Vehicle-to-vehicle (V2V) communication applications face significant challenges to security
and privacy since all types of possible breaches are common in connected and autonomous …

Vehicles in platoons need to process many tasks to support various real-time vehicular
applications. When a task arrives at a vehicle, the vehicle may not process the task due to its …

Cloud computing plays an important role in every IT sector. Many tech giants such as
Google, Microsoft, and Facebook as deploying their data centres around the world to …

In vehicular edge computing (VEC), some tasks can be processed either locally or on the
mobile edge computing (MEC) server at a base station (BS) or a nearby vehicle. In fact …

As a promising distributed learning paradigm, Federated Learning (FL) is expected to meet
the ever-increasing needs of Machine Learning (ML) based applications in Intelligent …

Federated edge learning (FEEL) technology for vehicular networks is considered as a
promising technology to reduce the computation workload while keeping the privacy of …

In this paper, we propose a novel approach for optimal resource management and caching
in ultra-reliable low-latency communication (URLLC)-enabled Internet of Vehicles (IoV) …

This article investigates the resource allocation of a reconfigurable intelligent surface (RIS)-
aided joint communication and sensing (JCAS) system in a coal mine scenario. In the JCAS …

Emerging compute-intensive and latency-sensitive vehicular applications are expected to be
deployed at the edge instead of the cloud to shorten the network latency. Mobile fog nodes …

With the increasing number of connected vehicles on the road, vehicular communications
have become an important research area. Federated learning (FL), a distributed machine …