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

13.9K
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...
13.9K
Sampling Plans01:23

Sampling Plans

838
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...
838
Optimal Foraging00:48

Optimal Foraging

13.3K
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
13.3K
Random Sampling Method01:09

Random Sampling Method

14.0K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures 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. Among the various sampling methods used by...
14.0K
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.0K
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...
1.0K
Sampling Methods: Overview01:06

Sampling Methods: Overview

2.0K
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
2.0K

You might also read

Related Articles

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

Sort by
Same author

Heterogeneous Structure Omnidirectional Strain Sensor Arrays With Cognitively Learned Neural Networks.

Advanced materials (Deerfield Beach, Fla.)·2023
Same author

SSD-EMB: An Improved SSD Using Enhanced Feature Map Block for Object Detection.

Sensors (Basel, Switzerland)·2021
Same author

SSD-TSEFFM: New SSD Using Trident Feature and Squeeze and Extraction Feature Fusion.

Sensors (Basel, Switzerland)·2020
Same author

A Behavior-Learned Cross-Reactive Sensor Matrix for Intelligent Skin Perception.

Advanced materials (Deerfield Beach, Fla.)·2020
Same author

Dilated Skip Convolution for Facial Landmark Detection.

Sensors (Basel, Switzerland)·2019
Same author

Frequency-Stable Ionic-Type Hybrid Gate Dielectrics for High Mobility Solution-Processed Metal-Oxide Thin-Film Transistors.

Materials (Basel, Switzerland)·2017
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: Jan 2, 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

1.0K

Energy-Efficient Cluster-Head Selection for Wireless Sensor Networks Using Sampling-Based Spider Monkey Optimization.

Jin-Gu Lee1, Seyha Chim1, Ho-Hyun Park1

  • 1School of Electrical and Electronics Engineering, Chung-Ang University; 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea.

Sensors (Basel, Switzerland)
|December 6, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for selecting cluster heads in wireless sensor networks (WSNs) to improve energy efficiency and network longevity. The sampling-based spider monkey optimization (SMO) method enhances network lifetime and stability.

Keywords:
SSMOECHSWSNsenergy efficient CH selectionnetwork lifetimenetwork stabilitysampling SMO

More Related Videos

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.
10:14

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

Published on: December 12, 2012

10.9K
Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

2.0K

Related Experiment Videos

Last Updated: Jan 2, 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

1.0K
Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.
10:14

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

Published on: December 12, 2012

10.9K
Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

2.0K

Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Wireless Sensor Networks (WSNs) face challenges in extending operational lifetime and stability due to energy consumption constraints.
  • Clustering improves energy efficiency in WSNs but existing cluster-head selection methods are complex and prone to issues like increased computation and inaccurate node selection.

Purpose of the Study:

  • To propose a novel, efficient cluster-head selection method for WSNs that overcomes the limitations of existing location-based approaches.
  • To enhance the lifetime and stability of WSNs through an optimized energy-efficient cluster head selection strategy.

Main Methods:

  • Introduced the sampling-based spider monkey optimization (SMO) method for cluster-head selection in WSNs.
  • Developed a new protocol named SSMOECHS (sampling-based spider monkey optimization and energy-efficient cluster head selection).
  • Evaluated SSMOECHS against LEACH-C, PSO-C, and SMOTECP in both homogeneous and heterogeneous WSN environments.

Main Results:

  • The proposed SSMOECHS method significantly improves network lifetime and stability compared to existing protocols.
  • Average improvements in network lifetime and stability periods were observed across different setups, demonstrating the effectiveness of the sampling-based SMO approach.

Conclusions:

  • The sampling-based SMO approach effectively resolves issues associated with traditional location-based cluster-head selection in WSNs.
  • SSMOECHS offers a superior solution for enhancing WSN energy efficiency, lifetime, and stability.