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Updated: May 14, 2025

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

Energy efficient multipath routing in IoT-wireless sensor network via hybrid optimization and deep learning-based

G A Senthil¹, R Prabha², R Renuka Devi³

¹Department of Information Technology, Agni College of Technology, Chennai, India.

Network (Bristol, England)

|April 12, 2025

Summary

This study introduces a Hybrid Beluga Whale-Coati Optimization (HBWCO) algorithm for efficient data transmission in Wireless Sensor Networks (WSNs). HBWCO enhances routing reliability and throughput by considering multiple performance factors.

Keywords:

Deep learning cluster head selection data transmission hybrid optimization multipath routing

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Area of Science:

Computer Science
Network Engineering
Artificial Intelligence

Background:

Efficient data transmission is crucial for Wireless Sensor Networks (WSNs).
Traditional routing protocols often prioritize energy efficiency over other performance metrics.
Existing methods may not adequately address performance degradation factors in WSNs.

Purpose of the Study:

To propose a novel Hybrid Beluga Whale-Coati Optimization (HBWCO) algorithm for optimizing energy-efficient data transmission in WSNs.
To enhance routing reliability and throughput in WSNs by considering multiple factors beyond just energy.
To introduce a robust route maintenance strategy for link breakages.

Main Methods:

Initialization of sensor nodes and field dimensions.
K-means clustering for node grouping and Deep Q-Net for energy level prediction.
Hybrid Beluga Whale-Coati Optimization (HBWCO) for multipath routing selection based on reliability, residual energy, predicted energy, throughput, and traffic intensity.
Source Link Breakage Warning (SLBW) strategy for route maintenance.

Main Results:

The HBWCO algorithm achieved a reliability of 0.948.
The HBWCO approach demonstrated a throughput of 3496.
The proposed method offers a comprehensive approach to enhancing network energy efficiency and data transmission.

Conclusions:

The HBWCO algorithm provides an effective solution for data transmission and routing reliability in WSNs.
This novel approach surpasses traditional methods in performance.
The integration of optimization algorithms with deep learning enhances WSN performance.