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• W3015354227 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." @default.
• W3015354227 created "2020-04-17" @default.
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• W3015354227 date "2020-04-12" @default.
• W3015354227 modified "2023-10-16" @default.
• W3015354227 title "A Data-Driven Adaptive Sampling Method Based on Edge Computing" @default.
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• W3015354227 doi "https://doi.org/10.3390/s20082174" @default.
• W3015354227 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7218728" @default.
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