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Different Approaches to Indoor Localization Based on Bluetooth Low Energy Beacons and Wi-Fi

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10449)

Abstract

Thanks to Global Navigation Satellite Systems, the position of a smartphone equipped with the particular receiver can be determined with an accuracy of a few meters outdoors, having a clear view of the sky. These systems, however, are not usable indoors, because there is no signal from satellites. Therefore, it is necessary to use other localization techniques indoors. This paper focuses on the use of Bluetooth Low Energy and Wi-Fi radio technologies. We have created a special mobile application for the Android operating system in order to evaluate these techniques. This application allows localization testing in a real environment within a building of a university campus. We compare multiple approaches based on K-Nearest Neighbors and Particle Filter algorithms that have been further modified. The combination of Low Energy Bluetooth and Wi-Fi appears to be a promising solution reaching the satisfying accuracy and minimal deployment costs.

Keywords

Indoor localization Indoor positioning Bluetooth Low Energy Internet of Things iBeacon K-Nearest Neighbors Particle Filter 

Notes

Acknowledgements

The authors of this paper would like to thank Tereza Krizova for proofreading. This work and the contribution were also supported by a project of Students Grant Agency – FIM, University of Hradec Kralove, Czech Republic (under ID: UHK-FIM-SP-2017). Radek Bruha is a student member of the research team.

References

  1. 1.
    Estimote: Mesh networking - Estimote developer. http://developer.estimote.com/managing-beacons/mesh/
  2. 2.
    Siddharth, S., Ali, A., El-Sheimy, N., Goodall, C., Syed, Z.: A robust sensor fusion algorithm for pedestrian heading improvement. In: Proceedings of 24th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2011). p. 1337-0 (2001)Google Scholar
  3. 3.
    Racko, J., Brida, P., Perttula, A., Parviainen, J., Collin, J.: Pedestrian dead reckoning with particle filter for handheld smartphone. In: 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–7. IEEE (2016)Google Scholar
  4. 4.
    Abu-Shaban, Z., Zhou, X., Abhayapala, T.D.: A novel TOA-based mobile localization technique under mixed LOS/NLOS conditions for cellular networks. IEEE Trans. Veh. Technol. 65(11), 8841–8853 (2016)CrossRefGoogle Scholar
  5. 5.
    Yiu, S., Dashti, M., Claussen, H., Perez-Cruz, F.: Wireless RSSI fingerprinting localization. Sig. Process. 131, 235–244 (2017)CrossRefGoogle Scholar
  6. 6.
    Matharu, N.S., Buttar, A.S.: An efficient approach for localization using trilateration algorithm based on received signal strength in wireless sensor network. Int. J. Comput. Sci. Netw. Secur. (IJCSNS) 16(8), 116 (2016)Google Scholar
  7. 7.
    Sanguino, T.D.J.M., Gómez, F.P.: Toward simple strategy for optimal tracking and localization of robots with adaptive particle filtering. IEEE/ASME Trans. Mechatron. 21(6), 2793–2804 (2016)CrossRefGoogle Scholar
  8. 8.
    Kumar, S., Hegde, R.M., Trigoni, N.: Gaussian process regression for fingerprinting based localization. Ad Hoc Netw. 51, 1–10 (2016)CrossRefGoogle Scholar
  9. 9.
    Song, C., Wang, J., Yuan, G.: Hidden Naive Bayes indoor fingerprinting localization based on best-discriminating AP selection. ISPRS Int. J. Geo-Inf. 5(10), 189 (2016)CrossRefGoogle Scholar
  10. 10.
    Yang, Z., Zhou, Z., Liu, Y.: From RSSI to CSI: indoor localization via channel response. ACM Comput. Surv. (CSUR) 46(2), 25 (2013)CrossRefGoogle Scholar
  11. 11.
    Machaj, J., Brida, P.: Performance comparison of similarity measurements for database correlation localization method. In: Nguyen, N.T., Kim, C.-G., Janiak, A. (eds.) ACIIDS 2011. LNCS, vol. 6592, pp. 452–461. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-20042-7_46CrossRefGoogle Scholar
  12. 12.
    Honkavirta, V., Perala, T., Ali-Loytty, S., Piché, R.: A comparative survey of WLAN location fingerprinting methods. In: 6th Workshop on Positioning, Navigation and Communication, WPNC 2009, pp. 243–251. IEEE (2009)Google Scholar
  13. 13.
    Gu, Z., Chen, Z., Zhang, Y., Zhu, Y., Lu, M., Chen, A.: Reducing fingerprint collection for indoor localization. Comput. Commun. 83, 56–63 (2016)CrossRefGoogle Scholar
  14. 14.
    Android.com: Bluetooth low energy - Android developers. https://developer.android.com/guide/topics/connectivity/bluetooth-le.html

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Informatics and Quantitative Methods, Faculty of Informatics and ManagementUniversity of Hradec KraloveHradec KraloveCzech Republic

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