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

被引：6

作者：

Korani, Zahra Mardani ^{[1
,2
]}

Moin, Armin ^{[3
]}

da Silva, Alberto Rodrigues ^{[4
]}

Ferreira, Joao Carlos ^{[1
,5
]}

机构：

[1] Inst Univ Lisboa ISCTE IUL, ISTAR, ISCTE, P-1649026 Lisbon, Portugal

[2] LNEC, Hydraul & Environm Dept, P-1700066 Lisbon, Portugal

[3] Tech Univ Munich, Sch Computat Informat & Technol CIT, D-80333 Munich, Germany

[4] Univ Lisbon, INESC ID, Inst Super Tecn, P-1000029 Lisbon, Portugal

[5] Inst Novas Tecnol, Inesc Inovacao, Inov, P-1000029 Lisbon, Portugal

来源：

SENSORS | 2023年 / 23卷 / 03期

关键词：

model-driven engineering; internet of things; data analytics and machine learning; time series; literature review; scoping review; DOMAIN-SPECIFIC LANGUAGE; INTERNET; THINGS; TIME; ANALYTICS; FRAMEWORK; DESIGN;

D O I：

10.3390/s23031458

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

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.

引用

页数：27

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