An efficient energy management system for android phone using bayesian networks

Many applications on smart phones have been developed for a user's convenience. They are using various sensors such as accelerometer, proximity, light and orientation sensors for context-awareness. Energy-efficient battery management systems become more important to support the sensors on a mobile phone because recent applications consume a large amount of energy for diverse functionalities. This paper proposes a context-aware battery management system which infers a user's situation and controls unnecessary functionalities using embedded sensors on a mobile phone. The proposed system uses probabilistic models to infer a user's situation and minimizes the cost of probability calculation according to the change of context. In order to show the feasibility of the proposed method, we conducted the evaluation with a real dataset collected from mobile device.