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

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

692
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...
692
Short-distance Transport of Resources02:12

Short-distance Transport of Resources

16.3K
Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.
16.3K
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

156
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.
156
Distributed Loads01:19

Distributed Loads

576
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
576
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

709
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...
709
Maximum Power Transfer01:16

Maximum Power Transfer

325
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...
325

You might also read

Related Articles

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

Sort by
Same author

A Survey on Reduction of Energy Consumption in Fog Networks-Communications and Computations.

Sensors (Basel, Switzerland)·2024
Same author

Poisoning Attacks against Communication and Computing Task Classification and Detection Techniques.

Sensors (Basel, Switzerland)·2024
Same author

Efficiency Maximization for Battery-Powered OFDM Transmitter via Amplifier Operating Point Adjustment.

Sensors (Basel, Switzerland)·2023
Same author

Federated Learning for 5G Radio Spectrum Sensing.

Sensors (Basel, Switzerland)·2022
Same author

Dynamic Transmit Profile Selection in Dense Wireless Networks.

Sensors (Basel, Switzerland)·2020
Same author

Stochastic Power Consumption Model of Wireless Transceivers.

Sensors (Basel, Switzerland)·2020

Related Experiment Video

Updated: Aug 13, 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

632

Communication and Computing Task Allocation for Energy-Efficient Fog Networks.

Bartosz Kopras1, Filip Idzikowski1, Bartosz Bossy1

  • 1Faculty of Computing and Telecommunications, Poznan University of Technology, 60-965 Poznań, Poland.

Sensors (Basel, Switzerland)
|January 21, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an optimal task allocation method for fog computing, minimizing energy use for wireless users. The proposed algorithm efficiently assigns tasks to fog nodes and the cloud, reducing energy consumption and task rejections.

Keywords:
cloudedge computingenergy efficiencyfog networklatency

More Related Videos

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.1K
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

Related Experiment Videos

Last Updated: Aug 13, 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

632
Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.1K
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

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Telecommunications

Background:

  • Cloud computing faces latency challenges for real-time applications.
  • Fog computing extends cloud capabilities closer to end-users for reduced latency.
  • Efficient task offloading in fog networks requires balancing computation, communication, and energy.

Purpose of the Study:

  • To propose an optimal task allocation strategy for fog computing environments.
  • To minimize energy consumption for wireless users offloading tasks to fog nodes and the cloud.
  • To address challenges in task allocation with diverse user requirements and network capabilities.

Main Methods:

  • Formulated the task allocation as a Mixed-Integer Nonlinear Programming problem.
  • Developed energy consumption and delay models reflecting real device characteristics.
  • Proposed an optimal algorithm and a heuristic algorithm for task allocation and fog node frequency optimization.

Main Results:

  • Splitting task processing between multiple fog nodes and the cloud is energy-efficient.
  • The proposed optimal algorithm achieves the lowest energy consumption and task rejection rates.
  • The heuristic algorithm provides optimal or near-optimal solutions across various scenarios.

Conclusions:

  • The developed optimal task allocation significantly reduces energy consumption in fog computing.
  • The proposed methods effectively manage complex task offloading requirements.
  • Fog computing offers a viable solution for low-latency, energy-efficient task processing.