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

Load-frequency control01:28

Load-frequency control

Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

You might also read

Related Articles

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

Sort by
Same author

Sensor-Based Technologies for the Detection of Unwanted Loneliness in Older Adults: A Systematic Review.

Sensors (Basel, Switzerland)·2026
Same author

BLE-Based Custom Devices for Indoor Positioning in Ambient Assisted Living Systems: Design and Prototyping.

Sensors (Basel, Switzerland)·2025
Same author

Novel sustainable magnetic material to improve the wireless charging of a lightweight drone.

RSC advances·2023
Same author

A Distributed Clustering Algorithm Guided by the Base Station to Extend the Lifetime of Wireless Sensor Networks.

Sensors (Basel, Switzerland)·2020
Same author

Smart Containers Schedulers for Microservices Provision in Cloud-Fog-IoT Networks. Challenges and Opportunities.

Sensors (Basel, Switzerland)·2020
Same author

A New Centralized Clustering Algorithm for Wireless Sensor Networks.

Sensors (Basel, Switzerland)·2019
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: Jun 20, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K

Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks.

Jose-Enrique Muñoz-Exposito1, Antonio-Jesus Yuste-Delgado1, Alicia Triviño-Cabrera2

  • 1Department of Telecommunication Engineering, Universidad de Jaén, 23700 Linares, Spain.

Sensors (Basel, Switzerland)
|September 14, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an optimized fuzzy logic clustering algorithm for wireless sensor networks (WSNs). The approach uses dynamic knowledge bases and particle swarm optimization to significantly extend network lifetime by improving cluster head selection.

Keywords:
clusteringfuzzy logicwireless sensor network

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

500
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K

Related Experiment Videos

Last Updated: Jun 20, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K
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

500
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless sensor networks (WSNs) require efficient resource management for longevity.
  • Clustering with careful cluster head (CH) selection is a key WSN resource management technique.

Purpose of the Study:

  • To present a novel centralized clustering algorithm for WSNs.
  • To enhance WSN lifetime through optimized fuzzy logic-based CH selection.

Main Methods:

  • A Type-1 fuzzy logic controller is used for CH probability inference.
  • The controller employs three distinct, optimized knowledge bases (KBs) tailored to different network stages.
  • Particle Swarm Optimization (PSO) tunes KB rule weights to maximize WSN lifetime.

Main Results:

  • The optimized fuzzy logic approach significantly improved WSN lifetime compared to existing methods.
  • PSO effectively adjusted KB rule weights, demonstrating its utility in complex WSN systems.
  • Analysis of rule weight changes provides insights for future controller design.

Conclusions:

  • The proposed optimized fuzzy logic clustering algorithm effectively extends WSN operational lifespan.
  • Dynamic, optimized knowledge bases are crucial for adaptive WSN resource management.
  • The method offers a robust solution for enhancing WSN performance and longevity.