IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i5p1263-d1088753.html
   My bibliography  Save this article

Modeling Languages for Internet of Things (IoT) Applications: A Comparative Analysis Study

Author

Listed:
  • Sadik Arslan

    (International Computer Institute, Ege University, Izmir 35100, Turkey
    FIS Systems, Software & Functions Group, BorgWarner Co., Ltd., Izmir 35410, Turkey)

  • Mert Ozkaya

    (Computer Engineering Department, Yeditepe University, Istanbul 34755, Turkey)

  • Geylani Kardas

    (International Computer Institute, Ege University, Izmir 35100, Turkey)

Abstract

Modeling languages have gained ever-increasing importance for the Internet of Things (IoT) domain for improving the productivity and quality of IoT developments. In this study, we analyzed 32 different modeling languages that have been designed for IoT software development in terms of a set of requirements that were categorized into three groups: language definition, language features, and tool support. Some key findings are as follows: (1) performance is the most supported quality property (28%); (2) most languages offer a visual notation set only, while 6% provide both textual and visual notation sets; (3) most languages (88%) lack formally precise semantic definitions; (4) most languages (94%) support the physical, deployment, and logical modeling viewpoints, while the behavior, logical, and information viewpoints are rarely supported; (5) almost none of the languages enable extensibility; (6) Java (34%) and C (21%) are the most preferred programming languages for model transformation; (7) consistency (77%) and completeness (64%) are the most supported properties for the automated checking of models; and (8) most languages (81%) are not supported with any websites for sharing case studies, source code, tools, tutorials, etc. The analysis results can be useful for language engineers, practitioners, and tool vendors for better understanding the existing languages for IoT, their weak and strong points, and IoT industries’ needs in future language and modeling toolset developments.

Suggested Citation

  • Sadik Arslan & Mert Ozkaya & Geylani Kardas, 2023. "Modeling Languages for Internet of Things (IoT) Applications: A Comparative Analysis Study," Mathematics, MDPI, vol. 11(5), pages 1-35, March.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1263-:d:1088753
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/5/1263/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/5/1263/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tomaž Kosar & Željko Kovačević & Marjan Mernik & Boštjan Slivnik, 2023. "The Impact of Code Bloat on Genetic Program Comprehension: Replication of a Controlled Experiment on Semantic Inference," Mathematics, MDPI, vol. 11(17), pages 1-20, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1263-:d:1088753. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.