Air-Ground Integrated Mobile Edge Networks: Architecture, Challenges and Opportunities
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Abstract (arXiv)
The ever-increasing mobile data demands have posed significant challenges in the current radio access networks, while the emerging computation-heavy Internet of things (IoT) applications with varied requirements demand more flexibility and resilience from the cloud/edge computing architecture. In this article, to address the issues, we propose a novel air-ground integrated mobile edge network (AGMEN), where UAVs are flexibly deployed and scheduled, and assist the communication, caching, and computing of the edge network. In specific, we present the detailed architecture of AGMEN, and investigate the benefits and application scenarios of drone-cells, and UAV-assisted edge caching and computing. Furthermore, the challenging issues in AGMEN are discussed, and potential research directions are highlighted.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Apr 2018.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1.
3 papers.
- 2023Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimizationcited by 56
- 2021EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks for Internet of Vehiclescited by 3
- 2024DeepAir: A Multi-Agent Deep Reinforcement Learning Based Scheme for an Unknown User Location Problemcited by 0
- 2024DeepAir: A Multi-Agent Deep Reinforcement Learning Based Scheme for an Unknown User Location Problem11 Aug 2024
- 2023Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization10 Aug 2023
- 2021EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks for Internet of Vehicles15 Jan 2021