Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12753
Title: Abnormal event detection scheme based on compressive sensing and GM(1,1) in Wireless Sensor Networks
Authors: Li, P
Wang, JX
Cao, JN 
Keywords: Anomaly event detection
Compressive sensing (CS)
Energy consumption
Grey Model(1,1) (GM(1,1))
Signal reconstruction
Wireless sensor networks (WSN)
Issue Date: 2015
Publisher: 中国科学院电子学研究所
Source: 电子与信息学报 (Journal of electronics and information technology), 2015, v. 37, no. 7, p. 1586-1590 How to cite?
Journal: 电子与信息学报 (Journal of electronics and information technology) 
Abstract: In order to solve the problems of the low accuracy and the high energy cost by the existing abnormal event detection algorithm in Wireless Sensor Networks (WSN), this paper proposes an abnormal event detection algorithm based on Compressive Sensing (CS) and Grey Model(1,1) (GM(1,1)). Firstly, the network is divided into the clusters, and the data are sampled based on compressive sensing and are forwarded to the Sink. According to the characteristics of the unknown data sparsity in WSN, this paper proposes a block-sparse signal reconstruction algorithm based on the adaptive step. Then the abnormal event is predicted based on the GM(1,1) at the Sink node, and the work status of the node is adaptively adjusted. The simulation results show that, compared with the other anomaly detection algorithms, the proposed algorithm has lower probability of false detection and missed detection, and effectively saves the energy of nodes, with assurance the reliability of abnormal event detection at the same time.
URI: http://hdl.handle.net/10397/12753
ISSN: 1009-5896
DOI: 10.11999/JEIT141219
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