[PDF] arxiv.org

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 …
被引用次数：114 相关文章 所有 3 个版本

Resource allocation based on deep reinforcement learning in IoT edge computing

X Xiong, K Zheng, L Lei, L Hou - IEEE Journal on Selected …, 2020 - ieeexplore.ieee.org
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 …
被引用次数：229 相关文章 所有 2 个版本

Novel method of mobile edge computation offloading based on evolutionary game strategy for IoT devices

Y Cui, D Zhang, T Zhang, L Chen, M Piao… - AEU-International Journal …, 2020 - Elsevier
Due to the limited computing resources and energy of IoT devices, complex computing tasks
are offloaded to sufficient computing servers, such as Cloud Center. However, offloading …
被引用次数：122 相关文章

Resource allocation with edge computing in IoT networks via machine learning

X Liu, J Yu, J Wang, Y Gao - IEEE Internet of Things Journal, 2020 - ieeexplore.ieee.org
In this article, we investigate resource allocation with edge computing in Internet-of-Things
(IoT) networks via machine learning approaches. Edge computing is playing a promising …
被引用次数：128 相关文章 所有 4 个版本
[PDF] ieee.org

Federated learning-based computation offloading optimization in edge computing-supported internet of things

Y Han, D Li, H Qi, J Ren, X Wang - Proceedings of the ACM Turing …, 2019 - dl.acm.org
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 …
被引用次数：215 相关文章 所有 4 个版本

Efficient and flexible management for industrial internet of things: A federated learning approach

Y Guo, Z Zhao, K He, S Lai, J Xia, L Fan - Computer Networks, 2021 - Elsevier
In this paper, we devise an efficient and flexible management for mobile edge computing
(MEC)-aided industrial Internet of Things (IIoT), from a federated learning approach. In the …
被引用次数：97 相关文章

iRAF: A deep reinforcement learning approach for collaborative mobile edge computing IoT networks

J Chen, S Chen, Q Wang, B Cao… - IEEE Internet of Things …, 2019 - ieeexplore.ieee.org
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 …
被引用次数：208 相关文章 所有 2 个版本

Edge QoE: Computation offloading with deep reinforcement learning for Internet of Things

H Lu, X He, M Du, X Ruan, Y Sun… - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
In edge-enabled Internet of Things (IoT), computation offloading service is expected to offer
users with better Quality of Experience (QoE) than traditional IoT. Unfortunately, the growing …
被引用次数：155 相关文章 所有 2 个版本

Smart computational offloading for mobile edge computing in next-generation Internet of Things networks

Z Ali, ZH Abbas, G Abbas, A Numani, M Bilal - Computer Networks, 2021 - Elsevier
Limited battery and computing resources of mobile devices (MDs) induce performance
limitations in mobile edge computing (MEC) networks. Computational offloading has the …
被引用次数：31 相关文章 所有 2 个版本
[PDF] arxiv.org

In-edge ai: Intelligentizing mobile edge computing, caching and communication by federated learning

X Wang, Y Han, C Wang, Q Zhao, X Chen… - Ieee …, 2019 - ieeexplore.ieee.org
Recently, along with the rapid development of mobile communication technology, edge
computing theory and techniques have been attracting more and more attention from global …
被引用次数：995 相关文章 所有 7 个版本