A Deep Reinforcement Learning-based Adaptive Charging Policy for WRSNs
Author:
Affiliation:
1. School of Information and Communication Technology, Hanoi University of Science and Technology
2. VinAI Research,Vietnam
Funder
Vietnam National Foundation for Science and Technology Development (NAFOSTED)
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9973399/9973403/09973511.pdf?arnumber=9973511
Reference24 articles.
1. ESync: Energy Synchronized Mobile Charging in Rechargeable Wireless Sensor Networks
2. TSCA: A Temporal-Spatial Real-Time Charging Scheduling Algorithm for On-Demand Architecture in Wireless Rechargeable Sensor Networks
3. Double warning thresholds for preemptive charging scheduling in Wireless Rechargeable Sensor Networks
4. Adaptive online mobile charging for node failure avoidance in wireless rechargeable sensor networks
5. An efficient scheduling scheme for mobile charger in on-demand wireless rechargeable sensor networks
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1. Deep reinforcement learning approach with hybrid action space for mobile charging in wireless rechargeable sensor networks;Expert Systems with Applications;2024-09
2. A DRL-based Partial Charging Algorithm for Wireless Rechargeable Sensor Networks;ACM Transactions on Sensor Networks;2024-07-08
3. An adaptive charging scheme for large-scale wireless rechargeable sensor networks inspired by deep Q-network;Neural Computing and Applications;2024-04-26
4. Deep Reinforcement Learning-Based Joint Sequence Scheduling and Trajectory Planning in Wireless Rechargeable Sensor Networks;IEEE Sensors Journal;2024-04-15
5. Mobile Charging Scheduling Approach for Wireless Rechargeable Sensor Networks Based on Multiple Discrete-Action Space Deep Q-Network;Applied Sciences;2023-07-23
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