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

595
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...
595
Distributed Loads01:19

Distributed Loads

475
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...
475
Relation Between the Distributed Load and Shear01:23

Relation Between the Distributed Load and Shear

571
Understanding the relationship between the distributed load and shear force in structural analysis is crucial for analyzing beams subjected to various loading conditions. Consider the case of a beam experiencing a distributed load, two concentrated loads, and a couple moment.
571
Work and Energy for Variable Forces01:10

Work and Energy for Variable Forces

3.3K
When an object is acted upon by a variable force, the amount of work done and the change in energy of the object can be more complex to calculate compared to when a constant force is applied. Work is the product of force and displacement, while energy is the capacity of a system to do work. When a constant force is applied to an object, the work done can be calculated as the product of the force and the distance moved in the direction of the force. However, when a variable force is applied, the...
3.3K
Beams with Unsymmetric Loadings01:17

Beams with Unsymmetric Loadings

103
Analyzing a supported beam under unsymmetrical loadings is essential in structural engineering to understand how beams respond to varied force distributions. This analysis involves calculating the deflection and identifying points where the slope of the beam is zero, which are crucial for ensuring structural stability and functionality.
The first moment-area theorem determines the slope at any point on the beam. This theorem indicates that the change in slope between two points on a beam...
103
Energy Budgets00:51

Energy Budgets

9.1K
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...
9.1K

You might also read

Related Articles

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

Sort by
Same author

Spinal neuromotor rehabilitation using a portable isokinetic training robot.

Nature·2026
Same author

An Interpretable PK-Informed Hybrid Model for Voriconazole Exposure Prediction: Roles of CYP2C19 Genotype and Inflammation.

Drug design, development and therapy·2026
Same author

Task offloading and resource allocation for cooperative communication and sensing in edge computing for mine.

Scientific reports·2026
Same author

A Hybrid Assistive-Resistive Isokinetic Training Robot for Full-Cycle Knee Rehabilitation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Selenium-Containing Nanoscale Hydrogen-Bonded Organic Framework Nanozymes for Multienzyme Cascade Antioxidant-Targeted Therapy of Cerebral Ischemia-Reperfusion Injury.

ACS nano·2026
Same author

Aromatic Ketone-Mediated Two-Electron Lithiation for Rapid and Room-Temperature Regeneration of Spent LiFePO<sub>4</sub> Cathodes.

Journal of the American Chemical Society·2025

Related Experiment Video

Updated: May 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

457

Multi-user joint task offloading and resource allocation based on mobile edge computing in mining scenarios.

Siqi Li1, Weidong Li2, Wanbo Zheng3

  • 1Faculty of Science, Kunming University of Science and Technology, Kunming, 650500, China.

Scientific Reports
|May 9, 2025
PubMed
Summary

This study introduces a partial offloading and collaborative mobile edge computing (MEC) method for industrial IoT devices in mining. The IGA-DDPG algorithm significantly reduces latency, energy consumption, and system costs while ensuring task completion.

Keywords:
Mining edge computingMulti-objective optimizationResource allocationTask offloading

More Related Videos

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques
07:52

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Published on: December 1, 2023

940
A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.5K

Related Experiment Videos

Last Updated: May 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

457
Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques
07:52

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Published on: December 1, 2023

940
A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.5K

Area of Science:

  • Industrial Internet of Things (IIoT)
  • Mobile Edge Computing (MEC)
  • Resource Management

Background:

  • Intelligent terminal devices in mining face performance challenges due to high network traffic and limited resources.
  • Meeting requirements for low transmission latency and low energy consumption is critical for these devices.
  • Existing solutions struggle to balance computational load and resource utilization.

Purpose of the Study:

  • To propose a novel method combining partial offloading with collaborative mobile edge computing (MEC) for IIoT devices in mining.
  • To optimize the offloading decision-making process to enhance resource utilization efficiency.
  • To minimize overall system cost while ensuring task completion latency does not exceed a predefined threshold.

Main Methods:

  • A partial offloading strategy leveraging device-to-device communication to partition tasks.
  • A two-layer alternating optimization framework: Improved Genetic Algorithm (IGA) for offloading decisions and Deep Deterministic Policy Gradient (DDPG) for strategy optimization.
  • Multi-objective optimization formulation considering latency and energy consumption weighted coefficients.

Main Results:

  • The proposed IGA-DDPG algorithm significantly outperforms five baseline algorithms.
  • Achieved an average reduction of 24.5% in latency, 26.3% in energy consumption, and 44.6% in overall system cost.
  • Consistently ensured a 100% task completion rate across various system configurations.

Conclusions:

  • The IGA-DDPG approach effectively addresses the performance challenges of intelligent terminal devices in mining IIoT.
  • The method enhances resource utilization by offloading tasks to collaborative devices and MEC servers.
  • This optimized offloading strategy provides substantial improvements in efficiency and cost-effectiveness.