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 …

Cooperative computational offloading in mobile edge computing for vehicles: A model-based dnn approach

S Munawar, Z Ali, M Waqas, S Tu… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Many advancements are being made in vehicular networks, such as self-driving, dynamic
route scheduling, real-time traffic condition monitoring, and on-board infotainment services …

A joint service migration and mobility optimization approach for vehicular edge computing

Q Yuan, J Li, H Zhou, T Lin, G Luo… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The vehicular edge computing is considered an enabling technology for intelligent and
connected vehicles since the optimization of communication and computing on edge has a …

Regional intelligent resource allocation in mobile edge computing based vehicular network

G Wang, F Xu - IEEE Access, 2020 - ieeexplore.ieee.org
The advancement of 5G technology has brought the prosperous development of Internet of
Vehicles (IoV). IoV services are not only computational intensive but also extremely sensitive …

Deep reinforcement learning for collaborative edge computing in vehicular networks

M Li, J Gao, L Zhao, X Shen - IEEE Transactions on Cognitive …, 2020 - ieeexplore.ieee.org
Mobile edge computing (MEC) is a promising technology to support mission-critical
vehicular applications, such as intelligent path planning and safety applications. In this …

Computation migration and resource allocation in heterogeneous vehicular networks: a deep reinforcement learning approach

H Wang, H Ke, G Liu, W Sun - IEEE Access, 2020 - ieeexplore.ieee.org
With the development of 5G technology, the requirements for data communication and
computation in emerging 5G-enabled vehicular networks are becoming increasingly …

Knowledge-driven service offloading decision for vehicular edge computing: A deep reinforcement learning approach

Q Qi, J Wang, Z Ma, H Sun, Y Cao… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
The smart vehicles construct Internet of Vehicle (IoV), which can execute various intelligent
services. Although the computation capability of a vehicle is limited, multi-type of edge …

Task offloading in vehicular edge computing networks via deep reinforcement learning

E Karimi, Y Chen, B Akbari - Computer Communications, 2022 - Elsevier
Given the rapid increase of various applications in vehicular networks, it is crucial to
consider a flexible architecture to improve the Quality of Service (QoS). Utilizing Multi-access …

Asynchronous deep reinforcement learning for collaborative task computing and on-demand resource allocation in vehicular edge computing

L Liu, J Feng, X Mu, Q Pei, D Lan… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Vehicular Edge Computing (VEC) is enjoying a surge in research interest due to the
remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the …

Computation offloading in heterogeneous vehicular edge networks: On-line and off-policy bandit solutions

A Bozorgchenani, S Maghsudi… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
With the rapid advancement of intelligent transportation systems (ITS) and vehicular
communications, vehicular edge computing (VEC) is emerging as a promising technology to …