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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Redefining IoT networks for improving energy and memory efficiency through compressive sensing paradigm.

S Balamurali1, M Kathirvelu2, SatheeshKumar Palanisamy3

  • 1Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, 641407, India. balamuraliselvaraj@gmail.com.

Scientific Reports
|July 27, 2025
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Summary

This study introduces NSPL-HCS, a novel framework for Wireless Sensor Networks (WSNs) that improves energy efficiency and network lifetime. The new method enhances data compression and transmission, overcoming resource limitations in the Internet of Things (IoT).

Keywords:
Compressive sensingEnergy efficiencyInternet of thingsMemory constraintsOptimization algorithmsWireless sensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) are crucial for the Internet of Things (IoT) but suffer from resource constraints (energy, memory).
  • Existing energy management techniques in WSNs lead to rapid battery depletion and reduced network lifespan.
  • Memory limitations in sensor nodes hinder data storage, impacting WSN productivity and scalability.

Purpose of the Study:

  • To address energy and memory limitations in WSNs for enhanced IoT applications.
  • To introduce a novel hybrid compressive sensing framework, NSPL-HCS, for improved WSN performance.
  • To optimize WSN operations including clustering, data compression, and transmission.

Main Methods:

  • Developed NSPL-HCS (Novel Smoothed Projected Landweber based Hybrid Compressive Sensing).
  • Integrated enhanced Particle Swarm Optimization and Grey Wolf Optimization with compressive sensing.
  • Enhanced key WSN operations: cluster creation, head selection, data compression, transmission, and reconstruction.

Main Results:

  • NSPL-HCS demonstrated significant improvements in throughput, residual energy, and the number of alive nodes.
  • Achieved better performance in terms of first and half dead nodes, leading to extended network lifetime.
  • Simulation results validated the framework's reliability and feasibility on test functions.

Conclusions:

  • NSPL-HCS effectively enhances WSN performance by optimizing energy consumption and data handling.
  • The framework overcomes critical resource limitations, paving the way for broader WSN adoption in IoT.
  • NSPL-HCS offers a reliable and feasible solution for improving the longevity and efficiency of WSNs.