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Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning

Department of Electronics and Communications Engineering, Tampere University of Technology, Korkeakoulunkatu 3, Tampere 33720, Finland
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
Author to whom correspondence should be addressed.
Received: 12 September 2017 / Revised: 28 September 2017 / Accepted: 29 September 2017 / Published: 3 October 2017
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Benchmark open-source Wi-Fi fingerprinting datasets for indoor positioning studies are still hard to find in the current literature and existing public repositories. This is unlike other research fields, such as the image processing field, where benchmark test images such as the Lenna image or Face Recognition Technology (FERET) databases exist, or the machine learning field, where huge datasets are available for example at the University of California Irvine (UCI) Machine Learning Repository. It is the purpose of this paper to present a new openly available Wi-Fi fingerprint dataset, comprised of 4648 fingerprints collected with 21 devices in a university building in Tampere, Finland, and to present some benchmark indoor positioning results using these data. The datasets and the benchmarking software are distributed under the open-source MIT license and can be found on the EU Zenodo repository. View Full-Text
Keywords: Wi-Fi datasets; fingerprinting; indoor positioning; multi-floor building; positioning software; crowdsourced data Wi-Fi datasets; fingerprinting; indoor positioning; multi-floor building; positioning software; crowdsourced data

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Lohan, E.S.; Torres-Sospedra, J.; Leppäkoski, H.; Richter, P.; Peng, Z.; Huerta, J. Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning. Data 2017, 2, 32.

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