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Enhancing Clustering Efficiency in Heterogeneous Wireless Sensor Network Protocols Using the K-Nearest Neighbours

Abdulla Juwaied1, Lidia Jackowska-Strumillo1, Artur Sierszeń1

  • 1Institute of Applied Computer Science, Lodz University of Technology, ul. Stefanowskiego 18, 90-537 Lodz, Poland.

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Summary
This summary is machine-generated.

This study introduces a novel K-Nearest Neighbours (KNN) algorithm to optimize clustering in Wireless Sensor Networks (WSNs). The approach enhances energy efficiency, reduces connection distances, and extends network lifespan for improved performance.

Keywords:
DECKNNLEACHSEPTEENcluster head positionclusteringenergy consumptionsensors

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) are critical for data collection but face challenges with energy consumption and network lifespan.
  • Effective clustering protocols are essential for secure connections and stable network lifetime in WSNs.
  • Existing protocols like LEACH, SEP, TEEN, and DEC have limitations in energy efficiency and connection optimization.

Purpose of the Study:

  • To introduce a novel K-Nearest Neighbours (KNN) algorithm for optimizing node selection and clustering in WSNs.
  • To improve energy efficiency, reduce network connection lengths, and extend the operational lifetime of heterogeneous WSNs.
  • To evaluate the efficacy of the KNN algorithm in enhancing four established WSN protocols: LEACH, SEP, TEEN, and DEC.

Main Methods:

  • Implementation of the K-Nearest Neighbours (KNN) algorithm to optimize clustering mechanisms within WSN protocols.
  • Modification and simulation of four distinct WSN protocols (LEACH, SEP, TEEN, DEC) using the proposed KNN approach.
  • Performance evaluation through MATLAB simulations focusing on energy consumption, connection distances, and network lifetime.

Main Results:

  • The KNN-optimized protocols demonstrated shorter distances between cluster heads and sensor nodes.
  • Significant reductions in overall energy consumption were observed across the modified protocols.
  • The proposed KNN-based approach led to a notable increase in the overall network lifetime.
  • Enhanced network operational efficiency and security were achieved through optimized clustering.

Conclusions:

  • The K-Nearest Neighbours (KNN) algorithm offers a robust and effective solution for energy management in Wireless Sensor Networks.
  • Optimizing node selection and clustering with KNN significantly improves key performance metrics in WSNs.
  • The proposed method provides a valuable enhancement for heterogeneous WSNs, extending their practical applicability and lifespan.