Indoor location prediction using multiple wireless received signal strengths

  • Authors:

  • Kha Tran;Dinh Phung;Brett Adams;Svetha Venkatesh

  • Affiliations:

  • Curtin University of Technology, Perth, WA, Australia;Curtin University of Technology, Perth, WA, Australia;Curtin University of Technology, Perth, WA, Australia;Curtin University of Technology, Perth, WA, Australia

  • Venue:
  • AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
  • Year:
  • 2008

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Abstract

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.