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ZigBee-based indoor localization system with the personal dynamic positioning method and modified particle filter estimation

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Abstract

We introduce a portable Wireless Sensor Network; which characterized by its great precision, fast detection, real time-monitoring and cheapness. The received signal strength indication (RSSI) is used for estimating the location of the target based on the trilateration algorithm. One of the biggest issues when acquiring a precise location is the numerous calculations that are required within particle filtering. Therefore, we have suggested a modified particle filtering (MPF) using a ZigBee model; in order to minimize both error and huge computations within the indoor environment based on the variance and gradient data-resampling. Increasing the particle weight near the estimated position using RSSI localization helps in avoiding undesired estimations. The MPF algorithm has been enhanced to predict a moving target within an indoor location with an average accuracy of approximately 1.5–2 m while consuming less power. The efficient number of particles has been improved, in addition to the estimated error; in comparison to the classical methods. The results prove that our algorithm can effectively meet the general indoor environmental demands with significant improvements over other algorithms and good position’s evaluation.

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References

  1. Hucharde, M., Paquier, V., Loeillet, A., Marangozov, V., & Nicolai, J-M. (2012). Indoor deployment of a wireless sensor networks for inventory and localization of mobile assets. In IEEE 2012 international conference on RFID-technologies and applications (RFID-TA). Meylan (pp. 369–372).

  2. Zemek, R., Anzai, D., Hara, S., Yanagihara, K., & Kitayama, K. (2008). RSSI-based localization without a prior knowledge of channel model parameters. International Journal of Wireless Information Networks, 15(3, 4), 128–136.

    Article  Google Scholar 

  3. Adeep, M., Islam, B., Haider, M., Tulip, F., & Ericson, M. (2012). An inductive link-based wireless power transfer system for biomedical applications. Active and Passive Electronic Components, 1(9), 1–11.

    Article  Google Scholar 

  4. Farahani, S. (2011). ZigBee wireless networks and transceivers. Burlington, MA: Newnes.

    Google Scholar 

  5. Alhmiedat, T., Abu Salem, A., & Abu Taleb, A. (2013). An improved decentralized approach for tracking multiple mobile targets through zigbee WSNs. International Journal of Wireless and Mobile Networks, 5(3), 61–76.

    Article  Google Scholar 

  6. Sung, W.-T., & Hsu, Y.-C. (2011). Designing an industrial real-time measurement and monitoring system based on embedded system and ZigBee. Expert Systems with Applications, 38(4), 4522–4529.

    Article  Google Scholar 

  7. Doucet, A., Godsill, S., & Andrieu, C. (2000). On sequential monte carlo sampling methods for Bayesian filtering. Statistics and Computing, 10(3), 197–208.

    Article  Google Scholar 

  8. Kwon, S., Yang, K., & Park, S. (2005). Robust mobile robot localization with combined kalman filter-pertorbation estimator. In IEEE/RSJ international conference on intelligent robost and systems. IROS (pp. 4003–4008).

  9. Harvey, A. (1989). Structural time series models and the kalman filter (1st ed.). Cambridge: Cambridge University Press.

    Google Scholar 

  10. Puskorius, G., & Feldkomp, L. (1995). Avoiding matrix inversions for thee decoupled extended kalman filter training algorithm. In Proceedings of the world congress on neural networks. Washington, DC (pp. 704–709).

  11. Iwasaki, I., & Kataoka, T. (1989). Application of an extended kalman filter to parameter identification of an induction motor. In IEEE IAS annual meeting conference record (pp. 248–253).

  12. Sarkka, S., Vehtari, A., & Lampinen, J. (2007). Rao-blackwellized particle filter for multiple target tracking. Inf-Fusion, 8(1), 2–15.

    Article  Google Scholar 

  13. Dung, N., Van, D., Thanh, N., & Wakasug, K. (2013). A new evaluation of particle filter algorithm and apply it to the wires sensor networks. In Proceedings on the computing management and telecommunications. HCM (pp. 169–174).

  14. Djuric, P., Jayesh, H., Yufei, H., et al. (2003). Particle filtering. IEEE Signal Processing Magazine, 20(5), 19–38.

    Article  Google Scholar 

  15. Baker, N. (2005). ZigBee and Bluetooth strengths and weaknesses fore industrial applications. Computer and Control Engineering, 16(2), 20–25.

