EPtask: Deep reinforcement learning based energy-efficient and priority-aware task scheduling for dynamic vehicular edge computing
The increasing complexity of vehicles has led to a growing demand for in-vehicle services
that rely on multiple sensors. In the Vehicular Edge Computing (VEC) paradigm, energy …
that rely on multiple sensors. In the Vehicular Edge Computing (VEC) paradigm, energy …
Deep reinforcement learning‐based resource allocation in multi‐access edge computing
Network architects and engineers face challenges in meeting the increasing complexity and
low‐latency requirements of various services. To tackle these challenges, multi‐access …
low‐latency requirements of various services. To tackle these challenges, multi‐access …
Reinforcement learning based resource management for 6G-enabled mIoT with hypergraph interference model
J Huang, C Yang, S Zhang, F Yang… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
For the future 6G-enabled massive Internet of Things (mIoT), how to effectively manage
spectrum resources to support huge data traffic under the large-scale overlapping caused by …
spectrum resources to support huge data traffic under the large-scale overlapping caused by …
Time-slotted task offloading and resource allocation for cloud-edge-end cooperative computing networks
W Fan, X Liu, H Yuan, N Li, Y Liu - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
In time-slotted edge computing systems, task scheduling is conducted at the end of each
time slot to make task offloading decisions and resource allocation for all the tasks pending …
time slot to make task offloading decisions and resource allocation for all the tasks pending …
An Improved Gravitational Search Algorithm for Task Offloading in a Mobile Edge Computing Network with Task Priority
L Xu, Y Liu, B Fan, X Xu, Y Mei, W Feng - Electronics, 2024 - mdpi.com
Mobile edge computing (MEC) distributes computing and storage resources to the edge of
the network closer to the user and significantly reduces user task completion latency and …
the network closer to the user and significantly reduces user task completion latency and …
Optimizing Mobility-Aware Task Offloading in Smart Healthcare for Internet of Medical Things Through Multi-Agent Reinforcement Learning
In the scenario of smart healthcare applications, the Internet of Medical Things (IoMT)
devices, equipped with limited resources, would offload numerous computation-heavy tasks …
devices, equipped with limited resources, would offload numerous computation-heavy tasks …
Joint Task Offloading and Resource Allocation in Multi-UAV Multi-Server Systems: An Attention-based Deep Reinforcement Learning Approach
The multi-access edge computing (MEC) provides opportunities for unmanned aerial
vehicles (UAVs) to perform computing-intensive and delay-sensitive applications. To further …
vehicles (UAVs) to perform computing-intensive and delay-sensitive applications. To further …
An intelligent task offloading method based on multi-agent deep reinforcement learning in ultra-dense heterogeneous network with mobile edge computing
S Pang, T Wang, H Gui, X He, L Hou - Computer Networks, 2024 - Elsevier
With the rapid development of IoT technology, various computation-intensive and latency-
sensitive tasks have emerged in large numbers, which impose higher requirements on the …
sensitive tasks have emerged in large numbers, which impose higher requirements on the …
Task execution latency minimization for energy-sensitive IoTs in wireless powered mobile edge computing: A DRL-based method
Wireless powered mobile edge computing (MEC) has become a vital component of future
6G networks, offering efficient computational capabilities to internet of things (IoT) devices …
6G networks, offering efficient computational capabilities to internet of things (IoT) devices …
A Survey of Computation Offloading With Task Types
Computation task offloading plays a crucial role in facilitating computation-intensive
applications and edge intelligence, particularly in response to the explosive growth of …
applications and edge intelligence, particularly in response to the explosive growth of …