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Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning

Author

Listed:
  • Germán Martín Mendoza-Silva

    (Institute of New Imaging Technologies, Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castellón de la Plana, Spain
    These authors contributed equally to this work.)

  • Philipp Richter

    (Laboratory of Electronics and Communications Engineering, Tampere University of Technology, Korkeakoulunkatu 3, 33720 Tampere, Finland
    These authors contributed equally to this work.)

  • Joaquín Torres-Sospedra

    (Institute of New Imaging Technologies, Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castellón de la Plana, Spain)

  • Elena Simona Lohan

    (Laboratory of Electronics and Communications Engineering, Tampere University of Technology, Korkeakoulunkatu 3, 33720 Tampere, Finland)

  • Joaquín Huerta

    (Institute of New Imaging Technologies, Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castellón de la Plana, Spain)

Abstract

WiFi fingerprinting, one of the most popular methods employed in indoor positioning, currently faces two major problems: lack of robustness to short and long time signal changes and difficult reproducibility of new methods presented in the relevant literature. This paper presents a WiFi RSS (Received Signal Strength) database created to foster and ease research works that address the above-mentioned two problems. A trained professional took several consecutive fingerprints while standing at specific positions and facing specific directions. The consecutive fingerprints may enable the study of short-term signals variations. The data collection spanned over 15 months, and, for each month, one type of training datasets and five types of test datasets were collected. The measurements of a dataset type (training or test) were taken at the same positions and directions every month, in order to enable the analysis of long-term signal variations. The database is provided with supporting materials and software, which give more information about the collection environment and eases the database utilization, respectively. The WiFi measurements and the supporting materials are available at the Zenodo repository under the open-source MIT license.

Suggested Citation

  • Germán Martín Mendoza-Silva & Philipp Richter & Joaquín Torres-Sospedra & Elena Simona Lohan & Joaquín Huerta, 2018. "Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning," Data, MDPI, vol. 3(1), pages 1-17, January.
    • Handle: RePEc:gam:jdataj:v:3:y:2018:i:1:p:3-:d:127119
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    Citations

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    Cited by:

    1. Germán Martín Mendoza-Silva & Miguel Matey-Sanz & Joaquín Torres-Sospedra & Joaquín Huerta, 2019. "BLE RSS Measurements Dataset for Research on Accurate Indoor Positioning," Data, MDPI, vol. 4(1), pages 1-17, January.
    2. Asim Abdullah & Muhammad Haris & Omar Abdul Aziz & Rozeha A. Rashid & Ahmad Shahidan Abdullah, 2023. "UTMInDualSymFi: A Dual-Band Wi-Fi Dataset for Fingerprinting Positioning in Symmetric Indoor Environments," Data, MDPI, vol. 8(1), pages 1-38, January.

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