Abnormal Event Detection Scheme Based on Compressive Sensing and GM (1,1) in Wireless Sensor Networks

Li Peng; Wang Jian-xin; Cao Jian-nong

doi:10.11999/JEIT141219

Volume 37 Issue 7

Jul. 2015

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Li Peng, Wang Jian-xin, Cao Jian-nong. Abnormal Event Detection Scheme Based on Compressive Sensing and GM (1,1) in Wireless Sensor Networks[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1586-1590. doi: 10.11999/JEIT141219

Citation:

Li Peng, Wang Jian-xin, Cao Jian-nong. Abnormal Event Detection Scheme Based on Compressive Sensing and GM (1,1) in Wireless Sensor Networks[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1586-1590. doi: 10.11999/JEIT141219

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Abnormal Event Detection Scheme Based on Compressive Sensing and GM (1,1) in Wireless Sensor Networks

doi: 10.11999/JEIT141219

1.
(School of Information Science and Engineering, Central South University, Changsha 410083, China)
2.

Received Date: 2014-09-17
Rev Recd Date: 2015-03-02
Publish Date: 2015-07-19

Abstract

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.
- Wireless Sensor Networks (WSN),
- Anomaly event detection,
- Compressive Sensing (CS),
- Grey Model(1,1) ),
- Signal reconstruction,
- Energy consumption

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