Machine Learning applied to Wi-Fi fingerprinting: The experiences of the Ubiqum Challenge

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-of-art Machine Learning methods provide good positioning results, but expertise on the problem is still needed.