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Cluster Sampling Method01:20

Cluster Sampling Method

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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...
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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.
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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Related Experiment Video

Updated: Oct 15, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Published on: September 8, 2023

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In-Network Data Aggregation for Ad Hoc Clustered Cognitive Radio Wireless Sensor Network.

Mohamad Rida Mortada1,2, Abbass Nasser1,2, Ali Mansour1

  • 1LABSTICC UMR CNRS 6285, ENSTA-Bretagne, 29806 Brest, France.

Sensors (Basel, Switzerland)
|October 26, 2021
PubMed
Summary
This summary is machine-generated.

This study optimizes clustered cognitive radio wireless sensor networks (CRSN) by analyzing spectrum sensing (SS) and in-network data aggregation (IDA). Results show IDA extends network lifespan and reduces collision rates and latency.

Keywords:
cognitive radioin-network data aggregationmultihop routingnetwork lifespanwireless sensor network

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Wireless Communications

Background:

  • Cognitive radio wireless sensor networks (CRSN) enable secondary users to access channels opportunistically.
  • Cluster heads (CHs) in CRSN perform spectrum sensing (SS), data gathering, and transmission to a base station.
  • Increasing clusters impacts energy consumption for SS and data transmission.

Purpose of the Study:

  • Investigate the impact of in-network data aggregation (IDA) on CRSN performance.
  • Determine the optimal number of clusters to extend network lifespan.
  • Analyze energy consumption, collision rates, and network latency.

Main Methods:

  • Theoretical derivation of collision rates and network latency.
  • Analysis of energy consumption for SS and data transmission in a clustered CRSN.
  • Optimization study for cluster number considering SS requirements and IDA effects.

Main Results:

  • In-network data aggregation (IDA) effectively extends CRSN network lifespan.
  • IDA significantly minimizes both primary/secondary transmission collision rates.
  • IDA contributes to reducing overall network latency.

Conclusions:

  • The proposed approach balances SS requirements and IDA benefits for enhanced CRSN performance.
  • Optimizing cluster numbers is crucial for maximizing network lifespan and efficiency.
  • IDA is a key technique for improving CRSN reliability and performance.