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A Data-driven Adaptive Sampling Method Based on Edge Computing.

Ping Lou1,2, Liang Shi1,2, Xiaomei Zhang1,2

  • 1School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.

Sensors (Basel, Switzerland)
|April 16, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive sampling method for the industrial internet of things (IIoT) using edge computing. The novel approach reduces data redundancy and energy consumption by adjusting sensor sampling frequencies based on real-time data analysis.

Keywords:
adaptive samplingdata acquisitionedge computingindustrial internet of thingslinear median jitter sum

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Area of Science:

  • * Industrial Internet of Things (IIoT)
  • * Edge Computing
  • * Sensor Data Acquisition

Background:

  • * Traditional sampling methods in IIoT often use constant sampling frequencies, neglecting dynamic changes in industrial environments.
  • * This leads to issues like sampling distortion, excessive data redundancy, and increased energy consumption in edge computing scenarios.
  • * Existing adaptive sampling methods may not fully address these challenges in complex industrial fields.

Purpose of the Study:

  • * To propose a data-driven adaptive sampling method for IIoT leveraging edge computing.
  • * To mitigate sampling distortion, edge data redundancy, and energy consumption.
  • * To enhance the efficiency and effectiveness of data acquisition in industrial settings.

Main Methods:

  • * Development of a data-driven adaptive sampling strategy executed at the edge node.
  • * Utilizing linear fitting on the latest sensor data to predict and adjust future sampling frequencies.
  • * Implementation of a strategy based on linear median jitter sum for dynamic frequency adjustment.
  • * Validation through an established edge data acquisition platform.

Main Results:

  • * The proposed adaptive sampling method demonstrated superior effectiveness compared to existing adaptive techniques.
  • * Significant reductions in edge data redundancy (over 13.92%) and energy consumption (over 12.86%) were achieved compared to constant sampling frequencies.
  • * Experimental validation confirmed the method's capability to optimize data acquisition in IIoT.

Conclusions:

  • * The proposed edge-based adaptive sampling method effectively addresses limitations of traditional constant sampling frequencies in IIoT.
  • * The approach offers substantial improvements in reducing data redundancy and energy usage.
  • * This method provides a more efficient and sustainable solution for data acquisition in industrial edge computing environments.