AI Opportunities for Increased Energy Autonomy of Low Power IoT Devices This paper is a focus paper opening the special session xe2x80x9cUltralow power pattern recognition for smart IoT applicationsxe2x80x9d. The goal of this paper is to provide a review of the trends in the use of the recently available deep learning techniques for the leveraging of the energy autonomy of low power wireless devices used in InternetofThings networks. The paper discusses two families of applications of Artificial Intelligence in power optimisation: (i) Contextaware adaptive optimisation of the consumed power by IoT devices and networks by forecasting the future of the power consumption and the future of the energy available through the batteryenergy harvesting, and (ii) Optimisation of energy harvesting devices in the context where available environment energy is characterized by stochastic andor irregular patterns. The second case is illustrated by the presentation of a novel concept of kinetic energy harvesting generating electricity out of vibrations related to human body motion.

Energy per Operation Optimization for EnergyHarvesting 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 selfpowered devices that operate on harvested energy. This paper considers anenergyoptimal gesture recognition application that runs on energyharvesting 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 2.4xc3x97 compared to a manual optimization.