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Related Concept Videos

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.
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Pharmacokinetic Models: Comparison and Selection Criterion01:26

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks.

Gulnaz Ahmed1, Jianhua Zou2, Xi Zhao3

  • 1School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China. gulnazahmed@stu.xjtu.edu.cn.

Sensors (Basel, Switzerland)
|March 1, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a Markov chain model-based optimal cluster heads (MOCHs) selection for Wireless Sensor Networks (WSNs). MOCHs enhances network lifetime and energy efficiency by ensuring uniform load distribution compared to other clustering protocols.

Keywords:
Markov chain-based modelenergy efficiencymulti-hop routingnon-associated nodesoptimal number

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Last Updated: Mar 7, 2026

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) face challenges with energy consumption and uneven load distribution, impacting network lifetime.
  • Hierarchical clustering architectures are effective for addressing these energy distribution issues in WSNs.

Purpose of the Study:

  • To introduce a novel clustering protocol, Markov chain model-based optimal cluster heads (MOCHs) selection, for WSNs.
  • To improve energy efficiency and extend network lifetime through optimal cluster head selection and uniform load distribution.

Main Methods:

  • A novel clustering protocol, MOCHs, is proposed, utilizing a Markov chain model for optimal cluster head selection.
  • The Base Station (BS) controls the number of cluster heads, while cluster heads manage members to ensure uniform load distribution.
  • Extensive simulations were conducted using five quality metrics: network lifetime, stable/unstable regions, throughput, number of cluster heads, and transmission time.

Main Results:

  • The MOCHs protocol demonstrated significant improvements in network lifetime compared to SEED, ABC, ZBR, and CEEC.
  • MOCHs achieved network lifetimes of 1095, 2630, 3599, and 2045 rounds longer than SEED, ABC, ZBR, and CEEC, respectively.
  • The proposed model outperformed existing methods in terms of energy efficiency and network throughput.

Conclusions:

  • The MOCHs protocol effectively addresses uneven energy distribution in WSNs, leading to extended network lifetime.
  • MOCHs offers superior energy efficiency and network throughput compared to SEED, ABC, ZBR, and CEEC.
  • The proposed strategy provides a robust solution for optimizing cluster head selection in hierarchical WSNs.