Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Transformers in Distribution System01:27

Transformers in Distribution System

102
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
102

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Patient-Reported Outcomes in Masculinizing Gender-Affirming Chest Surgery: Validation of the GENDER-Q Chest Scales.

The British journal of surgery·2026
Same author

Racial and Regional Differences of the Cheek for Facial Feminization: A Systematic Review.

The Journal of craniofacial surgery·2026
Same author

Further Validation of the GENDER-Q Voice Sound and Voice Distress Scales in 5424 Transgender and Gender Diverse Adults: An Examination of Construct Validity.

Journal of voice : official journal of the Voice Foundation·2026
Same author

PeMYB6 Competes with PeMYB114 for Interaction with PebHLH42 to Regulate Anthocyanin Biosynthesis in Passion Fruit.

Journal of agricultural and food chemistry·2026
Same author

The role of histidine metabolism in tumor progression and its clinical significance.

Biochemical and biophysical research communications·2026
Same author

Assessment of Behavioral Health Readiness for Gender-Affirming Surgery: A Cross-Sectional Survey of Mental Health Provider Practices.

Cureus·2026

Related Experiment Video

Updated: Jun 30, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

531

Towards Edge-Based Deep Learning in Industrial Internet of Things.

Fan Liang1, Wei Yu1, Xing Liu1

  • 1Towson University, USA.

IEEE Internet of Things Journal
|March 15, 2024
PubMed
Summary

This study introduces an edge computing deep learning model for Industrial Internet of Things (IIoT) systems. It reduces network congestion by processing data at the edge, maintaining high classification accuracy.

Keywords:
Distributed deep learningEdge ComputingFog ComputingIndustrial IoT

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K

Related Experiment Videos

Last Updated: Jun 30, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

531
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K

Area of Science:

  • Industrial Internet of Things (IIoT)
  • Deep Learning
  • Edge Computing

Background:

  • Industrial Internet of Things (IIoT) systems connect devices for industrial monitoring and control.
  • Deep learning in IIoT requires significant computation, typically cloud-based, leading to network congestion.
  • Transmitting IIoT data to the cloud for deep learning impacts network performance.

Purpose of the Study:

  • To propose an edge computing-based deep learning model for IIoT systems.
  • To reduce data transmission demands and mitigate network congestion in IIoT networks.
  • To optimize deep learning models for the limited computational power of edge nodes.

Main Methods:

  • Leveraged the fog/edge computing paradigm.
  • Developed an edge computing-based deep learning model.
  • Designed a mechanism to optimize deep learning models for reduced computational requirements.
  • Implemented a testbed in Google Cloud and deployed a Convolutional Neural Network (CNN) model.
  • Evaluated the approach using a real-world IIoT dataset.

Main Results:

  • The proposed model effectively reduces network traffic overhead in IIoT.
  • Classification accuracy is maintained comparable to baseline schemes.
  • The approach mitigates network congestion caused by data transmission.

Conclusions:

  • Edge computing offers a viable solution for deep learning in IIoT.
  • Optimized deep learning models can be deployed on edge nodes with limited resources.
  • The proposed method enhances IIoT network performance and supports intelligent applications.