New approach for selecting multi-point relays in the optimized link state routing protocol using self-organizing map artificial neural network: OLSR-SOM

Omar Barki, Z. Guennoun, A. Addaim
{"title":"New approach for selecting multi-point relays in the optimized link state routing protocol using self-organizing map artificial neural network: OLSR-SOM","authors":"Omar Barki, Z. Guennoun, A. Addaim","doi":"10.11591/ijai.v12.i2.pp648-655","DOIUrl":null,"url":null,"abstract":"In order to improve the selection of multi-point relays (MPRs) by a node node performing the computation (NPC) in the optimized link state routing (OLSR) protocol and therefore to guarantee more security for the routing in the mobile ad hoc network (MANET), we propose new approach that could distinguish between the strong and weak MPRs in the list of MPRs already selected using the standard algorithm described in RFC3626 document. This approach is based on self organizing map (SOM) artificial neural network that processes the collected data and then only selects the strong MPRs using a set of criteria allowing a reliable retransmission and a strong link and therefore better network performances. The obtained results, from the simulations that have been carried out using a customized network simulator 3 (NS3) network simulator, show an improvement in terms of throughput, packets delivery ratio (PDR) and the security of the network compared to the standard approach.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IAES International Journal of Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11591/ijai.v12.i2.pp648-655","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Decision Sciences","Score":null,"Total":0}
引用次数: 2

Abstract

In order to improve the selection of multi-point relays (MPRs) by a node node performing the computation (NPC) in the optimized link state routing (OLSR) protocol and therefore to guarantee more security for the routing in the mobile ad hoc network (MANET), we propose new approach that could distinguish between the strong and weak MPRs in the list of MPRs already selected using the standard algorithm described in RFC3626 document. This approach is based on self organizing map (SOM) artificial neural network that processes the collected data and then only selects the strong MPRs using a set of criteria allowing a reliable retransmission and a strong link and therefore better network performances. The obtained results, from the simulations that have been carried out using a customized network simulator 3 (NS3) network simulator, show an improvement in terms of throughput, packets delivery ratio (PDR) and the security of the network compared to the standard approach.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于自组织映射人工神经网络的优化链路状态路由协议中多点中继选择新方法:OLSR-SOM
为了改进在优化链路状态路由(OLSR)协议中执行计算(NPC)的节点节点对多点中继(MPR)的选择,从而保证移动自组织网络(MANET)中路由的更多安全性,我们提出了一种新的方法,该方法可以区分已经使用RFC3626文档中描述的标准算法选择的MPR列表中的强MPR和弱MPR。这种方法基于自组织映射(SOM)人工神经网络,该网络处理收集的数据,然后使用一组允许可靠重传和强链路的标准仅选择强MPR,从而获得更好的网络性能。从使用定制网络模拟器3(NS3)网络模拟器进行的模拟中获得的结果显示,与标准方法相比,在吞吐量、分组传送率(PDR)和网络安全性方面有所改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
IAES International Journal of Artificial Intelligence
IAES International Journal of Artificial Intelligence Decision Sciences-Information Systems and Management
CiteScore
3.90
自引率
0.00%
发文量
170
期刊最新文献
Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5 Eligibility of village fund direct cash assistance recipients using artificial neural network Reducing the time needed to solve a traveling salesman problem by clustering with a Hierarchy-based algorithm Glove based wearable devices for sign language-GloSign Hybrid travel time estimation model for public transit buses using limited datasets
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1