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This review examines model-driven engineering (MDE) tools for the Internet of Things (IoT). It finds that many MDE tools lack support for data analytics and machine learning (DAML) techniques crucial for smart IoT services.

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Area of Science:

  • Computer Science
  • Software Engineering

Background:

  • The Internet of Things (IoT) generates vast amounts of big data, necessitating advanced analytical capabilities.
  • Smart IoT applications heavily rely on data analytics and machine learning (DAML) techniques.
  • Sequential time series data, common in IoT from sensors, requires specialized analysis methods.

Purpose of the Study:

  • To review and classify existing literature on model-driven engineering (MDE) tools and languages for IoT.
  • To identify MDE approaches that support Data Analytics and Machine Learning (DAML) techniques at the modeling level.
  • To assess the integration of DAML, particularly time series analysis, within MDE for smart IoT services.

Main Methods:

  • A scoping review approach was employed to systematically analyze relevant literature.
  • Literature was classified based on proposed MDE approaches, tools, and languages for IoT.
  • The review specifically focused on the support for DAML techniques within these MDE artifacts.

Main Results:

  • Existing MDE tools and languages for IoT were identified and categorized.
  • The study highlights a gap in current MDE approaches regarding out-of-the-box support for DAML techniques.
  • Limited MDE solutions were found to explicitly incorporate time series analysis for IoT data.

Conclusions:

  • There is a need for MDE tools and languages that natively support Data Analytics and Machine Learning (DAML) for IoT development.
  • Future MDE research should focus on integrating advanced analytical capabilities, including time series analysis, to enhance smart IoT services.
  • Enhanced MDE support for DAML is crucial for realizing the full potential of data-driven IoT applications.