Offloading using traditional optimization and machine learning in federated cloud-edge-fog systems: A survey

B Kar, W Yahya, YD Lin, A Ali - IEEE Communications Surveys …, 2023 - ieeexplore.ieee.org
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 …

Machine and deep learning for resource allocation in multi-access edge computing: A survey

H Djigal, J Xu, L Liu, Y Zhang - IEEE Communications Surveys …, 2022 - ieeexplore.ieee.org
With the rapid development of Internet-of-Things (IoT) devices and mobile communication
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …

TCDA: Truthful combinatorial double auctions for mobile edge computing in industrial Internet of Things

L Ma, X Wang, X Wang, L Wang, Y Shi… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Mobile edge computing (MEC) emerges as an appealing paradigm to provide time-sensitive
computing services for industrial Internet of Things (IIoT) applications. How to guarantee …

Vehicular intelligence in 6G: Networking, communications, and computing

H Guo, X Zhou, J Liu, Y Zhang - Vehicular Communications, 2022 - Elsevier
With the deployment of 5G, researchers and experts begin to look forward to 6G. They
predict that 6G will be the key driving force for information interaction and social life after …

[HTML][HTML] Machine learning-based offloading strategy for lightweight user mobile edge computing tasks

S Zhou, W Jadoon, J Shuja - Complexity, 2021 - hindawi.com
This paper presents an in-depth study and analysis of offloading strategies for lightweight
user mobile edge computing tasks using a machine learning approach. Firstly, a scheme for …

Toward swarm coordination: Topology-aware inter-UAV routing optimization

L Hong, H Guo, J Liu, Y Zhang - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Swarm unmanned aerial vehicles (UAVs) is an approach to the coordination of multiple
UAVs as a system, which has huge advantages on mission capabilities, such as cooperative …

IoT service slicing and task offloading for edge computing

J Hwang, L Nkenyereye, N Sung… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
With the advancement of Internet-of-Things (IoT) technology, various domains, such as
smart factories and smart cars have used this new technology to provide value-added …

Dynamic task offloading for digital twin-empowered mobile edge computing via deep reinforcement learning

Y Chen, W Gu, J Xu, Y Zhang, G Min - China Communications, 2023 - ieeexplore.ieee.org
Limited by battery and computing resources, the computing-intensive tasks generated by
Internet of Things (IoT) devices cannot be processed all by themselves. Mobile edge …

Energy-efficient resource allocation in multi-UAV-assisted two-stage edge computing for beyond 5G networks

NN Ei, M Alsenwi, YK Tun, Z Han… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Unmanned aerial vehicle (UAV)-assisted multi-access edge computing (MEC) has become
one promising solution for energy-constrained devices to run the applications with high …

Edge computing resources reservation in vehicular networks: A meta-learning approach

D Chen, YC Liu, BG Kim, J Xie… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
With the development of autonomous vehicular technologies, the execution tasks become
more memory-consuming and computation-intensive. Simultaneously, a certain portion of …