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IoT-Edge Communication Protocol based on Low Latency for effective Data Flow and Distributed Neural Network in a Big Data Environment.

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Title: IoT-Edge Communication Protocol based on Low Latency for effective Data Flow and Distributed Neural Network in a Big Data Environment.
Authors: Kumar, Dr. Kailash1 (AUTHOR) k.kumar@seu.edu.sa
Source: Microprocessors & Microsystems. Mar2021, Vol. 81, pN.PAG-N.PAG. 1p.
Subjects: Artificial neural networks, Big data, Internet of things, Machine learning, Petroleum prospecting, Smart cities
Abstract: The sky-scraping increase in computer power includes in-depth study for all. In-depth learning provides accurate information at all times compared to other learning algorithms. Then, the Internet of Things (IoT) has grown in popularity in the field, for example, smart cities, exploration of oil, communications, etc. Edge / Fog IT support solve major challenges faced by the Internet of Things such as internet bandwidth, latency, and fixed network connection. Edge computing is spreading in a virtual environment, which requires a lot of time for machine learning. This paper aims to integrate the flow of data and disseminate the IoT Edge environment to reduce exploration and increase reliability from the data generation point of view. Based on this, a Troubleshooting of the Distributed Neural Network Model (DF-DDNN) was developed from the model of IoT Edge for the largest environment Data. Our suggested technique reduces latency by almost 33% compared to the long-developed cloud model of the Internet of Things. [ABSTRACT FROM AUTHOR]
Copyright of Microprocessors & Microsystems is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: The sky-scraping increase in computer power includes in-depth study for all. In-depth learning provides accurate information at all times compared to other learning algorithms. Then, the Internet of Things (IoT) has grown in popularity in the field, for example, smart cities, exploration of oil, communications, etc. Edge / Fog IT support solve major challenges faced by the Internet of Things such as internet bandwidth, latency, and fixed network connection. Edge computing is spreading in a virtual environment, which requires a lot of time for machine learning. This paper aims to integrate the flow of data and disseminate the IoT Edge environment to reduce exploration and increase reliability from the data generation point of view. Based on this, a Troubleshooting of the Distributed Neural Network Model (DF-DDNN) was developed from the model of IoT Edge for the largest environment Data. Our suggested technique reduces latency by almost 33% compared to the long-developed cloud model of the Internet of Things. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Microprocessors & Microsystems is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.micpro.2020.103642
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Big data
        Type: general
      – SubjectFull: Internet of things
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Petroleum prospecting
        Type: general
      – SubjectFull: Smart cities
        Type: general
    Titles:
      – TitleFull: IoT-Edge Communication Protocol based on Low Latency for effective Data Flow and Distributed Neural Network in a Big Data Environment.
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              Text: Mar2021
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              Y: 2021
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              Value: 81
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