Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Short-distance Transport of Resources

Short-distance Transport of Resources

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.

Distributed Loads: Problem Solving

Distributed Loads: Problem Solving

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...

You might also read

Related Articles

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

Sort by

Same author

Kinetics, mechanistic, toxicological and economical investigations of para-chlorophenol degradation by UV/persulfate and UV/persulfate/Fe<sup>2+</sup> processes.

BMC chemistry·2026

Same author

Sustainable adsorptive removal of eriochrome black T Dye using Cladophora glomerata biochar.

Scientific reports·2026

Same author

Retraction notice to "Improving wheat physio-biochemical attributes in ciprofloxacin-polluted saline soil using nZVI-modified biochar" [Ecotoxicol. Environ. Saf., 286 C, (2024) 117202].

Ecotoxicology and environmental safety·2026

Same author

Professional perspectives on PN among registered dietitians in Saudi Arabia: a mixed-methods assessment.

Frontiers in nutrition·2026

Same author

Stochastic and Statistical Analysis of Cnoidal, Snoidal, Dnoidal, Hyperbolic, Trigonometric and Exponential Wave Solutions of a Coupled Volatility Option-Pricing System.

Entropy (Basel, Switzerland)·2026

Same author

Synergistic phytohormone crosstalk enhances nickel detoxification, antioxidant defense and yield in isabgol (Plantago ovata).

Biodegradation·2026

Search research articles

Related Experiment Video

Updated: May 12, 2026

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

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

Published on: November 26, 2019

Machine Learning-Based Resource Management in Fog Computing: A Systematic Literature Review.

Fahim Ullah Khan¹, Ibrar Ali Shah¹, Sadaqat Jan¹

¹Department of Computer Software Engineering, University of Engineering and Technology, Mardan 23200, Pakistan.

Sensors (Basel, Switzerland)

|February 13, 2025

Summary

Deep learning (DL) dominates resource management in fog computing, outperforming traditional machine learning (ML) techniques. This review highlights DL

Keywords:

Internet of Things (IoT)cloud computing deep learning (DL)edge computing interpretability latency machine learning (ML)resource management scalability

More Related Videos

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

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

Published on: February 3, 2021

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Related Experiment Videos

Last Updated: May 12, 2026

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

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

Published on: November 26, 2019

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

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

Published on: February 3, 2021

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

Computer Science
Artificial Intelligence
Distributed Computing

Background:

Fog computing environments require efficient resource management.
Machine learning (ML) and deep learning (DL) offer potential solutions.
Optimizing resource allocation is crucial for fog computing performance.

Purpose of the Study:

To systematically review ML-based techniques for resource management in fog computing.
To identify prevalent ML/DL approaches and their applications.
To analyze key factors and challenges addressed in the literature.

Main Methods:

Systematic literature review following the PRISMA protocol.
Analysis of 68 extended research papers on ML/DL for fog computing resource management.
Categorization of techniques based on addressed factors and challenges.

Main Results:

Deep learning (DL) techniques are preferred, used in 66% of reviewed studies, compared to 34% for ML.
Latency (77%), energy consumption (44%), and QoS (33%) are the most frequently addressed factors.
Computational resources, latency, scalability, data quality, and model interpretability are key challenges addressed by various ML/DL methods.

Conclusions:

DL shows a strong trend for resource management in fog computing.
Addressing latency, energy, and QoS is paramount for optimization.
ML/DL techniques effectively tackle diverse challenges in fog computing resource management.