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Related Experiment Video

Updated: Jul 9, 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

Flamingo Jelly Fish search optimization-based routing with deep-learning enabled energy prediction in WSN data

Dhanabal Subramanian¹, Sangeetha Subramaniam², Krishnamoorthy Natarajan³

¹Department of Computer Science and Engineering, Kongunadu College of Engineering and Technology, Trichy, India.

Network (Bristol, England)

|December 4, 2023

Summary

This study introduces a novel dynamic clustering and routing model for wireless sensor networks (WSN) to enhance energy efficiency and network lifespan. The Flamingo Jellyfish Search Optimization (FJSO) model optimizes energy prediction and route selection, significantly improving network performance.

Keywords:

Deep Neuro-Fuzzy Network Flamingo search algorithm fuzzy system jellyfish search optimization

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Last Updated: Jul 9, 2025

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

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Area of Science:

Computer Science
Electrical Engineering
Network Engineering

Background:

Wireless Sensor Networks (WSN) face significant limitations due to finite energy resources, impacting overall network lifetime.
Effective energy management and efficient routing are critical for sustaining WSN operations.

Purpose of the Study:

To design and evaluate a dynamic clustering and routing model for WSNs to address energy limitations.
To improve network lifetime and performance through optimized energy prediction and route selection.

Main Methods:

Simulation of dynamic clustering in WSNs using energy, mobility, trust, and Link Life Time (LLT) models.
Utilization of a deep neuro-fuzzy network (DNFN) for residual energy prediction and fuzzy system for dynamic workload balancing.
Application of the Flamingo Jellyfish Search Optimization (FJSO) algorithm for tuning fuzzy system weights and identifying optimal data transmission routes.

Main Results:

The FJSO model achieved a maximum energy of 0.657J, minimum distance of 0.739m, and minimum delay of 0.649s.
The model demonstrated a trust score of 0.849 and a throughput of 0.885 Mbps.
Experimental validation using MATLAB confirmed the effectiveness of the proposed FJSO model.

Conclusions:

The developed dynamic clustering and routing model, optimized by FJSO, significantly enhances WSN performance.
The approach effectively balances node workloads, predicts energy consumption, and selects optimal routes, leading to improved network longevity and efficiency.