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Related Experiment Video

Updated: Aug 29, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks.

R Muthukkumar1, Lalit Garg2, K Maharajan3

  • 1Department of Information Technology, National Engineering College, Kovilpatti, Thoothukudi, Tamil Nadu, India.

Peerj. Computer Science
|September 12, 2022
PubMed
Summary

This study introduces a genetic algorithm-based energy-aware multi-hop clustering (GA-EMC) scheme for heterogeneous wireless sensor networks (WSNs). GA-EMC enhances network lifetime and stability while minimizing delay compared to existing methods.

Keywords:
ClusteringEnd-to-end delayGenetic algorithmHeterogeneous wireless sensor networksMulti-hop routingNetwork lifetimeThroughput

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Heterogeneous wireless sensor networks (HWSNs) present significant challenges for energy-aware clustering due to nodes with varying energy levels and consumption constraints.
  • Optimizing cluster heads (CHs) is crucial for minimizing energy consumption, reducing delay, and extending network lifetime in WSNs.
  • Existing clustering approaches face difficulties in addressing the unique demands of HWSNs.

Purpose of the Study:

  • To propose a novel genetic algorithm-based energy-aware multi-hop clustering (GA-EMC) scheme tailored for HWSNs.
  • To optimize the selection of cluster heads (CHs) and their positions within the network to improve energy efficiency.
  • To mitigate the 'hot spot' problem in WSNs by strategically deploying nodes.

Main Methods:

  • Developed a GA-EMC scheme utilizing a genetic algorithm to determine optimal CHs and their locations.
  • Calculated chromosome fitness based on distance, optimal CH selection, and residual energy of nodes.
  • Implemented multi-hop communication to enhance energy efficiency in HWSNs.
  • Strategically deployed supernodes in areas farther from the sink to balance energy consumption.

Main Results:

  • The GA-EMC scheme demonstrated a significantly extended network lifetime in simulations.
  • GA-EMC achieved improved network stability compared to existing clustering approaches.
  • The proposed scheme effectively minimized communication delay in heterogeneous WSN environments.

Conclusions:

  • The GA-EMC scheme offers a superior solution for energy-aware clustering in HWSNs.
  • The genetic algorithm effectively optimizes CH selection and network configuration for prolonged operational life.
  • GA-EMC provides a robust method for enhancing the performance and longevity of wireless sensor networks.