IDEAS home Printed from https://ideas.repec.org/a/zib/zbnbda/v1y2019i1p1-2.html
   My bibliography  Save this article

Artificial Intelligence Based Congestion Control Mechanism Via Bayesian Networks Under Opportunistic

Author

Listed:
  • Ahthasham Sajid

    (Department of Computer Science, FICT, BUITEMS, Quetta, Baluchistan, PAKISTAN)

  • Muhammad Zaheer

    (Department of Environmental Management & Policy, FOE, BUITEMS, Quetta, Baluchistan, PAKISTAN)

  • Mehwish Baloch

    (Department of Statistics, International Islamic University, Islamabad, PAKISTAN)

  • Javeria Arshad

    (Department of Computer Science, Quaid-e-Azam University, Islamabad, PAKISTAN)

Abstract

Nodes in Mobile Opportunistic Network (MON) must cache packets to deal with the key challenging issue of intermittent connection. Management strategy of handling buffer therefore plays a vital impact on the performance of MON, and it attracts more attention recently. Intermittent connection and lengthy postpone are the key challenges in such networks for this reason the internet congestion control mechanisms aren’t suitable for DTNs. An inefficient buffer management strategy would badly affect the performance of such networks. In recent years, to handle congestion buffer manipulate strategies in MON are considered an active vicinity for research studies.

Suggested Citation

  • Ahthasham Sajid & Muhammad Zaheer & Mehwish Baloch & Javeria Arshad, 2019. "Artificial Intelligence Based Congestion Control Mechanism Via Bayesian Networks Under Opportunistic," Big Data In Agriculture (BDA), Zibeline International Publishing, vol. 1(1), pages 1-2, January.
    • Handle: RePEc:zib:zbnbda:v:1:y:2019:i:1:p:1-2
    DOI: 10.26480/bda.01.2019.01.02
    as

    Download full text from publisher

    File URL: https://bigdatainagriculture.com/download/442/
    Download Restriction: no
    File URL: https://libkey.io/10.26480/bda.01.2019.01.02?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    DTN; ICN; MON;
    All these keywords.

    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:zib:zbnbda:v:1:y:2019:i:1:p:1-2. 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: Zibeline International Publishing (email available below). General contact details of provider: https://bigdatainagriculture.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.