基于强化学习的安全边缘启用多任务调度模型,用于万物互联网应用程序.
Thiruppathy Kesavan V1,2, Venkatesan R3, Wai Kit Wong4
1Faculty of Information Technology, Dhanalakshmi Srinivasan Engineering College, Perambalur, 621212, Tamil Nadu, India.
Scientific reports
|February 20, 2025
概括
安全边缘启用多任务调度 (SEE-MTS) 模型提高了万物互联网 (IoE) 的安全性和效率. 它优化了工作安排和能源使用,用于 IoE 数据管理,减少延迟和提高性能.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 网络安全 网络安全
背景情况:
- 万物互联网 (IoE) 产生了庞大的网络数据,要求分布式计算解决方案.
- 无线传感器网络对于 IoE 数据管理和工作安排至关重要.
- 安全漏洞和高能耗是 IoE 数据调度中的重大挑战.
研究的目的:
- 提出安全边缘启用多任务调度 (SEE-MTS) 模型,以实现有效的 IoE 工作分配.
- 通过边缘计算提高 IoE 应用程序的效率和数据管理.
- 解决安全问题,优化 IoE 环境中的能源消耗.
主要方法:
- 利用边缘计算来实现高效的数据处理和工作分配.
- 实施多任务调度 (MTS) 机制,以优化能源使用.
- 使用强化学习技术来最大限度地减少任务完成时间和数据使用.
- 采用动态更新,多键搜索,数据加密和结果验证以确保安全.
主要成果:
- 该SEE-MTS模型实现了4J的能量利用,2s的延迟和4s的反应时间.
- 证明了89%的能源效率和96%的安全水平.
- 将计算时间缩短到6秒,提高整体效率和安全性.
结论:
- 该SEE-MTS模型为IoE数据管理提供了显著的效率和安全性改进.
- 该模型有效地减少了能源消耗,延迟,反应时间和处理时间.
- 现实世界实施的潜在限制包括设备规模和数据量.
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