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A Distributed Clustering Algorithm Guided by the Base Station to Extend the Lifetime of Wireless Sensor Networks.

Antonio-Jesus Yuste-Delgado1, Juan-Carlos Cuevas-Martinez1, Alicia Triviño-Cabrera2

  • 1Department of Telecommunication Engineering, Universidad de Jaén, 23700 Linares, Spain.

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Summary
This summary is machine-generated.

This study introduces a novel distributed clustering algorithm for wireless sensor networks. By dynamically adjusting cluster heads and using a fuzzy-logic system, it significantly extends network operational life.

Keywords:
clusteringinterval Type-2 fuzzy systemwireless sensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Clustering algorithms are crucial for reducing energy consumption in Wireless Sensor Networks (WSNs).
  • Cluster head selection significantly impacts network performance and energy balance.
  • Existing centralized and distributed algorithms have limitations in efficiency and data requirements.

Purpose of the Study:

  • To propose a novel distributed clustering algorithm for WSNs that enhances network lifetime.
  • To dynamically form clusters and balance energy consumption among nodes.
  • To introduce a hybrid approach occasionally supported by the Base Station for network reconfiguration.

Main Methods:

  • A distributed clustering approach is presented, with occasional Base Station (BS) support.
  • The BS sends three messages to reconfigure the 'skip' value, adapting to network status.
  • Nodes use a fuzzy-logic system, specifically a Takagi-Sugeno-Kang model, to determine cluster head suitability.

Main Results:

  • The proposed algorithm dynamically forms clusters and balances energy consumption.
  • The fuzzy-logic system effectively manages cluster head selection.
  • Simulation results demonstrate a significant extension of network operability compared to existing methods.

Conclusions:

  • The novel distributed fuzzy-logic based clustering algorithm effectively extends WSN operational lifetime.
  • The hybrid approach combining distributed decisions with occasional BS support offers a robust solution.
  • The use of a Takagi-Sugeno-Kang fuzzy model provides an advantageous alternative to Mamdani models in this context.