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基于自我学习的增强集群方案,用于无线网络中实时流量数据的分发
Arpit Jain1, Tushar Mehrotra2, Ankur Sisodia3
1Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation (KLEF), Greenfield, Vaddeswaram, Guntur, Andhra Pradesh, 522302, India.
Heliyon
|July 14, 2023
概括
网络流量分析对数据流进行分类,以识别无线网络性能问题. 基于自我学习的增强集群计划 (ESLCS) 提高了实时流量分类的准确性,并减少了处理时间.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 数据挖掘 数据挖掘
背景情况:
- 网络流量分析对于识别无线网络性能异常至关重要.
- 有效的流量分类对于区分实时和非实时数据至关重要.
- 现有的算法往往没有针对网络流量分类进行有效分析.
研究的目的:
- 在无线网络中提高网络流量分类的准确性和效率.
- 为有效的网络流量分析解决现有算法的差距.
- 通过先进的集群和分类来改进实时交通数据的分布.
主要方法:
- 利用数据挖掘集群和分类算法来对流量数据进行分类.
- 提出了一个基于自我学习的增强集群计划 (ESLCS).
- 在ESLCS中采用了增强的无监督算法和自适应播种方法.
主要成果:
- 该ESLCS模型显示了增强的集群精度和真正率 (TPR).
- 实现了对分类时间 (CT) 和通讯开支 (CO) 的大幅减少.
- 在测试床评估中表现优于现有的同行路由技术.
结论:
- ESLCS有效地对无线网络中的实时和非实时流量数据进行分类.
- 拟议的方案显著提高了分类准确性,同时减少了处理时间和开销.
- ESLCS为高效的网络流量分析和性能提升提供了一个有前途的解决方案.
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