@inproceedings{Rojo2019,

title = {Machine Learning applied to Wi-Fi fingerprinting: The experiences of the Ubiqum Challenge },

author = {Jordi Rojo and Germán Martín Mendoza-Silva and Gabriel Ristow Cidral and Jorma Laiapea and Gerardo Parrello and Arnau Simó and Laura Stupin and Deniz Minican and María Farrés and Carmen Corvalán and

Florian Unger and Sara Marín- López and Ignacio Soteras and Daniel Castejón Bravo and Joaquín Torres-Sospedra},

isbn = { 978-1-7281-1788-1 },

year  = {2019},

date = {2019-12-01},

booktitle = {Proceedings of the Tenth International Conference on Indoor Positioning and Indoor Navigation, 30 Sept. – 3 Oct. 2019, Pisa, Italy. },

publisher = {IEEE},

abstract = {Wi-Fi Fingerprinting is widely adopted for smartphone-based indoor positioning systems due to the availability of already deployed infrastructure for communications. The UJIIndoorLoc database contains Wi-Fi data for indoor positioning in a large environment covering three multi-tier buildings collected with multiple devices. Since the evaluation set is private, the indoor positioning systems of developers and researchers can still be evaluated under the same evaluation conditions than the participants of the 2015 EvAAL-ETRI competition. This paper shows the results and the experiences of such kind of external evaluation based on a competition provided by the the students of the “Data Analytics and Machine Learning” program of the Ubiqum data academy, who applied machine learning models they learnt during the program. The results show that state-ofart Machine Learning methods provide good positioning results, but expertise on the problem is still needed},

keywords = {machine learning, Wi-Fi fingerprint},

pubstate = {published},

tppubtype = {inproceedings}

}