Skip to main content
SpringerLink
Log in

A Hierarchical Signal-Space Partitioning Technique for Indoor Positioning with WLAN to Support Location-Awareness in Mobile Map Services

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The precise and accurate performance of location estimation is a vital component of context-aware applications. Numerous mobile devices with built-in IEEE 802.11 Wi-Fi technology can be used to estimate a user’s location through a wireless local area network (WLAN) in indoor environments in which fixed access points are deployed. This study deals with improving the common techniques of such positioning once the acquisition of the fingerprint database in offline phase is performed. The main idea is to propose a methodology that includes two layers of classification: a concurrent hierarchical partitioning of both signal and physical space in a way that signal patterns in each part of building have the highest similarity, and a precise and independent positioning in a given part. A procedure for combining the proposed classifier with either artificial neural network (ANN) or Bayesian probabilistic model is then introduced. We also consider an alternative strategy for ANN learning by including all raw observations. The average distance error was successfully reduced in the proposed methodology by 32 % compared to the simple approach. We concluded that the physical partitioning should also consider the signal behavior. Toosi location-aware mobile system was ultimately implemented, providing different services (e.g., friend finder and nearest point of interest) based on the proposed technique via WLAN. The system benefits from the high level of interaction provided by Asynchronous JavaScript and XML (AJAX) technology. It is capable of transferring locational data and GIS map services efficiently to the mobile terminal.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Drakatos S., Pissinou N., Makki K., Douligeris C. (2007) A context-aware cache structure for mobile computing environments. Journal of Systems and Software 80(7): 1102–1119

    Article  Google Scholar 

  2. Ghiani G., Paternò F., Santoro C., Spano L. D. (2009) UbiCicero: A location-aware, multi-device museum guide. Interacting with Computers 21(4): 288–303

    Article  Google Scholar 

  3. Rueppel, U., & Stuebbe, K. M. (2008). BIM-based indoor-emergency-navigation-system for complex buildings. In ICCCBE-XII: Proceedings of the 12th international conference on computing in civil and building engineering, Beijing, China (Vol. 13, pp. 362–367).

  4. Djuknic G. M., Richton R. E. (2001) Geolocation and assisted GPS. IEEE Computer 34(2): 123–125

    Article  Google Scholar 

  5. Kjærgaard, M. B., Blunck, H., Godsk, T., Toftkjær, T., Christensen, D. L., & Grønbæk, K. (2010). Indoor positioning using GPS revisited. In Pervasive 2010: Proceedings of the 8th international conference on pervasive computing, Berlin, Germany (pp. 38–56).

  6. Li, B., Salter, J., Dempster, A. G., & Rizos, C. (2006). Indoor positioning techniques based on wireless LAN. In AusWireless’06: Proceedings of the 1st IEEE international conference on wireless broadband and ultra wideband communications, Sydney, Australia, paper 113.

  7. Swangmuang N., Krishnamurthy P. (2008) An effective location fingerprint model for wireless indoor localization. Pervasive and Mobile Computing 4(6): 836–850

    Article  Google Scholar 

  8. Hightower J., Borriello G. (2001) Location systems for ubiquitous computing. IEEE Computer 34(8): 57–66

    Article  Google Scholar 

  9. Lin, T. N., & Lin, P. C. (2005). Performance comparison of indoor positioning techniques based on location fingerprinting in wireless networks. In WirelessCom 2005: Proceedings of the international conference on wireless networks, communications and mobile computing, Hawaii, USA (Vol. 2, pp. 1569–1574).

  10. Pahlavan K., Li X., Makela J. P. (2002) Indoor geolocation science and technology. IEEE Communications Magazine 40(2): 112–118

    Article  Google Scholar 

  11. Roos T., Myllymaki P., Tirri H., Misikangas P., Sievanen J. (2002) A probabilistic approach to WLAN user location estimation. International Journal of Wireless Information Networks 9(3): 155–164

    Article  Google Scholar 

  12. Ji, Y., Biaz, S., Pandey, S., & Agrawal, P. (2006). ARIADNE: A dynamic indoor signal map construction and localization system. In MobiSys 2006: Proceedings of the 4th international conference on mobile systems, applications and services, Uppsala, Sweden (pp. 151–164).

  13. Borenović M. N., Nešković A. M. (2009) Positioning in WLAN environment by use of artificial neural networks and space partitioning. Annals of Telecommunications 64(9): 665–676

    Article  Google Scholar 

  14. Battiti, R., Nhat, T. L., & Villani, A. (2002). Location-aware computing: A neural network model for determining location in wireless LANs. Technical Report DIT-02-0083, Department of Information and Communication Technology, University of Trento.

  15. Kjærgaard, M. B. (2007). A taxonomy for radio location fingerprinting. In J. Hightower, B. Schiele, & T. Strang (Eds.), Lecture notes in computer science (pp. 139–156). Berlin: Springer.

