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

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 3; p. 1458
• Main Authors: Mardani Korani, Zahra, Moin, Armin, Rodrigues da Silva, Alberto, Ferreira, João Carlos
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28.01.2023; MDPI
• Subjects: Artificial intelligence; Automation; Big data; Data analysis; data analytics and machine learning; Design; Efficiency; Embedded systems; Engineering; Internet of Things; Languages; literature review; Machine learning; model-driven engineering; Modelling; Research methodology; scoping review; Sensors; Smartphones; Software; Software engineering; Systematic Review; Technology application; Time series; Trends; France; Portugal; Germany; Spain; data analytics and machine learning; time series; internet of things; literature review; model-driven engineering; scoping review
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23031458

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.