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An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity.

Gabriel Signoretti1, Marianne Silva1, Pedro Andrade1

  • 1UFRN-PPgEEC, Postgraduate Program in Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil.

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Summary
This summary is machine-generated.

A new Tiny Anomaly Compressor (TAC) algorithm offers efficient data compression for the Internet of Things (IoT). This TinyML approach achieves a 98.33% compression rate, outperforming existing methods.

Keywords:
LPWANTinyMLeccentricityevolving algorithminternet of thingsonline data compression

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Area of Science:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • Internet of Things (IoT) applications generate vast amounts of sensor data, posing challenges for storage and management.
  • TinyML enables machine learning on resource-constrained devices, facilitating real-time local data analysis.
  • Efficient data compression is crucial for managing IoT data on small devices and optimizing wireless communication.

Purpose of the Study:

  • To introduce a novel data compression algorithm for IoT environments leveraging the TinyML paradigm.
  • To present the Tiny Anomaly Compressor (TAC) algorithm, based on data eccentricity, requiring no prior data distribution assumptions.

Main Methods:

  • Developed the Tiny Anomaly Compressor (TAC) algorithm utilizing data eccentricity for IoT data compression.
  • Conducted a comparative analysis of TAC against Swing Door Trending (SDT) and Discrete Cosine Transform (DCT) using two real-world datasets.

Main Results:

  • The TAC algorithm achieved a maximum compression rate of 98.33%.
  • TAC demonstrated superior performance over SDT and DCT in terms of compression error and peak signal-to-noise ratio across all tested scenarios.

Conclusions:

  • The proposed TAC algorithm offers a highly effective solution for IoT data compression, particularly within the TinyML framework.
  • TAC's ability to achieve high compression rates and low error makes it suitable for resource-limited IoT devices and efficient network communication.