Big Data Small Footprint: The Design of A Low-Power Classifier for Detecting Transportation Modes

Sensors on mobile phones and wearables, and in general sen-sors on IoT (Internet of Things), bring forth a couple of new challenges to big data research. First, the power consump-tion for analyzing sensor data must be low, since most wear-ables and portable devices are power-strapped. Second, the velocity of analyzing big data on these devices must be high, otherwise the limited local storage may overflow. This paper presents our hardware-software co-design of a classifier for wearables to detect a person’s transporta-tion mode (i.e., still, walking, running, biking, and on a vehicle). We particularly focus on addressing the big-data small-footprint requirement by designing a classifier that is low in both computational complexity and memory require-ment. Together with a sensor-hub configuration, we are able to drastically reduce power consumption by 99%, while maintaining competitive mode-detection accuracy. The data used in the paper is made publicly available for conducting research