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

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 is challenging. To meet this need, in this article, we propose a novel architecture called UaalS, that is, unmanned aerial vehicles (UAVs) as a intelligent service for the air-ground integrated network, featuring the UAV as a key enabler to boost edge intelligence with the help of machine learning (ML) techniques. We envision that the proposed UaalS 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 low energy consumption of UAVs. Finally, we discuss the challenges and some open research issues in UaaIS.

Automated summary

The research proposes a novel architecture named UaalS, utilizing UAVs as a key enabler to boost edge intelligence with machine learning techniques, achieving seamless super-connectivity service. A case study demonstrates the efficiency and low energy consumption of UAVs participating in distributed machine learning model training.