Donghua Jiang

TL;DR: A blockchain based trust mechanism to help MEC address selfish edge attacks and faked service record attacks and a reinforcement learning (RL) based edge central processing unit (CPU) allocation algorithm without knowing the mobile service generation model and the network model in the dynamic edge computing process and a deep RL version to further improve the computational performance.

TL;DR: A hotbooting deep Q-network based 2-D mobile communication scheme is proposed that exploits experiences in similar scenarios to reduce the exploration time at the beginning of the game, and applies deep convolutional neural network and macro-action techniques to accelerate learning in dynamic situations.

TL;DR: This article investigates secure mobile crowdsensing and presents ways to use deep learning methods, such as stacked autoencoder, deep neural networks, convolutional neural Networks, and deep reinforcement learning, to improve approaches to MCS security, including authentication, privacy protection, faked sensing countermeasures, intrusion detection and anti-jamming transmissions in MCS.

TL;DR: A reinforcement learning-based antijamming relay scheme for UWSNs that enables an underwater relay to decide whether to leave the heavily jammed location and choose the relay power based on the state that consists of the bit error rate of the previous transmission, the previous relay power, the current transmit power of the sensor, and the jamming power measured by the relay node.

TL;DR: This article investigates secure mobile crowdsensing and presents how to use deep learning methods such as stacked autoencoder (SAE), deep neural network (DNN, and convolutional neural network) to improve the MCS security approaches including authentication, privacy protection, faked sensing countermeasures, intrusion detection and anti-jamming transmissions in MCS.