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BLE RSS Measurements Dataset for Research on Accurate 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.)

  • Miguel Matey-Sanz

    (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.)

  • Joaquín Torres-Sospedra

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

  • Joaquín Huerta

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

Abstract

RSS-based indoor positioning is a consolidated research field for which several techniques have been proposed. Among them, Bluetooth Low Energy (BLE) beacons are a popular option for practical applications. This paper presents a new BLE RSS database that was created to aid in the development of new BLE RSS-based positioning methods and to encourage their reproducibility and comparability. The measurements were collected in two university zones: an area among bookshelves in a library and an area of an office space. Each zone had its own batch of deployed iBKS 105 beacons, configured to broadcast advertisements every 200 ms. The collection in the library zone was performed using three Android smartphones of different brands and models, with beacons broadcasting at −12 dBm transmission power, while in the other zone the collection was performed using of one those smartphones with beacons configured to advertise at the −4 dBm, −12 dBm and −20 dBm transmission powers. Supporting materials and scripts are provided along with the database, which annotate the BLE readings, provide details on the collection, the environment, and the BLE beacon deployments, ease the database usage, and introduce the reader to BLE RSS-based positioning and its challenges. The BLE RSS database and its supporting materials are available at the Zenodo repository under the open-source MIT license.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jdataj:v:4:y:2019:i:1:p:12-:d:197201
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    References listed on IDEAS

    as
    1. 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.
    2. Zohaib Iqbal & Da Luo & Peter Henry & Samaneh Kazemifar & Timothy Rozario & Yulong Yan & Kenneth Westover & Weiguo Lu & Dan Nguyen & Troy Long & Jing Wang & Hak Choy & Steve Jiang, 2018. "Accurate real time localization tracking in a clinical environment using Bluetooth Low Energy and deep learning," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-13, October.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Achour Achroufene, 2023. "RSSI-based Hybrid Centroid-K-Nearest Neighbors localization method," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 82(1), pages 101-114, January.
    2. Antonio-Pedro Albín-Rodríguez & Yolanda-María De-La-Fuente-Robles & José-Luis López-Ruiz & Ángeles Verdejo-Espinosa & Macarena Espinilla Estévez, 2021. "UJAmI Location: A Fuzzy Indoor Location System for the Elderly," IJERPH, MDPI, vol. 18(16), pages 1-22, August.
    3. Aina Nadhirah Nor Hisham & Yin Hoe Ng & Chee Keong Tan & David Chieng, 2022. "Hybrid Wi-Fi and BLE Fingerprinting Dataset for Multi-Floor Indoor Environments with Different Layouts," Data, MDPI, vol. 7(11), pages 1-20, November.
    4. Viktoriia Shubina & Sylvia Holcer & Michael Gould & Elena Simona Lohan, 2020. "Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era," Data, MDPI, vol. 5(4), pages 1-40, September.
    5. Emilio Sansano-Sansano & Fernando J. Aranda & Raúl Montoliu & Fernando J. Álvarez, 2020. "BLE-GSpeed: A New BLE-Based Dataset to Estimate User Gait Speed," Data, MDPI, vol. 5(4), pages 1-15, December.
    6. Fernando J. Aranda & Felipe Parralejo & Fernando J. Álvarez & Joaquín Torres-Sospedra, 2020. "Multi-Slot BLE Raw Database for Accurate Positioning in Mixed Indoor/Outdoor Environments," Data, MDPI, vol. 5(3), pages 1-20, July.
    7. 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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