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

686
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...
686
Distributed Loads01:19

Distributed Loads

565
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...
565
Multimachine Stability01:25

Multimachine Stability

208
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
208
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

252
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:
252
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
Parallel Processing01:20

Parallel Processing

191
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...
191

You might also read

Related Articles

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

Sort by
Same author

Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification.

Sensors (Basel, Switzerland)·2022
Same author

Automated Detection of Myocardial Infarction and Heart Conduction Disorders Based on Feature Selection and a Deep Learning Model.

Sensors (Basel, Switzerland)·2022
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 5, 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

611

Energy-Aware and Secure Task Offloading for Multi-Tier Edge-Cloud Computing Systems.

Hatem A Alharbi1, Mohammad Aldossary2, Jaber Almutairi3

  • 1Department of Computer Engineering, College of Computer Science and Engineering, Taibah University, Al-Madinah 42353, Saudi Arabia.

Sensors (Basel, Switzerland)
|March 30, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for Unmanned Aerial Vehicle (UAV) task offloading in Edge-Cloud Computing (ECC). The proposed method enhances security and reduces energy consumption by using data compression and AES encryption.

Keywords:
compressionmobile edge computingsecuritytask offloadingunmanned aerial vehicle

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 5, 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

611
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:

  • Computer Science
  • Electrical Engineering
  • Aerospace Engineering

Background:

  • Unmanned Aerial Vehicles (UAVs) require significant computational power and energy, exceeding their onboard capabilities.
  • Edge-Cloud Computing (ECC) offers a solution by offloading tasks, but faces challenges with limited bandwidth and data security during transmission.

Purpose of the Study:

  • To propose a novel compression, security, and energy-aware task offloading framework for ECC environments supporting UAVs.
  • To address the limitations of bandwidth and enhance data protection in UAV-based ECC systems.

Main Methods:

  • Implemented an efficient data compression layer to minimize transmission data.
  • Integrated an Advanced Encryption Standard (AES) layer for robust data security.
  • Formulated task offloading, compression, and security as a mixed-integer problem to minimize system energy consumption under latency constraints.

Main Results:

  • The proposed framework significantly reduces energy consumption compared to existing benchmarks.
  • Achieved energy savings of 19%, 18%, 21%, 14.5%, 13.1%, and 12% against various benchmark models.
  • Demonstrated the scalability of the developed model for UAV applications in ECC.

Conclusions:

  • The developed framework effectively tackles bandwidth limitations and security threats in UAV-based ECC.
  • The joint optimization of task offloading, compression, and security leads to substantial energy savings.
  • The proposed approach provides a scalable and secure solution for resource-intensive UAV applications.