  16. Ahmad U., Gavrilov A. V., Lee S., Lee Y. (2008) A modular classification model for received signal strength based location systems. Neurocomputing 71(13–15): 2657–2669

    Article  Google Scholar 

  17. Mengual L., Marbán O., Eibe S. (2010) Clustering-based location in wireless networks. Expert Systems with Applications 37(9): 6165–6175

    Article  Google Scholar 

  18. Kushki A., Plataniotis K. N., Venetsanopoulos A. N. (2007) Kernel-based positioning in wireless local area networks. IEEE Transactions on Mobile Computing 6(6): 689–705

    Article  Google Scholar 

  19. Sadoun B., Al-Bayari O. (2007) Location based services using geographical information systems. Computer Communications 30(16): 3154–3160

    Article  Google Scholar 

  20. Steiniger S., Neun M., Edwardes A. (2006) Lecture notes: Foundations of location based services. Department of Geography, University of Zürich, Switzerland

    Google Scholar 

  21. Brunato M., Battiti R. (2005) Statistical learning theory for location fingerprinting in wireless LANs. Computer Networks 47(6): 825–845

    Article  MATH  Google Scholar 

  22. Gezici S. (2008) A survey on wireless position estimation. Wireless Personal Communications 44(3): 263–282

    Article  Google Scholar 

  23. Mok E., Retscher G. (2007) Location determination using WiFi fingerprinting versus WiFi trilateration. Journal of Location Based Services 1(2): 145–159

    Article  Google Scholar 

  24. Kodippili, N. S., & Dias, D. (2010). Integration of fingerprinting and trilateration techniques for improved indoor localization. In WOCN 2010: Proceedings of the 7th international conference on wireless and optical communications networks, Colombo, Sri Lanka (pp. 1–6).

  25. Want R. (2006) An introduction to RFID technology. IEEE Pervasive Computing 5(1): 25–33

    Article  Google Scholar 

  26. Ma Y. W., Lai C. F., Hsu J. M., Chen N. K., Huang Y. M. (2011) RFID-based positioning system for telematics location-aware applications. Wireless Personal Communications 59(1): 95–108

    Article  Google Scholar 

  27. Youssef, M. A., Agrawala, A., & Shankar, A. U. (2003). WLAN location determination via clustering and probability distributions. In PerCom’03: Proceedings of the IEEE international conference on pervasive computing and communications, Texas, USA (pp. 23–26).

  28. Duda R. O., Hart P. E., Stork D. G. (2000) Pattern classification. Wiley-Interscience, New York

    Google Scholar 

  29. Picton P. (2000) Neural networks. Macmillan, Palgrave, New York

    Google Scholar 

  30. Beale R., Jackson T. (1990) Neural computation: An introduction. Institute of Physics Publishing, Vermont

    Book  MATH  Google Scholar 

  31. Smola A. J., Schölkopf B. (2004) A tutorial on support vector regression. Statistics and Computing 14(3): 199–222

    Article  MathSciNet  Google Scholar 

  32. Al-Anazi A., Gates I. D. (2010) A support vector machine algorithm to classify lithofacies and model permeability in heterogeneous reservoirs. Engineering Geology 114(3–4): 267–277

    Article  Google Scholar 

  33. Vapnik V. (1999) The nature of statistical learning theory. Springer, New York

    Google Scholar 

  34. OpenNETCF. http://www.opennetcf.org.

  35. AspMap. http://www.vdstech.com/aspmap.htm.

  36. Jia Y., Zhao H., Niu C., Jiang Y., Gan H., Xing Z., Zhao X., Zhao Z. (2009) A WebGIS-based system for rainfall-runoff prediction and real-time water resources assessment for Beijing. Computers and Geosciences 35(7): 1517–1528

    Article  Google Scholar 

  37. Microsoft Corporation, Overview of automation. http://msdn.microsoft.com/en-us/library/ms221251(vs.71).aspx.

  38. Hagan M. T., Demuth H. B., Beale M. (1996) Neural network design. PWS Publishing Company, Boston

    Google Scholar 

  39. Marsaglia G., Tsang W. W., Wang J. (2003) Evaluating Kolmogorov’s distribution. Journal of Statistical Software 8(18): 1–4

    Google Scholar 

  40. Merwade V. M., Maidment D. R., Goff J. A. (2006) Anisotropic considerations while interpolating river channel bathymetry. Journal of Hydrology 331(3–4): 731–741

    Article  Google Scholar 

  41. Manodham T., Loyola L., Miki T. (2008) A novel wireless positioning system for seamless internet connectivity based on the WLAN infrastructure. Wireless Personal Communications 44(3): 295–309

    Article  Google Scholar 

  42. Halunga S. V., Vizireanu D. N. (2010) Performance evaluation for conventional and MMSE multiuser detection algorithms in imperfect reception conditions. Digital Signal Processing 20(1): 166–178

    Article  Google Scholar 

  43. Halunga S. V., Vizireanu D. N., Fratu O. (2010) Imperfect cross-correlation and amplitude balance effects on conventional multiuser decoder with turbo encoding. Digital Signal Processing 20(1): 191–200

    Article  Google Scholar 

  44. Lorincz, K., & Welsh, M. (2005). MoteTrack: A robust, decentralized approach to RFbased location tracking. In Proceedings of the 1st international workshop on location and context-awareness.

Download references

Author information

Authors and Affiliations

  1. Department of Geospatial Information System, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, No. 1346, Mirdamad Cross, Valiasr Street, 19967-15433, Tehran, Iran

    Mohammad H. Vahidnia, Mohammad R. Malek, Nazila Mohammadi & Ali A. Alesheikh

Authors
  1. Mohammad H. Vahidnia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad R. Malek
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nazila Mohammadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ali A. Alesheikh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad H. Vahidnia.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Vahidnia, M.H., Malek, M.R., Mohammadi, N. et al. A Hierarchical Signal-Space Partitioning Technique for Indoor Positioning with WLAN to Support Location-Awareness in Mobile Map Services. Wireless Pers Commun 69, 689–719 (2013). https://doi.org/10.1007/s11277-012-0607-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-012-0607-5

Keywords

Search

Navigation