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Indoor location prediction using multiple wireless received signal strengths

Tran, Kha, Phung, Dinh, Adams, Brett and Venkatesh, Svetha 2008, Indoor location prediction using multiple wireless received signal strengths, in AusDM 2008 : Proceedings of the 7th Australasian Data Mining Conference, Australian Computer Society, Gold Coast, Qld., pp. 187-192.

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Title Indoor location prediction using multiple wireless received signal strengths
Author(s) Tran, Kha
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Adams, Brett
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Australasian Data Mining Conference (7th : 2008 : Glenelg, S. Aust.)
Conference location Glenelg, S. Aust.
Conference dates 27-28 Nov. 2008
Title of proceedings AusDM 2008 : Proceedings of the 7th Australasian Data Mining Conference
Editor(s) Roddick, John F.
Li, Jiuyong
Christen, Peter
Kennedy, Paul
Publication date 2008
Conference series Australasian Data Mining Conference
Start page 187
End page 192
Total pages 6
Publisher Australian Computer Society
Place of publication Gold Coast, Qld.
Keyword(s) indoor positioning
WiFi signal
naive bayes
hidden naive bayes
indoor navigation
Summary This 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.
ISBN 9781920682682
ISSN 1445-1336
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2008, Australian Computer Society
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044766

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.