可靠的基于集群的数据收集框架,用于物联网大数据医疗保健应用

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
N. Pughazendi, K. Valarmathi, P.V. Rajaraman, S. Balaji
{"title":"可靠的基于集群的数据收集框架,用于物联网大数据医疗保健应用","authors":"N. Pughazendi, K. Valarmathi, P.V. Rajaraman, S. Balaji","doi":"10.3233/jifs-233505","DOIUrl":null,"url":null,"abstract":"Internet of Things (IoT) devices installed in hospital direct data unceasingly; in this manner, energy usage augments with the number of broadcasts too. In this paper, Reliable Cluster based Data Collection Framework (RCDCF) for IoT-Big Data Healthcare Applications (HA) is developed. During clustering process, the connected IoT devices are grouped into clusters. In clustering technique, the available IoT devices are gathered into groups. The device with high battery capacity and processing ability is selected as a cluster head (CH). Each member of the cluster is allocated multiple slots by applying a general function pooled by the Fog node and the entire devices. To perceive and eliminate outliers from the sensor data, Density-based spatial clustering of applications with noise (DBSCAN) method is utilized. To forecast the objective and subjective behaviours of the equipments, a Random Forest Deep Neural Network (RF-DNN) based classification model is utilized. By experimental results, it has been shown that RCDCF achieves 19% and 20% reduced energy consumption at Cloud and Fog centers, respectively. Moreover, RCDCF has 2.1% and 1.3% increased correctness of data at Cloud and Fog data centers, respectively, when compared to the existing framework.","PeriodicalId":54795,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"26 1","pages":"0"},"PeriodicalIF":1.7000,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Reliable cluster based data collection framework for IoT-big data healthcare applications\",\"authors\":\"N. Pughazendi, K. Valarmathi, P.V. Rajaraman, S. Balaji\",\"doi\":\"10.3233/jifs-233505\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Internet of Things (IoT) devices installed in hospital direct data unceasingly; in this manner, energy usage augments with the number of broadcasts too. In this paper, Reliable Cluster based Data Collection Framework (RCDCF) for IoT-Big Data Healthcare Applications (HA) is developed. During clustering process, the connected IoT devices are grouped into clusters. In clustering technique, the available IoT devices are gathered into groups. The device with high battery capacity and processing ability is selected as a cluster head (CH). Each member of the cluster is allocated multiple slots by applying a general function pooled by the Fog node and the entire devices. To perceive and eliminate outliers from the sensor data, Density-based spatial clustering of applications with noise (DBSCAN) method is utilized. To forecast the objective and subjective behaviours of the equipments, a Random Forest Deep Neural Network (RF-DNN) based classification model is utilized. By experimental results, it has been shown that RCDCF achieves 19% and 20% reduced energy consumption at Cloud and Fog centers, respectively. Moreover, RCDCF has 2.1% and 1.3% increased correctness of data at Cloud and Fog data centers, respectively, when compared to the existing framework.\",\"PeriodicalId\":54795,\"journal\":{\"name\":\"Journal of Intelligent & Fuzzy Systems\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2023-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Intelligent & Fuzzy Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/jifs-233505\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent & Fuzzy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/jifs-233505","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

物联网(IoT)设备不断安装在医院直接数据;通过这种方式,能源使用也随着广播数量的增加而增加。本文提出了一种基于可靠集群的物联网大数据医疗应用数据采集框架(RCDCF)。在聚类过程中,连接的物联网设备被分组成集群。在聚类技术中,可用的物联网设备被聚集成组。选择电池容量大、处理能力强的设备作为簇头(CH)。通过应用Fog节点和整个设备池化的通用功能,为集群的每个成员分配多个插槽。为了识别和消除传感器数据中的异常值,采用了基于密度的带噪声应用空间聚类(DBSCAN)方法。为了预测设备的客观和主观行为,采用基于随机森林深度神经网络(RF-DNN)的分类模型。实验结果表明,RCDCF在云中心和雾中心的能耗分别降低了19%和20%。此外,与现有框架相比,RCDCF在云和雾数据中心的数据正确性分别提高了2.1%和1.3%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reliable cluster based data collection framework for IoT-big data healthcare applications
Internet of Things (IoT) devices installed in hospital direct data unceasingly; in this manner, energy usage augments with the number of broadcasts too. In this paper, Reliable Cluster based Data Collection Framework (RCDCF) for IoT-Big Data Healthcare Applications (HA) is developed. During clustering process, the connected IoT devices are grouped into clusters. In clustering technique, the available IoT devices are gathered into groups. The device with high battery capacity and processing ability is selected as a cluster head (CH). Each member of the cluster is allocated multiple slots by applying a general function pooled by the Fog node and the entire devices. To perceive and eliminate outliers from the sensor data, Density-based spatial clustering of applications with noise (DBSCAN) method is utilized. To forecast the objective and subjective behaviours of the equipments, a Random Forest Deep Neural Network (RF-DNN) based classification model is utilized. By experimental results, it has been shown that RCDCF achieves 19% and 20% reduced energy consumption at Cloud and Fog centers, respectively. Moreover, RCDCF has 2.1% and 1.3% increased correctness of data at Cloud and Fog data centers, respectively, when compared to the existing framework.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Intelligent & Fuzzy Systems
Journal of Intelligent & Fuzzy Systems 工程技术-计算机:人工智能
CiteScore
3.40
自引率
10.00%
发文量
965
审稿时长
5.1 months
期刊介绍: The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信