IDEAS home Printed from https://ideas.repec.org/a/igg/jsda00/v11y2022i3p1-21.html
   My bibliography  Save this article

Introducing Fog Computing (FC) Technology to Internet of Things (IoT) Cloud-Based Anti-Theft Vehicles Solutions

Author

Listed:
  • Eissa Jaber Alreshidi

    (University of Ha'il, Saudi Arabia)

Abstract

Securing vehicles, especially against theft, has become a significant concern. Smart antitheft solutions have emerged to provide better protection. However, most existing smart vehicle antitheft solutions use (GSM) and (GPS) technologies to track stolen vehicles and these technologies are not sufficiently efficient in tracking vehicles in real-time. Hence, there is a need to optimise solutions to incorporate new technologies such as Internet of Things (IoT), Fog Computing (FC), and Face Recognition (FR) technologies. This paper introduces the new concept of Fog Computing to existing tracking systems and presents the design and the development of the Internet of Things (IoT) Cloud-based vehicle anti-theft system to pinpoint the exact location of the stolen vehicle in real-time. The proposed system extends the existing tracking systems to include advanced features influenced by advanced computing technologies such as Fog, Cloud, IoT and FR. Furthermore, it sheds light on the benefits of using FC combined with Cloud Computing (CC) to provide a more accurate and reliable tracking system.

Suggested Citation

  • Eissa Jaber Alreshidi, 2022. "Introducing Fog Computing (FC) Technology to Internet of Things (IoT) Cloud-Based Anti-Theft Vehicles Solutions," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 11(3), pages 1-21, August.
  • Handle: RePEc:igg:jsda00:v:11:y:2022:i:3:p:1-21
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSDA.287114
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:igg:jsda00:v:11:y:2022:i:3:p:1-21. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.