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 paper 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.
|Cite as: Tran, K., Phung, D., Adams, B. and Venkatesh, S. (2008). Indoor Location Prediction Using Multiple Wireless Received Signal Strengths. In Proc. Seventh Australasian Data Mining Conference (AusDM 2008), Glenelg, South Australia. CRPIT, 87. Roddick, J. F., Li, J., Christen, P. and Kennedy, P. J., Eds. ACS. 187-192. |
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