Power Management for Kinetic Energy Harvesting IoT

Abstract

Human kinetic energy is regarded as a promising sustainable energy source to solve the energy bottleneck of Internet of Things (IoT). The low power harvested from human motion and scarce hardware resource of IoT severely restrain the operation of kinetic energy harvesting IoT and stress the need for power management strategies to improve the energy efficiency. In this paper, we propose a novel power management framework for kinetic energy harvesting IoT, composed of an off-line inertial harvester optimization algorithm and an on-line joint sink selection and transmission power control module. By analyzing the characteristics of human daily motion and the inertial harvester model, the optimal inertial harvester parameters are determined to maximize the power generation from human daily motion. The on-line scheme improves energy efficiency by joint consideration of optimal sink selection (i.e., on-body sink or off-body sink) and transmission power control. The real world human motion data set is used to evaluate the proposed framework. The simulation results indicate that, compared with the existing approach, the proposed kinetic harvester optimization algorithm achieves 83.31% to 135.69% improvement in harvested power from the same human motion trace. In addition, the proposed on-line joint sink selection and transmission power control incurs 7.07% to 34.23% improvement in transmission energy efficiency.