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

Nodal Analysis01:10

Nodal Analysis

2.1K
Nodal analysis is a fundamental method in electrical engineering used to simplify the process of circuit analysis. This method revolves around the concept of using node voltages as the primary variables for circuit analysis. The objective is to determine the voltage at each node in a circuit, which can then be used to find other quantities of interest, such as currents through specific components.
Consider, for instance, a simple circuit composed of three nodes and three resistors, as shown in...
2.1K
Cluster Sampling Method01:20

Cluster Sampling Method

15.4K
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...
15.4K

You might also read

Related Articles

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

Sort by
Same author

Clinical presentation and mutational spectrum in a series of 166 patients with classical 21-hydroxylase deficiency from South China.

Clinica chimica acta; international journal of clinical chemistry·2018
Same author

Association Between Thyroid-Stimulating Hormone and Renal Function: a Mendelian Randomization Study.

Kidney & blood pressure research·2018
Same author

[Influence of enteral nutrition initiation timing on curative effect and prognosis of acute respiratory distress syndrome patients with mechanical ventilation].

Zhonghua wei zhong bing ji jiu yi xue·2018
Same author

A novel electrochemical aptamer-antibody sandwich assay for the detection of tau-381 in human serum.

The Analyst·2018
Same author

Instrumental variable analysis in the presence of unmeasured confounding.

Annals of translational medicine·2018
Same author

Tobacco product initiation is correlated with cross-product changes in tobacco harm perception and susceptibility: Longitudinal analysis of the Population Assessment of Tobacco and Health youth cohort.

Preventive medicine·2018
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: Mar 15, 2026

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

1.2K

An Effective Cuckoo Search Algorithm for Node Localization in Wireless Sensor Network.

Jing Cheng1, Linyuan Xia2

  • 1Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China. chengjing921@126.com.

Sensors (Basel, Switzerland)
|September 3, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a Cuckoo Search (CS) algorithm for wireless sensor network (WSN) localization, enhancing node positioning accuracy and efficiency. The optimized CS algorithm reduces localization errors and improves convergence rates for energy-limited sensor networks.

Keywords:
average localization errorconvergence ratecuckoo search algorithmlocalizationwireless sensor network

More Related Videos

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

6.2K
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

8.7K

Related Experiment Videos

Last Updated: Mar 15, 2026

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

1.2K
Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

6.2K
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

8.7K

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) are increasingly used in various applications, necessitating accurate node localization.
  • Energy efficiency and computational complexity are critical challenges in WSN localization due to limited node resources.
  • Existing localization methods often struggle with balancing accuracy, convergence speed, and energy consumption.

Purpose of the Study:

  • To propose an effective Cuckoo Search (CS) algorithm tailored for node localization in Wireless Sensor Networks (WSNs).
  • To enhance localization performance by reducing computational complexity and communication overhead, thereby extending the lifetime of energy-limited sensor nodes.
  • To improve the convergence rate and minimize localization errors compared to standard algorithms.

Main Methods:

  • A modified Cuckoo Search (CS) algorithm is developed for WSN node localization.
  • Step size modification is implemented to accelerate convergence towards the global optimal solution.
  • Mutation probability is introduced, based on solution fitness, to prevent local convergence.
  • Population range restriction is employed to minimize energy consumption from redundant searches.

Main Results:

  • The proposed CS algorithm demonstrates a faster convergence rate compared to the standard CS algorithm.
  • Experimental results show a significant reduction in average localization error when using the proposed CS algorithm.
  • The algorithm's performance is evaluated against the Particle Swarm Optimization (PSO) algorithm, with the CS algorithm showing superior results.
  • The study analyzes the impact of parameters such as anchor density, node density, and communication range on localization accuracy and success ratio.

Conclusions:

  • The modified Cuckoo Search (CS) algorithm offers an effective solution for node localization in Wireless Sensor Networks (WSNs).
  • The proposed approach successfully improves convergence speed and reduces average localization error, outperforming standard CS and PSO algorithms.
  • The findings highlight the potential of the optimized CS algorithm for enhancing the performance and longevity of energy-constrained WSNs.