Towards a Novel Air–Ground Intelligent Platform for Vehicular Networks: Technologies, Scenarios, and Challenges

Abstract

Modern cities require a tighter integration with Information and Communication Technologies (ICT) for bringing new services to the citizens. The Smart City is the revolutionary paradigm aiming at integrating the ICT with the citizen life; among several urban services, transports are one of the most important in modern cities, introducing several challenges to the Smart City paradigm. In order to satisfy the stringent requirements of new vehicular applications and services, Edge Computing (EC) is one of the most promising technologies when integrated into the Vehicular Networks (VNs). EC-enabled VNs can facilitate new latency-critical and data-intensive applications and services. However, ground-based EC platforms (i.e., Road Side Units—RSUs, 5G Base Stations—5G BS) can only serve a reduced number of Vehicular Users (VUs), due to short coverage ranges and resource shortage. In the recent past, several new aerial platforms with integrated EC facilities have been deployed for achieving global connectivity. Such air-based EC platforms can complement the ground-based EC facilities for creating a futuristic VN able to deploy several new applications and services. The goal of this work is to explore the possibility of creating a novel joint air-ground EC platform within a VN architecture for helping VUs with new intelligent applications and services. By exploiting most modern technologies, with particular attention towards network softwarization, vehicular edge computing, and machine learning, we propose here three possible layered air-ground EC-enabled VN scenarios. For each of the discussed scenarios, a list of the possible challenges is considered, as well possible solutions allowing to overcome all or some of the considered challenges. A proper comparison is also done, through the use of tables, where all the proposed scenarios, and the proposed solutions, are discussed.

Joint Air-Ground Distributed Federated Learning for Intelligent Transportation Systems

“…3) Random Assignment Based FL Process (RA-FL): In this approach, nodes involved in the FL process (i.e., VTs, RSUs, UAVs, and HAP) follow the random assignment strategy in (12). Thus, each VT selects the one EN from a set of RSUs, UAVs, and HAP covering it.…”

“…Some recent works have however considered the applications of FL over VNs while doing some assumptions [11]. A particular attention has been focused on multi-layered edge computing enabled VNs, capable of communicating with RSUs, UAVs, and HAPs, able to tackle the long-distance communication challenge introduced by the centralized FL models by distributing the learning process over multiple nodes (i.e., distributed FL) [12]. With limited range communications, VTs can also save energy/resources during the FL iterations, allowing to perform more iterations [13].…”

Overview of Distributed Machine Learning Techniques for 6G Networks

“…Various air and space networking platforms, such as unmanned aerial vehicles (UAVs); high altitude platforms such as balloons; and very low earth orbitbased sidelights (VLEO), have already shown great promise and are expected to play important roles. With this in mind, the authors of [78] proposed the novel air-ground distributed-computing-enabled intelligent platform for the vehicular scenario. This work also highlighted the important problem to be solved for the traditional centralized FL process and proposed different FL-based distributed networking solutions for solving vehicular problems.…”

Blockchain-Empowered AI for 6G-Enabled Internet of Vehicles

“…In [11], FL was indicated to be a solution of constrained energy operations in vehicular offloading. Apart from vehicular nodes, unmanned aerial vehicles (UAVs) were recommended to perform FL in air-ground intelligent vehicular networks in [12].…”