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dc.contributor.authorMascharka, David
dc.contributor.authorManley, Eric
dc.date.accessioned2016-03-23T13:34:44Z
dc.date.available2016-03-23T13:34:44Z
dc.date.issued2016-01
dc.identifier.citationMASCHARKA, D. AND MANLEY, E. LIPS: Learning based indoor positioning system using mobile phone-based sensors. In Proceedings of the IEEE Consumer Communications & Networking Conference, Las Vegas, Nevada, January, 2016, 975-978.en_US
dc.identifier.urihttp://hdl.handle.net/2092/2118
dc.description.abstractIn this paper we investigate the problem of localizing a mobile device based on readings from its sensors utilizing machine learning methodologies. We consider a real world environment, collect a dense set of 3110 datapoints, and examine the performance of a substantial number of machine learning algorithms. We found algorithms that have a mean error as accurate as 0.76 meters, outperforming other indoor localization systems. We also propose a hybrid instance-based approach that results in a speed increase by a factor of ten with no loss of accuracy in a live deployment over standard instance based methods. Further, we determine how less dense datasets affect accuracy, important for use in real-world environments. Finally, we demonstrate that these approaches are appropriate for real-world deployment by evaluating their performance in an online, in-motion experiment. The Learning Based Indoor Positioning System (LIPS) Android application source has been made available on the web.en_US
dc.language.isoen_USen_US
dc.titleLIPS: Learning based indoor positioning system using mobile phone-based sensorsen_US
dc.typePreprinten_US


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