2023/03/10 by Pengwei Yang, Amani Abusafia, Yang, Pengwei +5
Engineering · #Advanced MIMO Systems Optimization #Distributed #Energy Harvesting in Wireless Networks #FOS: Computer and information sciences #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #Parallel #Smart Grid Energy Management #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2303.05629
openalex publication_date 2023/03/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.