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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Crowdsourcing based terminal positioning using multidimensional data clustering and interpolation

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DOI: http://dx.doi.org/10.15439/2016F337

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 961967 ()

Full text

Abstract. Recent years were characterized by the rapid increase of mobile device usage in people's lives, contemporary mobile devices are equipped with many sensors and have high computational and processing capabilities. In a crowdsourcing architecture, mobile users participate constructively in specific information handling. Data collected by crowd and stored in a database may help offering new services such as operator's radio quality, user's claim and tracking. In this paper, we will focus on mobile location by clustering and interpolating to fit fingerprints to positions, our method will be trained in the offline phase and parameters will be updated periodically to track possible changes in propagation environment.

References

  1. G. Chatzimilioudis, A. Konstantinidis, C. Laoudias, and D. Zeinalipour-Yazti, " Crowdsourcing with smartphones", IEEE Internet Comput., vol. 16, no. 5, pp. 36-44, Sep.-Oct. 2012. http://dx.doi.org/10.1109/MIC.2012.70
  2. Georgiou, K.; Constambeys, T.; Laoudias, C.; Petrou, L.; Chatzimilioudis, G.; Zeinalipour-Yazti, D. "Anyplace: A Crowdsourced Indoor Information Service", Mobile Data Management (MDM), 2015 16th IEEE International Conference on, On page(s): 291 - 294 Volume: 1, 15-18 June 2015. http://dx.doi.org/10.1109/MDM.2015.80
  3. "Coordinate Conversions and Transformations including Formulas", IOGP Publication 373-7-2- Geomatics Guidance Note number 7, part 2 âĂŞ April 2015
  4. H. Liu, H. Darabi, P. Banerjee, and J. Liu, "Survey of wireless indoor positioning techniques and systems", Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 37, no. 6, pp. 1067-1080, nov. 2007. http://dx.doi.org/10.1109/TSMCC.2007.905750
  5. T. K. Sarkar, Z. Ji, K. Kim, A. Medouri, M. Salazar-Palma. "A Survey of Various Propagation Models for Mobile Communication" IEEE Antennas and Propagation Magazine, Vol. 45, No. 3, June 2003. http://dx.doi.org/10.1002/0471722839
  6. F. Graziosi and F. Santucci, "A general correlation model for shadow fading in mobile radio systems" IEEE Commun. Lett., vol. 6, no. 3, pp. 102-104, Mar. 2002. http://dx.doi.org/10.1109/4234.991146
  7. J. Yang and Y.Chen, "Indoor Localization Using Improved RSS-Based Lateration Methods", in Proc. IEEE Globecom, Nov. 2009, pp. 1-6. http://dx.doi.org/10.1109/GLOCOM.2009.5425237
  8. J.Yang, Y. Chen "Indoor localization using improved RSS-based lateration methods" Global Telecommunications Conference, 2009. GLOBECOM 2009, Nov. 30, 2009-Dec. 4, 2009. http://dx.doi.org/10.1109/GLOCOM.2009.5425237
  9. J. H. Lee and R. M. Buehrer, " Location estimation using differential RSS with spatially correlated shadowing," in Proc. IEEE GLOBECOM, Nov. 2009, pp. 1-6. http://dx.doi.org/10.1109/GLOCOM.2009.5425272
  10. A. Payal, C. S. Rai, and B. V. R. Reddy, “Analysis of some feedforward artificial neural network training algorithms for developing localization framework in wireless sensor networks,” Wireless Pers. Commun., vol. 82, no. 4, pp. 2519-2536, 2015.
  11. J. C. Bezdek, R. Ehrlich, W. Full "FCM: The fuzzy c-means clustering algorithm" Computer and Geoscience, Vol.10, No 2-3, pp 191-203,1984.
  12. D. S. Broomhead, D. Lowe "Multivariable Functional Interpolation and Adaptive Networks", Complex system 2: 321-355, 1988
  13. C. Laoudias, P. Kemppi, and C. Panayiotou, "Localization using radial basis function networks and signal strength fingerprints in WLAN," in IEEE GLOBECOM, 2009, pp. 1-6 http://dx.doi.org/10.1109/GLOCOM.2009.5425278