Date of Graduation


Document Type


Degree Name

Master of Science in Computer Science (MS)

Degree Level



Computer Science & Computer Engineering


Tingxin Yan

Committee Member

Michael S. Gashler

Second Committee Member

John Gauch


Applied sciences, Logical status inference, Mobile computing, Usage statistics


Logical statuses of mobile users, such as isBusy and isAlone, are the key enabler for a plethora of context-aware mobile applications. While on-board hardware sensors (such as motion, proximity, and location sensors) have been extensively studied for logical status inference, continuous usage typically requires formidable energy consumption, which degrades the user experience. In this thesis, we argue that smartphone usage statistics can be used for logical status inference with negligible energy cost. To validate this argument, we present a continuous inference engine that (1) intercepts multiple operating system events, in particular foreground app, notifications, screen states, and connected networks; (2) extracts informative features from OS events; and (3) efficiently

infers the logical status of mobile users. The proposed inference engine is implemented

for unmodified Android phones, and an evaluation on a four-week trial has shown promising accuracy in identifying four logical statuses of mobile users with over 87% accuracy while the average energy impact on the battery life is less than 0.5%.