Malfunction Immune Wi-Fi Localisation Method

Rafał Górak , Marcin Luckner


Indoor localisation systems based on a Wi–Fi local area wireless technology bring constantly improving results. However, the whole localisation system may fail when one or more Access Point (AP) malfunctions. In this paper we present how to limit the number of observed APs and how to create a malfunction immune localisation method. The presented solutions are an ensemble of random forests with an additional malfunction detection system. The proposed solution reduces a growth of the localisation error to 4 percent for the floor detection inside a six floor building and 2 metres for the horizontal detection in case of a gross malfunction of an AP infrastructure. The system without proposed improvements may give the errors greater than 30 percent and 7 metres respectively in case of not detected changes in the AP’s infrastructure.
Author Rafał Górak (FMIS / DFG)
Rafał Górak,,
- Department of Foundations Geometry
, Marcin Luckner (FMIS / DACSCM)
Marcin Luckner,,
- Department of Applied Computer Science and Computation Methods
Publication size in sheets0.5
Book Núñez Manuel, Nguyen Ngoc Thanh, Camacho David, Trawiński Bogdan (eds.): Computational Collective Intelligence, Lecture Notes In Computer Science, vol. 9329, 2015, SPRINGER-VERLAG, ISBN 978-3-319-24068-8, [978-3-319-24069-5], 515 p.
Abstract in PolishW pracy zaproponowany zostaje system lokalizacji wewnątrzbudynkowej opartej na istniejącej sieci Wi-Fi. System ma na celu automatyczne wykrycie awarii w sieci Wi-Fi i przebudowanie modelu lokalizacyjnego, tak aby uwzględniał zaistniałe zmiany w infrastrukturze. Poza tym w pracy zostaje zaproponowana metoda redukcji liczby APów potrzebnych do zbudowania modelu lokalizacyjnego.
Languageen angielski
Score (nominal)15
ScoreMinisterial score = 15.0, 01-10-2019, BookChapterSeriesAndMatConfByConferenceseries
Ministerial score (2013-2016) = 15.0, 01-10-2019, BookChapterSeriesAndMatConfByConferenceseries
Publication indicators WoS Citations = 8
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