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Related Experiment Video

Updated: May 8, 2025

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

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Published on: September 8, 2023

451

Hybrid optimization for efficient 6G IoT traffic management and multi-routing strategy.

J Logeshwaran1, Shobhit K Patel2, Om Prakash Kumar3

  • 1Department of Computer Science, Christ University, Bengaluru, Karnataka, 560029, India.

Scientific Reports
|December 27, 2024
PubMed
Summary

This study introduces a novel framework for smart city air pollution monitoring in 6G networks. It enhances Internet of Things (IoT) efficiency and network stability using quantum-inspired algorithms and deep reinforcement learning.

Keywords:
Air pollution monitoringDeep reinforcement learningDynamic cluster head selectionIoTMobile ad-hoc networkingQuantum entanglement and mobility metricQuantum genetic algorithmQuantum-inspired clustering algorithm

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Area of Science:

  • Computer Science
  • Telecommunications Engineering
  • Environmental Monitoring

Background:

  • The proliferation of Internet of Things (IoT) devices presents significant traffic management challenges in 6G communication systems.
  • Reliable air pollution monitoring in smart cities requires scalable and accurate data collection frameworks.

Purpose of the Study:

  • To develop an all-inclusive framework for reliable air pollution monitoring in smart cities leveraging 6G capabilities.
  • To enhance IoT data accuracy, coverage, and scalability through advanced techniques.

Main Methods:

  • Proposed Quantum-inspired Clustering Algorithm (QCA) and Quantum Entanglement and Mobility Metric (MoM) for efficient clustering.
  • Implemented Dynamic Cluster Head (CH) selection using Deep Reinforcement Learning (DRL) for network sustainability.
  • Utilized a hybrid Quantum Genetic Algorithm and Ant Colony Optimization (QGA-ACO) for data routing.

Main Results:

  • Achieved 95% deployment coverage, demonstrating broad network reach.
  • Obtained a cluster stability index of 0.97, indicating robust network performance.
  • Demonstrated 95% CH selection efficiency, outperforming traditional methods.

Conclusions:

  • The proposed framework effectively addresses IoT traffic and multi-routing challenges in 6G communication systems.
  • The integration of quantum-inspired algorithms and DRL ensures a robust and scalable IoT ecosystem for smart city applications.