IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v16y2024i12p466-d1543381.html
   My bibliography  Save this article

WiFi Fingerprint Indoor Localization Employing Adaboost and Probability-One Access Point Selection for Multi-Floor Campus Buildings

Author

Listed:
  • Shanyu Jin

    (College of Computer Science and Technology, Harbin Engineering University, Harbin 150009, China)

  • Dongwoo Kim

    (Department of Electronics Engineering, Hanyang University, ERICA, Ansan 15588, Republic of Korea)

Abstract

Indoor positioning systems have become increasingly important due to the rapid expansion of Internet of Things (IoT) technologies, especially for providing precise location-based services in complex environments such as multi-floor campus buildings. This paper presents a WiFi fingerprint indoor localization system based on AdaBoost, combined with a new access point (AP) filtering technique. The system comprises offline and online phases. During the offline phase, a fingerprint database is created using received signal strength (RSS) values for two four-floor campus buildings. In the online phase, the AdaBoost classifier is used to accurately estimate locations. To improve localization accuracy, APs that always appear in the measurement data are selected for applying the AdaBoost algorithm, aiming to eliminate noise from the fingerprint database. The performance of the proposed method is compared with other well-known machine learning-based positioning algorithms in terms of positioning accuracy and error distances. The results indicate that the average positioning accuracy of the proposed scheme reaches 95.55%, which represents an improvement of 5.55% to 16.21% over the other methods. Additionally, the two-dimensional positioning error can be reduced to 0.25 m.

Suggested Citation

  • Shanyu Jin & Dongwoo Kim, 2024. "WiFi Fingerprint Indoor Localization Employing Adaboost and Probability-One Access Point Selection for Multi-Floor Campus Buildings," Future Internet, MDPI, vol. 16(12), pages 1-13, December.
  • Handle: RePEc:gam:jftint:v:16:y:2024:i:12:p:466-:d:1543381
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/16/12/466/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/16/12/466/
    Download Restriction: no
    ---><---

    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:16:y:2024:i:12:p:466-:d:1543381. 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.