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A Novel On-Demand Charging Strategy Based on Swarm Reinforcement Learning in WRSNs

  • Published:2020 Issue: Volume:8 Page:84258-84271
  • ISSN:2169-3536
  • Container-title:IEEE Access
  • language:
  • Short-container-title:IEEE Access

Author:

Wei Zhen,Li Meng,Wei ZhenchunORCID,Cheng Lei,Lyu Zengwei,Liu Fei

Funder

National Key Research Development Program of China

Key Research and Development Project in Anhui Province, China

Fundamental Research Funds for the Central Universities, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Cited by 5 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. Caddisfalcon optimization algorithm for on-demand energy transfer in wireless rechargeable sensors based IoT networks;Sustainable Energy Technologies and Assessments;2024-04

2. On optimizing the charging trajectory of mobile chargers in wireless sensor networks: a deep reinforcement learning approach;Wireless Networks;2023-08-31

3. A state-of-the-art survey on wireless rechargeable sensor networks: perspectives and challenges;Wireless Networks;2022-06-21

4. Fuzzy-based On-demand Multi-node Charging Scheme To Reduce Death Rate Of Sensors In Wireless Rechargeable Sensor Networks;2021 10th International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON);2021-12-01

5. Review on Charge Scheduling Techniques for On-Demand Wireless Recheargeable Sensor Networks;2021 1st International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS);2021-07-15
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