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Data Collection Strategy Based on OSELM and Gray Wolf Optimization Algorithm for Wireless Sensor Networks.

Yang Bai1, Li Cao1, Shuxin Wang1

  • 1School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou, 325035, China.

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Summary
This summary is machine-generated.

This study introduces an energy-efficient clustering strategy for Health Wireless Sensor Networks (HWSNs). It optimizes data collection, reduces energy consumption, and enhances network reliability and lifetime.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Biomedical Engineering

Background:

  • Health Wireless Sensor Networks (HWSNs) face challenges in energy consumption and network lifetime.
  • Clustering is an effective method for improving data collection efficiency in HWSNs.
  • Optimizing cluster head selection and the number of clusters is crucial for HWSN performance.

Purpose of the Study:

  • To propose an energy-efficient and reliable clustering data collection strategy for HWSNs.
  • To enhance data collection efficiency and prolong network lifetime.
  • To reduce energy consumption in HWSNs.

Main Methods:

  • A clustering data collection strategy for HWSNs is proposed.
  • An extreme learning machine (ELM) neural network model is established by the sink.
  • Cluster member nodes select cluster heads based on remaining energy, neighbor count, and distance to the sink.
  • The online sequence extreme learning machine (OS-ELM) is used for adaptive cluster head selection.
  • The gray wolf optimization algorithm is employed to optimize the number of clusters.

Main Results:

  • The proposed algorithm significantly improves data collection efficiency.
  • Energy consumption is substantially reduced.
  • Network reliability is comprehensively enhanced.
  • The overall network lifetime is prolonged.

Conclusions:

  • The developed strategy effectively addresses energy consumption and efficiency issues in HWSNs.
  • The integration of OS-ELM and gray wolf optimization yields superior performance compared to existing methods.
  • The proposed approach offers a promising solution for reliable and long-lasting HWSN deployments.