Since appearance of smart phones such as Apple iPhone and Google Android phones, the market grows continuously. Android phone provides a Java-based open platform to develop applications and support various kinds of hardware. It is used to develop useful context-aware services in short time.
In many cases, smart phones consume a large amount of energy to support high performance processor, wireless communication, and various sensors such as GPS receiver, G-sensor, proximity sensor, accelerometer and light sensor. The users often have to re-charge the battery or prepare a spare battery. Nowadays, it becomes an issue to develop the techniques for efficient energy management on a mobile phone.
This paper proposes a system to manage some functions, which require a large amount of energy, such as Wi-Fi, Bluetooth, screen backlight and unnecessary applications. This system uses decision tree and rules to extract context from sensor data. It depends on Bayesian probabilistic models to determine whether a specific function will be stopped or not. For the feasibility of the proposed system, we applied it to Android phone and analyzed the effectiveness.
Our context-aware battery management system 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.
The proposed system consists of four components: sensor data collection, pre-processing, Bayesian network-based inference, and energy management. Decision tree algorithm is used to classify a user's transportation mode from acceleration, orientation, and magnetic field. Some features such as the difference between previous and current sensor values, average sensor value for a specific period, and standard deviation of the sensor value for a specific period are extracted for decision tree
A Bayesian network is used to process uncertainty with probability. It supports efficient probability calculation based on conditional independence assumption. It can be designed with domain knowledge and trained with data.
The energy management system has four modules to control Wi-Fi, Bluetooth, brightness of the display, and background applications. Each module includes a Bayesian network to estimate the probability to turn off a specific function and a controller to turn on or off the function.
To evaluate the proposed system, we applied it to a LG Optimus 2X Android phone and conducted simulations. Training data are collected for three days for classifier learning and parameter tuning. Test data set is gathered for two days to compare the reduction of energy consumption.
The proposed system integrates probabilistic inference and energy management into a module. It performs probabilistic inference only when context changes to reduce the cost of inference. The system shows better performance than other previous works in the simulation.
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