Deep learning in the industrial internet of things: Potentials, challenges, and emerging applications
Recent advances in the Internet of Things (IoT) are giving rise to a proliferation of
interconnected devices, allowing the use of various smart applications. The enormous …
interconnected devices, allowing the use of various smart applications. The enormous …
Path planning for UAV-mounted mobile edge computing with deep reinforcement learning
In this letter, we study an unmanned aerial vehicle (UAV)-mounted mobile edge computing
network, where the UAV executes computational tasks offloaded from mobile terminal users …
network, where the UAV executes computational tasks offloaded from mobile terminal users …
Deep reinforcement learning multi-UAV trajectory control for target tracking
J Moon, S Papaioannou, C Laoudias… - IEEE Internet of …, 2021 - ieeexplore.ieee.org
In this article, we propose a novel deep reinforcement learning (DRL) approach for
controlling multiple unmanned aerial vehicles (UAVs) with the ultimate purpose of tracking …
controlling multiple unmanned aerial vehicles (UAVs) with the ultimate purpose of tracking …
UAV anti-jamming video transmissions with QoE guarantee: A reinforcement learning-based approach
Unmanned aerial vehicles (UAVs) that are widely utilized for video capturing, processing
and transmission have to address jamming attacks with dynamic topology and limited …
and transmission have to address jamming attacks with dynamic topology and limited …
Multi-UAV mobile edge computing and path planning platform based on reinforcement learning
Unmanned Aerial vehicles (UAVs) are widely used as network processors in mobile
networks, but more recently, UAVs have been used in Mobile Edge Computing as mobile …
networks, but more recently, UAVs have been used in Mobile Edge Computing as mobile …
Bayesian optimization enhanced deep reinforcement learning for trajectory planning and network formation in multi-UAV networks
In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the
ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories …
ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories …
Joint buffer-aided hybrid-duplex relay selection and power allocation for secure cognitive networks with double deep Q-network
This paper applies the reinforcement learning in the joint relay selection and power
allocation in the secure cognitive radio (CR) relay network, where the data buffers and full …
allocation in the secure cognitive radio (CR) relay network, where the data buffers and full …
Task-oriented communication design in cyber-physical systems: A survey on theory and applications
Communication system design has been traditionally guided by task-agnostic principles,
which aim at efficiently transmitting as many correct bits as possible through a given …
which aim at efficiently transmitting as many correct bits as possible through a given …
Joint communication scheduling and velocity control in multi-UAV-assisted sensor networks: A deep reinforcement learning approach
Recently, Unmanned Aerial Vehicle (UAV) swarm has been increasingly studied to collect
data from ground sensors in remote and hostile areas. A key challenge is the joint design of …
data from ground sensors in remote and hostile areas. A key challenge is the joint design of …
Approximating Nash equilibrium for anti-UAV jamming Markov game using a novel event-triggered multi-agent reinforcement learning
In the downlink communication, it is currently challenging for ground users to cope with the
uncertain interference from aerial intelligent jammers. The cooperation and competition …
uncertain interference from aerial intelligent jammers. The cooperation and competition …