Recent advances in smart connected vehicles and Intelligent Transportation Systems (ITS)
are based upon the capture and processing of large amounts of sensor data. Modern …

Over the past decade, we have witnessed an enormous amount of research effort dedicated
to the design of efficient outlier detection techniques while taking into consideration …

Federated learning (FL) is a distributed machine learning approach that can achieve the
purpose of collaborative learning from a large amount of data that belong to different parties …

Vehicular edge computing (VEC) is a new computing paradigm that has great potential to
enhance the capability of vehicle terminals (VTs) to support resource-hungry in-vehicle …

Abstract The Internet of Vehicles (IoV) and Vehicle‐to‐Everything (V2X) concept have
emerged from IoT technology, which refers to connecting many vehicles with various …

To investigate the diversified technologies in Internet of Vehicles (IoVs) under intelligent
edge computing, brain-inspired computing techniques are proposed in this study, which is a …

Abstract Mobile Edge Computing (MEC) enables immersive XR with multimodal data by
coordinating communication, computation, and caching (3C) resources upcoming 6G. The …

In this paper, we study joint allocation of the spectrum, computing, and storing resources in a
multi-access edge computing (MEC)-based vehicular network. To support different vehicular …

With the emergence of computation-intensive vehicular applications, vehicular edge
computing (VEC) offers a new paradigm to augment the capabilities of vehicles. In this …

Vehicular edge computing (VEC) is an innovative computing paradigm with an exceptional
ability to improve the vehicles' capacity to manage computation-intensive applications with …