TY - GEN
T1 - Wireless sensor network fault detection via semi-supervised local kernel density estimation
AU - Zhao, Mingbo
AU - Chow, Tommy W.S.
PY - 2015/6/16
Y1 - 2015/6/16
N2 - Wireless sensor network (WSN) has become widely used in different applications. Fault detection of sensors is importance for maintaining a reliable WSN operation. And identification of faulty nodes in a WSN can be transformed into a pattern classification problem. In this paper, we introduce an effective label propagation procedure using semi-supervised local kernel density estimation. The proposed method estimates the posterior probability of a scene belonging to the faulty and it can preserve the manifold structure of dataset due to the utilization of kNN kernel for density estimation. Simulations based on a WSN are presented to show the effectiveness of the methods. The results demonstrate that our proposed algorithm can achieve better classification performance compared with other state-of-art semi-supervised learning methods.
AB - Wireless sensor network (WSN) has become widely used in different applications. Fault detection of sensors is importance for maintaining a reliable WSN operation. And identification of faulty nodes in a WSN can be transformed into a pattern classification problem. In this paper, we introduce an effective label propagation procedure using semi-supervised local kernel density estimation. The proposed method estimates the posterior probability of a scene belonging to the faulty and it can preserve the manifold structure of dataset due to the utilization of kNN kernel for density estimation. Simulations based on a WSN are presented to show the effectiveness of the methods. The results demonstrate that our proposed algorithm can achieve better classification performance compared with other state-of-art semi-supervised learning methods.
KW - Fault detection
KW - Graph based semi-supervised learning
KW - Pattern classification
KW - Wireless sensor network
UR - https://www.scopus.com/pages/publications/84937710380
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84937710380&origin=recordpage
U2 - 10.1109/ICIT.2015.7125308
DO - 10.1109/ICIT.2015.7125308
M3 - RGC 32 - Refereed conference paper (with host publication)
VL - 2015-June
SP - 1495
EP - 1500
BT - Proceedings of the IEEE International Conference on Industrial Technology
PB - IEEE
T2 - 2015 IEEE International Conference on Industrial Technology, ICIT 2015
Y2 - 17 March 2015 through 19 March 2015
ER -