PQTBA: Priority Queue based Token Bucket Algorithm for congestion control in IoT network

A. P, Vimala H S, J Shreyas
{"title":"PQTBA: Priority Queue based Token Bucket Algorithm for congestion control in IoT network","authors":"A. P, Vimala H S, J Shreyas","doi":"10.1109/I2CT57861.2023.10126166","DOIUrl":null,"url":null,"abstract":"The Internet of Things connects millions of devices in the areas of smart cities, e-health, transportation, and the military to fulfill a variety of human needs. To offer these services, a large amount of data must be transmitted to the IoT network servers. But the node processing power, buffer size, and server capacity limitations on IoT networks have a negative influence on throughput, latency, and energy consumption. Additionally, the IoT network’s performance is decreased by congestion caused by the high network traffic that results from the high volume of data. In order to handle congestion challenges in IoT networks, unique congestion control strategies—such as the queue management strategy—must be created. In this study, a novel Priority Queue-based Token Bucket Algorithm (PQTBA) is suggested as a means of controlling congestion in IoT networks. The PQTBA uses a preemptive/non-preemptive technique with a discretionary rule to categorize network traffic into priority groups in accordance with real-time requirements. Our proposed work performs con-siderably better than the most recent techniques in terms of throughput, packet loss ratio, and energy consumption.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I2CT57861.2023.10126166","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

The Internet of Things connects millions of devices in the areas of smart cities, e-health, transportation, and the military to fulfill a variety of human needs. To offer these services, a large amount of data must be transmitted to the IoT network servers. But the node processing power, buffer size, and server capacity limitations on IoT networks have a negative influence on throughput, latency, and energy consumption. Additionally, the IoT network’s performance is decreased by congestion caused by the high network traffic that results from the high volume of data. In order to handle congestion challenges in IoT networks, unique congestion control strategies—such as the queue management strategy—must be created. In this study, a novel Priority Queue-based Token Bucket Algorithm (PQTBA) is suggested as a means of controlling congestion in IoT networks. The PQTBA uses a preemptive/non-preemptive technique with a discretionary rule to categorize network traffic into priority groups in accordance with real-time requirements. Our proposed work performs con-siderably better than the most recent techniques in terms of throughput, packet loss ratio, and energy consumption.
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
PQTBA:基于优先队列的物联网网络拥塞控制令牌桶算法
物联网连接了智慧城市、电子医疗、交通和军事领域的数百万台设备,以满足人类的各种需求。为了提供这些服务,必须将大量数据传输到物联网网络服务器。但物联网网络中的节点处理能力、缓冲区大小和服务器容量限制会对吞吐量、延迟和能耗产生负面影响。此外,由于大量数据导致的高网络流量导致的拥塞导致物联网网络的性能下降。为了应对物联网网络中的拥塞挑战,必须创建独特的拥塞控制策略,例如队列管理策略。在这项研究中,提出了一种新的基于优先队列的令牌桶算法(PQTBA)作为控制物联网网络拥塞的手段。PQTBA采用抢占/非抢占技术,并结合自由裁量规则,根据实时需求将网络流量划分为多个优先级组。我们提出的工作在吞吐量、丢包率和能耗方面比最新的技术要好得多。
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
自引率
0.00%
发文量
0
期刊最新文献
Investigation on Impact of Partial Shading on Solar PV Array Character and Word Level Gesture Recognition of Indian Sign Language Electricity Theft Detection Employing Machine Learning Algorithms Precision Agriculture: Classifying Banana Leaf Diseases with Hybrid Deep Learning Models Multimodal Question Generation using Multimodal Adaptation Gate (MAG) and BERT-based 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