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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Sleep-wakeup based secure multipath routing in wsn using fennec fox optimized deep learning framework.

Mohammad Bilal J1, D Suresh2, R Karthikeyan3

  • 1Department of Information Technology, Annamalai University, Chidambaram, India. mohammadbilal1987@gmail.com.

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|December 4, 2025
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Summary
This summary is machine-generated.

This study introduces a novel framework for wireless sensor networks (WSNs) to conserve energy and extend network lifespan. The Secure Clustering and Sleep-Wakeup based Energy Efficient Routing (SCS-EEF) framework significantly reduces energy consumption and latency.

Keywords:
Deep learningFennec Fox optimizationFuzzy C meansSecure routingTemporal convolutional network

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) are crucial for applications like environmental monitoring and smart cities.
  • WSNs suffer from high energy consumption, limiting their operational lifetime and data integrity.
  • Existing routing methods often lead to congestion and rapid energy depletion due to redundant transmissions and collisions.

Purpose of the Study:

  • To propose a novel Secure Clustering and Sleep-Wakeup based Energy Efficient Routing (SCS-EEF) framework for WSNs.
  • To enhance energy efficiency and prolong the network lifetime of WSNs.
  • To address the challenges of redundant retransmissions and collisions in WSN data routing.

Main Methods:

  • Implemented Fuzzy C Means based Balanced Iterative Reducing and Clustering Using Hierarchies (Fuzzy-BIRCH) for improved cluster formation.
  • Utilized Fennec Fox Optimization (FFO) for optimal cluster head selection (CHS) and balanced energy distribution.
  • Developed an energy-saving dynamic sleep-wakeup schedule to minimize redundant transmissions.
  • Employed a hybrid Temporal Convolutional Network-BiGRU (TCN-BiGRU) for multipath route prediction based on data urgency.

Main Results:

  • The SCS-EEF framework demonstrated significant reductions in energy consumption: 39.3% over HBWCO, 29.2% over IBORSDFFNL, and 26.1% over EER-CGHHOA.
  • Latency was reduced by 10.7% compared to HBWCO, 9.42% over IBORSDFFNL, and 7.4% over EER-CGHHOA.
  • The proposed methods effectively improved cluster formation, energy distribution, and reduced premature node failures.

Conclusions:

  • The SCS-EEF framework offers a substantial improvement in energy efficiency and network lifetime for WSNs.
  • The integration of Fuzzy-BIRCH, FFO, and TCN-BiGRU effectively tackles WSN energy consumption and latency issues.
  • This research provides a promising solution for sustainable and efficient WSN operations in various applications.