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. Attached Files Name Description MIMEType Size Downloads phung-indoorlocation-2008.pdf Published version application/pdf 262.70KB 146 Title Indoor location prediction using multiple wireless received signal strengths Author(s) Tran, Kha Phung, Dinh orcid.org/0000-0002-9977-8247 Adams, Brett 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 Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30044766 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Tue, 01 May 2012, 10:56:47 EST Wed, 02 May 2012, 09:53:39 EST Wed, 02 May 2012, 09:55:41 EST Wed, 02 May 2012, 09:57:13 EST Wed, 02 May 2012, 10:30:25 EST Tue, 08 May 2012, 12:44:49 EST Tue, 28 Jan 2014, 14:36:01 EST Mon, 05 Sep 2016, 15:33:13 EST Tue, 06 Sep 2016, 18:50:15 EST Sat, 29 Apr 2017, 02:35:05 EST Tue, 05 Dec 2017, 12:31:54 EST Fri, 07 Sep 2018, 15:54:59 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 5 times in Scopus Search Google Scholar Access Statistics: 585 Abstract Views, 147 File Downloads  -  Detailed Statistics Created: Tue, 01 May 2012, 10:56:47 EST

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.