Abstract
Wireless signal strength fingerprinting has become an increasingly popular technique for realizing indoor localization systems using existing WiFi infrastructures. However, these systems typically require a time-consuming and costly training phase to build the radio map. Moreover, since radio signals change and fluctuate over time, map maintenance requires continuous re-calibration. We introduce a new concept called “asynchronous interval labeling” that addresses these problems in the context of user-generated place labels. By using an accelerometer to detect whether a device is moving or stationary, the system can continuously and unobtrusively learn from all radio measurements during a stationary period, thus greatly increasing the number of available samples. Movement information also allows the system to improve the user experience by deferring labeling to a later, more suitable moment. Initial experiments with our system show considerable increases in data collected and improvements to inferred location likelihood, with negligible overhead reported by users.
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Bolliger, P., Partridge, K., Chu, M., Langheinrich, M. (2009). Improving Location Fingerprinting through Motion Detection and Asynchronous Interval Labeling. In: Choudhury, T., Quigley, A., Strang, T., Suginuma, K. (eds) Location and Context Awareness. LoCA 2009. Lecture Notes in Computer Science, vol 5561. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01721-6_3
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DOI: https://doi.org/10.1007/978-3-642-01721-6_3
Publisher Name: Springer, Berlin, Heidelberg
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