Deep Reinforcement Learning Based Computation Offloading in UAV-Assisted Edge Computing
Traditional multi-access edge computing (MEC) often has difficulty processing large
amounts of data in the face of high computationally intensive tasks, so it needs to offload …
amounts of data in the face of high computationally intensive tasks, so it needs to offload …
Deep reinforcement learning for computation offloading and resource allocation in unmanned-aerial-vehicle assisted edge computing
S Li, X Hu, Y Du - Sensors, 2021 - mdpi.com
Computation offloading technology extends cloud computing to the edge of the access
network close to users, bringing many benefits to terminal devices with limited battery and …
network close to users, bringing many benefits to terminal devices with limited battery and …
Multi-objective deep reinforcement learning for computation offloading in UAV-assisted multi-access edge computing
X Liu, ZY Chai, YL Li, YY Cheng, Y Zeng - Information Sciences, 2023 - Elsevier
Unmanned aerial vehicle-assisted multi-access edge computing (UAV-MEC) plays an
important role in some complex environments such as mountainous and disaster areas …
important role in some complex environments such as mountainous and disaster areas …
Deep reinforcement learning-based computation offloading in uav swarm-enabled edge computing for surveillance applications
The rapid development of the Internet of Things and wireless communication has resulted in
the emergence of many latency-constrained and computation-intensive applications such as …
the emergence of many latency-constrained and computation-intensive applications such as …
Multi-agent deep reinforcement learning for task offloading in UAV-assisted mobile edge computing
Mobile edge computing can effectively reduce service latency and improve service quality
by offloading computation-intensive tasks to the edges of wireless networks. Due to the …
by offloading computation-intensive tasks to the edges of wireless networks. Due to the …
Distributed deep learning-based task offloading for UAV-enabled mobile edge computing
Unmanned Aerial Vehicle (UAV)-enabled mobile edge computing (MEC) is considered to
offer computational capabilities to the resource-constraints end-users. In this paper, we …
offer computational capabilities to the resource-constraints end-users. In this paper, we …
Intelligent task offloading and collaborative computation in multi-UAV-enabled mobile edge computing
J Xia, P Wang, B Li, Z Fei - China Communications, 2022 - ieeexplore.ieee.org
This article establishes a three-tier mobile edge computing (MEC) network, which takes into
account the cooperation between unmanned aerial vehicles (UAVs). In this MEC network …
account the cooperation between unmanned aerial vehicles (UAVs). In this MEC network …
A deep learning approach for task offloading in multi-UAV aided mobile edge computing
MA Ebrahim, GA Ebrahim, HK Mohamed… - IEEE …, 2022 - ieeexplore.ieee.org
Computation offloading has proven to be an effective method for facilitating resource-
intensive tasks on IoT mobile edge nodes with limited processing capabilities. Additionally …
intensive tasks on IoT mobile edge nodes with limited processing capabilities. Additionally …
Computation offloading optimization for UAV-assisted mobile edge computing: a deep deterministic policy gradient approach
Abstract Unmanned Aerial Vehicle (UAV) can play an important role in wireless systems as it
can be deployed flexibly to help improve coverage and quality of communication. In this …
can be deployed flexibly to help improve coverage and quality of communication. In this …
Joint task offloading, resource allocation, and trajectory design for multi-uav cooperative edge computing with task priority
H Hao, C Xu, W Zhang, S Yang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Mobile edge computing (MEC) has emerged as a solution to address the demands of
computation-intensive network services by providing computational capabilities at the …
computation-intensive network services by providing computational capabilities at the …