Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 210500195-6.doi: 10.11896/jsjkx.210500195

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Lossless Data Compression Method Based on Edge Computing

ZHANG Xiao-mei1,2, CAO Ying1, LOU Ping1,2, JIANG Xue-mei1,2, YAN Jun-wei1,2, LI Da1,2   

  1. 1 School of Information Engineering,Wuhan University of Technology,Wuhan 430070,China
    2 Hubei Key Laboratory of Broadband Wireless Communications and Sensor Networks,Wuhan 430070,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:ZHANG Xiao-mei,born in 1977,Ph.D,professor,postgraduate supervisor.Her main research interests include intelligent information processing and information security
    LI Da,born in 1980,Ph.D,associate professor.His main research interests include statistical signal processing,computer vision,machine learning, and pattern recognition.
  • Supported by:
    Application Foundation Frontier Special Project of Wuhan Science and Technology Bureau(2020010601012176) and National Natural Science Foundation of China(52075404).

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Abstract: Through the Industrial Internet of Things(IIoT),various industrial equipments,monitoring instruments and sensors can be connected to each other.The operating status of equipment can be fully sensed through monitoring instruments and sensors,and the equipment status can be analyzed and predicted based on sensing data.However,the analysis and processing of massive amounts of sensing data requires a lot of storage space and computing power.Sending it to the cloud platform will inevitably take up a lot of bandwidth and cause a large delay.It is difficult to meet the requirements for real-time analysis and diagnosis of device status.Therefore,for the state-aware data of industrial equipment,a lossless compression method based on optimal diffe-rential and linear fitting entropy reduction transform is proposed,and the perceptual data is compressed losslessly at the edge of data collection,so that the transmission efficiency is greatly improved and the perceptual data can be quickly transmitted to the cloud platform for analysis and processing.This method selects an effective entropy reduction transform from the optimal diffe-rence and curve fitting difference according to the variance of data difference sequence and the acquisition frequency,and uses Lempel-Ziv-Oberhumer(LZO) compression algorithm for secondary compression.The new method is tested on two different data sets.Experimental results show that the lowest and highest compression rate of this method can reach 77% and 93%,respectively.At the same time,the characteristics of its lossless reconstruction are verified.

Key words: Industrial Internet of things, Edge computing, Perceptual data, Reduced entropy transformation, LZO

CLC Number: 

  • TP391
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