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
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
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

670
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...
670

You might also read

Related Articles

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

Sort by
Same author

Targeting the lactate-lactylation-glucose uptake axis: Cryptotanshinone reprograms metabolic-epigenetic crosstalk in gastric cancer.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026
Same author

Enolase 1: A paradigm of metabolic enzyme moonlighting in tumorigenesis (Review).

International journal of oncology·2026
Same author

The herbal compound miltirone from Salvia miltiorrhiza targets DJ-1 to induce mitophagic dysfunction and ROS-dependent hippo activation in gastric cancer.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026
Same author

Brusatol inhibits gastric cancer by targeting P4HA2 and supressing glycolysis-driven histone lactylation.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2025
Same author

Mendelian randomization identifies genetically predicted calorie dietary preference as causal risk factors for cancer.

Discover oncology·2025
Same author

Targeting PGK1 as a Novel strategy to regulate the sensitivity of HER2 positive gastric cancer to lapatinib.

Frontiers in pharmacology·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jul 19, 2025

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

604

A Distributed Particle-Swarm-Optimization-Based Fuzzy Clustering Protocol for Wireless Sensor Networks.

Chuhang Wang1

  • 1College of Computer Science and Technology, Changchun Normal University, Changchun 130032, China.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces DPFCP, a novel protocol for wireless sensor networks (WSNs). DPFCP enhances energy efficiency and network longevity by optimizing cluster head selection and balancing energy consumption among nodes.

Keywords:
clusteringenergy efficiencyfuzzy logicnetwork lifetimeparticle swarm optimization

More Related Videos

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
08:13

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

8.2K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.3K

Related Experiment Videos

Last Updated: Jul 19, 2025

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

604
SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
08:13

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

8.2K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.3K

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) face significant energy constraints, necessitating efficient energy preservation and network lifetime maximization.
  • Existing clustering approaches struggle with energy efficiency and load balancing, limiting the operational lifespan of WSNs.
  • Effective energy management is crucial for the sustained performance and reliability of WSNs.

Purpose of the Study:

  • To propose a novel distributed particle swarm optimization-based fuzzy clustering protocol (DPFCP) for WSNs.
  • To enhance energy efficiency and balance energy consumption to extend the overall network lifetime.
  • To optimize cluster head selection and cluster formation using intelligent algorithms.

Main Methods:

  • Developed a distributed particle swarm optimization-based fuzzy clustering protocol (DPFCP).
  • Employed a Mamdani fuzzy logic system for cluster head (CH) nomination based on residual energy, node degree, and distances.
  • Utilized particle swarm optimization (PSO) to optimize fuzzy rules and an on-demand mechanism for cluster maintenance.

Main Results:

  • DPFCP demonstrated significant improvements in energy consumption, reducing it by an average of 13.06% to 38.20% compared to existing protocols.
  • Network lifetime was extended by 10.99% to 46.19% compared to benchmark protocols.
  • Reduced the standard deviation of residual energy across the network by 19.39% to 61.88%, indicating better energy balancing.

Conclusions:

  • The proposed DPFCP protocol effectively balances energy consumption in WSNs.
  • DPFCP significantly enhances overall network performance and maximizes network lifetime.
  • The intelligent optimization of fuzzy rules and cluster formation contributes to superior energy management in WSNs.