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首页> 外文期刊>International journal of intelligent unmanned systems >Route classification scheme based on covering rough set approach in mobile ad hoc network (CRS-MANET)
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Route classification scheme based on covering rough set approach in mobile ad hoc network (CRS-MANET)

机译：基于覆盖粗糙集方法的路线分类方案在移动临时网络中的粗糙集方法（CRS-MANET）

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摘要

Purpose - The purpose of this paper is to obtain correctly classified routes based on their parameters. Design/methodology/approach - In this paper, a covering rough set (CRS) approach is proposed for route classification in wireless ad hoc networks. In a wireless network, mobile nodes are deployed randomly in a simulation region. This work addresses the problem of route classification. Findings - The network parameters such as bandwidth, delay, packet byte rate and packet loss rate changes due to the frequent mobility of nodes lead to uncertainty in wireless networks. This type of uncertainty can be very well handled using a rough set concept. An ultimate aim of classification is to correctly predict the decision class for each instance in the data. Originality/value - The traditional classification algorithms, named K-nearest neighbor, J48, general rough set theory, naive Bayes, JRIP and multilayer perception, are used in this work for comparison and for the proposed CRS based on route classification approach revealing better accuracy than traditional classification algorithms.
机译：目的 - 本文的目的是根据其参数获得正确的分类路由。设计/方法/方法 - 在本文中，提出了一种用于无线临时网络中的路由分类的覆盖粗糙集（CRS）方法。在无线网络中，移动节点在仿真区域中随机部署。这项工作解决了路由分类问题。结果 - 由于节点的频繁移动性导致无线网络中的不确定性导致的带宽，延迟，数据包字节率和分组丢失率发生的网络参数导致的网络参数。这种类型的不确定性可以使用粗糙的集合概念非常好处理。分类的最终目标是正确预测数据中的每个实例的决策类。原创性/值 - 传统的分类算法，名为K-Collect邻居，J48，普通粗糙集理论，朴素贝叶斯，JRIP和多层感知，用于比较和基于路由分类方法的提议CRS，揭示了更好的准确性比传统的分类算法。

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