Multi level trust calculation with improved ant colony optimization for improving quality of service in wireless sensor network

Ahmed Jamal Ahmed, Ali Hashim Abbas, Sami Abduljabbar Rashid
{"title":"Multi level trust calculation with improved ant colony optimization for improving quality of service in wireless sensor network","authors":"Ahmed Jamal Ahmed, Ali Hashim Abbas, Sami Abduljabbar Rashid","doi":"10.11591/ijai.v12.i3.pp1224-1237","DOIUrl":null,"url":null,"abstract":"Wireless sensor network (WSN) is the most integral parts of current technology which are used for the real time applications. The major drawbacks in currect technologies are threads due to the creation of false trust values and data congestion. Maximum of the concept of WSNs primarily needs security and optimization. So, we are in the desire to develop a new model which is highly secured and localized. In this paper, we introduced a novel approach namely multi level trust calculation with improved ant colony optimization (MLT-IACO). This approach mainly sub-divided into two sections they are multi level trust calculation which is the combination three levels of trust such as direct trust, indirect trust and random repeat trust. Secondly, improved ant colony optimization technique is used to find the optimal path in the network. By transmitting the data in the optimal path, the congestion and delay of the network is reduced which leads to increase the efficiency. The outcome values are comparatively analyzed based the parameters such as packet delivery ratio, network throughput and average latency. While compared with the earlier research our MLT-IACO approach produce high packet delivery ratio and throughput as well as lower latency and routing overhead.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IAES International Journal of Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11591/ijai.v12.i3.pp1224-1237","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: 13

Abstract

Wireless sensor network (WSN) is the most integral parts of current technology which are used for the real time applications. The major drawbacks in currect technologies are threads due to the creation of false trust values and data congestion. Maximum of the concept of WSNs primarily needs security and optimization. So, we are in the desire to develop a new model which is highly secured and localized. In this paper, we introduced a novel approach namely multi level trust calculation with improved ant colony optimization (MLT-IACO). This approach mainly sub-divided into two sections they are multi level trust calculation which is the combination three levels of trust such as direct trust, indirect trust and random repeat trust. Secondly, improved ant colony optimization technique is used to find the optimal path in the network. By transmitting the data in the optimal path, the congestion and delay of the network is reduced which leads to increase the efficiency. The outcome values are comparatively analyzed based the parameters such as packet delivery ratio, network throughput and average latency. While compared with the earlier research our MLT-IACO approach produce high packet delivery ratio and throughput as well as lower latency and routing overhead.
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
基于改进蚁群优化的多级信任计算提高无线传感器网络的服务质量
无线传感器网络(WSN)是当前实时应用技术中最重要的组成部分。当前技术的主要缺点是由于创建错误的信任值和数据拥塞而导致的线程。无线传感器网络的最大概念首先需要安全性和优化性。因此,我们希望开发一种高度安全且本地化的新模式。本文提出了一种基于改进蚁群优化(MLT-IACO)的多级信任计算方法。该方法主要分为多级信任计算两部分,即多级信任计算是直接信任、间接信任和随机重复信任三级信任的组合。其次,采用改进蚁群优化技术在网络中寻找最优路径;通过在最优路径上传输数据,减少了网络的拥塞和延迟,从而提高了效率。根据分组传输率、网络吞吐量和平均时延等参数对结果值进行比较分析。与早期的研究相比,我们的MLT-IACO方法具有更高的数据包传送率和吞吐量,以及更低的延迟和路由开销。
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