Offloading using traditional optimization and machine learning in federated cloud–edge–fog systems: A survey
The huge amount of data generated by the Internet of Things (IoT) devices needs the
computational power and storage capacity provided by cloud, edge, and fog computing …
computational power and storage capacity provided by cloud, edge, and fog computing …
Privacy-preserving offloading scheme in multi-access mobile edge computing based on MADRL
G Wu, X Chen, Z Gao, H Zhang, S Yu, S Shen - Journal of Parallel and …, 2024 - Elsevier
With the development of industrialization and intelligence, the Industrial Internet of Things
(IIoT) has gradually become the direction for traditional industries to transform into modern …
(IIoT) has gradually become the direction for traditional industries to transform into modern …
Deep learning-based dynamic computation task offloading for mobile edge computing networks
This paper investigates the computation offloading problem in mobile edge computing
(MEC) networks with dynamic weighted tasks. We aim to minimize the system utility of the …
(MEC) networks with dynamic weighted tasks. We aim to minimize the system utility of the …
Deep Learning Aided Intelligent Reflective Surfaces for 6G: A Survey
The envisioned sixth-generation (6G) networks anticipate robust support for diverse
applications, including massive machine-type communications, ultra-reliable low-latency …
applications, including massive machine-type communications, ultra-reliable low-latency …
Non-cooperative game algorithms for computation offloading in mobile edge computing environments
Abstract Mobile Edge Computing (MEC) has become a promising technology for 5G
networks. Computation offloading is an essential issue of MEC, which enables mobile User …
networks. Computation offloading is an essential issue of MEC, which enables mobile User …
Deep reinforcement learning-based online resource management for uav-assisted edge computing with dual connectivity
Mobile Edge Computing (MEC) is a key technology towards delay-sensitive and
computation-intensive applications in future cellular networks. In this paper, we consider a …
computation-intensive applications in future cellular networks. In this paper, we consider a …
Multi-UAV-assisted federated learning for energy-aware distributed edge training
Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has largely
extended the border and capacity of artificial intelligence of things (AIoT) by providing a key …
extended the border and capacity of artificial intelligence of things (AIoT) by providing a key …
A survey on offloading in federated cloud-edge-fog systems with traditional optimization and machine learning
The huge amount of data generated by the Internet of things (IoT) devices needs the
computational power and storage capacity provided by cloud, edge, and fog computing …
computational power and storage capacity provided by cloud, edge, and fog computing …
[HTML][HTML] Efficient task migration and resource allocation in cloud–edge collaboration: a drl approach with learnable masking
Y Wang, J Chen, Z Wu, P Chen, X Li, J Hao - Alexandria Engineering …, 2025 - Elsevier
The paper addresses the challenges of task migration and resource allocation in
heterogeneous cloud–edge environments, where dynamic and stochastic conditions …
heterogeneous cloud–edge environments, where dynamic and stochastic conditions …
Learning-driven algorithms for responsive AR offloading with non-deterministic rewards in metaverse-enabled MEC
In the coming era of Metaverse, Augmented Reality (AR) has become a key enabler of
diverse applications including healthcare, education, smart cities, and entertainments. To …
diverse applications including healthcare, education, smart cities, and entertainments. To …