Seesaw: End-to-end Dynamic Sensing for IoT using Machine Learning

IoT edge devices' small form factor places tight constraints on their battery life. These devices are often equipped with multiple sensors, a few of them responsible for most of the energy usage. Naively lowering the sensing rate of these power-hungry sensors reduces energy consumption, but also degrades application's output quality. In this work, we observe that it is possible to leverage low-power sensors in a system to predict the impact of throttling a power-intensive sensor on application's output accuracy. We thus propose Seesaw, an end-to-end ML-based solution that automatically identifies correlations between power-intensive and lightweight sensors without human expertise. Further, Seesaw deploys a low-overhead decision tree predictor to determine the optimal sensing rates for power-intensive sensors, thus avoiding significant quality degradation. We show that Seesaw improves battery life for (1) video recording on mountable video cameras and (2) route tracking on fitness trackers by 32% and 66%, respectively, without significant accuracy loss.