An optimal content indexing approach for named data networking in software-defined IoT system

IF 2.1 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IET Smart Cities Pub Date : 2022-02-08 DOI:10.1049/smc2.12023
Rajan Kumar Dudeja, Amritpal Singh, Rasmeet Singh Bali, Gagangeet Singh Aujla
{"title":"An optimal content indexing approach for named data networking in software-defined IoT system","authors":"Rajan Kumar Dudeja,&nbsp;Amritpal Singh,&nbsp;Rasmeet Singh Bali,&nbsp;Gagangeet Singh Aujla","doi":"10.1049/smc2.12023","DOIUrl":null,"url":null,"abstract":"<p>The evolution of the Internet of Things (IoT) has increased the number of connected devices in the network. This has shifted the focus from IP-based network architecture towards content-centric networking (CCN). CCN eliminates the need for address-content binding in the conventional IP-based networks and allows the content to be accessed based on the name instead of the physical location. Named data networking (NDN) is a promising technique that can fulfil the increasing demand for connected devices through the CCN approach. NDN distributes the content on the network and focusses on the security of the content rather than the communication channel. However, the increase in traffic due to the escalation in the number of connected devices can lead to congestion in the network. The content distribution approach on the nodes is generalised and suitable for small networks. In the case of larger networks, an optimal approach is required to decide the optimal location to store the required content. However, a linear search approach is used to search (or lookup) the content in the assigned cache of the NDN node. In this work, the authors have combined the software-defined networking (SDN) with the NDN approach to overcome the above-highlighted challenge. Thus, the authors have designed an optimal content storage and indexing approach based on NDN-SDN coalesce in the IoT ecosystem. The proposed approach includes different phases, (a) a hashing-based content searching approach is formulated to reduce the look-up time of the content, (b) a red-black tree-based content storage approach is introduced for optimal utilisation of the assigned cache memory of the different NDN nodes, and (c) SDN controller facilitates automated network management and helps to administer the network requirements centrally and locate the content accordingly. The proposed approach was validated through the simulation experiments concerning network delay, packet rate, throughput, and cache hit ratio. The results obtained show the effectiveness of the proposed approach.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"4 1","pages":"36-46"},"PeriodicalIF":2.1000,"publicationDate":"2022-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12023","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Smart Cities","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/smc2.12023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 2

Abstract

The evolution of the Internet of Things (IoT) has increased the number of connected devices in the network. This has shifted the focus from IP-based network architecture towards content-centric networking (CCN). CCN eliminates the need for address-content binding in the conventional IP-based networks and allows the content to be accessed based on the name instead of the physical location. Named data networking (NDN) is a promising technique that can fulfil the increasing demand for connected devices through the CCN approach. NDN distributes the content on the network and focusses on the security of the content rather than the communication channel. However, the increase in traffic due to the escalation in the number of connected devices can lead to congestion in the network. The content distribution approach on the nodes is generalised and suitable for small networks. In the case of larger networks, an optimal approach is required to decide the optimal location to store the required content. However, a linear search approach is used to search (or lookup) the content in the assigned cache of the NDN node. In this work, the authors have combined the software-defined networking (SDN) with the NDN approach to overcome the above-highlighted challenge. Thus, the authors have designed an optimal content storage and indexing approach based on NDN-SDN coalesce in the IoT ecosystem. The proposed approach includes different phases, (a) a hashing-based content searching approach is formulated to reduce the look-up time of the content, (b) a red-black tree-based content storage approach is introduced for optimal utilisation of the assigned cache memory of the different NDN nodes, and (c) SDN controller facilitates automated network management and helps to administer the network requirements centrally and locate the content accordingly. The proposed approach was validated through the simulation experiments concerning network delay, packet rate, throughput, and cache hit ratio. The results obtained show the effectiveness of the proposed approach.

Abstract Image

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
软件定义物联网系统中命名数据网络的最佳内容索引方法
物联网(IoT)的发展增加了网络中连接设备的数量。这将焦点从基于ip的网络体系结构转向以内容为中心的网络(CCN)。在传统的基于ip的网络中,CCN消除了地址-内容绑定的需要,并允许基于名称而不是物理位置访问内容。命名数据网络(NDN)是一种很有前途的技术,它可以通过CCN方法满足对连接设备日益增长的需求。NDN将内容分发到网络上,关注内容的安全性而不是通信通道。但是,由于连接设备数量的增加而导致的流量增加可能导致网络拥塞。节点上的内容分发方法具有通用性,适用于小型网络。在较大网络的情况下,需要一种最佳方法来决定存储所需内容的最佳位置。但是,使用线性搜索方法来搜索(或查找)NDN节点的指定缓存中的内容。在这项工作中,作者将软件定义网络(SDN)与NDN方法相结合,以克服上述突出的挑战。因此,作者在物联网生态系统中设计了一种基于NDN-SDN融合的最佳内容存储和索引方法。建议的方法包括不同的阶段,(a)制定基于散列的内容搜索方法以减少内容的查找时间,(b)引入基于红黑树的内容存储方法以最佳利用不同NDN节点分配的缓存内存,以及(c) SDN控制器促进自动化网络管理,有助于集中管理网络需求并相应地定位内容。通过网络延迟、数据包速率、吞吐量和缓存命中率的仿真实验,验证了该方法的有效性。仿真结果表明了该方法的有效性。
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
IET Smart Cities
IET Smart Cities Social Sciences-Urban Studies
CiteScore
7.70
自引率
3.20%
发文量
25
审稿时长
21 weeks
期刊最新文献
Guest Editorial: Smart cities 2.0: How Artificial Intelligence and Internet of Things are transforming urban living Smart city fire surveillance: A deep state-space model with intelligent agents Securing smart cities through machine learning: A honeypot-driven approach to attack detection in Internet of Things ecosystems Smart resilience through IoT-enabled natural disaster management: A COVID-19 response in São Paulo state Optimising air quality prediction in smart cities with hybrid particle swarm optimization-long-short term memory-recurrent neural network model
×
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