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An Evolving Multivariate Time Series Compression Algorithm for IoT Applications.

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This study introduces two data compression methods for Tiny Machine Learning (TinyML) in vehicle monitoring. These methods reduce latency and energy use in Internet of Things (IoT) devices.

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Area of Science:

  • Computer Science
  • Embedded Systems
  • Data Compression

Background:

  • Internet of Things (IoT) systems face challenges with high latency and energy consumption due to large real-time data transmission.
  • Tiny Machine Learning (TinyML) offers a solution by enabling machine learning on resource-constrained embedded devices.
  • Vehicle monitoring applications within IoT generate substantial data, exacerbating transmission issues.

Purpose of the Study:

  • To develop and evaluate two novel online multivariate data compression approaches for TinyML applications.
  • To leverage the Typicality and Eccentricity Data Analytics (TEDA) framework for data compression.
  • To optimize compression performance without relying on predefined mathematical models or data distribution assumptions.

Main Methods:

  • Developed two online multivariate compression techniques based on data eccentricity within the TEDA framework.
  • Applied the compression approaches to the OBD-II Freematics ONE+ dataset for vehicle monitoring.
  • Evaluated both parallel and sequential compression strategies.

Main Results:

  • Both proposed compression approaches demonstrated significant improvements in execution time.
  • The methods achieved notable reductions in compression errors.
  • The study confirmed the effectiveness of eccentricity-based compression for TinyML.

Conclusions:

  • The developed compression approaches enhance the performance of embedded IoT systems, particularly in vehicular applications.
  • These findings contribute to improving the efficiency and sustainability of real-time data processing in resource-constrained environments.
  • The TEDA framework provides a robust foundation for developing advanced data compression techniques for TinyML.