Pengwei Yang's Conference Paper has now been published by IEEE.
Conference Proceeding on 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops).
Image credit: 2023 Percom WorkshopAbstract
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.
Presentation
- Monitoring Efficiency of IoT Wireless Charging
Authors and Affiliations
The University of Sydney, Sydney, NSW, 2000, Australia
Pengwei Yang, Amani Abusafia, Abdallah Lakhdari & Athman Bouguettaya
Acknowledgment
This research was partly made possible by LE220100078 grants from the Australian Research Council. The statements made herein are solely the responsibility of the authors.
Cite this paper
P. Yang, A. Abusafia, A. Lakhdari and A. Bouguettaya, “Monitoring Efficiency of IoT Wireless Charging,” 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), Atlanta, GA, USA, 2023, pp. 306-308, doi: 10.1109/PerComWorkshops56833.2023.10150276.
Links
Pengwei Yang
Research Associate at SCSLab (USYD)
Pengwei Yang is a Research Associate in the Sensors, Clouds, and Services Lab at the University of Sydney. His research interests include Crowdsourcing, Service-oriented Computing, Deep Learning, and Trustworthy Machine Learning.