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Event-Driven Deep Learning for Edge Intelligence (EDL-EI).
Shah, Sayed Khushal; Tariq, Zeenat; Lee, Jeehwan; Lee, Yugyung.
  • Shah SK; Department of Computer Science and Engineering, University of North Texas, Denton, TX 76207, USA.
  • Tariq Z; Department of Computer Science and Engineering, University of North Texas, Denton, TX 76207, USA.
  • Lee J; College of Architecture, Myongji University, Seoul 03674, Korea.
  • Lee Y; Department of Computer Science and Electrical Engineering, University of Missouri, Kansas City, MO 64110, USA.
Sensors (Basel) ; 21(18)2021 Sep 08.
Artigo em Inglês | MEDLINE | ID: covidwho-1468446
ABSTRACT
Edge intelligence (EI) has received a lot of interest because it can reduce latency, increase efficiency, and preserve privacy. More significantly, as the Internet of Things (IoT) has proliferated, billions of portable and embedded devices have been interconnected, producing zillions of gigabytes on edge networks. Thus, there is an immediate need to push AI (artificial intelligence) breakthroughs within edge networks to achieve the full promise of edge data analytics. EI solutions have supported digital technology workloads and applications from the infrastructure level to edge networks; however, there are still many challenges with the heterogeneity of computational capabilities and the spread of information sources. We propose a novel event-driven deep-learning framework, called EDL-EI (event-driven deep learning for edge intelligence), via the design of a novel event model by defining events using correlation analysis with multiple sensors in real-world settings and incorporating multi-sensor fusion techniques, a transformation method for sensor streams into images, and lightweight 2-dimensional convolutional neural network (CNN) models. To demonstrate the feasibility of the EDL-EI framework, we presented an IoT-based prototype system that we developed with multiple sensors and edge devices. To verify the proposed framework, we have a case study of air-quality scenarios based on the benchmark data provided by the USA Environmental Protection Agency for the most polluted cities in South Korea and China. We have obtained outstanding predictive accuracy (97.65% and 97.19%) from two deep-learning models on the cities' air-quality patterns. Furthermore, the air-quality changes from 2019 to 2020 have been analyzed to check the effects of the COVID-19 pandemic lockdown.
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Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / COVID-19 Tipo de estudo: Estudo observacional / Estudo prognóstico Limite: Humanos País/Região como assunto: América do Norte Idioma: Inglês Ano de publicação: 2021 Tipo de documento: Artigo País de afiliação: S21186023

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Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / COVID-19 Tipo de estudo: Estudo observacional / Estudo prognóstico Limite: Humanos País/Região como assunto: América do Norte Idioma: Inglês Ano de publicação: 2021 Tipo de documento: Artigo País de afiliação: S21186023