Energy-efficient resource allocation for heterogeneous edge-cloud computing

W Hua, P Liu, L Huang - IEEE Internet of Things Journal, 2023 - ieeexplore.ieee.org
With the rapid development of Internet of Things (IoT) technology, billions of mobile devices
(MDs) are putting a massive burden on limited radio resources. Mobile-edge computing …

Smart resource allocation for mobile edge computing: A deep reinforcement learning approach

J Wang, L Zhao, J Liu, N Kato - IEEE Transactions on emerging …, 2019 - ieeexplore.ieee.org
The development of mobile devices with improving communication and perceptual
capabilities has brought about a proliferation of numerous complex and computation …

Bringing deep learning at the edge of information-centric internet of things

H Khelifi, S Luo, B Nour, A Sellami… - IEEE …, 2018 - ieeexplore.ieee.org
Various Internet solutions take their power processing and analysis from cloud computing
services. Internet of Things (IoT) applications started discovering the benefits of computing …

Digital twin-aided intelligent offloading with edge selection in mobile edge computing

T Do-Duy, D Van Huynh, OA Dobre… - IEEE Wireless …, 2022 - ieeexplore.ieee.org
In this letter, we study a mobile edge computing (MEC) architecture with the assistance of
digital twin (DT) applied for industrial automation where multiple Internet-of-Things (IoT) …

Sdn-assisted mobile edge computing for collaborative computation offloading in industrial internet of things

C Tang, C Zhu, N Zhang, M Guizani… - IEEE Internet of …, 2022 - ieeexplore.ieee.org
Mobile edge computing (MEC) can provision augmented computational capacity in proximity
so as to better support Industrial Internet of Things (IIoT). Tasks from the IIoT devices can be …

A MEC offloading strategy based on improved DQN and simulated annealing for internet of behavior

X Yuan, H Tian, Z Zhang, Z Zhao, L Liu… - ACM Transactions on …, 2022 - dl.acm.org
The Internet of Medical Things (IoMT) and Artificial Intelligence (AI) have brought
unprecedented opportunities to meet massive behavioral data access and personalization …

Resource allocation for edge computing in IoT networks via reinforcement learning

X Liu, Z Qin, Y Gao - ICC 2019-2019 IEEE international …, 2019 - ieeexplore.ieee.org
In this paper, we consider resource allocation for edge computing in internet of things (IoT)
networks. Specifically, each end device is considered as an agent, which makes its …

Graph-reinforcement-learning-based task offloading for multiaccess edge computing

Z Sun, Y Mo, C Yu - IEEE Internet of Things Journal, 2021 - ieeexplore.ieee.org
Network applications involve massive heterogeneous data fusion and analysis. Artificial
intelligence can significantly improve the convenience and user experience, but it requires a …

Reinforcement learning-based mobile offloading for edge computing against jamming and interference

L Xiao, X Lu, T Xu, X Wan, W Ji… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Mobile edge computing systems help improve the performance of computational-intensive
applications on mobile devices and have to resist jamming attacks and heavy interference …

Joint offloading and resource allocation using deep reinforcement learning in mobile edge computing

X Zhang, X Zhang, W Yang - IEEE Transactions on Network …, 2022 - ieeexplore.ieee.org
Mobile edge computation offloading (MECO) has recently emerged as a promising method
to support computation-intensive and latency-sensitive applications, significantly saving the …