CONTEXT 2015: Modeling and Using Context pp 544-550 | Cite as

WHERE: An Autonomous Localization System with Optimized Size of the Fingerprint Database

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9405)

Abstract

Wi-Fi fingerprinting without site surveys is one interesting approach for indoor localization. Current approaches in this field either achieve high accuracy with a large fingerprint database, or yield lower accuracy when the database size is small. In this paper, we propose a novel RSS (Received Signal Strength)-range based approach for fingerprint building, which optimizes the size of the fingerprint database while maintaining the accuracy at the same level. In this approach, a fingerprint is a low-dimensional vector of RSS-ranges, which are extracted from a high-dimensional vector of Wi-Fi scans in the process of fingerprint building. The proposed approach is used and evaluated in the autonomous localization system, which we call WHERE. The evaluation results show the system can optimize the size of the fingerprint database while maintaining an accuracy of room-level.

Keywords

Global Navigation Satellite System Global Navigation Satellite System Learning Phase Shopping Mall Database Size 
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.
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Notes

Acknowledgments

This work has been co-funded by the Social Link Project within the Loewe Program of Excellence in Research, Hessen, Germany.

References

  1. 1.
    Ester, E., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA (1996)Google Scholar
  2. 2.
    Kaemarungsi, K.: Distribution of WLAN received signal strength indication for indoor location determination. In: 1st International Symposium on Wireless Pervasive Computing (ISWPC), Phuket, Thailand (2006)Google Scholar
  3. 3.
    Sklar, B.: Rayleigh fading channels in mobile digital communication systems. Part I: characterization. IEEE Commun. Mag. 35(7), 90–100 (1997)CrossRefGoogle Scholar
  4. 4.
    Xu, Y., Lau, S.L., Kusber, R., David, K.: DCCLA: autonomous indoor localization using unsupervised Wi-Fi fingerprinting. In: CONTEXT 2013, Annecy, France (2013)Google Scholar
  5. 5.
    Xu, Y., Kusber, R., David, K.: An enhanced density-based clustering algorithm for the autonomous indoor localization. In: MOBILe Wireless MiddleWARE, Operating Systems and Applications (Mobilware), Bologna, Italy (2013)Google Scholar
  6. 6.
    Xu, Y., Lau, S.L., Kusber, R., David, K.: An experimental investigation of indoor localization by unsupervised Wi-Fi signal clustering. In: Future Network and Mobile Summit, Berlin, Germany (2012)Google Scholar
  7. 7.
    Lau, S.L., Xu, Y., David, K.: Novel indoor localisation using an unsupervised Wi-Fi signal clustering method. In: Future Network and Mobile Summit, Warsaw, Poland (2011)Google Scholar
  8. 8.
    Lau, S.L., David, K.: Movement recognition using the accelerometer in smartphones. In: Future Network and Mobile. Florence, Italy (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Chair for Communication Technology (ComTec)University of KasselKasselGermany

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