Optimization for Energy-Harvesting Wearable IoT Devices.

Wearable internet of things (IoT) devices can enable a variety of biomedical applications,such as gesture recognition, health monitoring, and human activity tracking. Size and weightconstraints limit the battery capacity, which leads to frequent charging requirements and userdissatisfaction. Minimizing the energy consumption not only alleviates this problem, but alsopaves the way for self-powered devices that operate on harvested energy. This paper considers anenergy-optimal gesture recognition application that runs on energy-harvesting devices. We firstformulate an optimization problem for maximizing the number of recognized gestures when energybudget and accuracy constraints are given. Next, we derive an analytical energy model from thepower consumption measurements using a wearable IoT device prototype. Then, we prove thatmaximizing the number of recognized gestures is equivalent to minimizing the duration of gesturerecognition. Finally, we utilize this result to construct an optimization technique that maximizes thenumber of gestures recognized under the energy budget constraints while satisfying the recognitionaccuracy requirements. Our extensive evaluations demonstrate that the proposed analytical modelis valid for wearable IoT applications, and the optimization approach increases the number ofrecognized gestures by up to 2.4× compared to a manual optimization.