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...

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...

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...

You might also read

Related Articles

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

Sort by

Same journal

Solvent Extraction of Metals in the Circular Economy: Enhancing Resource Efficiency and Sustainability.

TheScientificWorldJournal·2026

Same journal

Agronomic Performance and Nutritive Value Evaluation of Desho Grass Varieties Under Supplementary Irrigation in Western Oromia, Ethiopia.

TheScientificWorldJournal·2026

Same journal

Physicians' and Hospital Administrators' Perspectives of Diagnosis-Related Groups (DRGs) in High-Income Countries: A Systematic Review.

TheScientificWorldJournal·2026

Same journal

The Eco-Friendly Preparation of Se, Zn, and Ag MONPs and Their Current Medical Applications and Drug Delivery for AD Diseases.

TheScientificWorldJournal·2026

Same journal

Fear of COVID-19: A Comparative Study Among University Students in Peru.

TheScientificWorldJournal·2026

Same journal

Opportunities and Challenges of Integrating Ethiopian Traditional Medicine System Into Modern Medicine: A Narrative Review.

TheScientificWorldJournal·2026

Search research articles

Related Experiment Video

Updated: Mar 31, 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

Synchronous Firefly Algorithm for Cluster Head Selection in WSN.

Madhusudhanan Baskaran¹, Chitra Sadagopan²

¹Department of Computer Science, Er.Perumal Manimekalai College of Engineering, Hosur, Tamil Nadu 635117, India.

Thescientificworldjournal

|October 24, 2015

Summary

This summary is machine-generated.

A new synchronous firefly algorithm enhances wireless sensor networks (WSNs) by optimizing cluster head selection. This method improves energy efficiency and reduces packet loss compared to existing techniques like LEACH.

More Related Videos

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.

Published on: December 12, 2012

Related Experiment Videos

Last Updated: Mar 31, 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

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.

Published on: December 12, 2012

Area of Science:

Computer Science
Network Engineering
Artificial Intelligence

Background:

Wireless Sensor Networks (WSNs) rely on cluster-based approaches for efficient data aggregation and energy conservation.
Cluster Heads (CHs) manage data transmission, but their increased workload leads to accelerated energy depletion and network degradation.
Existing methods like Low Energy Adaptive Clustering Hierarchy (LEACH) use probabilistic CH rotation to mitigate energy imbalance.

Purpose of the Study:

To propose a modified firefly heuristic, the synchronous firefly algorithm, for improved WSN performance.
To address the NP-Hard problem of optimal cluster head selection for efficient data aggregation and energy savings in WSNs.
To enhance overall network performance by optimizing energy consumption and data transmission.

Main Methods:

A modified firefly heuristic, termed the synchronous firefly algorithm, was developed and implemented.
Extensive simulations were conducted to evaluate the proposed algorithm's performance.
The algorithm's effectiveness was compared against established methods such as LEACH and Energy-Efficient Hierarchical Clustering (EEHC).

Main Results:

The synchronous firefly algorithm demonstrated superior performance compared to LEACH and EEHC.
Simulations indicated a significant reduction in packet loss ratio by an average of 9.63%.
The proposed method effectively improved the energy efficiency of the wireless sensor network.

Conclusions:

The synchronous firefly algorithm offers a promising solution for optimizing cluster head selection in WSNs.
This approach leads to substantial improvements in network longevity and data reliability.
The study validates the effectiveness of the proposed heuristic in overcoming energy drain challenges in WSNs.