Hierarchical modular Bayesian networks for low-power context-aware smartphone

Nowadays, smartphone has a tremendous number of applications using sensors and devices for several applications such as healthcare and game. However, serious consideration to increase the duration of battery use of phone is required because of the limited battery capacity. In this paper, we propose a hybrid system to increase the longevity of phone with hierarchical modular Bayesian networks that recognize the user's contexts, and device management rules that infer the unnecessary devices in smartphone. Inferring the user's contexts with sensor data and considering the device status, the context inferred and user's tendency, we determine the superfluous devices that are consuming the battery as dispensable. The experiments with the real log data collected from 28 people for 6 months verify that the proposed system performs the accuracy of 85.68% and the reduction of battery consumption of about 6%.