Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning
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 (Receive...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-01-01
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| Series: | Data |
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| Online Access: | http://www.mdpi.com/2306-5729/3/1/3 |
| _version_ | 1798034862236499968 |
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| author | Germán Martín Mendoza-Silva Philipp Richter Joaquín Torres-Sospedra Elena Simona Lohan Joaquín Huerta |
| author_facet | Germán Martín Mendoza-Silva Philipp Richter Joaquín Torres-Sospedra Elena Simona Lohan Joaquín Huerta |
| author_sort | Germán Martín Mendoza-Silva |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T20:50:18Z |
| format | Article |
| id | doaj.art-885221491cae424fb9d9295cd32eb518 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-04-11T20:50:18Z |
| publishDate | 2018-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-885221491cae424fb9d9295cd32eb5182022-12-22T04:03:53ZengMDPI AGData2306-57292018-01-0131310.3390/data3010003data3010003Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor PositioningGermán Martín Mendoza-Silva0Philipp Richter1Joaquín Torres-Sospedra2Elena Simona Lohan3Joaquín Huerta4Institute of New Imaging Technologies, Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castellón de la Plana, SpainLaboratory of Electronics and Communications Engineering, Tampere University of Technology, Korkeakoulunkatu 3, 33720 Tampere, FinlandInstitute of New Imaging Technologies, Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castellón de la Plana, SpainLaboratory of Electronics and Communications Engineering, Tampere University of Technology, Korkeakoulunkatu 3, 33720 Tampere, FinlandInstitute of New Imaging Technologies, Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castellón de la Plana, SpainWiFi 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.http://www.mdpi.com/2306-5729/3/1/3WiFi datasetsfingerprintingindoor positioningtemporal signal variationcollection campaigns |
| spellingShingle | Germán Martín Mendoza-Silva Philipp Richter Joaquín Torres-Sospedra Elena Simona Lohan Joaquín Huerta Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning Data WiFi datasets fingerprinting indoor positioning temporal signal variation collection campaigns |
| title | Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning |
| title_full | Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning |
| title_fullStr | Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning |
| title_full_unstemmed | Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning |
| title_short | Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning |
| title_sort | long term wifi fingerprinting dataset for research on robust indoor positioning |
| topic | WiFi datasets fingerprinting indoor positioning temporal signal variation collection campaigns |
| url | http://www.mdpi.com/2306-5729/3/1/3 |
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