IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v7y2022i11p156-d968118.html
   My bibliography  Save this article

Hybrid Wi-Fi and BLE Fingerprinting Dataset for Multi-Floor Indoor Environments with Different Layouts

Author

Listed:
  • Aina Nadhirah Nor Hisham

    (Faculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia)

  • Yin Hoe Ng

    (Faculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia)

  • Chee Keong Tan

    (School of Information Technology, Monash University Malaysia, Subang Jaya 47500, Malaysia)

  • David Chieng

    (Department of Electrical and Electronic Engineering, Faculty of Science and Engineering (FoSE), University of Nottingham Ningbo China, Ningbo 315100, China)

Abstract

Indoor positioning has garnered significant interest over the last decade due to the rapidly growing demand for location-based services. As a result, a multitude of techniques has been proposed to localize objects and devices in indoor environments. Wireless fingerprinting, which leverages machine learning, has emerged as one of the most popular positioning approaches due to its low implementation cost. The prevailing fingerprinting-based positioning mainly utilizes wireless fidelity (Wi-Fi) and Bluetooth low energy (BLE) signals. However, the RSS of Wi-Fi and BLE signals are very sensitive to the layout of the indoor environment. Thus, any change in the indoor layout could potentially lead to severe degradation in terms of localization performance. To foster the development of new positioning methods, several open-source location fingerprinting datasets have been made available to the research community. Unfortunately, none of these public datasets provides the received signal strength (RSS) measurements for indoor environments with different layouts. To fill this gap, this paper presents a new hybrid Wi-Fi and BLE fingerprinting dataset for multi-floor indoor environments with different layouts to facilitate the future development of new fingerprinting-based positioning systems that can provide adaptive positioning performance in dynamic indoor environments. Additionally, the effects of indoor layout change on the location fingerprint and localization performance are also investigated.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:11:p:156-:d:968118
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/7/11/156/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2306-5729/7/11/156/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.

    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:jdataj:v:7:y:2022:i:11:p:156-:d:968118. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.