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Updated: Jan 15, 2026

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

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Energy efficient clustering in industrial Iot using a quantum informed artificial hummingbird optimization algorithm.

S Rajkumar1, R Gopalakrishnan2

  • 1Department of Computer Science and Engineering, K. S. Rangasamy College of Technology, Tiruchengode, 637215, Tamil Nadu, India. rajkumars@ksrct.ac.in.

Scientific Reports
|October 10, 2025
PubMed
Summary
This summary is machine-generated.

A new Energy Efficient Quantum-Informed Artificial Hummingbird Optimization Algorithm (EEQIAHBOA) enhances Industrial Internet of Things (IIoT) networks. This method boosts energy efficiency by 32.12% and network lifetime by 35.62%.

Keywords:
Cluster head (CHs)ClusteringEnergy efficiencyInternet of things (IoT)Live IoT operating nodesQuantum-Informed artificial hummingbird optimization algorithm

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Last Updated: Jan 15, 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

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Area of Science:

  • Computer Science
  • Engineering
  • Artificial Intelligence

Background:

  • Industrial Internet of Things (IIoT) networks face challenges with complexity, real-time processing, and massive data volumes.
  • Clustering is crucial in IIoT for organizing devices and data to improve resource utilization and network management.
  • Swarm intelligence optimization algorithms are employed to select optimal Cluster Heads (CHs) and ensure reliable routing for enhanced performance.

Purpose of the Study:

  • To propose an Energy Efficient Quantum-Informed Artificial Hummingbird Optimization Algorithm (EEQIAHBOA) for IIoT networks.
  • To address the critical issue of energy preservation in IIoT systems.
  • To maximize the performance and reliability of industrial IoT applications through optimized clustering and routing.

Main Methods:

  • Development of the EEQIAHBOA, integrating quantum-informed principles with artificial hummingbird optimization.
  • Implementation of EEQIAHBOA as a routing algorithm, determining information heuristic factors and an efficient encoding scheme.
  • Application of EEQIAHBOA as a clustering algorithm, considering residual energy, node distance, and energy consumption for CH selection.

Main Results:

  • EEQIAHBOA demonstrated a 32.12% improvement in energy efficiency.
  • A 35.62% enhancement in network lifetime was achieved compared to baseline approaches.
  • The algorithm proved effective in gathering information and transmitting it to the base station for decision-making.

Conclusions:

  • The EEQIAHBOA offers a significant advancement in energy efficiency and network longevity for IIoT.
  • The proposed algorithm provides a reliable solution for routing and clustering in complex industrial environments.
  • EEQIAHBOA contributes to more cost-effective and dependable industrial applications by optimizing resource management and energy preservation.