Content Caching at the edge of vehicular networks has been considered as a promising
technology to satisfy the increasing demands of computation-intensive and latency-sensitive …

As a distributed deep learning paradigm, federated learning (FL) provides a powerful tool for
the accurate and efficient processing of on-board data in vehicular edge computing (VEC) …

In an internet of vehicle (IoV) scenario, vehicular edge computing (VEC) exploits the
computing capabilities of the vehicles and roadside unit (RSU) to enhance the task …

Vehicular-ad hoc networks (VANETs) hold great importance because of their potentials in
road safety improvement, traffic monitoring, and in-vehicle infotainment services. Due to high …

With the widespread application of infotainment services in intelligent connected vehicles
(ICVs), network traffic has grown exponentially, bringing huge burden and energy …

Autonomous driving has become an important research area for road traffic, whereas testing
of autonomous driving systems to ensure a safe and reliable operation remains an open …

This article develops a statistical large-scale fading (path loss) model of a dynamic vehicular
visible light communication (VVLC) system. The proposed model combines the impact of …

Vehicular Edge Computing (VEC) is a promising new paradigm that has attracted much
attention in recent years, which can enhance the storage and computing capabilities of …

We develop a hierarchical description of traffic flow control by means of driver-assist
vehicles aimed at the mitigation of speed-dependent road risk factors. Microscopic feedback …

Harnessing information about the user mobility pattern and daily demand can enhance the
network capability to improve the quality of experience (QoE) at Vehicular Ad-Hoc Networks …