Journal of Mechanical Science and Technology

, Volume 28, Issue 1, pp 323–329 | Cite as

A multi-class classification approach for target localization in wireless sensor networks

  • Woojin Kim
  • Jaemann Park
  • H. Jin Kim
  • Chan Gook Park


Target localization problem, in which the location of an unknown target is estimated, is one of the key issues in applications of wireless sensor networks (WSNs). Target localization methods that directly use raw sensor data can suffer from uncertainty or disturbance caused by the surrounding environmental elements and noise. Especially, when using WSNs, the limited communication capacity can impose a significant limit on the amount of data that can be processed. Considering these issues, various methods have been proposed, especially using machine learning techniques such as neural networks or support vector machines. In this paper, we employ a multi-class classification algorithm for target localization, in which a pseudo probability map is constructed using modified support vector domain description. A local classification strategy which uses information from local neighbors only is proposed in order to reduce communication costs. Experimental results using an acoustic WSN are compared with Platt’s method to validate the multi-class classification algorithm.


Wireless sensor networks Target localization Support vector learning Multi-class classification 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    G. J. Pottie and W. J. Kaiser, Wireless integrated network sensors, Communications of the ACM, 43(5) May (2000) 551–58.CrossRefGoogle Scholar
  2. [2]
    F. Viani, P. Rocca, G. Oliveri and A. Massa, Pervasive remote sensing through WSNs, EuCAP 2012, Prague, Czech Republic, March 26–30 (2012).Google Scholar
  3. [3]
    F. Viani, P. Rocca, G. Oliveri, D. Trinchero and A. Massa, Localization, tracking, and imaging of targets in wireless sensor networks: An invited review, Radio Science, 46 (2011).Google Scholar
  4. [4]
    M. Walpola, Z. Hao and Z. Jinsong, Self organization algorithm for unattended acoustic sensor networks in ground target tracking, Proc. IEEE Wireless Communication and Networking Conference, March (2007) 2350–354.Google Scholar
  5. [5]
    X. Sheng and Y. Hu, Energy based acoustic source localization, Proc. 2 nd Workshop on Information Processing in Sensor Networks, April (2003) 285–300.Google Scholar
  6. [6]
    F. Viani, P. Rocca, M. Benedetti, G. Oliveri and A. Massa, Electromagnetic passive localization and tracking of moving targets in a WSN-infrastructured environment, Inverse Problems — Special Issue on Electromagnetic Inverse Problems: Emerging Methods and Novel Applications, 26, 074003, March (2010) 1–15.Google Scholar
  7. [7]
    K. Mechitov, S. Sundresh, Y. Kwon and G. Agha, Cooperative tracking with binary-detection sensor networks, Proc. The 1 st International Conference on Embedded Networked Sensor Systems (2003) 332–333.Google Scholar
  8. [8]
    S. Simic, A learning theory approach to sensor networks, IEEE Pervasive Computing, 2(4) (2003) 44–49.CrossRefGoogle Scholar
  9. [9]
    X. Nguyen, M. Jordan and B. Sinopoli, A kernel-based learning approach to ad-hoc sensor network localization, ACM Transactions on Sensor Networks, 1, August (2005) 134–152.CrossRefGoogle Scholar
  10. [10]
    J. Chen, R. Hudson and K. Yao, A maximum likelihood parametric approach to source localization, Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing 2001 (2001) 1043–1046.Google Scholar
  11. [11]
    D. Gu and Z. Wang, Distributed regression over sensor networks: A support vector machine approach, Proc. IEEE/RSJ international Conference on Intelligent Robots and Systems, September (2008) 3286–3291.Google Scholar
  12. [12]
    J. Predd, S. Kulkarni and H. Poor, Distributed learning in wireless sensor networks, IEEE Signal Processing Magazine, 23(4) (2006) 56–59.CrossRefGoogle Scholar
  13. [13]
    C. W. Hsu and C. J. Lin, A comparison of methods for multiclass support vector machines, IEEE Trans. Neural Networks, 13(2) (2002) 415–425.CrossRefGoogle Scholar
  14. [14]
    K. B. Duan and S. Sathiya Keerthi, Which is the best multiclass SVM method?: an empirical study, Multiple Classifier System, 3541, Springer, Berlin (2005) 278–285.CrossRefGoogle Scholar
  15. [15]
    V. N. Vapnik, Statistical learning theory, Wiley, New York (1998).zbMATHGoogle Scholar
  16. [16]
    U. Krebel, Pairwise classification and support vector machines, Advances in Kernel Methods: Support Vector Learning, MIT Press, Cambridge, MA, (1999) 255–268.Google Scholar
  17. [17]
    D. M. Tax and R. P. Duin, Support vector data description, Machine Learning, 54 (2004) 45–66.CrossRefzbMATHGoogle Scholar
  18. [18]
    W. Kang and J. Choi, Domain density description for multiclass pattern classification with reduced computational load, Pattern Recognition, 41(6) (2008) 1997–2007.CrossRefzbMATHMathSciNetGoogle Scholar
  19. [19]
    J. Platt, Probabilistic outputs for support vector machines and comparison to regularized likelihood methods, Advances in Large Margin Classifiers, MIT Press, Cambridge, MA, (1999).Google Scholar
  20. [20]
    F. Viani, L. Lizzi, P. Rocca, M. Benedetti, M. Donelli and A. Massa, Object tracking through RSSI measurements in wireless sensor networks, Electronics Letters, 44(10) (2008) 653–654.CrossRefGoogle Scholar
  21. [21]
    W. Kim, J. Park and H. J. Kim, Target localization using ensemble support vector regression in wireless sensor networks, IEEE Wireless Communications and Networking Conference, April (2010) 1–5.Google Scholar

Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Woojin Kim
    • 1
  • Jaemann Park
    • 1
  • H. Jin Kim
    • 1
  • Chan Gook Park
    • 1
  1. 1.School of Mechanical and Aerospace EngineeringSeoul National UniversitySeoulKorea

Personalised recommendations