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Personal and Ubiquitous Computing

, Volume 13, Issue 7, pp 499–508 | Cite as

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

  • Youngsoo Kim
  • Sangbae Jeong
  • Daeyoung Kim
  • Tomás Sánchez López
Original Article
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Accepted:

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.

Keywords

Sensor network Target classification Sensor fusion Gaussian mixture model (GMM) Classification and regression tree (CART) 
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Notes

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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Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  • Youngsoo Kim
    • 1
  • Sangbae Jeong
    • 2
  • Daeyoung Kim
    • 1
  • Tomás Sánchez López
    • 1
  1. 1.Real-time and Embedded System LaboratoryInformation and Communications UniversityDaejonKorea
  2. 2.Electronics Engineering DepartmentGyeongsang National UniversityJinjuKorea

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