When UAV Swarm Meets Edge-Cloud Computing: The QoS Perspective
Abstract
In this article, we propose a hybrid computing model, UAV-Edge-Cloud, bringing edge/cloud computing and UAV swarm together to achieve high quality of service (QoS) guarantees. First, we design this novel hybrid computing framework to provide powerful resources to support resource-intensive applications and real-time tasks at edge networks. Next, we discuss some potential applications for smart cities and raise open research issues of the proposed hybrid framework. We then study a joint task placement and routing problem for latency-critical applications as a case study. Finally, the simulation results show that our approach can improve the QoS of UAV swarms effectively.
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Reduced sent data size and distance reduces cost and the significant energy consumption associated with data transfer to the central cloud.
“This reduced sent data size and distance not only reduces cost but reduces the significant energy consumption associated with the data transfer to the central cloud [45].”
From A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022) · cites this paper as [45]
Edge AI reduces energy consumption associated with data transfer by enabling preprocessing at the edge, which reduces the amount of data sent to the remote cloud.
“performing AI processing at the edge reduces energy consumption.This is due to edge AI enabling preprocessing at the edge which results in less data needing to be sent to the remote cloud and, therefore, causing significantly less energy consumption associated with data transfer.”
From A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022) · cites this paper as [45]
Edge AI reduces operational cost by reducing the amount of data sent, because cloud-side computation cost can be significant when dealing with large volumes of data.
“edge AI can reduce the cost by reducing the amount of data that needs to sent (by preprocessing at the edge) as there is a cloud side computation cost that can be significant when dealing with large volumes of data [45].”
From A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022) · cites this paper as [45]
The reduced data size sent to the remote cloud due to edge AI preprocessing helps with operational cost, and this is particularly relevant to formation control.
“Also, the reduced data size sent to remote cloud due to edge AI's preprocessing helps with regard to operational cost as there is a cloud-side computation cost that can be significant when dealing with large volumes of data [45].This is particularly relevant to formation control.”
From A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022) · cites this paper as [45]
Cites
Edge computing: Vision and challenges
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2579198 · Cited by 8,733 · see citing works
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Applications of artificial intelligence and machine learning in smart cities
Ullah, Z., Al-Turjman, F., Mostarda, L., & Gagliardi, R. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.02.069 · Cited by 756 · see citing works
A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges
McEnroe, P., Wang, S., & Liyanage, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3176400 · Cited by 482 · see citing works
Islam, A., & Shin, S. Y. (2019) · IEEE Access · doi:10.1109/access.2019.2930774 · Cited by 99 · see citing works
- Authors
- Chen, W., Liu, B., Huang, H., Guo, S., & Zheng, Z.
- Affiliations
- Sun Yat-sen University (CN)
- Hong Kong Polytechnic University (HK)
- Venue
- IEEE Network
- Published
- 2019-03
- Volume
- 33
- Issue
- 2
- Pages
- 36-43
- ISSN
- 0890-8044 (ISSN-L)
- 1558-156X
- OpenAlex
- W2931293404
- Cited by
- 184
- References
- 1
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 19 · 2023: 27 · 2022: 32 · 2021: 50 · 2020: 26 · 2019: 14
- FWCI
- 16.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Network (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Aerospace Engineering › UAV Applications and Optimization
- Physical Sciences › Engineering › Aerospace Engineering › Satellite Communication Systems
- Keywords
- UAV swarms, edge-cloud computing, quality of service, task placement, routing, delay-sensitive applications, smart cities, real-time tasks, resource-intensive applications
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