Journal
IEEE NETWORK
Volume 33, Issue 2, Pages 111-117Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MNET.2019.1800254
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Categories
Funding
- NSFC [61773126, 61603099, 61701125]
- Pearl River SAMP
- T Nova Program of Guangzhou [201806010176]
- European Union [824019]
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In recent years, green energy management systems (smart grid, smart buildings, and so on) have received huge research and industrial attention with the explosive development of smart cities. By introducing Internet of Things (IoT) technology, smart cities are able to achieve exquisite energy management by ubiquitous monitoring and reliable communications. However, long-term energy efficiency has become an important issue when using an IoT-based network structure. In this article, we focus on designing an IoT-based energy management system based on edge computing infrastructure with deep reinforcement learning. First, an overview of IoT-based energy management in smart cities is described. Then the framework and software model of an IoT-based system with edge computing are proposed. After that, we present an efficient energy scheduling scheme with deep reinforcement learning for the proposed framework. Finally, we illustrate the effectiveness of the proposed scheme.
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