IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v13y2017i4p1550147717703967.html
   My bibliography  Save this article

An improved incremental least-mean-squares algorithm for distributed estimation over wireless sensor networks

Author

Listed:
  • Mou Wu
  • Liansheng Tan
  • Rong Yang
  • Runze Wan

Abstract

Distributed parameter estimation problem has attracted much attention due to its important foundation of some applications in wireless sensor networks. In this work, we first investigate the performance tradeoff between existing incremental least-mean-squares algorithm and traditional steepest-descent algorithm from the aspects of initial convergence rate, steady-state convergence behavior, and oscillation, and present the motivations for improvements. Thereby we propose an improved incremental least-mean-squares distributed estimation algorithm that starts from the incremental least-mean-squares algorithm and works toward the objective of improvement on initial convergence rate and steady-state performance. The proposed algorithm meets the requirements of diminishing step size for wireless sensor networks without any increase in communication overhead and the mean stability condition is derived for a practical guideline. A target localization model in wireless sensor networks is used to illustrate the effect of the proposed algorithm. Simulation results confirm the performance improvement of our method, as well as the effectiveness of application in wireless sensor networks.

Suggested Citation

  • Mou Wu & Liansheng Tan & Rong Yang & Runze Wan, 2017. "An improved incremental least-mean-squares algorithm for distributed estimation over wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 13(4), pages 15501477177, April.
    • Handle: RePEc:sae:intdis:v:13:y:2017:i:4:p:1550147717703967
    DOI: 10.1177/1550147717703967
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1550147717703967
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1550147717703967?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
    ---><---

    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:13:y:2017:i:4:p:1550147717703967. 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.