Research on Unmanned Aerial Vehicle Emergency Support System and Optimization Method Based on Gaussian Global Seagull Algorithm
In emergency rescue scenarios, drones can be equipped with different payloads as needed to aid in tasks such as disaster reconnaissance, situational awareness, communication support, and material assistance. However, rescue missions typically face challenges such as limited reconnaissance boundaries...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/8/12/763 |
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| author | Songyue Han Mingyu Wang Junhong Duan Jialong Zhang Dongdong Li |
| author_facet | Songyue Han Mingyu Wang Junhong Duan Jialong Zhang Dongdong Li |
| author_sort | Songyue Han |
| collection | DOAJ |
| description | In emergency rescue scenarios, drones can be equipped with different payloads as needed to aid in tasks such as disaster reconnaissance, situational awareness, communication support, and material assistance. However, rescue missions typically face challenges such as limited reconnaissance boundaries, heterogeneous communication networks, complex data fusion, high task latency, and limited equipment endurance. To address these issues, an unmanned emergency support system tailored for emergency rescue scenarios is designed. This system leverages 5G edge computing technology to provide high-speed and flexible network access along with elastic computing power support, reducing the complexity of data fusion across heterogeneous networks. It supports the control and data transmission of drones through the separation of the control plane and the data plane. Furthermore, by applying the Tammer decomposition method to break down the system optimization problem, the Global Learning Seagull Algorithm for Gaussian Mapping (GLSOAG) is proposed to jointly optimize the system’s energy consumption and latency. Through simulation experiments, the GLSOAG demonstrates significant advantages over the Seagull Optimization Algorithm (SOA), Particle Swarm Optimization (PSO), and Beetle Antennae Search Algorithm (BAS) in terms of convergence speed, optimization accuracy, and stability. The system optimization approach effectively reduces the system’s energy consumption and latency costs. Overall, our work alleviates the pain points faced in rescue scenarios to some extent. |
| format | Article |
| id | doaj-art-e9d4992a26904bebb84b7675f068d739 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-e9d4992a26904bebb84b7675f068d7392024-12-27T14:21:53ZengMDPI AGDrones2504-446X2024-12-0181276310.3390/drones8120763Research on Unmanned Aerial Vehicle Emergency Support System and Optimization Method Based on Gaussian Global Seagull AlgorithmSongyue Han0Mingyu Wang1Junhong Duan2Jialong Zhang3Dongdong Li4Air Defense and Antimissile School, Air Force Engineering University, Xi’an 710000, ChinaAir Defense and Antimissile School, Air Force Engineering University, Xi’an 710000, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, ChinaTest Center, National University of Defense Technology, Xi’an 710100, ChinaPeople’s Liberation Army Unit 32705, ChinaIn emergency rescue scenarios, drones can be equipped with different payloads as needed to aid in tasks such as disaster reconnaissance, situational awareness, communication support, and material assistance. However, rescue missions typically face challenges such as limited reconnaissance boundaries, heterogeneous communication networks, complex data fusion, high task latency, and limited equipment endurance. To address these issues, an unmanned emergency support system tailored for emergency rescue scenarios is designed. This system leverages 5G edge computing technology to provide high-speed and flexible network access along with elastic computing power support, reducing the complexity of data fusion across heterogeneous networks. It supports the control and data transmission of drones through the separation of the control plane and the data plane. Furthermore, by applying the Tammer decomposition method to break down the system optimization problem, the Global Learning Seagull Algorithm for Gaussian Mapping (GLSOAG) is proposed to jointly optimize the system’s energy consumption and latency. Through simulation experiments, the GLSOAG demonstrates significant advantages over the Seagull Optimization Algorithm (SOA), Particle Swarm Optimization (PSO), and Beetle Antennae Search Algorithm (BAS) in terms of convergence speed, optimization accuracy, and stability. The system optimization approach effectively reduces the system’s energy consumption and latency costs. Overall, our work alleviates the pain points faced in rescue scenarios to some extent.https://www.mdpi.com/2504-446X/8/12/763Gaussian global seagull algorithmresource optimization methodUAV5Gmobile edge computingemergency support |
| spellingShingle | Songyue Han Mingyu Wang Junhong Duan Jialong Zhang Dongdong Li Research on Unmanned Aerial Vehicle Emergency Support System and Optimization Method Based on Gaussian Global Seagull Algorithm Drones Gaussian global seagull algorithm resource optimization method UAV 5G mobile edge computing emergency support |
| title | Research on Unmanned Aerial Vehicle Emergency Support System and Optimization Method Based on Gaussian Global Seagull Algorithm |
| title_full | Research on Unmanned Aerial Vehicle Emergency Support System and Optimization Method Based on Gaussian Global Seagull Algorithm |
| title_fullStr | Research on Unmanned Aerial Vehicle Emergency Support System and Optimization Method Based on Gaussian Global Seagull Algorithm |
| title_full_unstemmed | Research on Unmanned Aerial Vehicle Emergency Support System and Optimization Method Based on Gaussian Global Seagull Algorithm |
| title_short | Research on Unmanned Aerial Vehicle Emergency Support System and Optimization Method Based on Gaussian Global Seagull Algorithm |
| title_sort | research on unmanned aerial vehicle emergency support system and optimization method based on gaussian global seagull algorithm |
| topic | Gaussian global seagull algorithm resource optimization method UAV 5G mobile edge computing emergency support |
| url | https://www.mdpi.com/2504-446X/8/12/763 |
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