IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v9y2013i5p176293.html
   My bibliography  Save this article

Traffic Prediction-Based Fast Rerouting Algorithm for Wireless Multimedia Sensor Networks

Author

Listed:
  • Zhiyuan Li
  • Junlei Bi
  • Siguang Chen

Abstract

Rerouting has become an important challenge to Wireless Multimedia Sensor Networks (WMSNs) due to the constraints on energy, bandwidth, and computational capabilities of sensor nodes and frequent node and link failures. In this paper, we propose a traffic prediction-based fast rerouting algorithm for use between the cluster heads and a sink node in WMSNs (TPFR). The proposed algorithm uses the autoregressive moving average (ARMA) model to predict a cluster head's network traffic. When the predicted value is greater than the predefined network traffic threshold, both adaptive retransmission trigger (ART) that contributes to switch to a better alternate path in time and trigger efficient retransmission behaviors are enabled. Performance comparison of TPFR with ant-based multi-QoS routing (AntSensNet) and power efficient multimedia routing (PEMuR) shows that they: (a) maximize the overall network lifespan by load balancing and not draining energy from some specific nodes, (b) provide high quality of service delivery for multimedia streams by switching to a better path towards a sink node in time, (c) reduce useless data retransmissions when node failures or link breaks occur, and (d) maintain lower routing overhead.

Suggested Citation

  • Zhiyuan Li & Junlei Bi & Siguang Chen, 2013. "Traffic Prediction-Based Fast Rerouting Algorithm for Wireless Multimedia Sensor Networks," International Journal of Distributed Sensor Networks, , vol. 9(5), pages 176293-1762, May.
    • Handle: RePEc:sae:intdis:v:9:y:2013:i:5:p:176293
    DOI: 10.1155/2013/176293
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1155/2013/176293
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2013/176293?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

    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:sae:intdis:v:9:y:2013:i:5:p:176293. 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: SAGE Publications (email available below). General contact details of provider: .

    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.