Abstract
The rise of edge computing has promoted the development of the industrial internet of things (IIoT). Supported by edge computing technology, data acquisition can also support more complex and perfect application requirements in industrial field. Most of traditional sampling methods use constant sampling frequency and ignore the impact of changes of sampling objects during the data acquisition. For the problem of sampling distortion, edge data redundancy and energy consumption caused by constant sampling frequency of sensors in the IIoT, a data-driven adaptive sampling method based on edge computing is proposed in this paper. The method uses the latest data collected by the sensors at the edge node for linear fitting and adjusts the next sampling frequency according to the linear median jitter sum and adaptive sampling strategy. An edge data acquisition platform is established to verify the validity of the method. According to the experimental results, the proposed method is more effective than other adaptive sampling methods. Compared with constant sampling frequency, the proposed method can reduce the edge data redundancy and energy consumption by more than 13.92% and 12.86%, respectively.
Highlights
With the arrival of the era of industrial internet of things (IIoT) [1] and big data [2], more and more data can be collected and analyzed
Aiming at the problem of sampling distortion, edge data redundancy [17] and energy consumption caused by the constant sampling frequency of traditional IIoT, a data-driven adaptive sampling method based on edge computing is proposed in this paper
According tosampling the sampling curve, method this paper better restore the actual curve of the object and we find that the sampling by the of adaptive sampling method proposed this of paper the can sampling curve is closer curves to the obtained actual change the sampling object
Summary
With the arrival of the era of industrial internet of things (IIoT) [1] and big data [2], more and more data can be collected and analyzed. If all the data on the edge side is uploaded to the cloud computing center for analysis and processing, it will cause a serious problem of insufficient network resources. This large amount of data poses new challenges to the development of cloud computing [10,11]. Aiming at the problem of sampling distortion, edge data redundancy [17] and energy consumption caused by the constant sampling frequency of traditional IIoT, a data-driven adaptive sampling method based on edge computing is proposed in this paper.
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