Data Flow and Distributed Deep Neural Network based low latency IoT-Edge computation model for big data environment
Title
Data Flow and Distributed Deep Neural Network based low latency IoT-Edge computation model for big data environment
Subject
Big data
Petroleum transportation
Deep neural networks
Data handling
Internet of things
Learning algorithms
Data Analytics
Edge computing
Fog computing
Data transfer
Cloud analytics
Description
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. 2020 Elsevier Ltd
94
Creator
Veeramanikandan
Sankaranarayanan, Suresh
Rodrigues, Joel J.P.C.
Sugumaran, Vijayan
Kozlov, Sergei
Publisher
Engineering Applications of Artificial Intelligence
Date
2020
Type
journalArticle
Identifier
9521976
10.1016/j.engappai.2020.103785
URL
http://dx.doi.org/10.1016/j.engappai.2020.103785
Citation
Veeramanikandan et al., “Data Flow and Distributed Deep Neural Network based low latency IoT-Edge computation model for big data environment,” Lamar University Midstream Center Research, accessed May 18, 2024, https://lumc.omeka.net/items/show/29097.