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  • 标题:Docker based Intelligent Fall Detection using Edge-Fog Infrastructure
  • 本地全文:下载
  • 作者:Divya. V ; Leena Sri. R
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2020
  • 卷号:53
  • 期号:1
  • 页码:45-50
  • DOI:10.1016/j.ifacol.2020.06.008
  • 语种:English
  • 出版社:Elsevier
  • 摘要:The uses of automation and sensors have become the order of the day which has led to a new paradigm of Internet of things. The computation of these data from the sensors has increased greatly with the need for real-time analytics of the generated data. To facilitate the integration of the Internet of Things( IoT) data and the analytics in real-time has brought the need for the use of dockers. Offloading latency-sensitive tasks to the edge devices can lead to great improvement in performance in terms of network utility, overall latency, and energy utilization. Certain tasks have to run on the end devices where the algorithms are deployed for real-time detection. The dockers are lightweight that containerizes the algorithms giving the ability to run on any platform. The fog devices are low configured devices with minimal communication and computation ability such that of a raspberry pi. Our proposed work deals with the fall detection with ensemble detection algorithm running on the edge devices containerized using dockers. The simulated results are presented in the paper along with the proposed fog architecture with the performance analysis in terms of accuracy and Bandwidth utility.
  • 关键词:Edge;Fog Computing;Dockers;Fall Detection;Ensemble Learning
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