Monitoring Efficiency of IoT Wireless Charging
This work addresses energy efficiency monitoring for IoT devices, but it is incremental as it applies existing machine learning methods to a new dataset without major innovations.
The paper tackles the problem of predicting actual received energy in IoT wireless charging by proposing an energy estimation framework using XGBoost and Neural Network models, with results showing the Neural Network model performs better.
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.