动态任务卸载边缘意识优化框架,用于在边缘计算平台上增强无人机操作
B Suganya1, R Gopi2, A Ranjith Kumar3
1Faculty of Artificial Intelligence & Data Science, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, 621112, India.
Scientific reports
|July 16, 2024
概括
本研究介绍了无人飞行器 (UAV) 操作的动态任务卸载框架,优化资源使用和效率. 由人工智能驱动的边缘计算方法显著降低了延迟并提高了任务性能.
科学领域:
- 计算机科学 计算机科学
- 航空航天工程 航空航天工程
- 人工智能的人工智能
背景情况:
- 无人机操作的传统集中处理架构面临着延迟,带宽和可扩展性的挑战.
- 确保地面站和无人机之间稳健的通信,数据隐私和安全性是复杂的.
- 优化资源分配和及时数据捕获对于任务的成功至关重要.
研究的目的:
- 提出一个新的动态任务卸载边缘意识优化框架 (DTOE-AOF) 以提高无人机操作.
- 整合边缘计算和人工智能 (AI) 以提高无人机的效率和节约资源.
- 解决无人机任务执行中集中架构的局限性.
主要方法:
- 开发了动态任务卸载边缘意识优化框架 (DTOE-AOF).
- 集成的边缘计算基础设施与人工智能驱动的决策和动态任务卸载机制.
- 基于近距离,资源可用性和任务紧迫性,向边缘节点和无人机动态分配计算任务.
主要成果:
- DTOE-AOF显著降低了延迟时间,并节省了机载无人机资源.
- 与传统的集中式方法相比,任务效率和响应时间明显提高.
- 通过模拟研究证实了提高资源利用率和运营效率.
结论:
- 通过利用边缘计算和人工智能,DTOE-AOF有效地优化无人机操作.
- 该框架为各种应用提供了可扩展和高效的解决方案,包括精密农业,应急管理和基础设施检查.
- 拟议的系统增强了无人机在关键场景中快速获取数据和执行任务的能力.
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