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Linear Interval Approximation for Smart Sensors and IoT Devices.

Marin B Marinov1, Nikolay Nikolov2, Slav Dimitrov2

  • 1Faculty of Electronic Engineering and Technologies, Technical University of Sofia, 1756 Sofia, Bulgaria.

Sensors (Basel, Switzerland)
|February 15, 2022
PubMed
Summary
This summary is machine-generated.

This study presents a novel adaptive method for approximating sensor characteristics, reducing data points for microcontrollers. This approach optimizes memory usage and enables efficient processing on resource-constrained devices.

Keywords:
IoTapproximationlinearization techniquespiecewise approximationrecourse constrained devicessmart sensors

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Area of Science:

  • Engineering
  • Computer Science
  • Signal Processing

Background:

  • Resource constraints in smart devices necessitate efficient algorithms.
  • Accurate sensor data processing is crucial for intelligent systems.
  • Existing methods may not be optimal for low-power, memory-limited microcontrollers.

Purpose of the Study:

  • To develop an adaptive piecewise linear approximation for differentiable sensor characteristics.
  • To minimize the number of data points required to represent sensor behavior.
  • To create a discreet inverse sensor characteristic with a controlled approximation error.

Main Methods:

  • Adaptive piecewise linear interval approximation of sensor functions.
  • Minimization of points defining the characteristic while adhering to a maximum error tolerance.
  • Directly deriving a linearized form of the inverse sensor characteristic.

Main Results:

  • The proposed method enables the use of low-cost microcontrollers for industrial sensor devices.
  • Optimal range subdivision significantly reduces microcontroller memory requirements for storing linearized characteristics.
  • The algorithm is adaptable to various sensor types beyond temperature sensors.

Conclusions:

  • The developed algorithm improves the performance of resource-constrained devices.
  • It offers flexibility in error distribution across approximation intervals.
  • The approach is suitable for differentiable sensor characteristics with piecewise concave/convex properties.