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.

翻译：众包无线能量传输是一种新颖且便捷的解决方案，可用于为附近的物联网设备充电。目前已有多项应用支持点对点无线能量传输，但均未考虑能量无线传输过程中的能效问题。本文提出了一种能量估计框架，用于预测实际接收能量。该框架采用两种机器学习算法——XGBoost与神经网络（Neural Network）——对接收能量进行估计。结果表明，神经网络模型在接收能量预测方面优于XGBoost。我们通过采集真实无线能量数据集对所构建模型进行训练与评估。