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

Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

712
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
712
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

693
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
693
Parallel Processing01:20

Parallel Processing

203
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
203
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

265
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
265
Ampere's Law: Problem-Solving01:31

Ampere's Law: Problem-Solving

3.7K
Ampere's law states that for any closed looped path, the line integral of the magnetic field along the path equals the vacuum permeability times the current enclosed in the loop. If the fingers of the right hand curl along the direction of the integration path, the current in the direction of the thumb is considered positive. The current opposite to the thumb direction is considered negative.
Specific steps need to be considered while calculating the symmetric magnetic field distribution...
3.7K

You might also read

Related Articles

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

Sort by
Same author

Integrated network pharmacology and serum metabolomics to reveal the protective mechanism of methanolic extract of BaiYangJie on cisplatin-induced acute kidney injury in mice.

Experimental and therapeutic medicine·2026
Same author

SSA-DCNet: a cross-session MI-EEG classification network based on deformable convolution and spatial-shift attention.

Biomedical engineering letters·2026
Same author

DP-MDLA Net: Detection of smooth pursuit abnormalities in Parkinson's disease.

Digital health·2026
Same author

Features extraction and fusion by attention mechanism for software defect prediction.

PloS one·2025
Same author

Optimized Design of a Triangular Shear Piezoelectric Sensor Using Non-Dominated Sorting Genetic Algorithm-II(NSGA-II).

Sensors (Basel, Switzerland)·2025
Same author

Mechanical equipment fault diagnosis method based on improved deep residual shrinkage network.

PloS one·2024
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Aug 17, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

634

Digital-Twin-Assisted Edge-Computing Resource Allocation Based on the Whale Optimization Algorithm.

Shaoming Qiu1, Jiancheng Zhao1, Yana Lv1

  • 1Communication and Network Laboratory, Dalian University, Dalian 116622, China.

Sensors (Basel, Switzerland)
|December 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a digital-twin-assisted model for efficient edge-computing resource allocation. The improved whale optimization algorithm significantly reduces power consumption and improves task allocation efficiency in Internet of Things networks.

Keywords:
Internet of Thingsdigital twinedge computingresource allocation

More Related Videos

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.1K

Related Experiment Videos

Last Updated: Aug 17, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

634
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.1K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Network Engineering

Background:

  • The proliferation of smart Internet of Things (IoT) devices generates substantial computing tasks at the network edge.
  • Inefficient edge-computing resource allocation leads to high power consumption and wasted resources.
  • Optimizing resource allocation is crucial for user task offloading in edge-computing systems.

Purpose of the Study:

  • To propose a digital-twin (DT)-assisted edge-computing resource allocation model.
  • To establish a joint optimization function considering power consumption, delay, and resource allocation imbalance.
  • To enhance the efficiency and reduce the resource waste in edge-computing systems.

Main Methods:

  • Development of a digital-twin-assisted edge-computing resource allocation model.
  • Establishment of a joint optimization function for power consumption, delay, and resource allocation imbalance.
  • Implementation of an improved whale optimization algorithm with a greedy initialization strategy and redesigned search strategy.

Main Results:

  • The improved whale optimization algorithm reduced resource allocation objective function value by 12.6%.
  • Power consumption was reduced by 15.2%, and the average resource allocation imbalance rate decreased by 15.6%.
  • Digital-twin assistance reduced overall power consumption to 89.6% of the level without DT assistance.

Conclusions:

  • The proposed digital-twin-assisted model effectively optimizes edge-computing resource allocation.
  • The improved whale optimization algorithm enhances convergence speed and allocation accuracy.
  • This approach significantly improves the efficiency of edge-computing resource allocation by reducing power consumption and resource waste.