Personal name classification in web queries
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A model-based WiFi localization method
Proceedings of the 2nd international conference on Scalable information systems
Proximity classification for mobile devices using wi-fi environment similarity
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
Reusable framework for location-aware mobile applications
Mobility '09 Proceedings of the 6th International Conference on Mobile Technology, Application & Systems
Neighbor-assisted location calibration mechanism in wireless network
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
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Environmental variations cause significant fluctuations in WiFi signals in the same location over time, rendering traditional RF-to-location pre-trained maps quickly obsolete. To solve this problem, we use a two-phase approach to determining the user's location. The first phase utilizes traditional patternmatching to identify the general location, and a second phase applies logistic regression to distinguish between finer-grained locations. An adaptive calibration system allows the user to re-train and dynamically update the signal strength maps to account for the fluctuated signals. We show that our two-phase approach is able to achieve generally high accuracy (95%) and over in areas of high signal fluctuations due to heavy access point and human density.