Skip to main content
SpringerLink
Log in

Measuring the uncertainty of RFID data based on particle filter and particle swarm optimization

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

The management of the uncertainties over data is an urgent problem of novel applications such as cyber-physical system, sensor network and RFID data management. In order to adapt the characteristics of evolving over time of sensor data in real-time location tracing service based on RFID, a measuring algorithm for the Uncertainty of RFID Data-PPMU (a particle filter and particle swarm optimization-based measuring uncertainty algorithm for RFID Data) is proposed in this paper. PPMU can change the number of samples adaptively on the basis of K–L distance to adapt the evolution of RFID data, and PPMU introduces an improved PSO (particle swarm optimization) method to enhance the efficiency of re-sampling phase of SIRPF (sequential importance re-sampling particle filter). Meanwhile, PPMU defines a fitness function base on Conventional Weighted Aggregation for PSO that balances the importance between the priori density and likelihood density to detect the most optimal samples among candidate sample sets. It provides a measurement with confidence factor for initial tuples in the probability RFID database. Experiments on real dataset show the proposed method can effectively measure the underlying uncertainty over RFID data. Compared with existing algorithms, PPMU can be further improved particle degradation and particle impoverishment problem.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Derakhshan, R., Orlowska, M. E., & Xue, L. (2007). RFID data management: Challenges and opportunities. In Proceeding of IEEE International conference on RFID 2007(IEEE RFID 2007), 26–28 March, Texas, USA.

  2. Benjelloun, O., Sarma, A., Halevy, A., & Widom, J. (2006). Uldbs: Databases with uncertainty and lineage. In Proceeding of the 32th International Conference on very large data base (VLDB’06), 12–15 September, Seoul, Korea.

  3. Zhou, A.-Y., Jin, C.-Q., Wang, G.-R., & Li, J.-Z. (2009). A survey on the management of uncertain data. Chinese Journal of Computers, 32(1), 1–16.

    Article  Google Scholar 

  4. Sarma, A. D., Theobald, M., & Widom, J. (2008). Exploiting lineage for confidence computation in uncertain and probabilistic databases. In Proceedings of the 24th IEEE International conference on data engineering (ICDE’08), 7–12 April, Cancun, Mexico.

  5. Hue, C., Cadre, J., & Perez, P. (2002). Sequential Monte Carlo methods for multiple target tracking and data fusion. IEEE Transactions on Signal Processing, 50(2), 309–325.

    Article  Google Scholar 

  6. Orton, M., & Fitzgerald, W. (2002). A Bayesian approach to tracking multiple targets using sensor arrays and particle filters. IEEE Transactions on Signal Processing, 50(2), 216–223.

    Article  MathSciNet  Google Scholar 

  7. Penny, D., & Williams, M. (2000). A sequential approach to Multisensor resource management using particle filters. In Proceedings of SPIE on signal and data processing of small targets. July-August, San Diego, CA, USA.

  8. Hu, C.-H., Zhang, Q., & Qiao, Y.-K. (2008). A strong tracking particle filter with application to fault prediction. Acta Electronica Sinica, 34(12), 1522–1528.

    MathSciNet  Google Scholar 

  9. Higuchi, T. (1997). Monte Carlo filtering using genetics algorithm operators. Journal of Statistical Computation and Simulation, 59(1), 1–23.

    Article  MathSciNet  MATH  Google Scholar 

  10. Yuan, Z.-J., Zheng, N.-N., & Jia, X.-C. (2003). The gauss hermite particle filter. Acta Electronica Sinica, 31(7), 970–973.

    Google Scholar 

  11. Christopher, R., Letchner, J., Balazinksa, M., & Suciu, D. (2008). Event queries on correlated probabilistic streams. In Proceedings of the ACM SIGMOD International conference on management of data (SIGMOD’08), 9–12 June, Vancouver, Canada.

  12. Fang, Z., Tong, G., & Xu, X. (2007). Particle swarm optimized particle filter. Control and Decision, 22(3), 273–277.

    MATH  Google Scholar 

  13. Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-gaussian bayesian state estimation. IEE–Proceedings–F, 140(2), 107–113.

  14. Fox, D. (2003). Adapting the sample size in particle filters through KLD-sampling. The International Journal of Robotics Research, 22(12), 985–1003.

    Article  Google Scholar 

  15. Geweke, J. (1989). Bayesian inference in econometric models using Monte Carlo integration. Econometrica, 57, 1317–1339.

    Article  MathSciNet  MATH  Google Scholar 

  16. Doucet, A., de Freitas, N., & Gordon, N. (2001). An introduction to sequential Monte Carlo methods. In A. Doucet, N. de Freitas, N. Gordon, & A. Smith (Eds.), Sequential Monte Carlo methods in practice. New York: Springer. http://www.amazon.com/Sequential-Practice-Statistics-Engineering-Information/dp/0387951466/ref=sr_1_2?s=books&ie=UTF8&qid=1323445739&sr=1-2.

  17. Parsopoulos, K. E., & Vrahatis, M. N. (2002). Particle swarm optimization method in multiobjective problems. In Proceedings of the ACM 2002 Symposium on Appl. Comp. (SAC’02), 10–14 March, Madrid, Spain.

  18. Weisstein Eric, W. (2011). Wilcoxon rank sum test. From MathWorld–A Wolfram Web Resource. See http://mathworld.wolfram.com/WilcoxonRankSumTest.html.

Download references

Acknowledgments

Supported by the National Natural Science Foundation of China under Grant No. 60803001, 61170035, 60850002, National Natural Science Foundation of Jiangsu under Grant No. BK2011225, the Special Grants of China Postdoctoral Science Foundation (No. 200902517), the Jiangsu Province Postdoctoral Science Foundation under Grant No. 0801043B, the special project of Nanjing Scientific Committee Foundation (No. 020142010), the Qing Lan Project Foundation of Jiangsu Province 2010, the Star of Excellence Zijin Foundation of NJUST 2009.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China

    Yongli Wang

  2. School of Information Science and Engineering, Ningbo University, Ningbo, China

    Jiangbo Qian

Authors
  1. Yongli Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiangbo Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yongli Wang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, Y., Qian, J. Measuring the uncertainty of RFID data based on particle filter and particle swarm optimization. Wireless Netw 18, 307–318 (2012). https://doi.org/10.1007/s11276-011-0401-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-011-0401-4

Keywords

Search

Navigation