google Scholar
  14. 14.
    Octacomm Inc.: http://www.octacomm.netGoogle Scholar
  15. 15.
    Octacomm Inc.: Understanding of Embbeded System, (2006)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2008

Authors and Affiliations

  • Seung-chan Shin
    • 1
  • Byung-rak Son
    • 1
  • Won-geun Kim
    • 1
  • Jung-gyu Kim
    • 1
  1. 1.Department of Information Communication EngineeringDaegu UniversityRepublic of Korea

Personalised recommendations