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UTMInDualSymFi: A Dual-Band Wi-Fi Dataset for Fingerprinting Positioning in Symmetric Indoor Environments

Author

Listed:
  • Asim Abdullah

    (Telecommunication Software and Systems Research Group, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai 81310, Johor Bahru, Malaysia)

  • Muhammad Haris

    (Faculty of Computing, Universiti Teknologi Malaysia, Skudai 81310, Johor Bahru, Malaysia
    Department of Computer Science & Bioinformatics, Khushal Khan Khattak University, Karak 27200, Pakistan)

  • Omar Abdul Aziz

    (Wireless Communication Centre, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai 81310, Johor Bahru, Malaysia)

  • Rozeha A. Rashid

    (Telecommunication Software and Systems Research Group, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai 81310, Johor Bahru, Malaysia)

  • Ahmad Shahidan Abdullah

    (Telecommunication Software and Systems Research Group, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai 81310, Johor Bahru, Malaysia)

Abstract

Recent studies on indoor positioning using Wi-Fi fingerprinting are motivated by the ubiquity of Wi-Fi networks and their promising positioning accuracy. Machine learning algorithms are commonly leveraged in indoor positioning works. The performance of machine learning based solutions are dependent on the availability, volume, quality, and diversity of related data. Several public datasets have been published in order to foster advancements in Wi-Fi based fingerprinting indoor positioning solutions. These datasets, however, lack dual-band Wi-Fi data within symmetric indoor environments. To fill this gap, this research work presents the UTMInDualSymFi dataset, as a source of dual-band Wi-Fi data, acquired within multiple residential buildings with symmetric deployment of access points. UTMInDualSymFi comprises the recorded dual-band raw data, training and test datasets, radio maps and supporting metadata. Additionally, a statistical radio map construction algorithm is presented. Benchmark performance was evaluated by implementing a machine-learning-based positioning algorithm on the dataset. In general, higher accuracy was observed, on the 5 GHz data scenarios. This systematically collected dataset enables the development and validation of future comprehensive solutions, inclusive of novel preprocessing, radio map construction, and positioning algorithms.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:1:p:14-:d:1022177
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. Osterrieder, Philipp & Budde, Lukas & Friedli, Thomas, 2020. "The smart factory as a key construct of industry 4.0: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 221(C).
    5. 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.
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