Constrained Deep Reinforcement Learning for Energy Sustainable IoT Networks

Dear Colleagues, 

It is known to all that revealing valuable information and making a wise decision from a complicated situation is the critical contribution of Artificial Intelligence. Recently, there have been many fantastic deep learning frameworks (such as Artificial Neural Network) proposed to resolve complex issues and obtain surprising results. Unfortunately, a real applied environment has more resource limitations, especially in an IoT environment. Endless data stream and poor terminal computation ability cause the traditional deep learning framework to be hardly applied. This Special Issue focuses on the environment of Energy Sustainable IoT, the critical challenge of the proposed methods is the endless and mess input data from an IoT network. Furthermore, it needs to continuously update the training model to fit the latest update. We encourage researchers to share their latest state-of-the-art solutions to interesting problems in this domain.

Prof. Dr. Jerry Chun Wei Lin
Dr. Ming-Tai Wu
Dr. Mu-En Wu
Guest Editors

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• IoT
• deep learning
• artificial intelligence
• energy sustainable
• artificial neural network