H Ning, Y An, Y Wei, N Wu, C Mu, H Cheng… - Vehicular …, 2023 - Elsevier
As the preferred underpinning technology of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs) have been widely used to cope with various traffic …
Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these …
K Fang, B Yang, H Zhu, Z Lin… - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
Upon the spreading of intelligent transportation technology, reconfigurable intelligent surface (RIS) can be applied in traditional vehicular networks to assist autonomous driving …
Z Zhang, Y Lai, Y Chen, J Wei, Y Wang - Ad Hoc Networks, 2023 - Elsevier
A Sybil attack is caused by a malicious vehicle node stealing fake identities and continuously generating fake vehicles on the road to create the illusion of congestion, which …
J Lan, D Zhao, D Tian - International Journal of Robust and …, 2023 - Wiley Online Library
This article considers mixed platoons consisting of both human‐driven vehicles (HVs) and automated vehicles (AVs). The uncertainties and randomness in human driving behaviors …
A Katiyar, D Singh, RS Yadav - Wireless Networks, 2022 - Springer
Clustering improves network stability and scalability significantly by efficiently handling the fast topological change in VANET. Multi-hop clustering is introduced in VANET to handle …
Y Zhang, J Zhang - Applied Sciences, 2022 - mdpi.com
With the continuous development of connected and automated vehicles (CAVs) and Internet of Vehicle (IoV) technologies, various application scenarios have put forward higher …
R Zhang, L Wu, S Cao, D Wu, J Li - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The combination of mobile edge computing and 5G heterogeneous networks (5G HetNets) provides new vehicular task offloading research solutions. Most existing task offloading …
Q Liu, Y Li, M Bilal, X Liu, Y Zhang… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
In recent years, AI and deep learning (DL) methods have been widely used for object classification, recognition, and segmentation of high-resolution multispectral remote sensing …