Distributed deep learning-based offloading for mobile edge computing networks
This paper studies mobile edge computing (MEC) networks where multiple wireless devices
(WDs) choose to offload their computation tasks to an edge server. To conserve energy and …
(WDs) choose to offload their computation tasks to an edge server. To conserve energy and …
DMRO: A deep meta reinforcement learning-based task offloading framework for edge-cloud computing
With the explosive growth of mobile data and the unprecedented demand for computing
power, resource-constrained edge devices cannot effectively meet the requirements of …
power, resource-constrained edge devices cannot effectively meet the requirements of …
Deep reinforcement learning for intelligent internet of vehicles: An energy-efficient computational offloading scheme
The emerging vehicular services call for updated communication and computing platforms.
Fog computing, whose infrastructure is deployed in close proximity to terminals, extends the …
Fog computing, whose infrastructure is deployed in close proximity to terminals, extends the …
[HTML][HTML] Deep learning at the mobile edge: Opportunities for 5G networks
M McClellan, C Cervelló-Pastor, S Sallent - Applied Sciences, 2020 - mdpi.com
Mobile edge computing (MEC) within 5G networks brings the power of cloud computing,
storage, and analysis closer to the end user. The increased speeds and reduced delay …
storage, and analysis closer to the end user. The increased speeds and reduced delay …
Deep learning for hybrid 5G services in mobile edge computing systems: Learn from a digital twin
In this paper, we consider a mobile edge computing system with both ultra-reliable and low-
latency communications services and delay tolerant services. We aim to minimize the …
latency communications services and delay tolerant services. We aim to minimize the …
Distributed probabilistic offloading in edge computing for 6G-enabled massive Internet of Things
Mobile-edge computing (MEC) is expected to provide reliable and low-latency computation
offloading for massive Internet of Things (IoT) with the next generation networks, such as the …
offloading for massive Internet of Things (IoT) with the next generation networks, such as the …
Knowledge-driven service offloading decision for vehicular edge computing: A deep reinforcement learning approach
The smart vehicles construct Internet of Vehicle (IoV), which can execute various intelligent
services. Although the computation capability of a vehicle is limited, multi-type of edge …
services. Although the computation capability of a vehicle is limited, multi-type of edge …
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 …
Throughput maximization of delay-aware DNN inference in edge computing by exploring DNN model partitioning and inference parallelism
Mobile Edge Computing (MEC) has emerged as a promising paradigm catering to
overwhelming explosions of mobile applications, by offloading compute-intensive tasks to …
overwhelming explosions of mobile applications, by offloading compute-intensive tasks to …
Toward reinforcement-learning-based service deployment of 5G mobile edge computing with request-aware scheduling
5G wireless network technology will not only significantly increase bandwidth but also
introduce new features such as mMTC and URLLC. However, high request latency will …
introduce new features such as mMTC and URLLC. However, high request latency will …