Collaborative UAVs Multi-task Video Processing Optimization Based on Enhanced Distributed Actor-Critic Networks
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916483571908608 |
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| author | Rong, Ziqi Zheng, Qiushi Shen, Zhishu Li, Xiaolong Zhang, Tiehua Lei, Zheng Jin, Jiong |
| author_facet | Rong, Ziqi Zheng, Qiushi Shen, Zhishu Li, Xiaolong Zhang, Tiehua Lei, Zheng Jin, Jiong |
| contents | With the rapid advancement of the Internet of Things (IoT) and Artificial Intelligence (AI), intelligent information services are being increasingly integrated across various sectors, including healthcare, industry, and transportation. Traditional solutions rely on centralized cloud processing, which encounters considerable challenges in fulfilling the Quality of Service (QoS) requirements of Computer Vision (CV) tasks generated in the resource-constrained infrastructure-less environments. In this paper, we introduce a distributed framework called CoUAV-Pro for multi-task video processing powered by Unmanned Aerial Vehicles (UAVs). This framework empowers multiple UAVs to meet the service demands of various computer vision (CV) tasks in infrastructure-less environments, thereby eliminating the need for centralized processing. Specifically, we develop a novel task allocation algorithm that leverages enhanced distributed actor-critic networks within CoUAV-Pro, aiming to optimize task processing efficiency while contending with constraints associated with UAV's energy, computational, and communication resources. Comprehensive experiments demonstrate that our proposed solution achieves satisfactory performance levels against those of centralized methods across key metrics including task acquisition rates, task latency, and energy consumption. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_10815 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Collaborative UAVs Multi-task Video Processing Optimization Based on Enhanced Distributed Actor-Critic Networks Rong, Ziqi Zheng, Qiushi Shen, Zhishu Li, Xiaolong Zhang, Tiehua Lei, Zheng Jin, Jiong Distributed, Parallel, and Cluster Computing With the rapid advancement of the Internet of Things (IoT) and Artificial Intelligence (AI), intelligent information services are being increasingly integrated across various sectors, including healthcare, industry, and transportation. Traditional solutions rely on centralized cloud processing, which encounters considerable challenges in fulfilling the Quality of Service (QoS) requirements of Computer Vision (CV) tasks generated in the resource-constrained infrastructure-less environments. In this paper, we introduce a distributed framework called CoUAV-Pro for multi-task video processing powered by Unmanned Aerial Vehicles (UAVs). This framework empowers multiple UAVs to meet the service demands of various computer vision (CV) tasks in infrastructure-less environments, thereby eliminating the need for centralized processing. Specifically, we develop a novel task allocation algorithm that leverages enhanced distributed actor-critic networks within CoUAV-Pro, aiming to optimize task processing efficiency while contending with constraints associated with UAV's energy, computational, and communication resources. Comprehensive experiments demonstrate that our proposed solution achieves satisfactory performance levels against those of centralized methods across key metrics including task acquisition rates, task latency, and energy consumption. |
| title | Collaborative UAVs Multi-task Video Processing Optimization Based on Enhanced Distributed Actor-Critic Networks |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2411.10815 |