Multi-agent resource allocation strategy for UAV swarm-based cooperative sensing
Driven by the development of intelligent internet of things (IoT) technology, unmanned aerial vehicle (UAV) swarms have been widely used for sensing and monitoring in emergency and rescue scenarios.The UAVs automatically sense and discover mission targets in the mission area, recruiting neighboring...
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2023-03-01
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| Series: | 物联网学报 |
| Subjects: | |
| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2023.00326/ |
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| _version_ | 1841533824976027648 |
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| author | Zhihong WANG Supeng LENG Kai XIONG |
| author_facet | Zhihong WANG Supeng LENG Kai XIONG |
| author_sort | Zhihong WANG |
| collection | DOAJ |
| description | Driven by the development of intelligent internet of things (IoT) technology, unmanned aerial vehicle (UAV) swarms have been widely used for sensing and monitoring in emergency and rescue scenarios.The UAVs automatically sense and discover mission targets in the mission area, recruiting neighboring UAVs to form perception and computation task groups to collaboratively complete the perception, acquisition and processing of data.However, repetitive sensory data and imbalance in the supply and demand of computational resources between multiple tasks cause additional computational and communication overheads and increase the end-to-end processing latency.To address this challenge, a multi-task resource allocation approach combining bionics and multi-agent independent reinforcement learning was proposed, making collaborative resource allocation decisions based on local task information.The method represents the resource requirements of individual tasks as situational information concentrations and dynamically updates the heterogeneous resource requirements of each task by spreading the situational information across task groups.At the same time, it combines multi-agent independent reinforcement learning methods for intelligent decision making in order to collaboratively allocate the heterogeneous resources of each task.Simulation results show that this solution can not only effectively reduce the task execution time, but also significantly improve the computational resource utilization. |
| format | Article |
| id | doaj-art-830eb131f77e46aeb5a523cbbe66fbb2 |
| institution | Kabale University |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2023-03-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-830eb131f77e46aeb5a523cbbe66fbb22025-01-15T02:54:36ZzhoChina InfoCom Media Group物联网学报2096-37502023-03-017182659579204Multi-agent resource allocation strategy for UAV swarm-based cooperative sensingZhihong WANGSupeng LENGKai XIONGDriven by the development of intelligent internet of things (IoT) technology, unmanned aerial vehicle (UAV) swarms have been widely used for sensing and monitoring in emergency and rescue scenarios.The UAVs automatically sense and discover mission targets in the mission area, recruiting neighboring UAVs to form perception and computation task groups to collaboratively complete the perception, acquisition and processing of data.However, repetitive sensory data and imbalance in the supply and demand of computational resources between multiple tasks cause additional computational and communication overheads and increase the end-to-end processing latency.To address this challenge, a multi-task resource allocation approach combining bionics and multi-agent independent reinforcement learning was proposed, making collaborative resource allocation decisions based on local task information.The method represents the resource requirements of individual tasks as situational information concentrations and dynamically updates the heterogeneous resource requirements of each task by spreading the situational information across task groups.At the same time, it combines multi-agent independent reinforcement learning methods for intelligent decision making in order to collaboratively allocate the heterogeneous resources of each task.Simulation results show that this solution can not only effectively reduce the task execution time, but also significantly improve the computational resource utilization.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2023.00326/UAV swarmresource allocationindependent reinforcement learningbionicsmulti-agent |
| spellingShingle | Zhihong WANG Supeng LENG Kai XIONG Multi-agent resource allocation strategy for UAV swarm-based cooperative sensing 物联网学报 UAV swarm resource allocation independent reinforcement learning bionics multi-agent |
| title | Multi-agent resource allocation strategy for UAV swarm-based cooperative sensing |
| title_full | Multi-agent resource allocation strategy for UAV swarm-based cooperative sensing |
| title_fullStr | Multi-agent resource allocation strategy for UAV swarm-based cooperative sensing |
| title_full_unstemmed | Multi-agent resource allocation strategy for UAV swarm-based cooperative sensing |
| title_short | Multi-agent resource allocation strategy for UAV swarm-based cooperative sensing |
| title_sort | multi agent resource allocation strategy for uav swarm based cooperative sensing |
| topic | UAV swarm resource allocation independent reinforcement learning bionics multi-agent |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2023.00326/ |
| work_keys_str_mv | AT zhihongwang multiagentresourceallocationstrategyforuavswarmbasedcooperativesensing AT supengleng multiagentresourceallocationstrategyforuavswarmbasedcooperativesensing AT kaixiong multiagentresourceallocationstrategyforuavswarmbasedcooperativesensing |