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Updated: Feb 28, 2026

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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HEOCP: Hybrid Energy-Optimized Clustering Protocol for WSNs Using Analytical Modeling and Deep Learning Integration.

Yen-Wu Ti1, Rei-Heng Cheng1, Songlin Wei1,2

  • 1School of Information Engineering, Xiamen Ocean Vocational College, Xiamen 361100, China.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
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This summary is machine-generated.

This study introduces a Hybrid Energy-Optimized Clustering Protocol (HEOCP) for Wireless Sensor Networks (WSNs). HEOCP enhances IoT device longevity by intelligently managing energy consumption through hybrid analytical and deep learning methods.

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) are crucial for the Internet of Things (IoT).
  • Limited sensor node energy significantly restricts WSN operational lifetime.
  • Efficient energy management is essential for sustainable IoT applications.

Purpose of the Study:

  • To introduce a Hybrid Energy-Optimized Clustering Protocol (HEOCP) for extending WSN lifetime.
  • To enhance energy efficiency and network performance in IoT deployments.
  • To develop a sustainable energy management paradigm for WSNs.

Main Methods:

  • Developed an analytical framework for distance-constrained cluster-head (CH) eligibility and optimal cluster count.
  • Employed a genetic algorithm (GA) for optimal CH configuration.
Keywords:
LEACH protocolgenetic algorithmwireless sensor networks

Related Experiment Videos

Last Updated: Feb 28, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K
  • Utilized a ResNet-50 deep network for training GA configurations and real-time CH prediction.

    • Main Results:

    • HEOCP extends network lifetime by up to 60% compared to LEACH and GA-based methods.
    • Effectively delays first-node death and improves overall energy efficiency.
    • The hybrid GA-ResNet framework demonstrates high scalability and computational efficiency.

    Conclusions:

    • Integrating analytical energy modeling with deep learning offers a powerful approach for intelligent WSN energy management.
    • HEOCP provides a sustainable and effective solution for large-scale IoT deployments.
    • The proposed protocol significantly enhances the longevity and efficiency of WSNs.