在物联网中使用随机森林和集群技术进行智能资源分配.
Nahideh Derakhshanfard1, Lida Hosseinzadeh2, Fahimeh Rashid Jafari2
1Department of Computer Engineering, Ta.C, Islamic Azad University, Tabriz, Iran. n.derakhshan@iaut.ac.ir.
Scientific reports
|August 20, 2025
概括
本研究介绍了使用集群和机器学习的物联网 (IoT) 智能资源分配方法. 这种方法提高了效率,减少了能源使用,并在动态物联网网络中改善了响应时间.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 网络工程 网络工程
背景情况:
- 物联网 (IoT) 设备的普及带来了重大资源管理挑战.
- 像有限的能量,带宽和计算能力这样的限制需要有效的资源配置策略.
- 现有的方法 (例如,进化算法,多代理强化学习) 由于复杂性和成本,与物联网网络的动态性质作斗争.
研究的目的:
- 为物联网 (IoT) 网络提出智能资源分配方法.
- 在动态和可扩展的物联网环境中解决当前方法的低效性.
- 为了提高预测准确性,减少能源消耗,减少响应时间.
主要方法:
- 集群 (K-Means) 和机器学习 (随机森林) 技术的整合.
- 基于能源消耗和带宽要求集群物联网设备.
- 训练一个随机森林模型来预测资源需求以实现最佳分配.
主要成果:
- 对于资源需求,实现了94%的预测准确度.
- 与现有方法相比,减少了20%的能源消耗.
- 在动态物联网环境中减少了10%的响应时间.
结论:
- 拟议的方法有效地管理动态和可扩展的物联网网络中的资源.
- 结合K-Means集群和Random Forest机器学习,为物联网资源分配提供了一个卓越的解决方案.
- 在精度,能源效率和响应时间方面取得了显著的改进.
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