Computer Science

Computer Science ›› 2018, Vol. 45 ›› Issue (11): 304-311.doi: 10.11896／j.issn.1002-137X.2018.11.049

• Interdiscipline & Frontier • Previous Articles     Next Articles

Dynamic Data Compression Strategy Based on Internet of Vehicle

HUANG Zhi-qing1,2, LI Meng-jia1,2, TIAN Rui1,2, ZHANG Yan-xin3, WANG Wei-dong1,2   

  1. (Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China)1
    (Beijing Engineering Research Center for IoT Software and System,Beijing University of Technology,Beijing 100124,China)2
    (Advanced Control Systems Laboratory,School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China)3
  • Received:2017-11-19 Published:2019-02-25

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Abstract: Internet of Vehicle (IoV) can effectively cover the sensing area,so it is applied to large-scale urban sensing.Meanwhile,in order to solve the problem that it is difficult for the IoV to transfer a large amount of data,the compressive sensing (CS) is used to compress the data with spatio-temporal correlation by some researchers.However,the current researches on the applications of CS in the IoV do not consider dynamic changes in data characteristic and vehicle distribution,which may lead to unacceptable errors.In order to ensure the accuracy of data reconstruction,this paper proposed a dynamic CS approach in the IoV.The approach can automatically analyze the relationship among data chara-cteristic,vehicle distribution and the number of measurements.Then,based on the CS,a function of adjusting the number of measurements is added.Through the analysis of data characteristic and vehicle distribution,theparameters of the observation matrixin CS is adjusted in real time so as to improve the accuracy of reconstruction to achieve higher quality data transmission.The experiment shows that the proposed dynamic CS method improves the reconstruction accuracy by 15.3% compared with the existing CS method in the IoV.

Key words: Compressive sensing, Dynamic compression, Internet of vehicle, Reconstruction accuracy, Urban sensing

CLC Number: 

  • TP393
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