    Article  Google Scholar 

  16. Lee, H. J., et al. (2009). Ubiquitous healthcare service using ZigBee and mobile phone for elderly patients. International Journal of Medical Informatics, 78(3), 193–198.

    Article  Google Scholar 

  17. Han, D.-M., & Lim, J.-H. (2010). Smart home energy management system using IEEE 802.15.4 and ZigBee. IEEE Transactions on Consumer Electronics, 56(3), 1403–1410.

    Article  Google Scholar 

  18. Subaashini, K., Dhivya, G., & Pitchiah, R. (2012). ZigBee RF signal strength for indoor location sensing—Experiments and results. In 14th international conference on advanced communication technology (ICACT). Pyeongchang (pp. 50–57).

  19. Alhmiedat, T., & Yang, S. (2007). A survey localization and tracking mobile targets through wireless sensor network. In Proceedings of the PGNet conference. Liverpool (pp. 48–53).

  20. Pal, A. (2010). Localization algorithms in wireless sensor networks: Current approaches and future challenges. Network Protocols and Algorithms, 2(1), 45–73.

    Article  Google Scholar 

  21. Han, J., Choi, C.-S., & Lee, I. (2011). More efficient home energy management system based on ZigBee communication and infrared remote controls. IEEE Transactions on Consumer Electronics, 57(1), 85–89.

    Article  Google Scholar 

  22. Lau, S-Y., Lin, T-H., Huang, T-Y., Ng, I-H., & Huang, P. (2009). A measurement study of ZigBee-based indoor localization systems under RF interference. In Proceedings of the 4th ACM international workshop on experimental evaluation and characterization. New York, NY (pp. 35–42).

  23. Sarwar, B., Nallagownden, P., Baharudin, Z., & Muthuvalu, M. (2015). PC based energy efficient wireless transceiver module with ZigBee. Applied Mechanics and Materials, 785, 724–728.

    Article  Google Scholar 

  24. Dardari, D., Falletti, E., & Luise, M. (2011). Satellite and terrestrial radio positioning techniques: A signal processing perspective. London: Elsevier.

    Google Scholar 

  25. Mitilineos, A., Kyriazanos, D., Segou, O., Goufas, J., & Thomopoulos, S. (2010). Indoor localization with wireless sensor networks. Progress in Electromagnetics Research, 109, 441–447.

    Article  Google Scholar 

  26. Dargi, W., & Poellabauer, C. (2010). Fundamentals of wireless sensor networks: Theory and practice. West Sussex: Wiley.

    Book  Google Scholar 

  27. Woo, J., Kim, Y., & Lim, M. (2006). Localization of mobile robot using particle filter. In International joint conference. SI CE-IASE (pp. 3031–3034).

  28. Doucet, A., Freitas, N., & Gordon, N. (2001). Sequential monte carlo methods in practice. Berlin: Springer.

    Book  MATH  Google Scholar 

  29. Jianhua, S., & Liping, H. (2010). STM32W radio frequency zigbee single chip microcomputer principle and application. Beijing: Beijing University of Aeronautics and Astronautics Press.

    Google Scholar 

  30. Alhmiedat, T., Abutaleb, A., & Bsoul, M. (2012). A study on threats detection and tracking for military applications using WSNs. International Journal of Computer Applications, 40(15), 12–18.

    Article  Google Scholar 

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Acknowledgements

All the required tools, which are in the entity of project coded as MF.13.01, for the experiments and the realization of this study, were financed by The Scientific Research Projects Governing Unit of Gaziantep University.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Gaziantep University, Gaziantep, Turkey

    Mahmut Aykaç, Ergun Erçelebi & Noor Baha Aldin

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  1. Mahmut Aykaç
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  2. Ergun Erçelebi
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  3. Noor Baha Aldin
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Correspondence to Mahmut Aykaç.

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Aykaç, M., Erçelebi, E. & Baha Aldin, N. ZigBee-based indoor localization system with the personal dynamic positioning method and modified particle filter estimation. Analog Integr Circ Sig Process 92, 263–279 (2017). https://doi.org/10.1007/s10470-017-0969-4

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  • DOI: https://doi.org/10.1007/s10470-017-0969-4

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