UAV Cluster-Assisted Task Offloading for Emergent Disaster Scenarios
Natural disasters often have an unpredictable impact on human society and can even cause significant problems, such as damage to communication equipment in disaster areas. In such post-disaster emergency rescue situations, unmanned aerial vehicles (UAVs) are considered an effective tool by virtue of...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/8/4724 |
| _version_ | 1827746088899051520 |
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| author | Minglin Shi Xiaoqi Zhang Jia Chen Hongju Cheng |
| author_facet | Minglin Shi Xiaoqi Zhang Jia Chen Hongju Cheng |
| author_sort | Minglin Shi |
| collection | DOAJ |
| description | Natural disasters often have an unpredictable impact on human society and can even cause significant problems, such as damage to communication equipment in disaster areas. In such post-disaster emergency rescue situations, unmanned aerial vehicles (UAVs) are considered an effective tool by virtue of high mobility, easy deployment, and flexible communication. However, the limited size of UAVs leads to bottlenecks in battery capacity and computational power, making it challenging to perform overly complex computational tasks. In this paper, we propose a UAV cluster-assisted task-offloading model for disaster areas, by adopting UAV clusters as aerial mobile edge servers to provide task-offloading services for ground users. In addition, we also propose a deep reinforcement learning-based UAV cluster-assisted task-offloading algorithm (DRL-UCTO). By modeling the energy efficiency optimization problem of the system model as a Markov decision process and jointly optimizing the UAV flight trajectory and task-offloading policy to maximize the reward value, DRL-UCTO can effectively improve the energy use efficiency of UAVs under limited-resource conditions. The simulation results show that the DRL-UCTO algorithm improves the UAV energy efficiency by about 79.6% and 301.1% compared with the DQN and Greedy algorithms, respectively. |
| first_indexed | 2024-03-11T05:16:35Z |
| format | Article |
| id | doaj.art-09145081dd164d2fbc9363279fec06e9 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:16:35Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-09145081dd164d2fbc9363279fec06e92023-11-17T18:08:19ZengMDPI AGApplied Sciences2076-34172023-04-01138472410.3390/app13084724UAV Cluster-Assisted Task Offloading for Emergent Disaster ScenariosMinglin Shi0Xiaoqi Zhang1Jia Chen2Hongju Cheng3College of Computer and Data Science, Fuzhou University, Fuzhou 350108, ChinaCollege of Computer and Data Science, Fuzhou University, Fuzhou 350108, ChinaCollege of Computer and Data Science, Fuzhou University, Fuzhou 350108, ChinaCollege of Computer and Data Science, Fuzhou University, Fuzhou 350108, ChinaNatural disasters often have an unpredictable impact on human society and can even cause significant problems, such as damage to communication equipment in disaster areas. In such post-disaster emergency rescue situations, unmanned aerial vehicles (UAVs) are considered an effective tool by virtue of high mobility, easy deployment, and flexible communication. However, the limited size of UAVs leads to bottlenecks in battery capacity and computational power, making it challenging to perform overly complex computational tasks. In this paper, we propose a UAV cluster-assisted task-offloading model for disaster areas, by adopting UAV clusters as aerial mobile edge servers to provide task-offloading services for ground users. In addition, we also propose a deep reinforcement learning-based UAV cluster-assisted task-offloading algorithm (DRL-UCTO). By modeling the energy efficiency optimization problem of the system model as a Markov decision process and jointly optimizing the UAV flight trajectory and task-offloading policy to maximize the reward value, DRL-UCTO can effectively improve the energy use efficiency of UAVs under limited-resource conditions. The simulation results show that the DRL-UCTO algorithm improves the UAV energy efficiency by about 79.6% and 301.1% compared with the DQN and Greedy algorithms, respectively.https://www.mdpi.com/2076-3417/13/8/4724UAV clustertask offloadingtrajectory optimizationdeep reinforcement learningemergent disaster scenarios |
| spellingShingle | Minglin Shi Xiaoqi Zhang Jia Chen Hongju Cheng UAV Cluster-Assisted Task Offloading for Emergent Disaster Scenarios Applied Sciences UAV cluster task offloading trajectory optimization deep reinforcement learning emergent disaster scenarios |
| title | UAV Cluster-Assisted Task Offloading for Emergent Disaster Scenarios |
| title_full | UAV Cluster-Assisted Task Offloading for Emergent Disaster Scenarios |
| title_fullStr | UAV Cluster-Assisted Task Offloading for Emergent Disaster Scenarios |
| title_full_unstemmed | UAV Cluster-Assisted Task Offloading for Emergent Disaster Scenarios |
| title_short | UAV Cluster-Assisted Task Offloading for Emergent Disaster Scenarios |
| title_sort | uav cluster assisted task offloading for emergent disaster scenarios |
| topic | UAV cluster task offloading trajectory optimization deep reinforcement learning emergent disaster scenarios |
| url | https://www.mdpi.com/2076-3417/13/8/4724 |
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