The Fusion of Deep Reinforcement Learning and Edge Computing for Real-time Monitoring and Control Optimization in IoT Environments-Reference-Cited by-同舟云学术

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The Fusion of Deep Reinforcement Learning and Edge Computing for Real-time Monitoring and Control Optimization in IoT Environments

Published:2024-02-23 Issue: Volume: Page:193-196
ISSN:
Container-title:2024 3rd International Conference on Energy and Power Engineering, Control Engineering (EPECE)
language:
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Author:

Xu Jingyu¹,Wan Weixiang²,Pan Linying³,Sun Wenjian⁴,Liu Yuxiang⁵

Affiliation:

1. Northern Arizona University,1900 S Knoles Dr,Flagstaff,Arizona,USA

2. University of Electronic Science Technology of China,Chengdu,China

3. Trine university,Phoenix,Arizona,USA

4. Yantai University,Tokyo,Japan

5. Northwestern University,Atlanta,Georgia,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10624604/10624605/10624703.pdf?arnumber=10624703

Reference10 articles.

1. DRLE: Decentralized Reinforcement Learning at the Edge for Traffic Light Control in the IoV

2. A Novel Adaptive Traffic Signal Control Based on Cloud/Fog/Edge Computing

3. Advanced Deep Learning for Resource Allocation and Security Aware Data Offloading in Industrial Mobile Edge Computing

4. Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles

5. SO‐VMEC: Service offloading in virtual mobile edge computing using deep reinforcement learning

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