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Model-Driven Engineering Techniques and Tools for Machine Learning-Enabled IoT Applications: A Scoping Review

This paper reviews the literature on model-driven engineering (MDE) tools and languages for the internet of things (IoT). Due to the abundance of big data in the IoT, data analytics and machine learning (DAML) techniques play a key role in providing smart IoT applications. In particular, since a sig...

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Detalles Bibliográficos
Autores principales: Mardani Korani, Zahra, Moin, Armin, Rodrigues da Silva, Alberto, Ferreira, João Carlos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9919367/
https://www.ncbi.nlm.nih.gov/pubmed/36772500
http://dx.doi.org/10.3390/s23031458
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author Mardani Korani, Zahra
Moin, Armin
Rodrigues da Silva, Alberto
Ferreira, João Carlos
author_facet Mardani Korani, Zahra
Moin, Armin
Rodrigues da Silva, Alberto
Ferreira, João Carlos
author_sort Mardani Korani, Zahra
collection PubMed
description This paper reviews the literature on model-driven engineering (MDE) tools and languages for the internet of things (IoT). Due to the abundance of big data in the IoT, data analytics and machine learning (DAML) techniques play a key role in providing smart IoT applications. In particular, since a significant portion of the IoT data is sequential time series data, such as sensor data, time series analysis techniques are required. Therefore, IoT modeling languages and tools are expected to support DAML methods, including time series analysis techniques, out of the box. In this paper, we study and classify prior work in the literature through the mentioned lens and following the scoping review approach. Hence, the key underlying research questions are what MDE approaches, tools, and languages have been proposed and which ones have supported DAML techniques at the modeling level and in the scope of smart IoT services.
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spelling pubmed-99193672023-02-12 Model-Driven Engineering Techniques and Tools for Machine Learning-Enabled IoT Applications: A Scoping Review Mardani Korani, Zahra Moin, Armin Rodrigues da Silva, Alberto Ferreira, João Carlos Sensors (Basel) Systematic Review This paper reviews the literature on model-driven engineering (MDE) tools and languages for the internet of things (IoT). Due to the abundance of big data in the IoT, data analytics and machine learning (DAML) techniques play a key role in providing smart IoT applications. In particular, since a significant portion of the IoT data is sequential time series data, such as sensor data, time series analysis techniques are required. Therefore, IoT modeling languages and tools are expected to support DAML methods, including time series analysis techniques, out of the box. In this paper, we study and classify prior work in the literature through the mentioned lens and following the scoping review approach. Hence, the key underlying research questions are what MDE approaches, tools, and languages have been proposed and which ones have supported DAML techniques at the modeling level and in the scope of smart IoT services. MDPI 2023-01-28 /pmc/articles/PMC9919367/ /pubmed/36772500 http://dx.doi.org/10.3390/s23031458 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Systematic Review
Mardani Korani, Zahra
Moin, Armin
Rodrigues da Silva, Alberto
Ferreira, João Carlos
Model-Driven Engineering Techniques and Tools for Machine Learning-Enabled IoT Applications: A Scoping Review
title Model-Driven Engineering Techniques and Tools for Machine Learning-Enabled IoT Applications: A Scoping Review
title_full Model-Driven Engineering Techniques and Tools for Machine Learning-Enabled IoT Applications: A Scoping Review
title_fullStr Model-Driven Engineering Techniques and Tools for Machine Learning-Enabled IoT Applications: A Scoping Review
title_full_unstemmed Model-Driven Engineering Techniques and Tools for Machine Learning-Enabled IoT Applications: A Scoping Review
title_short Model-Driven Engineering Techniques and Tools for Machine Learning-Enabled IoT Applications: A Scoping Review
title_sort model-driven engineering techniques and tools for machine learning-enabled iot applications: a scoping review
topic Systematic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9919367/
https://www.ncbi.nlm.nih.gov/pubmed/36772500
http://dx.doi.org/10.3390/s23031458
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