Task Offloading Strategy Based on Mobile Edge Computing in UAV Network
When an unmanned aerial vehicle (UAV) performs tasks such as power patrol inspection, water quality detection, field scientific observation, etc., due to the limitations of the computing capacity and battery power, it cannot complete the tasks efficiently. Therefore, an effective method is to deploy...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/5/736 |
| _version_ | 1797500002408333312 |
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| author | Wei Qi Hao Sun Lichen Yu Shuo Xiao Haifeng Jiang |
| author_facet | Wei Qi Hao Sun Lichen Yu Shuo Xiao Haifeng Jiang |
| author_sort | Wei Qi |
| collection | DOAJ |
| description | When an unmanned aerial vehicle (UAV) performs tasks such as power patrol inspection, water quality detection, field scientific observation, etc., due to the limitations of the computing capacity and battery power, it cannot complete the tasks efficiently. Therefore, an effective method is to deploy edge servers near the UAV. The UAV can offload some of the computationally intensive and real-time tasks to edge servers. In this paper, a mobile edge computing offloading strategy based on reinforcement learning is proposed. Firstly, the Stackelberg game model is introduced to model the UAV and edge nodes in the network, and the utility function is used to calculate the maximization of offloading revenue. Secondly, as the problem is a mixed-integer non-linear programming (MINLP) problem, we introduce the multi-agent deep deterministic policy gradient (MADDPG) to solve it. Finally, the effects of the number of UAVs and the summation of computing resources on the total revenue of the UAVs were simulated through simulation experiments. The experimental results show that compared with other algorithms, the algorithm proposed in this paper can more effectively improve the total benefit of UAVs. |
| first_indexed | 2024-03-10T03:55:34Z |
| format | Article |
| id | doaj.art-dd5b89609e3b4b9da2ec3c70fd1534e5 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T03:55:34Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-dd5b89609e3b4b9da2ec3c70fd1534e52023-11-23T10:56:26ZengMDPI AGEntropy1099-43002022-05-0124573610.3390/e24050736Task Offloading Strategy Based on Mobile Edge Computing in UAV NetworkWei Qi0Hao Sun1Lichen Yu2Shuo Xiao3Haifeng Jiang4Department of Information Technology, Jiangsu Union Technical Institute, Xuzhou 221000, ChinaSchool of Computer Sciences and Technology, China University of Mining and Technology, Xuzhou 221000, ChinaSchool of Sciences, Xi’an Jiaotong-Liverpool University, Suzhou 215123, ChinaSchool of Computer Sciences and Technology, China University of Mining and Technology, Xuzhou 221000, ChinaSchool of Computer Sciences and Technology, China University of Mining and Technology, Xuzhou 221000, ChinaWhen an unmanned aerial vehicle (UAV) performs tasks such as power patrol inspection, water quality detection, field scientific observation, etc., due to the limitations of the computing capacity and battery power, it cannot complete the tasks efficiently. Therefore, an effective method is to deploy edge servers near the UAV. The UAV can offload some of the computationally intensive and real-time tasks to edge servers. In this paper, a mobile edge computing offloading strategy based on reinforcement learning is proposed. Firstly, the Stackelberg game model is introduced to model the UAV and edge nodes in the network, and the utility function is used to calculate the maximization of offloading revenue. Secondly, as the problem is a mixed-integer non-linear programming (MINLP) problem, we introduce the multi-agent deep deterministic policy gradient (MADDPG) to solve it. Finally, the effects of the number of UAVs and the summation of computing resources on the total revenue of the UAVs were simulated through simulation experiments. The experimental results show that compared with other algorithms, the algorithm proposed in this paper can more effectively improve the total benefit of UAVs.https://www.mdpi.com/1099-4300/24/5/736unmanned aerial vehiclemobile edge computingstackelberg game |
| spellingShingle | Wei Qi Hao Sun Lichen Yu Shuo Xiao Haifeng Jiang Task Offloading Strategy Based on Mobile Edge Computing in UAV Network Entropy unmanned aerial vehicle mobile edge computing stackelberg game |
| title | Task Offloading Strategy Based on Mobile Edge Computing in UAV Network |
| title_full | Task Offloading Strategy Based on Mobile Edge Computing in UAV Network |
| title_fullStr | Task Offloading Strategy Based on Mobile Edge Computing in UAV Network |
| title_full_unstemmed | Task Offloading Strategy Based on Mobile Edge Computing in UAV Network |
| title_short | Task Offloading Strategy Based on Mobile Edge Computing in UAV Network |
| title_sort | task offloading strategy based on mobile edge computing in uav network |
| topic | unmanned aerial vehicle mobile edge computing stackelberg game |
| url | https://www.mdpi.com/1099-4300/24/5/736 |
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