传感器驱动资源优化框架用于智能雾启用IoHT系统的智能雾
Salman Khan1, Ibrar Ali Shah2, Woong-Kee Loh1
1School of Computing, Gachon University, Seongnam 13120, Republic of Korea.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
雾计算可以减少实时应用程序的延迟时间. 一个修改的粒子群集优化 (MPSO) 算法优化了雾医疗保健系统的资源配置,改善了响应时间和降低了成本.
科学领域:
- 计算机科学 计算机科学
- 分布式计算 (Distributed Computing) 是一种分布式计算.
- 人工智能的人工智能
背景情况:
- 云计算面临着影响实时应用程序的延迟问题.
- 雾计算通过在用户附近处理数据,提供低延迟解决方案.
- 医疗保健应用程序要求快速响应时间,使延迟成为关键问题.
研究的目的:
- 为延迟敏感的医疗保健应用程序开发一个优化的资源配置和调度框架.
- 解决医疗环境中雾设备的资源限制问题.
- 提高对关键医疗保健服务的雾计算的效率和性能.
主要方法:
- 设计了一个优化的资源分配和调度框架.
- 一个修改的粒子优化 (MPSO) 算法被用于优化.
- 通过广泛的模拟,使用iFogSim工具包评估了拟议的框架.
主要成果:
- 基于MPSO的方法将制量减少了高达8%.
- 与现有算法相比,执行成本降低了高达3%.
- 拟议的技术在提高医疗保健的雾计算性能方面表现出有效性.
结论:
- 雾计算非常适合实时医疗保健应用,因为其延迟减少.
- 使用MPSO优化资源管理显著提高了雾系统的性能.
- 该研究强调了雾计算在高效和快速响应的医疗保健提供方面的潜力.
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