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相关实验视频

Updated: Jul 13, 2025

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
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在用于智能农业的软件定义物联网网络中,启用人工智能的交通控制优先级.

Fahad Masood1,2, Wajid Ullah Khan2, Sana Ullah Jan3

  • 1Department of Electronics, Quaid i Azam University, Islamabad 45320, Pakistan.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
概括

本研究介绍了一种结合机器学习 (ML) 和强化学习 (RL) 的新型框架,用于优化软件定义网络 (SDN) 中的流量路由. 这种方法提高了资源配置并减少了延迟,特别是在紧急交通中.

关键词:
这就是为什么物联网物联网物联网.在SDN中,SDN是SDN.紧急/关键数据 紧急/关键数据机器学习是机器学习.强化学习是一种强化学习.智能农业系统是一个智能农业系统.

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科学领域:

  • 计算机科学 计算机科学
  • 网络工程 网络工程
  • 机器学习 机器学习

背景情况:

  • 智能农业系统利用传感器网络和物联网设备收集环境数据,以提高农业效率.
  • 传统的数据处理方法与智能农业系统中数据的复杂性和动态性作斗争.
  • 在软件定义网络 (SDN) 中优化流量路由对于高效的数据管理和资源分配至关重要.

研究的目的:

  • 提出一个整合机器学习 (ML) 和强化学习 (RL) 的新框架,以优化SDN中的流量路由.
  • 使用各种ML模型将数据流量分为紧急,正常和按需类别.
  • 通过减少延迟和改善资源配置来提高网络性能,特别是对于时间敏感的流量.

主要方法:

  • 使用的ML模型包括物流回归 (LR),随机森林 (RF),k-最近邻居 (KNN),支持矢量机 (SVM),天真湾 (NB) 和决策树 (DT) 来进行流量分类.
  • 在SDN范式中利用Q-learning (QL) 算法,这是一个基本的RL版本,用于基于分类流量的路由优化.
  • 评估了ML模型的性能,并注意到随机森林 (RF) 和决策树 (DT) 的卓越准确性.

主要成果:

  • 拟议的ML和RL集成框架有效地优化SDN环境中的流量路由.
  • 与其他ML模型相比,随机森林 (RF) 和决策树 (DT) 在分类交通数据方面表现出更高的准确性.
  • 集成显著改善了资源配置,减少了网络延迟,并优先处理紧急流量的交付.

结论:

  • 结合了基于ML的数据分类和QL算法,为SDN中的动态流量路由提供了强大的解决方案.
  • SDN的适应性使路由算法能够适应实时网络条件和流量特征.
  • 这种方法对于提高智能农业系统和其他数据密集型应用程序的效率和响应能力至关重要.