Enabling energy-efficient context recognition with configuration folding

Existing context recognition applications for personal mobile devices are usually fine-tuned to recognize the set of characteristics required to support a particular user task. Outside of a laboratory environment, however, users are often involved in multiple tasks at a time which requires the simultaneous execution of several applications. Yet, due to the energy constraints of most personal mobile devices this approach is inherently limited in scale. In this paper, we show how this problem can be avoided by applying a component-based approach to application development and execution. We present a component system that enables developers to build individual applications in isolation without compromising runtime efficiency. When applications are executed simultaneously, the component system applies a novel technique called configuration folding to automatically remove redundancies. Our experimental evaluation of configuration folding shows that it can save up to 48 percent of energy when applied to a music and a speech detection application, thus, amortizing energy costs after a few seconds of execution.