A Low-Power Context-Aware System for Smartphone Using Hierarchical Modular Bayesian Networks

Various applications using sensors and devices on smartphone are being developed. However, since limited battery capacity does not allow to utilize the phone all the time, studies to increase use-time of phone are very active. In this paper, we propose a hybrid system to increase the longevity of phone. User’s context is recognized through hierarchical modular Bayesian networks, and unnecessary devices are inferred through device management rules. Inferring the user’s context using sensor data, and considering device status, context inferred and user’s tendency, we determine the device which is consuming the battery most. In the experiments with the real log data collected from 28 people for six months, we evaluated the proposed system resulting in the accuracy of 85.68 % and the improvement of battery consumption of about 6 %.