Exploiting usage statistics for energy-efficient logical status inference on mobile phones.

ABSTRACTLogical 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, the continuous usage incurs formidable energy consumption and therefore user experience degradation. In this paper, we argue that smartphone usage statistics can be used for logical status inference with negligible energy cost. To validate this argument, this paper presents 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%.