IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v12y2016i2p7061486.html
   My bibliography  Save this article

NMCT: A Novel Monte Carlo-Based Tracking Algorithm Using Potential Proximity Information

Author

Listed:
  • Qiang Niu
  • Tian Huan
  • Pengpeng Chen

Abstract

Currently, many services benefit greatly from the availability of accurate tracking. Tracking in wireless sensor networks remains a challenging issue. Most tracking methods often require a large number of anchors and do not take advantage of potential localization information, leading to poor accuracy. To solve this problem, this paper proposes a Novel Monte Carlo-based Tracking (NMCT) algorithm with area-based and neighbor-based filtering, which fully extracts the proximity information embedded in the neighborhood sensing. We describe the entire system design in detail and conduct extensive simulations. The results show that the proposed algorithm outperforms the typical schemes under a wide range of conditions.

Suggested Citation

  • Qiang Niu & Tian Huan & Pengpeng Chen, 2016. "NMCT: A Novel Monte Carlo-Based Tracking Algorithm Using Potential Proximity Information," International Journal of Distributed Sensor Networks, , vol. 12(2), pages 7061486-706, February.
    • Handle: RePEc:sae:intdis:v:12:y:2016:i:2:p:7061486
    DOI: 10.1155/2016/7061486
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1155/2016/7061486
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2016/7061486?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:12:y:2016:i:2:p:7061486. 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.