iRAF: A deep reinforcement learning approach for collaborative mobile edge computing IoT networks
Recently, as the development of artificial intelligence (AI), data-driven AI methods have
shown amazing performance in solving complex problems to support the Internet of Things …
shown amazing performance in solving complex problems to support the Internet of Things …
Resource allocation based on deep reinforcement learning in IoT edge computing
By leveraging mobile edge computing (MEC), a huge amount of data generated by Internet
of Things (IoT) devices can be processed and analyzed at the network edge. However, the …
of Things (IoT) devices can be processed and analyzed at the network edge. However, the …
Smart resource allocation for mobile edge computing: A deep reinforcement learning approach
The development of mobile devices with improving communication and perceptual
capabilities has brought about a proliferation of numerous complex and computation …
capabilities has brought about a proliferation of numerous complex and computation …
Task-driven resource assignment in mobile edge computing exploiting evolutionary computation
The IoT network allows IoT devices to communicate with other devices, applications, and
services by exploiting existing network infrastructure. Recently, a promising paradigm, MEC …
services by exploiting existing network infrastructure. Recently, a promising paradigm, MEC …
DeepEdge: A new QoE-based resource allocation framework using deep reinforcement learning for future heterogeneous edge-IoT applications
Edge computing is emerging to empower the future of Internet of Things (IoT) applications.
However, due to heterogeneity of applications, it is a significant challenge for the edge cloud …
However, due to heterogeneity of applications, it is a significant challenge for the edge cloud …
Deep reinforcement learning based mobile edge computing for intelligent Internet of Things
R Zhao, X Wang, J Xia, L Fan - Physical Communication, 2020 - Elsevier
In this paper, we investigate mobile edge computing (MEC) networks for intelligent internet
of things (IoT), where multiple users have some computational tasks assisted by multiple …
of things (IoT), where multiple users have some computational tasks assisted by multiple …
Task offloading and resource allocation for mobile edge computing by deep reinforcement learning based on SARSA
In recent years, computation offloading has become an effective way to overcome the
constraints of mobile devices (MDs) by offloading delay-sensitive and computation-intensive …
constraints of mobile devices (MDs) by offloading delay-sensitive and computation-intensive …
Federated learning-based computation offloading optimization in edge computing-supported internet of things
Recent visualizations of smart cities, factories, healthcare system and etc. raise challenges
on the capability and connectivity of massive Internet of Things (IoT) devices. Hence, edge …
on the capability and connectivity of massive Internet of Things (IoT) devices. Hence, edge …
Multi-agent DRL for task offloading and resource allocation in multi-UAV enabled IoT edge network
The Internet of Things (IoT) edge network has connected lots of heterogeneous smart
devices, thanks to unmanned aerial vehicles (UAVs) and their groundbreaking emerging …
devices, thanks to unmanned aerial vehicles (UAVs) and their groundbreaking emerging …
Deep-graph-based reinforcement learning for joint cruise control and task offloading for aerial edge internet of things (edgeiot)
This article puts forth an aerial edge Internet of Things (EdgeIoT) system, where an
unmanned aerial vehicle (UAV) is employed as a mobile-edge server to process mission …
unmanned aerial vehicle (UAV) is employed as a mobile-edge server to process mission …