Significant motion-based adaptive sampling module for mobile sensing framework

Many mobile sensing frameworks have been developed to help researcher doing their mobile sensing research. However, energy consumption is still an issue in the mobile sensing research, and the existing frameworks do not provide enough solution for solving the issue. We have surveyed several mobile sensing frameworks and carefully chose one framework to improve. We have designed an adaptive sampling module for a mobile sensing framework to help solve the energy consumption issue. However, in this study, we limit our design to an adaptive sampling module for the location and motion sensors. In our adaptive sampling module, we utilize the significant motion sensor to help the adaptive sampling. We experimented with two sampling strategies that utilized the significant motion sensor to achieve low-power consumption during the continuous sampling. The first strategy is to utilize the sensor naively only while the second one is to add the duty cycle to the naive approach. We show that both strategies achieve low energy consumption, but the one that is combined with the duty cycle achieves better result.