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Optimal Cluster Head Selection in WSN with Convolutional Neural Network-Based Energy Level Prediction.

Sasikumar Gurumoorthy1, Parimella Subhash2, Rocio Pérez de Prado3

  • 1Department of Computer Science and Engineering, Jerusalem College of Engineering, Chennai 600100, India.

Sensors (Basel, Switzerland)
|December 23, 2022
PubMed
Summary

This study introduces an optimal cluster head selection model for Wireless Sensor Networks (WSN) using Bald Eagle Assisted SSA (BEA-SSA). The method enhances energy efficiency and security, achieving a high Packet Delivery Ratio (PDR) compared to existing algorithms.

Keywords:
RSSIWSNimproved DCNNsecuritytrust evaluation

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Security

Background:

  • Wireless Sensor Networks (WSN) are crucial for data collection, but energy consumption and security remain significant challenges.
  • Existing clustering protocols like Low Energy Adaptive Clustering Hierarchy (LEACH) face limitations due to arbitrary cluster head selection, impacting network efficiency.
  • The need for robust and energy-aware routing protocols is paramount for the sustained operation of WSNs.

Purpose of the Study:

  • To develop an optimal cluster head selection (CHS) model for secure and energy-aware routing in Wireless Sensor Networks (WSN).
  • To enhance the reliability and efficiency of WSNs by improving cluster head selection criteria.
  • To address the limitations of arbitrary cluster head selection in existing protocols.

Main Methods:

  • An optimal CHS model was developed, considering factors like distance, energy, security risk, delay, trust (direct and indirect), and Received Signal Strength Indicator (RSSI).
  • Energy levels were predicted using an improved Deep Convolutional Neural Network (DCNN).
  • The Bald Eagle Assisted Sparrow Search Algorithm (BEA-SSA) was employed for selecting the optimal cluster head in WSN.

Main Results:

  • The BEA-SSA model demonstrated superior performance in cluster head selection based on trust, RSSI, and security metrics.
  • The proposed model achieved a high Packet Delivery Ratio (PDR) of 0.98 for 100 nodes at 500 rounds.
  • Performance was significantly better than established optimization algorithms including GWO, MOFPL, SSA, BES, ROA, HGS, SSO, RCSO, and FCR.

Conclusions:

  • The BEA-SSA model provides an effective solution for optimal cluster head selection in WSNs, enhancing both security and energy efficiency.
  • The integration of DCNN for energy prediction and BEA-SSA for selection offers a robust approach to WSN routing challenges.
  • The study validates the effectiveness of the proposed method, paving the way for more reliable and sustainable WSN deployments.