Crowdsourcing wireless energy is a novel and convenient solution to charge nearby IoT devices. Several applications have been proposed to enable peer-to-peer wireless energy charging. However, none of them considered the energy efficiency of the wireless transfer of energy. In this paper, we propose an energy estimation framework that predicts the actual received energy. Our framework uses two machine learning algorithms, namely XGBoost and Neural Network, to estimate the received energy. The result shows that the Neural Network model is better than XGBoost at predicting the received energy. We train and evaluate our models by collecting a real wireless energy dataset.
@article{arxiv.2303.05629,
title = {Monitoring Efficiency of IoT Wireless Charging},
author = {Pengwei Yang and Amani Abusafia and Abdallah Lakhdari and Athman Bouguettaya},
journal= {arXiv preprint arXiv:2303.05629},
year = {2023}
}
Comments
3 pages, 4 figures. This is an accepted demo paper and it will appear in The 21st International Conference on Pervasive Computing and Communications (PerCom 2023)