State-machine driven opportunistic sensing by mobile devices
Radhika Loomba, Lei Shi, Brendan Jennings · 2014
As mobile devices increasingly incorporate a range of sensors, there is significant potential to apply opportunistic sensing techniques to allow collections of these devices to provide context information to applications. Focussing on a use case involving the use of mobile devices to sense and localize increasing levels of gases in a work environment, we show that the use of application-specific state machines that control the rate at which sensed data is reported, can lead to a significant reduction in battery consumption by the devices in comparison to continuous sensing approaches wherein the reporting rate remains constant.