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A Robust Location Tracking Using Ubiquitous RFID Wireless Network

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4159))

Abstract

A dangerous workplace like the iron production company needs a durable monitoring of workers to protect them from an critical accident. This paper concerns about a robust and accurate location tracking method using ubiquitous RFID wireless network. The sensed RSSI signals obtained from the RFID readers are very unstable in the complicated and propagation-hazard workplace like the iron production company. So, the existing particle filter can not provide a satisfactory location tracking performance. To overcome this limitation, we propose a double layered particle filter, where the lower layer classifies the block in which the tag is contained by the SVM classifier and the upper layer estimates the accurate location of tag owner by the particle filter within the classified block. This layered structure improves the location estimation and tracking performance because the evidence about the location from the lower layer makes a effective restrict on the range of possible locations of the upper layer. We implement the proposed location estimation and tracking system using the ubiquitous RFID wireless network in a noisy and complicated workplace (100m × 50m) where which 49 RFID readers and 9 gateways are located in the fixed locations and the maximally 100 workers owning active RFID tags are moving around the workplace. Many extensive experiments show that the proposed location estimation and tracking system is working well in a real-time and the position error is about 2m at maximum.

This work is financially supported by the Ministry of Education and Human Resources Development(MOE), the Ministry of Commerce, Industry and Energy(MOCIE) and the Ministry of Labor(MOLAB) through the fostering project of the Lab of Excellency, and also partially supported by the Regional Innovation System(RIS) of the Ministry of Commerce, Industry and Energy(MOCIE).

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References

  1. Hightower, J., Borriello, G.: Location systems for ubiquitous computing. Computer 34, 56–66 (2001)

    Article  Google Scholar 

  2. Hightower, J.: The location stack. Ph.D. thesis (2004)

    Google Scholar 

  3. Fox, D.: Bayesian techniques for location estimation, 16–18 (2003)

    Google Scholar 

  4. Guvenc, I., Abdallah, C., Jordan, R., Dedeoglu, O.: Enhancements to rss based indoor tracking systems using kalman filters. In: International Signal Processing Conference and Global Signal Processing Expo., pp. 91–102 (2003)

    Google Scholar 

  5. Hightower, J., Borriello, G.: Particle filters for location estimation in ubiquitous computing: A case study. In: Davies, N., Mynatt, E.D., Siio, I. (eds.) UbiComp 2004. LNCS, vol. 3205, Springer, Heidelberg (2004)

    Google Scholar 

  6. Burges, C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)

    Article  Google Scholar 

  7. Jordan, M.: An introduction to probabilistic graphical models (2003)

    Google Scholar 

  8. Fox, D., Hightower, J., Liao, L., Schulz, D., Borriello, G.: Bayesian filters for location estimation. IEEE Pervasive Computing 2, 24–33 (2003)

    Article  Google Scholar 

  9. Doucet, A., Godsill, S., Andrieu, C.: On sequential monte carlo sampling methods for bayesian filtering. Statistics and Computing 10, 197–208 (2000)

    Article  Google Scholar 

  10. Isard, M., Blake, A.: Condensation - conditional density propagation for visual tracking. International Journal of Computer Vision 29, 5–28

    Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Yun, K., Choi, S., Kim, D. (2006). A Robust Location Tracking Using Ubiquitous RFID Wireless Network. In: Ma, J., Jin, H., Yang, L.T., Tsai, J.JP. (eds) Ubiquitous Intelligence and Computing. UIC 2006. Lecture Notes in Computer Science, vol 4159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11833529_12

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  • DOI: https://doi.org/10.1007/11833529_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38091-7

  • Online ISBN: 978-3-540-38092-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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