Jove
Visualize
Contact Us

Related Concept Videos

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

687
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...
687
Distributed Loads01:19

Distributed Loads

567
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...
567
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
Parallel Processing01:20

Parallel Processing

194
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...
194
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
Energy Budgets00:51

Energy Budgets

9.6K
Organisms must balance energy intake with the energy required for growth, maintenance and reproduction. These trade-offs result in a variety of survivorship and reproductive strategies, including semelparity and iteroparity. Semelparous species, like annual plants, have only one reproductive episode in their lifetimes and consequently have short lifespans. Iteroparous species, by contrast, have many reproductive events during their lifetimes but have relatively few offspring. These two...
9.6K

You might also read

Related Articles

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

Sort by
Same author

Priority-Aware Multi-Objective Task Scheduling in Fog Computing Using Simulated Annealing.

Sensors (Basel, Switzerland)·2025
Same author

Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing.

Sensors (Basel, Switzerland)·2023
Same author

Prioritized Task-Scheduling Algorithm in Cloud Computing Using Cat Swarm Optimization.

Sensors (Basel, Switzerland)·2023
Same author

An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization.

Sensors (Basel, Switzerland)·2023
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
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 Experiment Video

Updated: Aug 7, 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

621

EEOA: Cost and Energy Efficient Task Scheduling in a Cloud-Fog Framework.

M Santhosh Kumar1, Ganesh Reddy Karri1

  • 1School of Computer Science and Engineering, VIT-AP University, Amaravathi 522237, Andhra Pradesh, India.

Sensors (Basel, Switzerland)
|March 11, 2023
PubMed
Summary
This summary is machine-generated.

A new electric earthworm optimization algorithm (EEOA) efficiently schedules Internet of Things (IoT) tasks in cloud-fog systems. This method significantly improves efficiency, reduces energy consumption, and lowers costs compared to existing algorithms.

Keywords:
CEA-CURIEHPC2Nearthworm optimization algorithmelectric fish optimizationinternet of things

More Related Videos

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

12.2K
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

Related Experiment Videos

Last Updated: Aug 7, 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

621
Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

12.2K
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

Area of Science:

  • Cloud-fog computing
  • Internet of Things (IoT)
  • Task Scheduling Algorithms

Background:

  • The rapid growth of IoT generates massive data, necessitating efficient task scheduling in cloud-fog environments.
  • Existing scheduling methods often neglect crucial factors like energy consumption and cost.
  • There is a need for advanced algorithms to manage heterogeneous workloads and enhance Quality of Service (QoS).

Purpose of the Study:

  • To propose a novel multi-objective task scheduling algorithm for IoT requests within a cloud-fog framework.
  • To address limitations of existing methods by incorporating energy efficiency and cost-effectiveness.
  • To enhance the overall efficiency and QoS of cloud-fog services.

Main Methods:

  • Developed the electric earthworm optimization algorithm (EEOA) by combining the Earthworm Optimization Algorithm (EOA) and Electric Fish Optimization (EFO).
  • Designed EEOA to optimize task scheduling considering execution time, cost, makespan, and energy consumption.
  • Evaluated the algorithm using real-world workloads like CEA-CURIE and HPC2N.

Main Results:

  • The proposed EEOA demonstrated significant improvements: 89% increase in efficiency, 94% reduction in energy consumption, and 87% decrease in total cost.
  • Performance was validated against existing algorithms across various benchmarks.
  • Simulation results confirm the superiority of EEOA in optimizing cloud-fog task scheduling.

Conclusions:

  • The electric earthworm optimization algorithm (EEOA) offers a superior scheduling scheme for IoT tasks in cloud-fog environments.
  • EEOA effectively balances performance metrics including efficiency, energy usage, and cost.
  • This research provides a promising solution for optimizing resource management in the era of big data and IoT.