A GMM-Based Target Classification Scheme for a Node in Wireless Sensor Networks

Youngsoo KIM  Sangbae JEONG  Daeyoung KIM  

IEICE TRANSACTIONS on Communications   Vol.E91-B    No.11    pp.3544-3551
Publication Date: 2008/11/01
Online ISSN: 1745-1345
DOI: 10.1093/ietcom/e91-b.11.3544
Print ISSN: 0916-8516
Type of Manuscript: Special Section PAPER (Special Section on Emerging Technologies for Practical Ubiquitous and Sensor Networks)
target classification,  sensor network,  Gaussian mixture model (GMM),  classification and regression tree (CART),  decision tree,  

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In this paper, an efficient node-level target classification scheme in wireless sensor networks (WSNs) is proposed. It uses acoustic and seismic information, and its performance is verified by the classification accuracy of vehicles in a WSN. Because of the hard limitation in resources, parametric classifiers should be more preferable than non-parametric ones in WSN systems. As a parametric classifier, the Gaussian mixture model (GMM) algorithm not only shows good performances to classify targets in WSNs, but it also requires very few resources suitable to a sensor node. In addition, our sensor fusion method uses a decision tree, generated by the classification and regression tree (CART) algorithm, to improve the accuracy, so that the algorithm drives a considerable increase of the classification rate using less resources. Experimental results using a real dataset of WSN show that the proposed scheme shows a 94.10% classification rate and outperforms the k-nearest neighbors and the support vector machine.