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Location Estimation and Filtering of Wireless Nodes in an Open Environment

  • A. Muhammad
  • M. S. Mazliham
  • Patrice Boursier
  • M. Shahrulniza
Part of the Communications in Computer and Information Science book series (CCIS, volume 253)

Abstract

The research is on the location estimation and filtering of wireless nodes in an open environment. This research is based on our previous findings in which we categorized the geographical area into thirteen different terrains/clutters based on the signal to noise ratio. As signal to noise ratio differs from terrain to terrain therefore data points are calculated for each terrain. A C# program is used with the WiFi architecture to calculate the available signal strength and the receive signal strength. Estimation is done by using triangulation method with the construction of three triangles. As each experiment is repeated five times which estimated five different positions due to the effect of signal to noise ratio, therefore fifteen locations are estimated based on three triangles. Filtering is further done by using average and mean of means calculations. Results show that terrains/clutters based location estimation and filtering produce better results. Only terrains with high attenuation such as sea, dense forest, highway/motorway and high dense urban areas has high error rate after filtering. This research work helps to minimize location error in an open environment.

Keywords

Location estimation location filtering terrains/clutters signal to noise ratio 

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References

  1. 1.
    Wang, S., Min, J., Yi, B.K.: Location Based Services for Mobiles: Technologies and Standards. In: IEEE ICC 2008- Beijing, LG Electronics MobileComm, U.S.A. Inc. (2008)Google Scholar
  2. 2.
    Roos, T., Myllymäki, P., Tirri, H., Misikangas, P., Sievänen, J.: A Probabilistic Approach to WLAN User Location Estimation. International Journal of Wireless Information Networks 9(3), 155–164, doi:10.1023/A:1016003126882Google Scholar
  3. 3.
    Laurendeau, C., Barbeau, M.: Insider attack attribution using signal strength-based hyperbolic location estimation. Security and Communication Networks 1, 337–349 (2008), doi:10.1002/sec.35CrossRefGoogle Scholar
  4. 4.
    Patwari, N., Hero III, A.O., Perkins, M., Correal, N.S., O’Dea, R.J.: Relative location estimation in wireless sensor networks. IEEE Transactions on Signal Processing 51(8), 2137–2148 (2003); ISSN: 1053-587XCrossRefGoogle Scholar
  5. 5.
    Muhammad, A., Mazliham, M.S., Boursier, P., Shahrulniza, M., Yusuf, J.C.M.: Location Estimation and Power Management of Cellular Nodes for rescue operation. In: ICET Kuala Lumpur, Malaysia, December 08-10 (2009)Google Scholar
  6. 6.
  7. 7.
    EU Institutions Press Release, Commission Pushes for Rapid Deployment of Location Enhanced 112 Emergency Services, DN: IP/03/1122, Brussels (2003)Google Scholar
  8. 8.
    Khalaf-Allah, M.: A Novel GPS-free Method for Mobile Unit Global Positioning in Outdoor Wireless Environments. Wireless Personal Communications Journal 44(3) (February 2008)Google Scholar
  9. 9.
    Gezici, S.: A Survey on Wireless Position Estimation. Wireless Personal Communications: An International Journal 44(3) (February 2008); ISSN: 0929-6212Google Scholar
  10. 10.
    Muhammad, A., Mazliham, M.S., Boursier, P., Shahrulniza, M., Yusuf, J.C.M.: Predicted and Corrected Location Estimation of Mobile Nodes Based on the Combination of Kalman Filter and the Bayesian Decision Theory. In: Cai, Y., Magedanz, T., Li, M., Xia, J., Giannelli, C. (eds.) Mobilware 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 48, pp. 313–325. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Dalela, P.K., Prasad, M.V.S.N., Mohan, A.: A New Method of Realistic GSM Network planning for Rural Indian Terrains. IJCSNS International Journal of Computer Science and Network Security 8(8) (August 2008)Google Scholar
  12. 12.
    Lecture notes on RF fundamentals Universitas Bina Darma, http://images.ilman
  13. 13.
    Integrated Publishing, Electrical Engineering Training Series, http://www.tpub.com/neets/book10/40c.htm
  14. 14.
    Muhammad, A., Mazliham, M.S., Boursier, P., Shahrulniza, M., Mustapha, J.C.: Terrain/Clutter Based Error Calculation in Location Estimation of Wireless Nodes by using Receive Signal Strength. In: 2nd International Conference on Computer Technology and Development (ICCTD), Cairo, Egypt, November 2-4 (2010); ISBN: 978-1-4244-8844-5 Google Scholar
  15. 15.
    Muhammad, A., Mazliham, M.S., Boursier, P., Shahrulniza, M., Mustapha, J.C.: Clutter based Enhance Error Rate Table (CERT) for Error Correction in Location Estimation of Mobile Nodes. In: International Conference on Information and Computer Networks, ICICN 2011, Guiyang, China, January 26-28 (2011); IEEE Catalog Number: CFP1145M-PRT ISBN: 978-1-4244-9514-6Google Scholar
  16. 16.
    Muhammad, A., Mazliham, M.S., Shahrulniza, M.: Power Management of Portable Devices by Using Clutter Based Information. IJCSNS, International Journal of Computer Science and Network Security 9(4), 237–244 (2009)Google Scholar
  17. 17.
    Muhammad, A., Mazliham, M.S., Shahrulniza, M., Amir, M.: Posterior Probabilities based Location Estimation (P2LE) Algorithm for Locating a Mobile Node in a Disaster Area. In: MULTICONF 2009, July 13-16, American Mathematical Society, Orlando (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • A. Muhammad
    • 1
    • 2
    • 5
  • M. S. Mazliham
    • 3
  • Patrice Boursier
    • 1
    • 4
  • M. Shahrulniza
    • 5
  1. 1.Laboratoire Informatique Image Interaction (L3i)Université de La RochelleFrance
  2. 2.Institute of Research and Postgraguate StudiesUniKL (BMI)Kuala LumpurMalaysia
  3. 3.Malaysia France Institute (UniKL MFI)Bandar Baru BangiMalaysia
  4. 4.Faculty of Computer Science & ITUniversiti MalayaKuala LumpurMalaysia
  5. 5.Malaysian Institute of Information Technology (UniKL MIIT)Kuala LumpurMalaysia

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