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

676
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...
676
Energy Conservation and Bernoulli's Equation01:16

Energy Conservation and Bernoulli's Equation

9.0K
Applying the conservation of energy principle or the work-energy theorem to an incompressible, inviscid fluid in laminar, steady, irrotational flow leads to Bernoulli's equation. It states that the sum of the fluid pressure, potential, and kinetic energy per unit volume is constant along a streamline.
All the terms in the equation have the dimension of energy per unit volume. The kinetic energy per unit volume is called the kinetic energy density, and the potential energy per unit volume is...
9.0K
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

138
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
138
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

676
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...
676
Maximum Power Transfer01:16

Maximum Power Transfer

292
Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
292
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

239
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:
239

You might also read

Related Articles

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

Sort by
Same author

Automated Adjustment of PPE Masks Using IoT Sensor Fusion.

Sensors (Basel, Switzerland)·2023
Same author

Integration of IoT Sensors to Determine Life Expectancy of Face Masks.

Sensors (Basel, Switzerland)·2022
Same author

An assigned responsibility system for robotic teleoperation control.

International journal of intelligent robotics and applications·2018
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: Jul 23, 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

605

Energy Efficient Node Selection in Edge-Fog-Cloud Layered IoT Architecture.

Rolden Fereira1, Chathurika Ranaweera1, Kevin Lee1

  • 1School of Information Technology, Deakin University, Geelong, VIC 3220, Australia.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
Summary
This summary is machine-generated.

This research introduces an energy-efficient node selection framework for Internet of Things (IoT) systems. It optimizes processing in edge-fog-cloud architectures, reducing energy consumption for billions of IoT devices.

Keywords:
ILPIoTcloudedge computingenergyfognode selectionoptimal

More Related Videos

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
10:15

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Published on: February 3, 2021

3.8K
Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.6K

Related Experiment Videos

Last Updated: Jul 23, 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

605
Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
10:15

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Published on: February 3, 2021

3.8K
Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.6K

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Energy Systems

Background:

  • Internet of Things (IoT) architectures prioritize performance and reliability, often overlooking energy efficiency.
  • The integration of edge, fog, and cloud computing enhances service quality but increases energy demands.
  • Billions of IoT devices contribute significantly to global energy consumption, necessitating energy-aware solutions.

Purpose of the Study:

  • To propose an optimization framework for selecting energy-efficient nodes in layered IoT architectures.
  • To integrate energy consumption as a key metric in the node selection process for IoT requests.
  • To address the energy impact of large-scale IoT deployments.

Main Methods:

  • Developed an optimization framework considering node energy consumption for request processing.
  • Evaluated the framework within an edge-fog-cloud layered architecture.
  • Utilized CPLEX simulations for performance evaluation.

Main Results:

  • The proposed framework successfully incorporates energy efficiency into node selection.
  • Demonstrated the ability to meet application requirements and network constraints while optimizing energy.
  • Provided insights into energy-efficient node selection mechanisms for complex IoT environments.

Conclusions:

  • Energy efficiency can be effectively integrated into IoT architecture design.
  • The developed framework offers a viable approach to reduce the energy footprint of IoT systems.
  • Optimized node selection is crucial for sustainable large-scale IoT deployments across various use cases like smart grids, autonomous vehicles, and eHealth.