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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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A Hybrid Mayfly-Aquila Optimization Algorithm Based Energy-Efficient Clustering Routing Protocol for Wireless Sensor

Gobi Natesan1, Srinivas Konda2, Rocío Pérez de Prado3

  • 1Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi 642003, Tamilnadu, India.

Sensors (Basel, Switzerland)
|September 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel energy-efficient clustering routing protocol for Wireless Sensor Networks (WSNs). The hybrid Mayfly-Aquila optimization algorithm enhances network lifetime and reduces energy consumption effectively.

Keywords:
Aquila optimization algorithmcluster headmayflyrouting protocolwireless sensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) face significant energy constraints due to battery-powered nodes, limiting network lifetime.
  • Clustering is a key strategy to extend WSN longevity and reduce energy consumption by organizing nodes.
  • Efficient Cluster Head (CH) selection and routing are critical for minimizing energy usage and maximizing network operational duration.

Purpose of the Study:

  • To propose an energy-efficient clustering routing protocol for Wireless Sensor Networks (WSNs).
  • To address the challenges of reducing energy consumption and extending the network lifetime in WSNs.
  • To introduce a hybrid optimization algorithm for optimal Cluster Head (CH) selection and routing.

Main Methods:

  • A hybrid Mayfly-Aquila optimization (MFA-AOA) algorithm is developed for WSN routing.
  • The Mayfly algorithm is utilized for optimal Cluster Head (CH) selection among network nodes.
  • The Aquila optimization algorithm is employed to determine the most efficient routes between CHs and the Base Station (BS).

Main Results:

  • The proposed MFA-AOA protocol demonstrated superior energy efficiency compared to existing methods.
  • Achieved reductions in energy consumption by 10.22%, 11.26%, and 14.28% in simulations.
  • Showcased improvements in normalized energy by 9.56%, 11.78%, and 13.76% over state-of-the-art approaches.

Conclusions:

  • The hybrid Mayfly-Aquila optimization algorithm offers a promising solution for energy-efficient routing in WSNs.
  • The proposed protocol effectively extends WSN network lifetime by optimizing CH selection and data routing.
  • This approach significantly enhances energy conservation, crucial for the practical deployment of WSNs.