Intelligent resource scheduling scheme for UAV swarm collaborative sensing
With the rapid development of the low-altitude economy, unmanned aerial vehicles (UAV) have been widely applied in monitoring and sensing tasks. However, the limited onboard computing resources of UAV constrain the efficient processing of sensing data. Moreover, overlapping observation areas in coll...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2025-03-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2025050/ |
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| _version_ | 1849711291911897088 |
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| author | ZHAO Pengcheng LI Tianyang LENG Supeng XIONG Kai |
| author_facet | ZHAO Pengcheng LI Tianyang LENG Supeng XIONG Kai |
| author_sort | ZHAO Pengcheng |
| collection | DOAJ |
| description | With the rapid development of the low-altitude economy, unmanned aerial vehicles (UAV) have been widely applied in monitoring and sensing tasks. However, the limited onboard computing resources of UAV constrain the efficient processing of sensing data. Moreover, overlapping observation areas in collaborative sensing introduce additional computational redundancy. Meanwhile, the highly dynamic network topology and fluctuating node resources significantly increase the complexity of resource coordination. To address these challenges, an intelligent resource scheduling scheme for UAV swarm collaborative sensing was proposed. Adaptive sensing mode selection, stepwise computation offloading, and competitive bandwidth allocation were integrated to achieve heterogeneous resource coordination across communication, sensing, and computation (CSC), thereby enhancing collaborative sensing efficiency. Furthermore, a multi-agent reinforcement learning (MARL) algorithm with an attention mechanism was employed to solve the optimization problem, enabling agents to extract critical environmental features more effectively. Simulation results demonstrate that, compared with benchmark schemes, the proposed scheme significantly reduces the execution time of sensing tasks while improving computational resource utilization. |
| format | Article |
| id | doaj-art-aa83edbbaa6242c4ac5ca73b8a499eb2 |
| institution | DOAJ |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2025-03-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-aa83edbbaa6242c4ac5ca73b8a499eb22025-08-20T03:14:39ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012025-03-0141172689689764Intelligent resource scheduling scheme for UAV swarm collaborative sensingZHAO PengchengLI TianyangLENG SupengXIONG KaiWith the rapid development of the low-altitude economy, unmanned aerial vehicles (UAV) have been widely applied in monitoring and sensing tasks. However, the limited onboard computing resources of UAV constrain the efficient processing of sensing data. Moreover, overlapping observation areas in collaborative sensing introduce additional computational redundancy. Meanwhile, the highly dynamic network topology and fluctuating node resources significantly increase the complexity of resource coordination. To address these challenges, an intelligent resource scheduling scheme for UAV swarm collaborative sensing was proposed. Adaptive sensing mode selection, stepwise computation offloading, and competitive bandwidth allocation were integrated to achieve heterogeneous resource coordination across communication, sensing, and computation (CSC), thereby enhancing collaborative sensing efficiency. Furthermore, a multi-agent reinforcement learning (MARL) algorithm with an attention mechanism was employed to solve the optimization problem, enabling agents to extract critical environmental features more effectively. Simulation results demonstrate that, compared with benchmark schemes, the proposed scheme significantly reduces the execution time of sensing tasks while improving computational resource utilization.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2025050/cooperative sensingUAV swarmresource schedulingmulti-agent reinforcement learningattention mechanism |
| spellingShingle | ZHAO Pengcheng LI Tianyang LENG Supeng XIONG Kai Intelligent resource scheduling scheme for UAV swarm collaborative sensing Dianxin kexue cooperative sensing UAV swarm resource scheduling multi-agent reinforcement learning attention mechanism |
| title | Intelligent resource scheduling scheme for UAV swarm collaborative sensing |
| title_full | Intelligent resource scheduling scheme for UAV swarm collaborative sensing |
| title_fullStr | Intelligent resource scheduling scheme for UAV swarm collaborative sensing |
| title_full_unstemmed | Intelligent resource scheduling scheme for UAV swarm collaborative sensing |
| title_short | Intelligent resource scheduling scheme for UAV swarm collaborative sensing |
| title_sort | intelligent resource scheduling scheme for uav swarm collaborative sensing |
| topic | cooperative sensing UAV swarm resource scheduling multi-agent reinforcement learning attention mechanism |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2025050/ |
| work_keys_str_mv | AT zhaopengcheng intelligentresourceschedulingschemeforuavswarmcollaborativesensing AT litianyang intelligentresourceschedulingschemeforuavswarmcollaborativesensing AT lengsupeng intelligentresourceschedulingschemeforuavswarmcollaborativesensing AT xiongkai intelligentresourceschedulingschemeforuavswarmcollaborativesensing |