A Resource Management Based on Deep Learning in Ubiquitous Power Internet of Things

This paper proposes a resource allocation method based on deep learning in power internet of things. The method uses density-based spatial clustering of noisy applications (DBSCAN) to analyze data, and then trains deep neural networks (DNN) based on the results of DBSCAN data. The results show that our method can significantly improve the user's quality of service (QoS).