Computer Science ›› 2020, Vol. 47 ›› Issue (1): 276-280.doi: 10.11896/jsjkx.180901667

• Computer Network • Previous Articles     Next Articles

Data Abnormality Processing in Wireless Sensor Networks Based on Distributed Compressed Sensing

HOU Ming-xing1,QI Hui1,HUANG Bin-ke2   

  1. (Department of Computer Science and Technology,Taiyuan Normal University,Jinzhong,Shanxi 030619,China)1;
    (School of Electronics and Information Engineering,Xi’an Jiaotong University,Xi’an 710049,China)2
  • Received:2018-09-06 Published:2020-01-19
  • About author:HOU Ming-xing,born in 1990,postgraduate,is member of China Computer Federation (CCF).Her main research interests include compressed sensing,wireless sensor network and machine learning;QI Hui,born in 1981,postgraduate,associate professor,postgraduate supervisor,is member of China Computer Fe-deration (CCF).Her main research interests include machine learning and cloud computing.
  • Supported by:
    This work was supported by the Project of Educational Committee of Shanxi Province (J2018159),Key Research and Development Program of Shanxi Province (201803D31055),Key Research and Development Program of Shanxi Province (201803D121088).

Abstract: In wireless sensor networks,massive data acquisition,transmission and processing not only pose severe challenges to the processing ability and power consumption of sensors,but also suffer data anomalies frequently due to sensor failures or sudden changes of environmental factors,which cannot be effectively dealt with by traditional data processing methods.Regarding the problems above,this paper proposed two kinds of data abnormality models and corresponding processing method based on distri-buted compressed sensing (DSC) for wireless sensor networks.When the data collected by multiple sensors contains abnormal components,the DCS reconstructs the same normal component of data only once and the different abnormal components individual-ly,which avoids the repeated processing of the same normal component and improves the processing efficiency of the data containing abnormal components.In addition,DCS makes full use of the correlation of data,which can effectively reduce the amount of data acquisition and enhance the robustness against data anomalies.Numerical simulation results of two kinds of data abnormality models show that compared with the traditional compressed sensing based on single set of measurement,the data processing method based on DCS improves the accuracy of abnormal data reconstruction and reduces the amount of data by about 33%,which proves the effectiveness of the proposed method.

Key words: Compressed sampling, Data abnormality, Distributed compressed sensing, Joint sparsity, Wireless sensor networks

CLC Number: 

  • TP393
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