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Secure Clustering Strategy Based on Improved Particle Swarm Optimization Algorithm in Internet of Things.

Zhanbiao Bao1

  • 1Center of Education Technology, Henan University of Economics and Law, Zhengzhou, Henan 450046, China.

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This study introduces a secure clustering strategy for the Internet of Things (IoT) using improved particle swarm optimization (PSO). The method enhances network lifetime by optimizing cluster head and forwarding node selection based on energy and load balance.

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Internet of Things (IoT) networks face challenges in energy efficiency and security.
  • Clustering is a key strategy for managing IoT network resources and prolonging lifetime.
  • Existing clustering methods often struggle with optimal node selection and energy consumption.

Purpose of the Study:

  • To propose a secure and energy-efficient clustering strategy for IoT environments.
  • To enhance the network lifetime and performance of IoT networks.
  • To optimize the selection of cluster heads and forwarding nodes.

Main Methods:

  • A novel fitness function considering residual energy and load balance for cluster head election.
  • Utilizing an optimized adaptive learning factor in Particle Swarm Optimization (PSO) to improve convergence speed.
  • Implementing a forwarding node election mechanism based on optimal energy and location for reduced consumption.

Main Results:

  • The proposed secure clustering strategy significantly prolongs IoT network lifetime compared to existing methods.
  • The method demonstrates effective optimization in cluster head and forwarding node selection.
  • Achieved an average node degree of less than 2.5, indicating efficient network topology.

Conclusions:

  • The improved PSO-based secure clustering strategy offers a viable solution for energy-efficient IoT networks.
  • Optimizing node selection through energy and load balancing is crucial for network longevity.
  • The proposed approach effectively balances performance and resource management in IoT deployments.