Abnormal event detection scheme based on compressive sensing and GM(1,1) in Wireless Sensor Networks

Peng Li, Jian Xin Wang, Jiannong Cao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)


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.
Original languageChinese (Simplified)
Pages (from-to)1586-1590
Number of pages5
JournalDianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology
Issue number7
Publication statusPublished - 1 Jan 2015


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

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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