IDEAS home Printed from https://ideas.repec.org/a/hin/complx/3721579.html
   My bibliography  Save this article

Effective Data Transmission and Control Based on Social Communication in Social Opportunistic Complex Networks

Author

Listed:
  • Weiyu Yang
  • Jia Wu
  • Jingwen Luo

Abstract

In opportunistic complex networks, information transmission between nodes is inevitable through broadcast. The purpose of broadcasting is to distribute data from source nodes to all nodes in the network. In opportunistic complex networks, it is mainly used for routing discovery and releasing important notifications. However, when a large number of nodes in the opportunistic complex networks are transmitting information at the same time, signal interference will inevitably occur. Therefore, we propose a low-latency broadcast algorithm for opportunistic complex networks based on successive interference cancellation techniques to improve propagation delay. With this kind of algorithm, when the social network is broadcasting, this algorithm analyzes whether the conditions for successive interference cancellation are satisfied between the broadcast links on the assigned transmission time slice. If the conditions are met, they are scheduled at the same time slice, and interference avoidance scheduling is performed when conditions are not met. Through comparison experiments with other classic algorithms of opportunistic complex networks, this method has outstanding performance in reducing energy consumption and improving information transmission efficiency.

Suggested Citation

  • Weiyu Yang & Jia Wu & Jingwen Luo, 2020. "Effective Data Transmission and Control Based on Social Communication in Social Opportunistic Complex Networks," Complexity, Hindawi, vol. 2020, pages 1-20, June.
  • Handle: RePEc:hin:complx:3721579
    DOI: 10.1155/2020/3721579
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2020/3721579.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/8503/2020/3721579.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2020/3721579?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
    ---><---

    Citations

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


    Cited by:

    1. Yedong Shen & Fangfang Gou & Jia Wu, 2022. "Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks," Mathematics, MDPI, vol. 10(10), pages 1-27, May.

    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:hin:complx:3721579. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.