UAVs as a Service: Boosting Edge Intelligence for Air-Ground Integrated Networks

TL;DR

This paper proposes a novel UAV-as-a-Service architecture to enhance edge intelligence in 6G air-ground networks, enabling intelligent provisioning of communication, computing, and caching services through UAVs and machine learning.

Contribution

It introduces the UaaS architecture that leverages UAVs and ML for intelligent service provisioning in 6G networks, including a case study on distributed ML training.

Findings

Efficient model training with negligible UAV energy consumption.

UAVs effectively support edge computing and caching services.

The architecture addresses key challenges in 6G network integration.

Abstract

The air-ground integrated network is a key component of future sixth generation (6G) networks to support seamless and near-instant super-connectivity. There is a pressing need to intelligently provision various services in 6G networks, which however is challenging. To meet this need, in this article, we propose a novel architecture called UaaS (UAVs as a Service) for the air-ground integrated network, featuring UAV as a key enabler to boost edge intelligence with the help of machine learning (ML) techniques. We envision that the proposed UaaS architecture could intelligently provision wireless communication service, edge computing service, and edge caching service by a network of UAVs, making full use of UAVs' flexible deployment and diverse ML techniques. We also conduct a case study where UAVs participate in the model training of distributed ML among multiple terrestrial users, whose result shows that the model training is efficient with a negligible energy consumption of UAVs, compared to the flight energy consumption. Finally, we discuss the challenges and open research issues in the UaaS.