Data Flow and Distributed Deep Neural Network based low latency IoT-Edge computation model for big data environment

The trillion-fold increase in computing power brings the accessibility of deep learning to everyone. Deep learning offers precise information almost all the time when compared to other learning algorithms. On the other hand, the popularity of Internet of Things (IoT) has increased in various areas s...

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Published in:	Engineering applications of artificial intelligence Vol. 94; p. 103785
Main Authors:	Veeramanikandan, Sankaranarayanan, Suresh, Rodrigues, Joel J.P.C., Sugumaran, Vijayan, Kozlov, Sergei
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.09.2020
Subjects:	Big data Data flow processing Data in motion Deep Neural Networking Edge computing Fog computing Internet of Things Latency Fog computing Deep Neural Networking Big data Data in motion Internet of Things Data flow processing Edge computing Latency
ISSN:	0952-1976, 1873-6769
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Abstract	The trillion-fold increase in computing power brings the accessibility of deep learning to everyone. Deep learning offers precise information almost all the time when compared to other learning algorithms. On the other hand, the popularity of Internet of Things (IoT) has increased in various areas such as Smart City, Oil Mining, and Transportation. Edge/Fog computing environment helps to handle significant challenges faced by the IoT, viz. latency, bandwidth consumption, and everlasting network connectivity. For analytics in Edge computing, which is distributed in nature, the trend is more towards distributed machine learning. This research work is focused on the integration of data flow and distributed deep learning in the IoT-Edge environment to bring down the latency and increase accuracy starting from the data generation phase. To this end, a novel Data Flow and Distributed Deep Neural Network (DF-DDNN) based IoT-Edge model for big data environment has been proposed. Our proposed method has resulted in latency reduction of up to 33% when compared to the existing traditional IoT-Cloud model. •Data Flow and Distributed Deep Learning based Edge computation model for Big Data.•Data Flow based data gathering at the Edge with reduced bandwidth.•Intelligent decisions at various levels of Edge in a distributed manner.•Aggregation schemes for improving the overall performance of the system.•AI-assisted Edge services for resource-constrained heterogeneous devices.
AbstractList	The trillion-fold increase in computing power brings the accessibility of deep learning to everyone. Deep learning offers precise information almost all the time when compared to other learning algorithms. On the other hand, the popularity of Internet of Things (IoT) has increased in various areas such as Smart City, Oil Mining, and Transportation. Edge/Fog computing environment helps to handle significant challenges faced by the IoT, viz. latency, bandwidth consumption, and everlasting network connectivity. For analytics in Edge computing, which is distributed in nature, the trend is more towards distributed machine learning. This research work is focused on the integration of data flow and distributed deep learning in the IoT-Edge environment to bring down the latency and increase accuracy starting from the data generation phase. To this end, a novel Data Flow and Distributed Deep Neural Network (DF-DDNN) based IoT-Edge model for big data environment has been proposed. Our proposed method has resulted in latency reduction of up to 33% when compared to the existing traditional IoT-Cloud model. •Data Flow and Distributed Deep Learning based Edge computation model for Big Data.•Data Flow based data gathering at the Edge with reduced bandwidth.•Intelligent decisions at various levels of Edge in a distributed manner.•Aggregation schemes for improving the overall performance of the system.•AI-assisted Edge services for resource-constrained heterogeneous devices.
ArticleNumber	103785
Author	Sugumaran, Vijayan Rodrigues, Joel J.P.C. Veeramanikandan Sankaranarayanan, Suresh Kozlov, Sergei
Author_xml	– sequence: 1 surname: Veeramanikandan fullname: Veeramanikandan organization: Aegis School of Data Science, Bangalore, India – sequence: 2 givenname: Suresh surname: Sankaranarayanan fullname: Sankaranarayanan, Suresh email: pessuresh@hotmail.com organization: SRM Institute of Science and Technology, Chennai, India – sequence: 3 givenname: Joel J.P.C. surname: Rodrigues fullname: Rodrigues, Joel J.P.C. organization: Federal University of Piauí (UFPI), Teresina - PI, Brazil – sequence: 4 givenname: Vijayan surname: Sugumaran fullname: Sugumaran, Vijayan organization: Oakland University, USA – sequence: 5 givenname: Sergei surname: Kozlov fullname: Kozlov, Sergei organization: ITMO University, St. Petersburg, Russia
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