IDEAS home Printed from https://ideas.repec.org/a/igg/jdst00/v10y2019i4p17-30.html
   My bibliography  Save this article

IoT and Big Data Technologies for Monitoring and Processing Real-Time Healthcare Data

Author

Listed:
  • Abdelhak Kharbouch

    (International University of Rabat-University of Moulay Ismail, Rabat, Morocco)

  • Youssef Naitmalek

    (International University of Rabat-Higher National School of Computer Science and Systems Analysis, Rabat, Morocco)

  • Hamza Elkhoukhi

    (International University of Rabat-University of Moulay Ismail, Rabat, Morocco)

  • Mohamed Bakhouya

    (International University of Rabat, Rabat, Morocco)

  • Vincenzo De Florio

    (Vrije Universiteit Brussel, Brussels, Belgium & Global Brain Institute, Brussels, Belgium)

  • Moulay Driss El Ouadghiri

    (Université Moulay Ismail (UMI), Meknes City, Morocco)

  • Steven Latre

    (University of Antwerp-IMEC, Antwerp, Belgium)

  • Chris Blondia

    (Universiteit Antwerpen-IMEC, Antwerp, Belgium)

Abstract

Recent advances in pervasive technologies, such as wireless, ad hoc networks, and wearable sensor devices, allow the connection of everyday things to the Internet, commonly denoted as the Internet of Things (IoT). The IoT is seen as an enabler to the development of intelligent and context-aware services and applications. However, handling dynamic and frequent context changes is a difficult task without a real-time event/data acquisition and processing platform. Big data technologies and data analytics have been recently proposed for timely analyzing information (i.e., data, events) streams. The main aim is to make users' life more comfortable according to their locations, current requirements, and ongoing activities. In this article, combining IoT techniques and Big data technologies into a holistic platform for continuous and real-time health-care data monitoring and processing is introduced. Real-testing experiments have been conducted and results are reported to show the usefulness of this platform in a real-case scenario.

Suggested Citation

  • Abdelhak Kharbouch & Youssef Naitmalek & Hamza Elkhoukhi & Mohamed Bakhouya & Vincenzo De Florio & Moulay Driss El Ouadghiri & Steven Latre & Chris Blondia, 2019. "IoT and Big Data Technologies for Monitoring and Processing Real-Time Healthcare Data," International Journal of Distributed Systems and Technologies (IJDST), IGI Global, vol. 10(4), pages 17-30, October.
  • Handle: RePEc:igg:jdst00:v:10:y:2019:i:4:p:17-30
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDST.2019100102
    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:jdst00:v:10:y:2019:i:4:p:17-30. 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.