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

Reducing Line Loss01:18

Reducing Line Loss

184
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
184
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
Block Diagram Reduction01:22

Block Diagram Reduction

258
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
258
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
Downsampling01:20

Downsampling

209
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
209

You might also read

Related Articles

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

Sort by
Same author

Design and Implementation of an AI-Enabled Sensor for the Prediction of the Behaviour of Software Applications in Industrial Scenarios.

Sensors (Basel, Switzerland)·2024
Same author

Generation of a dataset for DoW attack detection in serverless architectures.

Data in brief·2023
Same journal

ArtifactOps and ArtifactDL: a methodology and a language for conceptualizing and operationalising different types of pipelines.

Journal of cloud computing (Heidelberg, Germany)·2025
Same journal

Cloud Enterprise Dynamic Risk Assessment (CEDRA): a dynamic risk assessment using dynamic Bayesian networks for cloud environment.

Journal of cloud computing (Heidelberg, Germany)·2023
Same journal

Criminal law regulation of cyber fraud crimes-from the perspective of citizens' personal information protection in the era of edge computing.

Journal of cloud computing (Heidelberg, Germany)·2023
Same journal

A convolutional neural network based online teaching method using edge-cloud computing platform.

Journal of cloud computing (Heidelberg, Germany)·2023
Same journal

An artificial intelligence lightweight blockchain security model for security and privacy in IIoT systems.

Journal of cloud computing (Heidelberg, Germany)·2023
Same journal

Efficient lattice-based revocable attribute-based encryption against decryption key exposure for cloud file sharing.

Journal of cloud computing (Heidelberg, Germany)·2023
See all related articles

Related Experiment Video

Updated: Aug 4, 2025

Data Communication Based on MQTT in a Polymer Extrusion Process
08:15

Data Communication Based on MQTT in a Polymer Extrusion Process

Published on: July 15, 2022

3.5K

Data transmission reduction formalization for cloud offloading-based IoT systems.

Aya Elouali1, Higinio Mora Mora1, Francisco José Mora-Gimeno1

  • 1Department of Computer Science Technology and Computation, University of Alicante, Alicante, Spain.

Journal of Cloud Computing (Heidelberg, Germany)
|April 3, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel data transmission reduction model for Internet of Things (IoT) devices. It minimizes network latency and bandwidth usage by intelligently transmitting only significant changes or lighter data representations.

Keywords:
Cloud computingData offloadingData transmission reductionIoT cameras

More Related Videos

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
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 4, 2025

Data Communication Based on MQTT in a Polymer Extrusion Process
08:15

Data Communication Based on MQTT in a Polymer Extrusion Process

Published on: July 15, 2022

3.5K
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
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
  • Network Engineering
  • Internet of Things

Background:

  • Internet of Things (IoT) devices often have limited resources and high processing demands.
  • Network constraints like latency and bandwidth consumption are significant challenges for IoT computation offloading.
  • Data transmission reduction is a key strategy to mitigate these network-related issues.

Purpose of the Study:

  • To propose a generalized formal model for data transmission reduction in IoT systems.
  • To develop a model that is independent of specific system architectures and data types.
  • To address network limitations in computation offloading for resource-constrained IoT devices.

Main Methods:

  • Developed a formal data transmission reduction model based on two core principles.
  • Principle 1: Data is transmitted only when a significant change is detected.
  • Principle 2: Transmitting a compressed or lighter data entity that allows the cloud to infer the captured data without full reception.

Main Results:

  • Presented the mathematical formulation of the proposed data transmission reduction model.
  • Included general evaluation metrics for assessing model performance.
  • Provided detailed projections and analysis for real-world IoT use cases.

Conclusions:

  • The proposed model offers a generalized approach to reduce data transmission for IoT computation offloading.
  • This method effectively tackles network latency and bandwidth concerns.
  • The formalization and use-case projections demonstrate practical applicability for enhancing IoT efficiency.