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Indoor location prediction using multiple wireless received signal strengths
conference contribution
posted on 2008-01-01, 00:00 authored by K Tran, Quoc-Dinh Phung, B Adams, Svetha VenkateshSvetha VenkateshThis paper presents a framework for indoor location prediction system using multiple wireless signals available freely in public or office spaces. We first propose an abstract architectural design for the system, outlining its key components and their functionalities. Different from existing works, such as robot indoor localization which requires as precise localization as possible, our work focuses on a higher grain: location prediction. Such a problem has a great implication in context-aware systems such as indoor navigation or smart self-managed mobile devices (e.g., battery management). Central to these systems is an effective method to perform location prediction under different constraints such as dealing with multiple wireless sources, effects of human body heats or mobility of the users. To this end, the second part of this pa- per presents a comparative and comprehensive study on different choices for modeling signals strengths and prediction methods under different condition settings. The results show that with simple, but effective modeling method, almost perfect prediction accuracy can be achieved in the static environment, and up to 85% in the presence of human movements. Finally, adopting the proposed framework we outline a fully developed system, named Marauder, that support user interface interaction and real-time voice-enabled location prediction.
History
Event
Australasian Data Mining Conference (7th : 2008 : Glenelg, S. Aust.)Pagination
187 - 192Publisher
Australian Computer SocietyLocation
Glenelg, S. Aust.Place of publication
Gold Coast, Qld.Start date
2008-11-27End date
2008-11-28ISSN
1445-1336ISBN-13
9781920682682Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2008, Australian Computer SocietyEditor/Contributor(s)
J Roddick, J Li, P Christen, P KennedyTitle of proceedings
AusDM 2008 : Proceedings of the 7th Australasian Data Mining ConferenceUsage metrics
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