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

Sampling Plans

Sampling Plans

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...

Cluster Sampling Method

Cluster Sampling Method

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...

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

Dual roles of GDNF in enteric glial cell plasticity: direct transdifferentiation via the CaMKII/NeuroD1 pathway and cooperative regulation in a neural stem cell-inducing medium.

Stem cell research & therapy·2026

Same author

Predictive modeling of chronic foot ulcer outcomes using longitudinal photoacoustic imaging.

Npj imaging·2026

Same author

DNER as a novel protein contributes to HSCR pathogenesis: multi-omics combined Mendelian randomization analysis.

Pediatric research·2026

Same author

Utilizing Machine Learning for Predicting PrEP Use Status Among Sexual and Gender Minority Young Adults.

Prevention science : the official journal of the Society for Prevention Research·2026

Same author

TaCSP-H1 Regulates Flowering Time by Directly Promoting the Expression of TaTOE1 in Wheat.

Plant biotechnology journal·2025

Same author

Optimizing Deep Neural Networks for EEG-Based Speech Recognition: A Multimodal Approach to Assistive Communication.

IEEE journal of biomedical and health informatics·2025

Search research articles

Related Experiment Video

Updated: May 25, 2026

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

Cluster Size Optimization in Sensor Networks with Decentralized Cluster-Based Protocols.

Navid Amini¹, Alireza Vahdatpour, Wenyao Xu

¹Computer Science Department, University of California, Los Angeles.

Computer Communications

|January 24, 2012

Summary

This summary is machine-generated.

This study determines the optimal cluster size for wireless sensor networks to minimize energy consumption. It analyzes cluster-based protocols like LEACH, offering energy-saving insights for network lifetime.

Related Experiment Videos

Last Updated: May 25, 2026

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:

Wireless Sensor Networks
Communication Protocols
Network Optimization

Background:

Network lifetime and energy efficiency are critical in cluster-based wireless sensor networks.
Decentralized data transmission through cluster heads to a base station is common.
Existing protocols like LEACH, LEACH-Coverage, and DBS lack centralized support.

Purpose of the Study:

To analytically determine the optimal cluster size that minimizes total energy expenditure in wireless sensor networks.
To provide closed-form expressions for optimal cluster size in various network configurations.
To investigate the impact of cluster number variability on energy consumption.

Main Methods:

Analytical derivation of optimal cluster size.
Formulation of closed-form expressions for energy minimization.
Extensive simulations to validate analytical results.
Analysis of cluster number variability and its effect on energy consumption.

Main Results:

The study provides analytical results for optimal cluster size in different network configurations.
Simulations confirm the analytical predictions regarding energy expenditure.
The relationship between cluster number variability and energy consumption is explored.

Conclusions:

The optimal cluster size can be analytically determined to enhance energy efficiency in wireless sensor networks.
The findings offer valuable insights for designing energy-efficient cluster-based communication protocols.
Understanding cluster number variability is crucial for maximizing network lifetime.