IDEAS home Printed from https://ideas.repec.org/a/ids/ijnvor/v31y2024i2p87-105.html
   My bibliography  Save this article

Cognitively-inspired intelligent decision-making framework in cognitive IoT network

Author

Listed:
  • Vidyapati Jha
  • Priyanka Tripathi

Abstract

Numerous Internet of Things (IoT) applications require brain-empowered intelligence. This necessity has caused the emergence of a new area called cognitive IoT (CIoT). Reasoning, planning, and selection are typically involved in decision-making within the network bandwidth limit. Consequently, data minimisation is needed. Therefore, this research proposes a novel technique to extract conscious data from a massive dataset. First, it groups the data using k-means clustering, and the entropy is computed for each cluster. The most prominent cluster is then determined by selecting the cluster with the highest entropy. Subsequently, it transforms each cluster element into an informative element. The most informative data is chosen from the most prominent cluster that represents the whole massive data, which is further used for intelligent decision-making. The experimental evaluation is conducted on the 21.25 years of environmental dataset, revealing that the proposed method is efficient over competing approaches.

Suggested Citation

  • Vidyapati Jha & Priyanka Tripathi, 2024. "Cognitively-inspired intelligent decision-making framework in cognitive IoT network," International Journal of Networking and Virtual Organisations, Inderscience Enterprises Ltd, vol. 31(2), pages 87-105.
    • Handle: RePEc:ids:ijnvor:v:31:y:2024:i:2:p:87-105
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=142239
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:ijnvor:v:31:y:2024:i:2:p:87-105. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=22 .

    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.