Edge-Enabled Two-Stage Scheduling Based on Deep Reinforcement Learning for Internet of Everything-Reference-Cited by-同舟云学术

Edge-Enabled Two-Stage Scheduling Based on Deep Reinforcement Learning for Internet of Everything

Published:2023-02-15 Issue:4 Volume:10 Page:3295-3304
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Zhou Xiaokang¹^ORCID,Liang Wei²^ORCID,Yan Ke³^ORCID,Li Weimin⁴^ORCID,Wang Kevin I-Kai⁵^ORCID,Ma Jianhua⁶,Jin Qun⁷^ORCID

Affiliation:

1. Faculty of Data Science, Shiga University, Hikone, Japan

2. Base of International Science and Technology Innovation and Cooperation on Big Data Technology and Management, Hunan University of Technology and Business, Changsha, China

3. College of Design and Engineering, National University of Singapore, Singapore

4. School of Computer Engineering and Science, Shanghai University, Shanghai, China

5. Department of Electrical, Computer Software Engineering, The University of Auckland, Auckland, New Zealand

6. Faculty of Computer and Information Sciences, Hosei University, Tokyo, Japan

7. Faculty of Human Sciences, Waseda University, Tokorozawa, Japan

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Key Research and Development Program of Hunan Province of China

Open Fund of Key Laboratory of Hunan Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10038283/09785600.pdf?arnumber=9785600

Reference31 articles.

1. Reinforcement Learning for Real-Time Optimization in NB-IoT Networks

2. Deep Correlation Mining Based on Hierarchical Hybrid Networks for Heterogeneous Big Data Recommendations

3. Intelligent IoT Connectivity: Deep Reinforcement Learning Approach

4. MMDP: A Mobile-IoT Based Multi-Modal Reinforcement Learning Service Framework

5. A Johnson's-Rule-Based Genetic Algorithm for Two-Stage-Task Scheduling Problem in Data-Centers of Cloud Computing

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