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Angle Based Critical Nodes Detection (ABCND) for Reliable Industrial Wireless Sensor Networks.

Shailendra Shukla1

  • 1Computer Science and Engineering Department, MNNIT Allahabad, Prayagraj, India.

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|May 11, 2023
PubMed
Summary

Detecting critical nodes in Wireless Sensor Networks (WSN) is vital for preventing failures. The proposed ABCND algorithm efficiently identifies these critical nodes using Received Signal Strength Indicator (RSSI) data, enhancing network reliability.

Keywords:
Critical Node DetectionEnergyIndustrial Wireless Sensor Networks(IWSN)Reliability

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Area of Science:

  • Computer Science
  • Network Engineering
  • Cybersecurity

Background:

  • Node failures in Wireless Sensor Networks (WSN) pose significant risks, including economic loss and safety hazards.
  • Effective management of WSN topology, particularly critical node detection and protection, is crucial for ensuring network reliability and preventing cascading failures.

Purpose of the Study:

  • To address the challenge of critical node detection in WSNs.
  • To propose and evaluate a novel two-phase algorithm, ABCND, for enhanced critical node detection and network resilience.

Main Methods:

  • Phase I: A 2D Critical Node (C-N) detection algorithm utilizing neighbor Received Signal Strength Indicator (RSSI) information.
  • Phase II: A correlation-based reliable RSSI approach to bolster node resilience against adversarial threats.
  • Implementation and comparison of the proposed ABCND algorithm against existing state-of-the-art C-N detection methods.

Main Results:

  • The ABCND algorithm demonstrates efficient convergence and critical node detection times.
  • Achieved 90% to 95% accuracy in identifying critical nodes.
  • Consumed 50% less energy compared to existing algorithms for critical node detection.

Conclusions:

  • The proposed ABCND algorithm offers a significant improvement in energy efficiency and accuracy for critical node detection in WSNs.
  • ABCND enhances overall network reliability and resilience, mitigating the risks associated with node failures.