A performance guaranteed indoor positioning system using conformal prediction and the WiFi signal strength

Khuong An Nguyen

    Research output: Contribution to journalArticlepeer-review


    Indoor navigation provides the positioning service to the indoor users, where the GPS coverage is not available. The challenges for most signal-based indoor positioning systems are the unpredictable signal propagation caused by the complex building interiors, and the dynamic of the environment caused by the peoples' movements. However, most existing systems made no assumption about the quality of their predictions, which is crucial in such noisy indoor environment.

    To address this challenge, this article proposes a confidence measure to reflect the uncertainty of the positioning prediction. More importantly, the users may control the size of the prediction set by setting the confidence level tailoring to their personal requirement. The proposed approach in this article has been validated in three real office buildings with challenging indoor environments, which indicated that it performed up to 20% more accurate than traditional Naïve Bayes and Weighted K-nearest neighbours (W-KNN) algorithms.
    Original languageEnglish
    Pages (from-to)41-65
    JournalJournal of Information and Telecommunication
    Issue number1
    Publication statusPublished - 3 Mar 2017

    Bibliographical note

    © 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of Ton Duc Thang University.
    This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/
    licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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