Ye Chen
Xiamen University
11 Papers
Ye Chen is an academic researcher from Xiamen University. The author has contributed to research in topics: Computer science & Reinforcement learning. The author has an hindex of 7, co-authored 10 publications. Previous affiliations of Ye Chen include Hefei University of Technology.
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Papers
Learning-Based Computation Offloading for IoT Devices With Energy Harvesting
TL;DR: A reinforcement learning (RL) based offloading scheme for an IoT device with EH to select the edge device and the offloading rate according to the current battery level, the previous radio transmission rate to each edge device, and the predicted amount of the harvested energy.
DQN-Based Power Control for IoT Transmission against Jamming
Ye Chen,Yanda Li,Dongjin Xu,Liang Xiao +3 more
- 03 Jun 2018
TL;DR: Experimental results show that this scheme improves the signal-to-interference-plus-noise of the IoT signals compared with the benchmark Q-learning based power control scheme against jamming.
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Towards Smart Educational Recommendations with Reinforcement Learning in Classroom
Su Liu,Ye Chen,Hui Huang,Liang Xiao,Xiaojun Hei +4 more
- 01 Dec 2018
TL;DR: A cyber-physical-social system that uses multiple sensors such as cameras and a quiz creator to track the learning process of the students and applies reinforcement learning techniques to provide learning guidance based on the multi-modal sensing data in smart classroom is proposed.
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Protecting Semantic Trajectory Privacy for VANET with Reinforcement Learning
Weihang Wang,Minghui Min,Liang Xiao,Ye Chen,Huaiyu Dai +4 more
- 20 May 2019
TL;DR: An reinforcement learning (RL) based differential privacy mechanism that randomizes the released vehicle locations to protect the semantic trajectory of the vehicle and uses RL to select the obfuscation policy.
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Reinforcement Learning Based Power Control for In-Body Sensors in WBANs Against Jamming
TL;DR: Simulation results show that the proposed scheme can efficiently increase the utilities and decrease the transmission energy consumptions for the in-body sensors and the WBAN coordinator, and simultaneously reduce the attack possibility of the jammer compared with a standard Q-learning-based sensor power control scheme.
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