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Accuracy-aware wireless indoor localization

Published: 01 February 2016 Publication History

Abstract

Fingerprint-based indoor localization has attracted extensive research efforts due to its potential for deployment without extensive infrastructure support. However, the accuracies of these different systems vary and it is difficult to compare and evaluate these systems systematically. In this work, we propose a Gaussian process based approach that takes the radio map and the localization algorithm as an input, and outputs the expected accuracy of the localization system. With an efficient error estimation algorithm, many applications such as landmark detection, localization algorithm selection and access point subset selection can be performed. Our evaluations show that our approach provides sufficient accuracy and can serve as a useful tool for system evaluation and performance tuning when developing fingerprint-based indoor localization systems.

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  • (2019)The Seamlessness of Outdoor and Indoor Localization Approaches based on a Ubiquitous Computing EnvironmentProceedings of the 2nd International Conference on Information Science and Systems10.1145/3322645.3322690(316-324)Online publication date: 16-Mar-2019
  • (2019)Precise Trajectories Derivation Using Smartphone SensorsProceedings of the 11th International Conference on Computer Modeling and Simulation10.1145/3307363.3307402(196-199)Online publication date: 16-Jan-2019
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  1. Accuracy-aware wireless indoor localization

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    Information & Contributors

    Information

    Published In

    cover image Journal of Network and Computer Applications
    Journal of Network and Computer Applications  Volume 62, Issue C
    February 2016
    185 pages

    Publisher

    Academic Press Ltd.

    United Kingdom

    Publication History

    Published: 01 February 2016

    Author Tags

    1. Accuracy awareness
    2. Gaussian process
    3. Indoor localization

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    View all
    • (2021)ezNavi: An Easy-to-Operate Indoor Navigation System Based on Pedestrian Dead Reckoning and Crowdsourced User TrajectoriesIEEE Transactions on Mobile Computing10.1109/TMC.2019.294682120:2(488-501)Online publication date: 9-Jan-2021
    • (2019)The Seamlessness of Outdoor and Indoor Localization Approaches based on a Ubiquitous Computing EnvironmentProceedings of the 2nd International Conference on Information Science and Systems10.1145/3322645.3322690(316-324)Online publication date: 16-Mar-2019
    • (2019)Precise Trajectories Derivation Using Smartphone SensorsProceedings of the 11th International Conference on Computer Modeling and Simulation10.1145/3307363.3307402(196-199)Online publication date: 16-Jan-2019
    • (2018)EvaLocProceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/3286978.3286999(372-381)Online publication date: 5-Nov-2018
    • (2017)Gait-KeyACM Transactions on Sensor Networks10.1145/302395413:1(1-27)Online publication date: 26-Jan-2017
    • (2017)PallasIEEE Transactions on Mobile Computing10.1109/TMC.2016.255045216:2(466-481)Online publication date: 1-Feb-2017
    • (2017)Industrial Internet: A Survey on the Enabling Technologies, Applications, and ChallengesIEEE Communications Surveys & Tutorials10.1109/COMST.2017.269134919:3(1504-1526)Online publication date: 1-Jul-2017
    • (2017)From mapping to indoor semantic queriesJournal of Network and Computer Applications10.1016/j.jnca.2016.11.02180:C(141-151)Online publication date: 15-Feb-2017
    • (2017)Compressive sensing based data quality improvement for crowd-sensing applicationsJournal of Network and Computer Applications10.1016/j.jnca.2016.10.00477:C(123-134)Online publication date: 1-Jan-2017

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