Indoor location prediction using multiple wireless received signal strengths

Abstract

This paper presents a framework for indoor loca-tion prediction system using multiple wireless signals available freely in public or office spaces. We first pro-pose 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 differ-ent 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 mod-eling method, almost perfect prediction accuracy can be achieved in the static environment, and up to 85% in the presence of human movements. Finally, adopt-ing the proposed framework we outline a fully de-veloped system, named Marauder, that support user interface interaction and real-time voice-enabled lo-cation prediction.