Indoor Location Prediction Using Multiple Wireless Received Signal Strengths

This paper presents a framework for indoor location prediction system using multiple wireless signals available freely in public or office spaces. We first proabstract 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....

• Type: Conference
• Year: 2008
• Where: AUSDM
• Authors: Kha Tran, Dinh Q. Phung, Brett Adams, Svetha Venkatesh
• Keywords: AUSDM 2008, Data Mining, Indoor Location Prediction, Location Prediction, Voice-enabled Location Prediction