IEICE TRANSACTIONS on Communications
A GMM-Based Target Classification Scheme for a Node in Wireless Sensor Networks
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E91-B No.11 pp.3544-3551
- Publication Date
- 2008/11/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1093/ietcom/e91-b.11.3544
- Type of Manuscript
- Special Section PAPER (Special Section on Emerging Technologies for Practical Ubiquitous and Sensor Networks)
- Category
Authors
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Youngsoo KIM, Sangbae JEONG, Daeyoung KIM, "A GMM-Based Target Classification Scheme for a Node in Wireless Sensor Networks" in IEICE TRANSACTIONS on Communications,
vol. E91-B, no. 11, pp. 3544-3551, November 2008, doi: 10.1093/ietcom/e91-b.11.3544.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/communications/10.1093/ietcom/e91-b.11.3544/_p
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@ARTICLE{e91-b_11_3544,
author={Youngsoo KIM, Sangbae JEONG, Daeyoung KIM, },
journal={IEICE TRANSACTIONS on Communications},
title={A GMM-Based Target Classification Scheme for a Node in Wireless Sensor Networks},
year={2008},
volume={E91-B},
number={11},
pages={3544-3551},
abstract={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.},
keywords={},
doi={10.1093/ietcom/e91-b.11.3544},
ISSN={1745-1345},
month={November},}
Copy
TY - JOUR
TI - A GMM-Based Target Classification Scheme for a Node in Wireless Sensor Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 3544
EP - 3551
AU - Youngsoo KIM
AU - Sangbae JEONG
AU - Daeyoung KIM
PY - 2008
DO - 10.1093/ietcom/e91-b.11.3544
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E91-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2008
AB - 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.
ER -
English
