IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v9y2017i4p86-d119270.html
   My bibliography  Save this article

Study of Mobility Enhancements for RPL in Convergecast Scenarios

Author

Listed:
  • Jinpeng Wang

    (LIMOS/CNRS, Université Clermont Auvergne, 63170 Aubière, France)

  • Gérard Chalhoub

    (LIMOS/CNRS, Université Clermont Auvergne, 63170 Aubière, France)

Abstract

In recent years, mobility support has become an important requirement in various wireless sensor network (WSN) applications. However, due to the strict resource constraints of power, memory, and processing resources in WSNs, routing protocols are mainly designed without considering mobility. Low-Power and Lossy Networks (LLNs) are a special type of WSNs that tolerate data loss. The Routing Protocol for Low-Power and Lossy Networks (RPL) is a routing protocol for LLNs that adapts IPv6 (Internet Protocol version 6) and runs on top of the IEEE (Institute of Electrical and Electronics Engineers) 802.15.4 standard. RPL supports multipoint-to-point traffic and point-to-multipoint traffic. In this paper we propose a mobility enhancement mechanism in order to improve data collection applications in highly mobile scenarios. The enhancement is based on signal strength monitoring and depth updating in order to improve the routing protocol performance in mobile scenarios. This enhancement helps routing protocols to cope better with topology changes and makes proactive decisions on updating next-hop neighbours. We integrated this mechanism into the RPL and compared it with other existing RPL mobility support enhancements. Results obtained through simulation using Cooja show that our work outperforms other existing RPL mobility supports on different performance metrics. Results also prove the efficiency of our proposal in highly mobile scenarios.

Suggested Citation

  • Jinpeng Wang & Gérard Chalhoub, 2017. "Study of Mobility Enhancements for RPL in Convergecast Scenarios," Future Internet, MDPI, vol. 9(4), pages 1-15, November.
  • Handle: RePEc:gam:jftint:v:9:y:2017:i:4:p:86-:d:119270
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/9/4/86/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/9/4/86/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Jinpeng Wang & Gérard Chalhoub & Michel Misson, 2019. "Adaptive Downward/Upward Routing Protocol for Mobile-Sensor Networks," Future Internet, MDPI, vol. 11(1), pages 1-13, January.

    More about this item

    Keywords

    LLN; RPL; WSN; mobility; convergecast;
    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:gam:jftint:v:9:y:2017:i:4:p:86-:d:119270. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.