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 …

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 …

Profit maximization of independent task offloading in MEC-enabled 5G internet of vehicles

G Sun, Z Wang, H Su, H Yu, B Lei… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The development of the Internet of Vehicles (IoVs) has attracted much attention due to the
increasing number of connected cars. IoV refers to the interconnection of vehicles with other …

Artificial intelligence for edge service optimization in internet of vehicles: A survey

X Xu, H Li, W Xu, Z Liu, L Yao… - Tsinghua Science and …, 2021 - ieeexplore.ieee.org
The Internet of Vehicles (IoV) plays a crucial role in providing diversified services because of
its powerful capability of collecting real-time information. Generally, collected information is …

Resource allocation in 5G IoV architecture based on SDN and fog-cloud computing

B Cao, Z Sun, J Zhang, Y Gu - IEEE transactions on intelligent …, 2021 - ieeexplore.ieee.org
In the traditional cloud-based Internet of Vehicles (IoV) architecture, it is difficult to guarantee
the low latency requirements of the current intelligent transportation system (ITS). As a …

UAV-enabled covert federated learning

X Hou, J Wang, C Jiang, X Zhang… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Integrating unmanned aerial vehicles (UAVs) with federated learning (FL) has been seen as
a promising paradigm for dealing with the massive amounts of data generated by intelligent …

RL/DRL meets vehicular task offloading using edge and vehicular cloudlet: A survey

J Liu, M Ahmed, MA Mirza, WU Khan… - IEEE Internet of …, 2022 - ieeexplore.ieee.org
The last two decades have seen a clear trend toward crafting intelligent vehicles based on
the significant advances in communication and computing paradigms, which provide a safer …

Priority-aware task offloading in vehicular fog computing based on deep reinforcement learning

J Shi, J Du, J Wang, J Wang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Vehicular fog computing (VFC) has been expected as a promising scheme that can increase
the computational capability of vehicles without relying on servers. Comparing with …

Deep reinforcement learning-based energy-efficient edge computing for internet of vehicles

X Kong, G Duan, M Hou, G Shen… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Mobile network operators (MNOs) allocate computing and caching resources for mobile
users by deploying a central control system. Existing studies mainly use programming and …

A comprehensive survey on vehicular networking: Communications, applications, challenges, and upcoming research directions

NH Hussein, CT Yaw, SP Koh, SK Tiong… - IEEE Access, 2022 - ieeexplore.ieee.org
Nowadays, advanced communication technologies are being utilized to develop intelligent
transportation management and driving assistance. Through the ability to exchange traffic …