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

Cluster Sampling Method01:20

Cluster Sampling Method

12.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.0K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.3K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.3K
Aggregates Classification01:29

Aggregates Classification

344
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
344
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

129
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
129
Sampling Plans01:23

Sampling Plans

208
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
208
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

96
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
96

You might also read

Related Articles

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

Sort by
Same author

A Possible Role of Clinical Factors in Choosing the Best Treatment Modality in Cesarean Scar Pregnancy.

Diagnostics (Basel, Switzerland)·2025
Same author

The Role of the Aryl Hydrocarbon Receptor in Vascular Factors Related to Preeclampsia in a Smoking Mouse Model.

Current issues in molecular biology·2024
Same author

Telecommuting-related health outcomes during the COVID-19 pandemic in South Korea: a national population-based cross-sectional study.

BMC public health·2023
Same author

Working From Home, Work-Life Balance, and Depression/Anxiety Among Korean Workers in the COVID-19 Pandemic Period: A Mediation Analysis.

Journal of occupational and environmental medicine·2022
Same author

Association between sickness presenteeism and depressive symptoms in Korean workers during the COVID-19 pandemic: A cross-sectional study.

Journal of affective disorders·2022
Same author

COVID-19 and vaccination during pregnancy: a systematic analysis using Korea National Health Insurance claims data.

Obstetrics & gynecology science·2022

Related Experiment Video

Updated: Jul 16, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.4K

Consensus-based clustering and data aggregation in decentralized network of multi-agent systems.

Joshua Julian Damanik1, Ming Chong Lim1, Hyeon-Mun Jeong1

  • 1Aerospace Engineering Department, Korea Advanced Institute of Science & Technology, Daejeon, South Korea.

Peerj. Computer Science
|September 14, 2023
PubMed
Summary
This summary is machine-generated.

This study presents a decentralized data aggregation algorithm for multi-agent systems. It improves accuracy in clustered networks using a trust value, enabling efficient COUNT and SUM operations.

Keywords:
AggregationClusteringConsensusDistributedMulti-agent systemsOptimizationSituational awareness

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

598
ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.5K

Related Experiment Videos

Last Updated: Jul 16, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.4K
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

598
ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.5K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Systems

Background:

  • Multi-agent systems (MAS) are crucial for various applications but face challenges in optimizing large, heterogeneously connected networks within clustered environments.
  • Decentralized planning algorithms in clustered MAS require accurate cluster information and noise compensation from other clusters.

Purpose of the Study:

  • To propose a novel decentralized data aggregation algorithm for clustered multi-agent systems.
  • To enhance the accuracy and efficiency of COUNT and SUM aggregation operations in decentralized environments.

Main Methods:

  • A decentralized data aggregation algorithm utilizing a consensus method for COUNT and SUM operations.
  • Introduction of a trust value for accurate cluster-level aggregation.
  • Inclusion of a correction parameter to optimize solution accuracy and computation time.

Main Results:

  • The algorithm demonstrates convergence on aggregated data with acceptable accuracy and convergence time.
  • Successful evaluation in simulations involving large, sparse networks and limited bandwidth.
  • Validation of the trust value mechanism for improved aggregation precision.

Conclusions:

  • The proposed algorithm effectively addresses data aggregation challenges in clustered multi-agent systems.
  • The developed tools offer a foundation for robust decentralized task assignment in complex multi-agent, multi-task environments.
  • This work contributes to the advancement of efficient and accurate decentralized computation in networked systems.