使用K-Means聚类优化AODV路由算法的路由发现(c++)

上传者: wq6qeg88 | 上传时间: 2022-05-11 09:04:26 | 文件大小: 187KB | 文件类型: ZIP
在 AODV 路由中,路由发现是通过泛洪方法完成的,即向发送者传输范围内的所有节点广播路由请求 (RREQ) 包。它通常会导致不必要的 RREQ 数据包重传和响应生成的回复 (RREP) 数据包,从而导致数据包冲突和网络拥塞。在这个项目中,我为 AODV 提出了一种优化的路由发现方法。关键思想是使用 K-Means 聚类算法来选择 RREQ 数据包转发器的最佳集群,而不是广播。该方法的目的是减少网络中不必要的控制包传输,从而减少网络的拥塞和端到端延迟。 K-Means 聚类中使用的特征: 到目的地的距离 传输错误数 空闲缓冲空间 根据邻居的这些特征选择最佳集群。

文件下载

资源详情

[{"title":"( 36 个子文件 187KB ) 使用K-Means聚类优化AODV路由算法的路由发现(c++)","children":[{"title":"NS3-AODV-Optimized-Route-Discovery-Using-K-Means-Clustering-main","children":[{"title":"scratch","children":[{"title":"aodvKmeansExample.cc <span style='color:#111;'> 14.08KB </span>","children":null,"spread":false}],"spread":true},{"title":"Results","children":[{"title":"pps-delay.png <span style='color:#111;'> 27.58KB </span>","children":null,"spread":false},{"title":"nodes-del_ratio.png <span style='color:#111;'> 28.84KB </span>","children":null,"spread":false},{"title":"nodes-delay.png <span style='color:#111;'> 28.50KB </span>","children":null,"spread":false},{"title":"pps-del_ratio.png <span style='color:#111;'> 21.86KB </span>","children":null,"spread":false}],"spread":true},{"title":"aodvKmeans","children":[{"title":"model","children":[{"title":"aodvKmeans-id-cache.cc <span style='color:#111;'> 1.86KB </span>","children":null,"spread":false},{"title":"aodvKmeans-rqueue.cc <span style='color:#111;'> 4.23KB </span>","children":null,"spread":false},{"title":"aodvKmeans-packet.cc <span style='color:#111;'> 14.47KB </span>","children":null,"spread":false},{"title":"aodvKmeans-routing-protocol.h <span style='color:#111;'> 16.62KB </span>","children":null,"spread":false},{"title":"aodvKmeans-routing-protocol.cc <span style='color:#111;'> 86.22KB </span>","children":null,"spread":false},{"title":"aodvKmeans-rtable.cc <span style='color:#111;'> 18.13KB </span>","children":null,"spread":false},{"title":"aodvKmeans-rtable.h <span style='color:#111;'> 14.35KB </span>","children":null,"spread":false},{"title":"aodvKmeans-dpd.h <span style='color:#111;'> 2.18KB </span>","children":null,"spread":false},{"title":"aodvKmeans-dpd.cc <span style='color:#111;'> 1.26KB </span>","children":null,"spread":false},{"title":"aodvKmeans-packet.h <span style='color:#111;'> 18.28KB </span>","children":null,"spread":false},{"title":"aodvKmeans-neighbor.h <span style='color:#111;'> 4.96KB </span>","children":null,"spread":false},{"title":"aodvKmeans-id-cache.h <span style='color:#111;'> 3.03KB </span>","children":null,"spread":false},{"title":"aodvKmeans-neighbor.cc <span style='color:#111;'> 4.72KB </span>","children":null,"spread":false},{"title":"aodvKmeans-rqueue.h <span style='color:#111;'> 6.77KB </span>","children":null,"spread":false}],"spread":false},{"title":"test","children":[{"title":"loopback.cc <span style='color:#111;'> 5.85KB </span>","children":null,"spread":false},{"title":"examples-to-run.py <span style='color:#111;'> 606B </span>","children":null,"spread":false},{"title":"aodvKmeans-test-suite.cc <span style='color:#111;'> 25.91KB </span>","children":null,"spread":false},{"title":"aodvKmeans-id-cache-test-suite.cc <span style='color:#111;'> 3.55KB </span>","children":null,"spread":false},{"title":"aodvKmeans-regression.cc <span style='color:#111;'> 7.13KB </span>","children":null,"spread":false},{"title":"bug-772.h <span style='color:#111;'> 2.68KB </span>","children":null,"spread":false},{"title":"bug-772.cc <span style='color:#111;'> 6.74KB </span>","children":null,"spread":false},{"title":"aodvKmeans-regression.h <span style='color:#111;'> 11.69KB </span>","children":null,"spread":false}],"spread":true},{"title":"wscript <span style='color:#111;'> 1.54KB </span>","children":null,"spread":false},{"title":"doc","children":[{"title":"aodvKmeans.h <span style='color:#111;'> 1.39KB </span>","children":null,"spread":false},{"title":"aodvKmeans.rst <span style='color:#111;'> 4.58KB </span>","children":null,"spread":false}],"spread":true},{"title":"examples","children":[{"title":"wscript <span style='color:#111;'> 267B </span>","children":null,"spread":false},{"title":"aodvKmeans.cc <span style='color:#111;'> 6.86KB </span>","children":null,"spread":false}],"spread":true},{"title":"helper","children":[{"title":"aodvKmeans-helper.h <span style='color:#111;'> 2.79KB </span>","children":null,"spread":false},{"title":"aodvKmeans-helper.cc <span style='color:#111;'> 3.03KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"README.md <span style='color:#111;'> 3.97KB </span>","children":null,"spread":false},{"title":".gitattributes <span style='color:#111;'> 66B </span>","children":null,"spread":false}],"spread":true}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明