Machine Learning applied to Wi-Fi fingerprinting: The experiences of the Ubiqum Challenge
| Autor: | Laura Stupin, Jorma Laiapea, Germán Martín Mendoza-Silva, Florian Unger, Deniz Minican, Jordi Rojo, Carmen Corvalan, Gerardo Parrello, Daniel Castejon Bravo, Maria Farres, Ignacio Soteras, Sara Marin Lopez, Arnau Simo, Gabriel Ristow Cidral, Joaquín Torres-Sospedra |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry 010401 analytical chemistry 02 engineering and technology Machine learning computer.software_genre 01 natural sciences 0104 chemical sciences Set (abstract data type) Competition (economics) 0202 electrical engineering electronic engineering information engineering Data analysis 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | IPIN |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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