6th International ICST Conference on Communications and Networking in China

Research Article

Compressed Sensing for Abnormal Event Detection in Wireless Networks

  • @INPROCEEDINGS{10.1109/ChinaCom.2011.6158112,
        author={Yu Xia and Zhifeng Zhao and Xiao Wang and honggang Zhang},
        title={Compressed Sensing for Abnormal Event Detection in Wireless Networks},
        proceedings={6th International ICST Conference on Communications and Networking in China},
        publisher={IEEE},
        proceedings_a={CHINACOM},
        year={2012},
        month={3},
        keywords={compressed sensing event detection wireless networks iterative scheme for event detection (ised) detection probability false alarm},
        doi={10.1109/ChinaCom.2011.6158112}
    }
    
  • Yu Xia
    Zhifeng Zhao
    Xiao Wang
    honggang Zhang
    Year: 2012
    Compressed Sensing for Abnormal Event Detection in Wireless Networks
    CHINACOM
    IEEE
    DOI: 10.1109/ChinaCom.2011.6158112
Yu Xia1, Zhifeng Zhao1,*, Xiao Wang1, honggang Zhang1
  • 1: Zhejiang University
*Contact email: zhaozf@zju.edu.cn

Abstract

Compressed sensing (CS) is a recently developed theory that has earned increasing interests in the area of wireless communications and signal processing. It states that the main information of a signal can be recovered from a relatively small number of linear projections. Event detection is an important application of wireless networks which has also attracted much attention. Recent research shows that CS based mechanism can be applied to event detection if the signature of event is sparse in a certain domain. In this article, we propose a novel scheme for abnormal event detection in the noise-involved networking environment by an ameliorated reconstruction method, with no prior information regarding the targeted wireless networks. We also analyze the trade-off between detection probability and false alarm. Finally we give a novel metric which helps to evaluate the results of detection. Simulation shows that our scheme proves to be effective and we can also acquire the best state for appropriate detection with the global data processed.