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

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...

You might also read

Related Articles

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

Sort by

Same author

Revolutionizing food science: a review on the role of nanotechnology in enhancing safety, quality, and sustainability.

Journal of food science and technology·2026

Same author

Biogenic Synthesis of Cerium Oxide Nanoparticles: Characterization, Biological Activities and Their Corrosion Inhibition Properties.

Microscopy research and technique·2025

Same author

Utility of low-cost sensor measurement for predicting ambient PM<sub>2.5</sub> concentrations: evidence from a monitoring network in Accra, Ghana.

Environmental science: atmospheres·2025

Same author

Low-Cost PM<sub>2.5</sub> Sensor Performance Characteristics against Meteorological Influence in Sub-Saharan Africa: Evidence from the Air Sensor Evaluation and Training Facility for the West Africa Project.

Environmental science & technology·2025

Same author

Clinical profiling, treatment characteristics and outcome in Behcet's Disease (BD)-A retrospective cohort study from Karnataka Rheumatology Association (KRA).

Clinical rheumatology·2024

Same author

Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM<sub>2.5</sub> Monitoring in Accra, Ghana.

Environmental science & technology·2023

Search research articles

Related Experiment Video

Updated: Mar 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

An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network.

C Vimalarani¹, R Subramanian², S N Sivanandam³

¹Department of Computer Science and Engineering, SNS College of Technology, Coimbatore 641 035, India.

Thescientificworldjournal

|February 17, 2016

Summary

This summary is machine-generated.

This study introduces an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm to extend the operational life of Wireless Sensor Networks (WSNs). The EPSO-CEO method optimizes energy consumption through intelligent clustering and cluster head selection, significantly improving network performance.

More Related Videos

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Related Experiment Videos

Last Updated: Mar 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

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Area of Science:

Computer Science
Electrical Engineering
Network Engineering

Background:

Wireless Sensor Networks (WSNs) are crucial for monitoring diverse environments, relying on battery-powered sensor nodes.
Energy conservation is paramount for maximizing the lifetime and performance of WSNs.
Existing clustering algorithms often face challenges in efficient energy management.

Purpose of the Study:

To propose an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for WSNs.
To minimize power consumption in WSNs through optimized clustering and cluster head selection.
To enhance the overall lifetime and efficiency of Wireless Sensor Networks.

Main Methods:

Developed an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm.
Utilized Particle Swarm Optimization (PSO) for efficient clustering and cluster head selection.
Evaluated performance metrics against existing clustering algorithms.

Main Results:

The EPSO-CEO algorithm demonstrated significant reductions in energy consumption within WSNs.
Optimized clustering and cluster head selection led to improved network performance.
Comparative analysis validated the effectiveness of the proposed algorithm over competitive methods.

Conclusions:

The EPSO-CEO algorithm effectively conserves energy in Wireless Sensor Networks.
This approach enhances the longevity and operational efficiency of WSNs.
The findings suggest a promising solution for energy-constrained WSN applications.