Using Prediction to Conserve Energy in Recognition on Mobile Devices
As devices are expected to be aware of their environment, the challenge becomes how to accommodate these abilities with the power constraints which plague modern mobile devices. The authors present a framework for an embedded approach to context recognition which reduces power consumption. This is accomplished by identifying class-sensor dependencies, and using prediction methods to identify likely future classes, thereby identifying sensors which can be temporarily turned off. Different methods for prediction, as well as integration with several classifiers is analyzed and the methods are evaluated in terms of computational load and loss in quality of context.