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An efficient scheme of target classification and information fusion in wireless sensor networks

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Abstract

In this paper, an efficient target classification and fusion scheme for wireless sensor networks (WSNs) is proposed and evaluated. When a classification algorithm for WSN nodes is designed, parametric approaches such as Gaussian mixture model (GMM) should be more preferred to non-parametric ones due to the hard limitation in resources. The GMM algorithm not only shows good performances for target classification in WSNs but it also requires very small resources. Based on the classifier, a decision tree generated by the classification and regression tree algorithm is used to fuse the information from heterogeneous sensors. This node-level classification scheme provides a satisfactory classification rate, 94.10%, with little resources. Finally, a confidence-based fusion algorithm improves the overall accuracy by fusing the information among sensor nodes. Our experimental results show that the proposed group-level fusion algorithm improves the accuracy by an average of 4.17% accuracy with randomly selected nodes.

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References

  1. Duarte M, Hu Y-H (2004) Vehicle classification in distributed sensor networks. J Parallel Distribut Comput 64(7):826–838

    Article  Google Scholar 

  2. Meesookho C, Narayanan S, Raghavendra CS (2002) Collaborative classification applications in sensor networks. In: Second IEEE sensor array and multichannel signal processing workshop, pp 370–374

  3. Li D, Wong KD, Hu YH, Sayeed AM (2002) Detection, classification and tracking of targets in distributed sensor networks. IEEE Signal Processing Magazine, pp 17–29

  4. Brooks RR, Griffin C, Friedlander D (2003) Distributed target classification and tracking in sensor networks. Proc IEEE, pp 1163–1171

  5. Arora A et al (2004) A line in the sand: a wireless sensor network for target detection, classification, and tracking. Comput Networks J 46(5):605–634

    Article  Google Scholar 

  6. Dasarathy BV (1990) Nearest neighbor: pattern classification techniques. IEEE Comput Soc. ISBN 0-8186-8930-7

  7. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Mining Knowledge Discovery 2:121–167

    Article  Google Scholar 

  8. Duarte M, Hu Y (2004) Distance based decision fusion in a distributed wireless sensor network. Telecomm Syst 26:339–350

    Article  Google Scholar 

  9. Klein LA (2004) Sensor and data fusion. SPIE. ISBN 0-8194-5435-4

  10. Reynolds DA, (1992) Gaussian mixture modeling approach to text-independent speaker identification. Ph.D. thesis, Georgia Institute of Technology

  11. Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth, CA

    MATH  Google Scholar 

  12. Kay SM (1993) Fundamentals of statistical signal processing: estimation theory. Prentice Hall, NJ. ISBN 0-13-345711-7

  13. Zheng F, Zhang G, Song Z (2001) Comparison of different implementations of MFCC. J Comput Sci Technol 16(6):582–589

    Article  MATH  Google Scholar 

  14. Falk TH, Chan WY (2005) A sequential feature selection algorithm for GMM-based Speech Quality Estimation, European Signal Processing Conference

  15. Xuan G, Zhang W, Chai P (2001) EM algorithms of Gaussian mixture model and hidden markov model. In: Proceedings of the international conference on image processing, pp 145–148

  16. Duda RO, Hart PE, Stork DG (2000) Pattern classification, 2nd edn. John-Wiley, pp 282–347

  17. Loh W-Y, Nunta Vanichsetakul N (1988) Tree-structured classification via generalized discriminant analysis. J Am Stat Associat 83(403):715–728

    Article  MATH  Google Scholar 

Download references

Acknowledgments

This research was supported by the MKE (Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the IITA (Institute of Information Technology Advancement) (IITA-2008-C1090-0801-0047) and the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government(MOST) (No. R0A-2007-000-10038-0)

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Authors and Affiliations

  1. Real-time and Embedded System Laboratory, Information and Communications University, 119 Munjiro, Yuseong-gu, Daejon, 305-732, Korea

    Youngsoo Kim, Daeyoung Kim & Tomás Sánchez López

  2. Electronics Engineering Department, Gyeongsang National University, 900 Gajwa-dong, Jinju, 660-701, Korea

    Sangbae Jeong

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  1. Youngsoo Kim
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  2. Sangbae Jeong
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  3. Daeyoung Kim
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  4. Tomás Sánchez López
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Correspondence to Youngsoo Kim.

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Kim, Y., Jeong, S., Kim, D. et al. An efficient scheme of target classification and information fusion in wireless sensor networks. Pers Ubiquit Comput 13, 499–508 (2009). https://doi.org/10.1007/s00779-009-0225-8

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  • DOI: https://doi.org/10.1007/s00779-009-0225-8

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