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：众包无线能源是一种新颖且方便的解决方案,可以向附近的 IoT 设备收费。 已经提出了几个应用程序, 以提供对等无线能源的收费。 但是, 没有一个应用程序考虑了无线能源传输的能效。 在本文中, 我们提出了一个能源估算框架, 预测实际收到的能源。 我们的框架使用两个机器学习算法, 即 XGBoost 和 Neal 网络, 来估计收到的能源。 结果显示神经网络模型比 XGBoost 更好地预测收到的能源。 我们通过收集真正的无线能源数据集来培训和评估我们的模型 。</s>
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