Deep Reinforcement Learning-Based Joint Sequence Scheduling and Trajectory Planning in Wireless Rechargeable Sensor Networks-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning-Based Joint Sequence Scheduling and Trajectory Planning in Wireless Rechargeable Sensor Networks

Published:2024-04-15 Issue:8 Volume:24 Page:13699-13711
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Jiang Chengpeng¹^ORCID,Chen Wencong²^ORCID,Wang Ziyang³^ORCID,Xiao Wendong¹^ORCID

Affiliation:

1. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China

2. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China

3. Xilingol Vocational College, Xilinhot, China

Funder

National Natural Science Foundations of China

Foshan Science and Technology Innovation Special Project

Basic and Applied Basic Research Foundation of Guangdong Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7361/10500449/10466524.pdf?arnumber=10466524

Reference34 articles.

1. Wireless Sensor’s Civil Applications, Prototypes, and Future Integration Possibilities: A Review

2. A Critical Analysis of Research Potential, Challenges, and Future Directives in Industrial Wireless Sensor Networks

3. Wireless Power Transfer via Strongly Coupled Magnetic Resonances

4. Mobile data gathering and energy harvesting in rechargeable wireless sensor networks

5. Minimizing the number of mobile chargers for large-scale wireless rechargeable sensor networks

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Charging Scheduling Method for Wireless Rechargeable Sensor Networks Based on Energy Consumption Rate Prediction for Nodes;Sensors;2024-09-12

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