Dinkelbach-guided deep reinforcement learning for secure communication in UAV-aided MEC networks
W Lu, Y Ding, Y Feng, G Huang, N Zhao… - … 2022-2022 IEEE …, 2022 - ieeexplore.ieee.org
Unmanned aerial vehicle-aided (UAV-aided) mobile edge computing (MEC) network can
greatly reduce the data growth pressure of Internet of Things (IoT) and expand the wireless …
greatly reduce the data growth pressure of Internet of Things (IoT) and expand the wireless …
Multi-Dimensional Resource Management for Distributed MEC Networks in Jamming Environment: A Hierarchical DRL Approach
This article investigates the problem of multidimensional resource management in
multiaccess mobile edge computing (MEC) networks against external dynamic jamming …
multiaccess mobile edge computing (MEC) networks against external dynamic jamming …
Reinforcement learning based UAV trajectory and power control against jamming
Unmanned aerial vehicles (UAVs) are vulnerable to jamming attacks that aim to interrupt the
communications between the UAVs and ground nodes and to prevent the UAVs from …
communications between the UAVs and ground nodes and to prevent the UAVs from …
Deep reinforcement learning for multi-hop offloading in UAV-assisted edge computing
In this article, we propose a unmanned aerial vehicle (UAV)-assisted multi-hop edge
computing (UAV-assisted MEC) system in which a UE can offload its task to multiple UAVs in …
computing (UAV-assisted MEC) system in which a UE can offload its task to multiple UAVs in …
Distributed reinforcement learning based framework for energy-efficient UAV relay against jamming
Unmanned aerial vehicle (UAV) network is vulnerable to jamming attacks, which may cause
severe damage like communication outages. Due to the energy constraint, the source UAV …
severe damage like communication outages. Due to the energy constraint, the source UAV …
Fairness-aware task loss rate minimization for multi-UAV enabled mobile edge computing
C Zhu, G Zhang, K Yang - IEEE Wireless Communications …, 2022 - ieeexplore.ieee.org
In practical systems, a computing task generated by an Internet of Things device (IoTD) is
usually given a valid period (vap). The tasks that cannot be executed within the vap will be …
usually given a valid period (vap). The tasks that cannot be executed within the vap will be …
Deep Reinforcement Learning Empowered Trajectory and Resource Allocation Optimization for UAV-Assisted MEC Systems
H Sun, M Chen, Y Pan, Y Cang… - IEEE Wireless …, 2024 - ieeexplore.ieee.org
In this paper, we address the energy minimization problem for the UAV-assisted MEC
system under the long-term dynamic environment by jointly optimizing UAV trajectory …
system under the long-term dynamic environment by jointly optimizing UAV trajectory …
Resource allocation strategy for multi-UAV-assisted MEC system with dense mobile users and MCR-WPT
L Liang, Y Zhao, K Jian, H You… - 2023 IEEE Wireless …, 2023 - ieeexplore.ieee.org
Mobile edge computing (MEC) moves computeintensive tasks to the edge of wireless
networks, which can effectively reduce service latency and improve quality of service. A …
networks, which can effectively reduce service latency and improve quality of service. A …
A hybrid secure resource allocation and trajectory optimization approach for mobile edge computing using federated learning based on WEB 3.0
The use of unmanned aerial vehicles (UAVs) in Internet-of-Things (IoT) has grown, but
security for UAV communications still a challenge due to the distributed nature of line-of …
security for UAV communications still a challenge due to the distributed nature of line-of …
Green MEC networks design under UAV attack: A deep reinforcement learning approach
In this paper, we propose a novel optimization framework for a secure and green mobile
edge computing (MEC) network, through a deep reinforcement learning approach, where …
edge computing (MEC) network, through a deep reinforcement learning approach, where …