Edge computing with artificial intelligence: A machine learning perspective

H Hua, Y Li, T Wang, N Dong, W Li, J Cao - ACM Computing Surveys, 2023 - dl.acm.org
Recent years have witnessed the widespread popularity of Internet of things (IoT). By
providing sufficient data for model training and inference, IoT has promoted the development …

Unlocking the power of industrial artificial intelligence towards Industry 5.0: Insights, pathways, and challenges

J Leng, X Zhu, Z Huang, X Li, P Zheng, X Zhou… - Journal of Manufacturing …, 2024 - Elsevier
With the continuous development of human-centric, resilient, and sustainable manufacturing
towards Industry 5.0, Artificial Intelligence (AI) has gradually unveiled new opportunities for …

Zero touch management: A survey of network automation solutions for 5G and 6G networks

E Coronado, R Behravesh… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
Mobile networks are facing an unprecedented demand for high-speed connectivity
originating from novel mobile applications and services and, in general, from the adoption …

Task offloading paradigm in mobile edge computing-current issues, adopted approaches, and future directions

MY Akhlaqi, ZBM Hanapi - Journal of Network and Computer Applications, 2023 - Elsevier
Many enterprise companies migrate their services and applications to the cloud to benefit
from cloud computing advantages. Meanwhile, the rapidly increasing number of connected …

A survey of incentive mechanism design for federated learning

Y Zhan, J Zhang, Z Hong, L Wu, P Li… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Federated learning is promising in enabling large-scale machine learning by massive
clients without exposing their raw data. It can not only enable the clients to preserve the …

Deep reinforcement learning for Internet of Things: A comprehensive survey

W Chen, X Qiu, T Cai, HN Dai… - … Surveys & Tutorials, 2021 - ieeexplore.ieee.org
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …

Comprehensive survey on machine learning in vehicular network: Technology, applications and challenges

F Tang, B Mao, N Kato, G Gui - IEEE Communications Surveys …, 2021 - ieeexplore.ieee.org
Towards future intelligent vehicular network, the machine learning as the promising artificial
intelligence tool is widely researched to intelligentize communication and networking …

A low-latency edge computation offloading scheme for trust evaluation in finance-level artificial intelligence of things

X Zhu, F Ma, F Ding, Z Guo, J Yang… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
The finance-level Artificial Intelligence of Things (AIoT) is going to become a novel media in
the 6G-driven digital society. Inside the financial AIoT environment, large-scale crowd credit …

Satellite edge computing with collaborative computation offloading: An intelligent deep deterministic policy gradient approach

H Zhang, R Liu, A Kaushik… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
Enabling a satellite network with edge computing capabilities can complement the
advantages further of a single terrestrial network and provide users with a full range of …

[HTML][HTML] Deep reinforcement learning-based scheduling for optimizing system load and response time in edge and fog computing environments

Z Wang, M Goudarzi, M Gong, R Buyya - Future Generation Computer …, 2024 - Elsevier
Edge/fog computing, as a distributed computing paradigm, satisfies the low-latency
requirements of ever-increasing number of IoT applications and has become the …