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Analysis of RF-based Indoor Localization with Multiple Channels and Signal Strengths

  • José M. Claver
  • Santiago Ezpeleta
  • José V. Martí
  • Juan J. Pérez-Solano
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 146)

Abstract

In this paper, the influence and improvement of the localization accuracy achieved using a fingerprint database with information coming from different channels and radio signal strength levels is evaluated. This study uses IEEE 802.15.4 networks with different power levels and carrier frequency channels in the 2.4 GHz band. Experimental results show that selecting part of this information with a cleverer data processing can provide similar or better localization accuracy than using the whole database.

Keywords

Indoor location Fingerprinting IEEE 802.15.4 

References

  1. 1.
    Alippi, C., Mottarella, A., Vanini, G.: A RF map-based localization algorithm for indoor environments. In: IEEE International Symposium on Circuits and Systems, ISCAS 2005, vol. 1, pp. 652–655 (2005)Google Scholar
  2. 2.
    Azizyan, M., Constandache, I., Choudhury, R.R.: Surroundsense: mobile phone localization via ambience fingerprinting. In: Proceedings of the 15th Annual International Conference on Mobile Computing and Networking, MobiCom 2009, pp. 261–272. ACM, New York (2009)Google Scholar
  3. 3.
    Bahl, P., Padmanabhan, V.N.: Radar: an in-building RF-based user location and tracking system. In: Proceedings INFOCOM 2000, Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 2, pages 775–784. IEEE (2000)Google Scholar
  4. 4.
    Lorincz, K., Welsh, M.: MoteTrack: a robust, decentralized approach to RF-based location tracking. In: Strang, T., Linnhoff-Popien, C. (eds.) LoCA 2005. LNCS, vol. 3479, pp. 63–82. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  5. 5.
    Marti, J.V., Sales, J., Marin, R., Jimenez-Ruiz, E.: Localization of mobile sensors and actuators for intervention in low-visibility conditions: the zigbee fingerprinting approach. Int. J. Distrib. Sens. Netw. 1–10, 2012 (2012)Google Scholar
  6. 6.
    Milioris, D., Tzagkarakis, G., Papakonstantinou, A., Papadopouli, M., Tsakalides, P.: Low-dimensional signal-strength fingerprint-based positioning in wireless LANs. Ad Hoc Netw. Spec. Issue: Model. Anal. Simul. Wirel. Mob. Syst. 12, 100–114 (2014)CrossRefGoogle Scholar
  7. 7.
    Oussar, Y., Ahriz, I., Denby, B., Dreyfus, G.: Indoor localization based on cellular telephony RSSI fingerprints containing very large numbers of carriers. EURASip J. Wirel. Commun. Network. 2011(1), 81 (2011)CrossRefGoogle Scholar
  8. 8.
    Yao, Q., Wang, F.-Y., Gao, H., Wang, K., Zhao. H.: Location estimation in zigbee network based on fingerprinting. In: IEEE International Conference on Vehicular Electronics and Safety, 2007, ICVES. pp 1–6 (2007)Google Scholar

Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015

Authors and Affiliations

  • José M. Claver
    • 1
  • Santiago Ezpeleta
    • 1
  • José V. Martí
    • 2
  • Juan J. Pérez-Solano
    • 1
  1. 1.Department of Computer ScienceUniversity of ValenciaBurjassotSpain
  2. 2.Computer Science and Engineering DepartmentJaume I UniversityCastellónSpain

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