Fan Zhiyong, Lin Yuanmo, Ai Yuxun, Xu Hang
Information Construction and Management Center, Putian University, Putian, 351100, Fujian, China.
Department of Electromechanical and Information Engineering, Putian University, Putian, 351100, Fujian, China.
Sci Rep. 2024 Jul 30;14(1):17606. doi: 10.1038/s41598-024-67886-x.
The timely and reliable handling of post-disaster emergency monitoring tasks is crucial for effective rescue operations. UAV-assisted edge computing plays a pivotal role in the rapid deployment of such systems. However, challenges persist due to communication and computation resource bottlenecks when dealing with delay-sensitive monitoring tasks. In dynamic post-disaster environments, effective task scheduling and resource allocation decisions directly impact the system's ability to process tasks. Therefore, this paper proposes an adaptive task migration decision-making system for emergency monitoring tasks in UAV-assisted edge computing. Firstly, We decomposed the optimization objectives based on the task processing workflow, then devised a stepwise delay risk control and resource recovery mechanism based on early discarding. Secondly, by integrating multi-agent reinforcement learning (MARL), optimal strategies for task offloading, UAV queue scheduling, and communication resource allocation are learned to enhance the decision system's environmental awareness and maximize the successful completion of emergency monitoring tasks. Simulation experiments demonstrate that the algorithm significantly improves the success rate of migration tasks and data processing capacity, thereby validating its convergence and effectiveness.
及时且可靠地处理灾后应急监测任务对于有效的救援行动至关重要。无人机辅助的边缘计算在这类系统的快速部署中起着关键作用。然而，在处理对延迟敏感的监测任务时，由于通信和计算资源瓶颈，挑战依然存在。在动态的灾后环境中，有效的任务调度和资源分配决策直接影响系统处理任务的能力。因此，本文提出了一种用于无人机辅助边缘计算中应急监测任务的自适应任务迁移决策系统。首先，我们基于任务处理工作流程分解了优化目标，然后设计了一种基于早期丢弃的逐步延迟风险控制和资源恢复机制。其次，通过集成多智能体强化学习（MARL），学习任务卸载、无人机队列调度和通信资源分配的最优策略，以增强决策系统的环境感知能力，并最大限度地成功完成应急监测任务。仿真实验表明，该算法显著提高了迁移任务的成功率和数据处理能力，从而验证了其收敛性和有效性。
