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适应性任务迁移策略,延迟风险控制和强化学习,用于紧急监控
Zhiyong Fan1, Yuanmo Lin2, Yuxun Ai3
1Information Construction and Management Center, Putian University, Putian, 351100, Fujian, China.
Scientific reports
|July 30, 2024
概括
本研究介绍了一种无人机辅助边缘计算的自适应系统,以改善灾后监测. 它增强了关键紧急响应行动的任务迁移和数据处理.
科学领域:
- 计算机科学 计算机科学
- 机器人技术 机器人技术 机器人技术
- 应急管理 应急管理
背景情况:
- 灾后有效的紧急监测对于救援行动至关重要.
- 无人机辅助边缘计算提供了快速部署,但在延迟敏感任务中面临通信和计算瓶.
- 动态的灾难环境需要强大的任务安排和资源分配,以实现高效的处理.
研究的目的:
- 提出适应性任务迁移决策系统,用于无人机辅助边缘计算中的紧急监控.
- 解决延迟敏感监控任务中的通信和计算资源限制.
- 提高系统的环境意识,并最大限度地完成紧急监测任务的成功完成.
主要方法:
- 基于任务处理工作流程的优化目标的分解.
- 通过早期丢弃,逐步开发延迟风险控制和资源回收机制.
- 整合多代理强化学习 (MARL) 以实现最佳的任务卸载,无人机队列安排和通信资源分配.
主要成果:
- 拟议的系统显著提高了迁移任务的成功率.
- 在无人机辅助边缘计算环境中展示了增强的数据处理能力.
- 模拟实验验证了算法在动态灾后场景中的融合和有效性.
结论:
- 适应性任务迁移系统有效地管理资源,并为紧急监控安排任务.
- 马尔集成增强了无人机辅助边缘计算在灾害响应中的决策能力.
- 开发的方法为及时和可靠的灾后监测提供了强大的解决方案.
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