Energy Accuracy Trade-offs in Querying Sensor Data for Continuous Sensing Mobile Systems

A large number of context-inference applications run on off-the-shelf smart-phones and infer context from the data acquired by means of the sensors embedded in these devices. The use of efficient and effective sampling technique is of key importance for these applications. Aggressive sampling can ensure a more fine-grained and accurate reconstruction of context information but, at the same time, continuous querying of sensor data might lead to rapid battery depletion.
In this paper, we present an adaptive sensor sampling methodology which relies on dynamic selection of sam- pling functions depending on history of context events. We also report on the experimental evaluation of a set of functions that control the rate at which the data are sensed from the Bluetooth device, accelerometer, and microphone sensors and we show that a dynamic adaptation mechanism provides a better trade-offs compared to simpler function based rate control methods. Furthermore, we show that the suitability of these mechanisms varies for each of the sensors, and the accuracy and energy consumption values stabilize after reaching a certain level.