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Updated: May 14, 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

Dynamic community evolution analysis for performance optimization in large scale RFID networks.

M Thurai Pandian1, H Anwar Basha2, Tanvir Habib Sardar3

  • 1School of Computing, SRM Institute of Science and Technology, Tiruchirapalli, Tamil Nadu, India.

Scientific Reports
|May 12, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a Novel Community Evolution Analysis (NCEA) method for selecting cluster heads in large-scale Radio Frequency Identification (RFID) networks. NCEA improves energy efficiency and performance in intelligent transportation systems and IoT applications.

Keywords:
Cluster headComplex networksEvolution frameworkNCEARFIDWeak and strong events

Related Experiment Videos

Last Updated: May 14, 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:

  • Large-scale Radio Frequency Identification (RFID) networks are crucial for object tracking in Intelligent Transportation Systems (ITS) and Internet of Things (IoT) applications.
  • Existing clustering methods struggle with identifying weak events, impacting cluster head selection efficiency.
  • Satellite interference is a concern for RFID-based road mapping.

Purpose of the Study:

  • To propose a Novel Community Evolution Analysis (NCEA) method for robust cluster head selection in large-scale RFID networks.
  • To enhance energy management and overall network performance.
  • To develop an RFID-based road mapping solution independent of satellite interference.

Main Methods:

  • The Novel Community Evolution Analysis (NCEA) method was developed for cluster head selection.
  • NCEA incorporates both strong and weak event detection.
  • Cluster heads are selected based on high energy and proximity to neighboring nodes, considering events like formation, disappearance, and merging.

Main Results:

  • The NCEA approach achieved 98% accuracy.
  • Vulnerability rate was 20%, with a success rate of 89%.
  • Observed latency of 11.4 seconds and a throughput of 93%.

Conclusions:

  • The NCEA method effectively addresses limitations of traditional clustering by incorporating weak event detection.
  • The proposed method significantly enhances energy efficiency and performance in large-scale RFID networks.
  • NCEA demonstrates a viable solution for RFID-based road mapping in ITS and IoT environments.