Resource Allocation and 3D Deployment of UAVs-Assisted MEC Network with Air-Ground Cooperation
Equipping an unmanned aerial vehicle (UAV) with a mobile edge computing (MEC) server is an interesting technique for assisting terminal devices (TDs) to complete their delay sensitive computing tasks. In this paper, we investigate a UAV-assisted MEC network with air–ground cooperation, where both UA...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/7/2590 |
| _version_ | 1797437722782072832 |
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| author | Jinming Huang Sijie Xu Jun Zhang Yi Wu |
| author_facet | Jinming Huang Sijie Xu Jun Zhang Yi Wu |
| author_sort | Jinming Huang |
| collection | DOAJ |
| description | Equipping an unmanned aerial vehicle (UAV) with a mobile edge computing (MEC) server is an interesting technique for assisting terminal devices (TDs) to complete their delay sensitive computing tasks. In this paper, we investigate a UAV-assisted MEC network with air–ground cooperation, where both UAV and ground access point (GAP) have a direct link with TDs and undertake computing tasks cooperatively. We set out to minimize the maximum delay among TDs by optimizing the resource allocation of the system and by three-dimensional (3D) deployment of UAVs. Specifically, we propose an iterative algorithm by jointly optimizing UAV–TD association, UAV horizontal location, UAV vertical location, bandwidth allocation, and task split ratio. However, the overall optimization problem will be a mixed-integer nonlinear programming (MINLP) problem, which is hard to deal with. Thus, we adopt successive convex approximation (SCA) and block coordinate descent (BCD) methods to obtain a solution. The simulation results have shown that our proposed algorithm is efficient and has a great performance compared to other benchmark schemes. |
| first_indexed | 2024-03-09T11:26:44Z |
| format | Article |
| id | doaj.art-924f9ab63ffb43f19913fb7a3c0a9de3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:26:44Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-924f9ab63ffb43f19913fb7a3c0a9de32023-12-01T00:01:32ZengMDPI AGSensors1424-82202022-03-01227259010.3390/s22072590Resource Allocation and 3D Deployment of UAVs-Assisted MEC Network with Air-Ground CooperationJinming Huang0Sijie Xu1Jun Zhang2Yi Wu3Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, ChinaFujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, ChinaJiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaFujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, ChinaEquipping an unmanned aerial vehicle (UAV) with a mobile edge computing (MEC) server is an interesting technique for assisting terminal devices (TDs) to complete their delay sensitive computing tasks. In this paper, we investigate a UAV-assisted MEC network with air–ground cooperation, where both UAV and ground access point (GAP) have a direct link with TDs and undertake computing tasks cooperatively. We set out to minimize the maximum delay among TDs by optimizing the resource allocation of the system and by three-dimensional (3D) deployment of UAVs. Specifically, we propose an iterative algorithm by jointly optimizing UAV–TD association, UAV horizontal location, UAV vertical location, bandwidth allocation, and task split ratio. However, the overall optimization problem will be a mixed-integer nonlinear programming (MINLP) problem, which is hard to deal with. Thus, we adopt successive convex approximation (SCA) and block coordinate descent (BCD) methods to obtain a solution. The simulation results have shown that our proposed algorithm is efficient and has a great performance compared to other benchmark schemes.https://www.mdpi.com/1424-8220/22/7/2590mobile edge computingUAV communicationresource allocation3D deploymentair-ground cooperation |
| spellingShingle | Jinming Huang Sijie Xu Jun Zhang Yi Wu Resource Allocation and 3D Deployment of UAVs-Assisted MEC Network with Air-Ground Cooperation Sensors mobile edge computing UAV communication resource allocation 3D deployment air-ground cooperation |
| title | Resource Allocation and 3D Deployment of UAVs-Assisted MEC Network with Air-Ground Cooperation |
| title_full | Resource Allocation and 3D Deployment of UAVs-Assisted MEC Network with Air-Ground Cooperation |
| title_fullStr | Resource Allocation and 3D Deployment of UAVs-Assisted MEC Network with Air-Ground Cooperation |
| title_full_unstemmed | Resource Allocation and 3D Deployment of UAVs-Assisted MEC Network with Air-Ground Cooperation |
| title_short | Resource Allocation and 3D Deployment of UAVs-Assisted MEC Network with Air-Ground Cooperation |
| title_sort | resource allocation and 3d deployment of uavs assisted mec network with air ground cooperation |
| topic | mobile edge computing UAV communication resource allocation 3D deployment air-ground cooperation |
| url | https://www.mdpi.com/1424-8220/22/7/2590 |
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