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Updated: May 28, 2026

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

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

Published on: September 8, 2023

Charger placement optimization in wireless sensor networks using hybrid graph coloring and Enhanced Aquila

P Neelagandan¹, S Balaji²

¹Department of Mathematics, Vellore Institute of Technology, Chennai Campus, Chennai, Tamil Nadu, 600127, India.

Scientific Reports

|May 26, 2026

Summary

This study presents a two-stage optimization framework for efficient wireless sensor network (WSN) charger deployment. The proposed method ensures high sensor coverage and energy efficiency for sustainable WSNs.

Keywords:

Degree of saturation Enhanced Aquila Optimization Grundy coloring Optimal position of chargers Wireless chargers

Related Experiment Videos

Last Updated: May 28, 2026

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

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

Published on: September 8, 2023

Area of Science:

Computer Science
Electrical Engineering
Network Engineering

Background:

Wireless sensor networks (WSNs) require efficient charger deployment for sustainability.
Existing methods lack optimal solutions for charger placement and quantity.
Intelligent deployment is crucial for network longevity and performance.

Purpose of the Study:

To propose a two-stage optimization framework for charger deployment in WSNs.
To determine the minimal number of chargers and their optimal positions.
To enhance WSN sustainability through efficient energy management.

Main Methods:

A hybrid algorithm combining degree-based saturation and Grundy coloring for charger quantity determination.
An Enhanced Aquila Optimization algorithm for optimal charger placement under coverage and power constraints.
Comparative analysis against standard optimization algorithms.

Main Results:

The Enhanced Aquila Optimization algorithm achieved 99% sensor coverage, surpassing existing methods.
Improved coverage by 6% compared to the standard Aquila Optimization algorithm.
Demonstrated faster convergence and significant performance improvements.

Conclusions:

The proposed framework offers a practical, scalable, and energy-efficient solution for WSN charger deployment.
The Enhanced Aquila Optimization algorithm significantly enhances network coverage and efficiency.
This approach contributes to the sustainability of wireless sensor networks.