IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v15y2019i1p1550147718824304.html
   My bibliography  Save this article

iBeacon indoor localization using trusted-ranges model

Author

Listed:
  • Tuan D Vy
  • Yoan Shin

Abstract

In this article, we propose an efficient approach to address mobile indoor localization using received signal strength from iBeacon combined with trusted-ranges model. In order to overcome the inconsistency of radio signal propagation, the trusted-ranges model supplies reliable ranges of received signal strength values from a certain number of nearest neighbor iBeacon nodes by classifying received signal strength values into various levels of range. By observing the signal propagation, the trusted-ranges model is built to provide important information for the training phase. Based on this, a partition scheme is applied to effectively determine the position of mobile devices. The experimental results show fast, robust, and accurate localization performance in the proposed method.

Suggested Citation

  • Tuan D Vy & Yoan Shin, 2019. "iBeacon indoor localization using trusted-ranges model," International Journal of Distributed Sensor Networks, , vol. 15(1), pages 15501477188, January.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:1:p:1550147718824304
    DOI: 10.1177/1550147718824304
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1550147718824304
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1550147718824304?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. Abdulraqeb Alhammadi & Fazirulhisyam Hashim & Mohd. Fadlee A Rasid & Saddam Alraih, 2020. "A three-dimensional pattern recognition localization system based on a Bayesian graphical model," International Journal of Distributed Sensor Networks, , vol. 16(9), pages 15501477198, September.

    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:15:y:2019:i:1:p:1550147718824304. 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.