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Energy to Drive Translocation

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Related Experiment Video

Updated: Jun 18, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

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

Published on: September 8, 2023

Quantum-inspired hybrid optimization framework for energy-efficient clustering and routing in wireless sensor

Ayush Mahanta1, Rohan Gupta2, Rachit Manchanda1

  • 1Department of Computer Science Engineering, University Institute of Engineering, ChandigarhUniversity, Mohali, Punjab, 140413, India.

Scientific Reports
|June 16, 2026
PubMed
Summary

This study introduces QIHOR-WSN, a novel framework optimizing wireless sensor networks (WSNs) for extended lifespan. By integrating quantum-inspired and genetic algorithms, it significantly enhances energy efficiency and network stability.

Keywords:
ClusteringEnergy efficiencyGenetic algorithmHybrid metaheuristicInternet of thingsLoad balancingMulti-objective optimizationNetwork lifetimeQuantum particle swarm optimizationQuantum-inspired optimizationRoutingWireless sensor networks

Related Experiment Videos

Last Updated: Jun 18, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

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) face energy depletion and limited network lifetime in large-scale, heterogeneous deployments.
  • Existing methods often treat clustering and routing separately, leading to suboptimal performance and poor adaptability.
  • Resource constraints and uneven node distribution exacerbate these challenges.

Purpose of the Study:

  • To propose QIHOR-WSN, a Quantum-Inspired Hybrid Optimization Framework for joint clustering and routing in WSNs.
  • To address the dual challenges of energy depletion and network lifetime.
  • To improve adaptability to dynamic network conditions and enhance overall network performance.

Main Methods:

  • Integration of Quantum Particle Swarm Optimization (QPSO) with a Genetic Algorithm (GA) for balanced exploration-exploitation.
  • Development of a multi-objective fitness criterion considering residual energy, node density, communication distance, and network stability.
  • Introduction of a density-load-aware radio energy model accounting for spatial heterogeneity and interference.

Main Results:

  • QIHOR-WSN consistently outperformed five baseline protocols in simulations.
  • Achieved 41-120% network lifetime improvement (FND) against classical protocols (LEACH, DEEC).
  • Demonstrated 21.5% longer FND, 10.2% lower energy consumption, 27.9% extended high-PDR operation, and 22.6% lower end-to-end delay compared to a hybrid baseline.

Conclusions:

  • QIHOR-WSN offers significant improvements in network lifetime, energy efficiency, and data reliability.
  • The framework's robustness, scalability, and adaptability make it suitable for next-generation WSN applications.
  • Confirms the practical suitability for industrial IoT, environmental monitoring, and smart city infrastructure.