Joint UAV Deployment and Task Offloading Scheme for Multi-UAV-Assisted Edge Computing
With the development of the Internet of Things (IoT), IoT devices are increasingly being deployed in scenarios with large footprints, remote locations, and complex geographic environments. In these scenarios, base stations are usually not easily deployed and are easily destroyed, so unmanned aerial...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
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| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/7/5/284 |
| _version_ | 1827741427636895744 |
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| author | Fan Li Juan Luo Ying Qiao Yaqun Li |
| author_facet | Fan Li Juan Luo Ying Qiao Yaqun Li |
| author_sort | Fan Li |
| collection | DOAJ |
| description | With the development of the Internet of Things (IoT), IoT devices are increasingly being deployed in scenarios with large footprints, remote locations, and complex geographic environments. In these scenarios, base stations are usually not easily deployed and are easily destroyed, so unmanned aerial vehicle (UAV)-based edge computing is a good solution. However, the UAV cannot accomplish the computing tasks and efficiently achieve better resource allocation considering the limited communication and computing resources of the UAV. In this paper, a multi-UAV-assisted mobile edge computing (MEC) system is considered where multiple UAVs cooperate to provide a service to IoT devices. We formulate an optimization function to minimize the energy consumption of a multi-UAV-assisted MEC system. The optimization function is a complex problem with non-convex and multivariate coupling. Thus, a joint UAV deployment and task scheduling optimization algorithm are designed to achieve optimal values of UAV numbers, the hovering position of each UAV, and the best strategy for offloading and resource allocation. Experimental results demonstrate that the algorithm has positive convergence performance and can accomplish more tasks under the constraint of delay compared to the two benchmark algorithms. The proposed algorithm can effectively reduce the system energy consumption compared to the two state-of-the-art algorithms. |
| first_indexed | 2024-03-11T03:47:23Z |
| format | Article |
| id | doaj.art-1be9c3b78c1c48abaf21f1c6c902ceb9 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-11T03:47:23Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-1be9c3b78c1c48abaf21f1c6c902ceb92023-11-18T01:06:46ZengMDPI AGDrones2504-446X2023-04-017528410.3390/drones7050284Joint UAV Deployment and Task Offloading Scheme for Multi-UAV-Assisted Edge ComputingFan Li0Juan Luo1Ying Qiao2Yaqun Li3College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, ChinaWith the development of the Internet of Things (IoT), IoT devices are increasingly being deployed in scenarios with large footprints, remote locations, and complex geographic environments. In these scenarios, base stations are usually not easily deployed and are easily destroyed, so unmanned aerial vehicle (UAV)-based edge computing is a good solution. However, the UAV cannot accomplish the computing tasks and efficiently achieve better resource allocation considering the limited communication and computing resources of the UAV. In this paper, a multi-UAV-assisted mobile edge computing (MEC) system is considered where multiple UAVs cooperate to provide a service to IoT devices. We formulate an optimization function to minimize the energy consumption of a multi-UAV-assisted MEC system. The optimization function is a complex problem with non-convex and multivariate coupling. Thus, a joint UAV deployment and task scheduling optimization algorithm are designed to achieve optimal values of UAV numbers, the hovering position of each UAV, and the best strategy for offloading and resource allocation. Experimental results demonstrate that the algorithm has positive convergence performance and can accomplish more tasks under the constraint of delay compared to the two benchmark algorithms. The proposed algorithm can effectively reduce the system energy consumption compared to the two state-of-the-art algorithms.https://www.mdpi.com/2504-446X/7/5/284edge computingtask schedulingUAV-assistedUAV deployment |
| spellingShingle | Fan Li Juan Luo Ying Qiao Yaqun Li Joint UAV Deployment and Task Offloading Scheme for Multi-UAV-Assisted Edge Computing Drones edge computing task scheduling UAV-assisted UAV deployment |
| title | Joint UAV Deployment and Task Offloading Scheme for Multi-UAV-Assisted Edge Computing |
| title_full | Joint UAV Deployment and Task Offloading Scheme for Multi-UAV-Assisted Edge Computing |
| title_fullStr | Joint UAV Deployment and Task Offloading Scheme for Multi-UAV-Assisted Edge Computing |
| title_full_unstemmed | Joint UAV Deployment and Task Offloading Scheme for Multi-UAV-Assisted Edge Computing |
| title_short | Joint UAV Deployment and Task Offloading Scheme for Multi-UAV-Assisted Edge Computing |
| title_sort | joint uav deployment and task offloading scheme for multi uav assisted edge computing |
| topic | edge computing task scheduling UAV-assisted UAV deployment |
| url | https://www.mdpi.com/2504-446X/7/5/284 |
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