A multi-class classification approach for target localization in wireless sensor networks
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.
KeywordsWireless sensor networks Target localization Support vector learning Multi-class classification
Unable to display preview. Download preview PDF.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- U. Krebel, Pairwise classification and support vector machines, Advances in Kernel Methods: Support Vector Learning, MIT Press, Cambridge, MA, (1999) 255–268.Google Scholar
- 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
- 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