Collaborative offloading method for digital twin empowered cloud edge computing on Internet of Vehicles
L Gu, M Cui, L Xu, X Xu - Tsinghua Science and Technology, 2022 - ieeexplore.ieee.org
Digital twinning and edge computing are attractive solutions to support computing-intensive
and service-sensitive Internet of Vehicles applications. Most of the existing Internet of …
and service-sensitive Internet of Vehicles applications. Most of the existing Internet of …
Secure digital twin migration in edge-based autonomous driving system
Y Zhou, J Wu, X Lin, AK Bashir… - IEEE Consumer …, 2022 - ieeexplore.ieee.org
Digital twin (DT) technology is being applied increasingly in the Internet of Vehicles
environment, but it still faces many challenges in terms of efficiency and security. In the field …
environment, but it still faces many challenges in terms of efficiency and security. In the field …
Dynamic mode selection and resource allocation approach for 5G-vehicle-to-everything (V2X) communication using asynchronous federated deep reinforcement …
I Rasheed - Vehicular Communications, 2022 - Elsevier
Abstract 5G vehicle-to-everything (V2X) connectivity is crucial to enable future complex
vehicular networking environment for enabling intelligent transportation systems (ITS). But …
vehicular networking environment for enabling intelligent transportation systems (ITS). But …
Graph neural networks for joint communication and sensing optimization in vehicular networks
In this paper, the problem of joint communication and sensing is studied in the context of
terahertz (THz) vehicular networks. In the studied model, a set of service provider vehicles …
terahertz (THz) vehicular networks. In the studied model, a set of service provider vehicles …
Boost decentralized federated learning in vehicular networks by diversifying data sources
Recently, federated learning (FL) has received intensive research because of its ability in
preserving data privacy for scattered clients to collaboratively train machine learning …
preserving data privacy for scattered clients to collaboratively train machine learning …
DSORL: Data Source Optimization With Reinforcement Learning Scheme for Vehicular Named Data Networks
Highly-dynamic (HD) map is an indispensable building block in the future of autonomous
driving, allowing for fine-grained environmental awareness, precise localization, and route …
driving, allowing for fine-grained environmental awareness, precise localization, and route …
Spectrum sharing in vehicular networks based on multi-agent reinforcement learning
This paper investigates the spectrum sharing problem in vehicular networks based on multi-
agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the …
agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the …
FedAPT: Joint Adaptive Parameter Freezing and Resource Allocation for Communication-Efficient Federated Vehicular Networks
Telematics technology development offers vehicles a range of intelligent and convenient
functions, including navigation and mapping services, intelligent driving assistance, and …
functions, including navigation and mapping services, intelligent driving assistance, and …
Collective reinforcement learning based resource allocation for digital twin service in 6G networks
Z Huang, D Li, J Cai, H Lu - Journal of Network and Computer Applications, 2023 - Elsevier
Abstract The 6th generation (6G) mobile communications technology will realize the
interconnection of humans, machines, things as well as virtual space. The development of …
interconnection of humans, machines, things as well as virtual space. The development of …
Task-driven semantic-aware green cooperative transmission strategy for vehicular networks
Considering the infrastructure deployment cost and energy consumption, it is unrealistic to
provide seamless coverage of the vehicular network. The presence of uncovered areas …
provide seamless coverage of the vehicular network. The presence of uncovered areas …