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

1
Institute of New Imaging Technologies, Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castellón de la Plana, Spain
2
Laboratory of Electronics and Communications Engineering, Tampere University of Technology, Korkeakoulunkatu 3, 33720 Tampere, Finland
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 24 November 2017 / Revised: 22 December 2017 / Accepted: 9 January 2018 / Published: 16 January 2018
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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. View Full-Text
Keywords: WiFi datasets; fingerprinting; indoor positioning; temporal signal variation; collection campaigns WiFi datasets; fingerprinting; indoor positioning; temporal signal variation; collection campaigns
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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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Mendoza-Silva, G.M.; Richter, P.; Torres-Sospedra, J.; Lohan, E.S.; Huerta, J. Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning. Data 2018, 3, 3.

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