Vehicular edge computing (VEC) is a novel computing paradigm, which is designed to
satisfy the growing computation and communication needs of vehicle systems. With the …

Due to their limited computation capabilities and battery life, Internet of Things (IoT) networks
face significant challenges in executing delay-sensitive and computation-intensive mobile …

The network area is extended from ground to air. In order to efficiently manage various kinds
of nodes, new network paradigms are needed such as cell-free massive multiple-input …

Mobile edge computing (MEC) offloads computation intensive tasks of individual users to
computing clouds to alleviate the computing loads. Virtual machines (VMs), in practice, are …

In recent years, vehicular networks have seen a proliferation of applications and services
such as image tagging, lane detection, and speech recognition. Many of these applications …

Developments in artificial intelligence techniques allow for an improvement in sustainable
mobility strategies with particular reference to energy consumption estimates of electric …

The performance of vehicular edge computing (VEC) depends on the effective optimization
of task offloading. However, uneven distribution of vehicular traffic, rapidly changing network …

Mobile edge computing (MEC) has emerged as a promising paradigm to enhance the
computational capabilities of resource-constrained secondary devices (RCSDs) in proximity …

Vehicular networks are a crucial component aimed to revolutionize the transportation system
through the integration of several services and technologies including autonomous driving …

Federated learning (FL) is a distributed training technique for machine learning (ML) models
that ensures ownership of training data for the devices or users. Data ownership is …