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A Kriged Fingerprinting for Wireless Body Area Network Indoor Localization

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Abstract

The accurate and efficient localization of a person equipped with wireless body area network (WBAN) is still a major issue in surveillance systems. Localization mechanisms using signal strength suffer from inaccuracy but those especially based on fingerprints are likely more accurate for indoor environment and some related mechanisms have been proposed. However, a simple and accurate mechanism is still unavailable for indoor environment except those using ultra wideband technology which is not yet implemented in all existing devices. This paper proposes a new mechanism based on fingerprinting for WBAN indoor localization named Kriged Fingerprinting. From the recorded data, this proposed model generates a huge unsaved database used to estimate the mobile node. It is a hybridism of fingerprinting, particle swarm optimization (PSO) and kriging. The fingerprinting method operates into two phases: the first is the training phase that serves to build a radio map in a database and the second phase is a matching phase. PSO is used as a matching algorithm and when a particle gets to an unrecorded location, the kriging mechanism is used to interpolate its locations values. In comparison with some works done for indoor localization mechanisms and based on fingerprinting, our proposal outperforms all in terms of accuracy: the mobile node is localized with a mean error value of 0.906 m. Hence, the kriging method brings more improvement in localization based on fingerprinting.

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References

  1. Rehman, O., Javaid, N., Bibi, A., & Khan, Z. A. (2012). Performance study of localization mechanisms in wireless body area sensor networks. In IEEE 11th international conference on trust, security and privacy in computing and communications.

  2. Khan, P., Hussain, M. A., & Kwak, K. S. (2009). Medical applications of wireless body area networks. In International journal of digital content technology and its applications (Vol. 3).

  3. Choi, J.-M., Kang, H.-J., & Choi, Y.-S. (2008). A study on the wireless body area network applications and channel models. In Second international conference on future generation communication and networking.

  4. Gomez, J., Tayebi, A., del Corte, A., Gutierrez, O., Gomez, J. M., & de Adana, F. S. (2013). A comparative study of localization methods in indoor environments. Wireless Personal Communications, 72, 2931–2944.

    Article  Google Scholar 

  5. Shi, Q., Huo, H., Fang, T., & Li, D. (2010). A distributed node localization scheme for wireless sensor networks. Wireless Personal Communications, 53, 15–33.

    Article  Google Scholar 

  6. Tatar, Y., & Yildirim, G. (2013). An alternative indoor localization mechanism based on fingerprint in wireless sensor networks. In International journal of advanced research in computer and communication engineering (Vol. 2), copyright to IJARCCE www.ijarcce.com 1288.

  7. Azizyan, M., Constandache, I., & Choudhury, R. R. (2009). Surroundsense: Mobile phone localization via ambience fingerprinting. In Annual international conference on mobile computing and networking, MobiCom’09, Beijing, China, copyright 2009 ACM 978-1-60558-702-8/09/09.

  8. Rashid, H., & Turuk, A. K. (2013). Localization of wireless sensor networks using a single anchor node. Wireless Personal Communications, 72, 975–986.

    Article  Google Scholar 

  9. Otsason, V., Varshavsky, A., LaMarca, A., & de Lara, E. (2005). Accurate GSM indoor localization. In UbiComp 2005, lNCS 3660 (pp. 141–158). Berlin: Springer.

  10. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceeding to IEEE international conference on neural networks, Perth, Australia (Vol. 4, pp. 1942–1948).

  11. Raghavendra, V., Ganesh, K., & Venayagamoorthy, K. (2010). Particle swarm optimization in wireless sensor networks: A brief survey. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews.

  12. Cressie, N. (1990). The origins of kriging. Mathmatical Geology, 22, 239–252.

    Article  MATH  MathSciNet  Google Scholar 

  13. Mase, S., & Takeda, J. (2001). Spatial data modeling. Kyoritsu Shuppan Co. Ltd.

  14. Hofmann-Wellenhof, B., Lichtenegger, H., & Collins, J. (1997). Global positioning system: Theory and practice (4th edn.). Berlin: Springer.

  15. Lin, C.-H., Liu, J.-C., Tsai, S.-H., & Lin, H.-Y. (2012). Research on the zigbee-based indoor location estimation technology. Communications in Computer and Information Science, 265, 82–86.

    Article  Google Scholar 

  16. Jain, V. K., Tapaswi, S., & Shukla, A. (2013). Performance analysis of received signal strength fingerprinting based distributed location estimation system for indoor wlan. Wireless Personal Communications, 70, 113–127.

    Article  Google Scholar 

  17. Meissner, P., Leitinger, E., Froehle, M., & Witrisal, K. (2013). Accurate and robust indoor localization systems using ultra-wideband signals. In European Conference on Navigation (ENC), Vienna.

  18. Waldmann, B., Weigel, R., Ebelt, R., & Vossiek, M. (2012). An ultra-wideband local positioning system for highly complex indoor environments. In International conference on localization and GNSS (ICL-GNSS), Starnberg, Germany (pp. 1–5).

  19. Eberhart, R. C., & Shi, Y. (2000). Comparing inertia weights and constriction factors in particle swarm optimization. In Proceedings of the 2000 congress on evolutionary computation (pp. 84–88).

  20. Hu, X., Shi, Y., & Eberhart, R. (2004). Recent advances in particle swarm. In Proceedings of the CEC2004 congress on evolutionary computation (Vol. 1, pp. 90–97).

  21. del Valle, Y., Venayagamoorthy, G. K., Mohagheghi, S., Hernandez, J. C., & Harley, R. G. (2008). Particle swarm optimization: Basic concepts, variants and applications in power systems. IEEE Transactions on Evolutionary Computation, 12, 171–195.

    Article  Google Scholar 

  22. [Online]. Available: http://www.metageek.net/products/inssider/

  23. Wang, X., Bischoff, O., Laur, R., & Paul, S. (2009). Localization in wireless ad-hoc sensor networks using multilateration with rssi for logistic applications. In Proceedings of the Eurosensors XXIII conference, Bremen, Germany (Vol. 1, pp. 461–464), Published by Elsevier B.V.

  24. Kothari, N., Kannan, B., & Dias, B. M. (2011). Robust indoor localization on a commercial smart-phone. In CMU-RI-TR-11-27, copyright 2010 Carnegie-Mellon University.

  25. Evennou, F., & Marx, F. (2006). Advanced integration of wifi and inertial navigation systems for indoor mobile positioning. Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing, 111, article ID 86706.

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Authors and Affiliations

  1. LETI-National School of Engineers of Sfax, University of Sfax, Route de la Soukra - Km 4, 3038 , Sfax, Tunisia

    Audace Manirabona & Lamia Chaari Fourati

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  1. Audace Manirabona
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  2. Lamia Chaari Fourati
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Correspondence to Lamia Chaari Fourati.

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Manirabona, A., Fourati, L.C. A Kriged Fingerprinting for Wireless Body Area Network Indoor Localization. Wireless Pers Commun 80, 1501–1515 (2015). https://doi.org/10.1007/s11277-014-2095-2

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  • DOI: https://doi.org/10.1007/s11277-014-2095-2

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