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Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning

Tutkimustuotosvertaisarvioitu

Standard

Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning. / Lohan, Elena-Simona; Torres-Sospedra, Joaquin; Leppäkoski, Helena; Richter, Philipp; Peng, Zhe; Huerta, Joaquin.

julkaisussa: MDPI Data, Vuosikerta 2, Nro 4, 03.10.2017.

Tutkimustuotosvertaisarvioitu

Harvard

Lohan, E-S, Torres-Sospedra, J, Leppäkoski, H, Richter, P, Peng, Z & Huerta, J 2017, 'Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning', MDPI Data, Vuosikerta. 2, Nro 4. https://doi.org/10.3390/data2040032

APA

Lohan, E-S., Torres-Sospedra, J., Leppäkoski, H., Richter, P., Peng, Z., & Huerta, J. (2017). Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning. MDPI Data, 2(4). https://doi.org/10.3390/data2040032

Vancouver

Author

Lohan, Elena-Simona ; Torres-Sospedra, Joaquin ; Leppäkoski, Helena ; Richter, Philipp ; Peng, Zhe ; Huerta, Joaquin. / Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning. Julkaisussa: MDPI Data. 2017 ; Vuosikerta 2, Nro 4.

Bibtex - Lataa

@article{744da5a2fcc74e64ac61ea9e0633a998,
title = "Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning",
abstract = "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 (https://zenodo.org/record/889798)",
author = "Elena-Simona Lohan and Joaquin Torres-Sospedra and Helena Lepp{\"a}koski and Philipp Richter and Zhe Peng and Joaquin Huerta",
note = "INT=elt,”Peng, Zhe”",
year = "2017",
month = "10",
day = "3",
doi = "10.3390/data2040032",
language = "English",
volume = "2",
journal = "MDPI Data",
issn = "2306-5729",
publisher = "MDPI",
number = "4",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning

AU - Lohan, Elena-Simona

AU - Torres-Sospedra, Joaquin

AU - Leppäkoski, Helena

AU - Richter, Philipp

AU - Peng, Zhe

AU - Huerta, Joaquin

N1 - INT=elt,”Peng, Zhe”

PY - 2017/10/3

Y1 - 2017/10/3

N2 - 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 (https://zenodo.org/record/889798)

AB - 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 (https://zenodo.org/record/889798)

U2 - 10.3390/data2040032

DO - 10.3390/data2040032

M3 - Article

VL - 2

JO - MDPI Data

JF - MDPI Data

SN - 2306-5729

IS - 4

ER -