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Coverage Optimization of Heterogeneous Wireless Sensor Network Based on Improved Wild Horse Optimizer.

Chuijie Zeng1, Tao Qin1, Wei Tan2

  • 1Electrical Engineering College, Guizhou University, Guiyang 550025, China.

Biomimetics (Basel, Switzerland)
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Summary
This summary is machine-generated.

This study introduces an Improved Wild Horse Optimizer (IWHO) to enhance network coverage and connectivity in heterogeneous wireless sensor networks (HWSNs). The IWHO algorithm significantly boosts optimization performance for HWSNs.

Keywords:
connectivity ratiocoverage optimizationcoverage ratioheterogeneous wireless sensor networkimproved wild horse optimizer

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Heterogeneous wireless sensor networks (HWSNs) face significant challenges in achieving adequate network coverage and connectivity.
  • Existing optimization algorithms may struggle with convergence speed and escaping local optima in complex network environments.

Purpose of the Study:

  • To propose an Improved Wild Horse Optimizer (IWHO) algorithm for enhancing coverage and connectivity in HWSNs.
  • To improve the accuracy and convergence speed of the Wild Horse Optimizer (WHO) through hybridization and advanced optimization strategies.

Main Methods:

  • Hybridization of the Wild Horse Optimizer (WHO) with the Golden Sine Algorithm (Golden-SA).
  • Incorporation of SPM chaotic mapping for increased population diversity during initialization.
  • Application of opposition-based learning and Cauchy variation strategy to prevent local optima and broaden the search space.

Main Results:

  • The IWHO algorithm demonstrated superior optimization capacity compared to seven other algorithms across 23 test functions.
  • Simulation experiments in varied environments confirmed the IWHO's effectiveness in improving sensor connectivity and coverage ratios.
  • Optimized HWSNs achieved coverage and connectivity ratios of 98.51% and 20.04%, respectively, with 97.79% coverage and 17.44% connectivity after introducing obstacles.

Conclusions:

  • The proposed IWHO algorithm offers a robust and effective solution for optimizing coverage and connectivity in HWSNs.
  • The hybrid approach and enhanced search strategies significantly improve upon existing optimization methods for wireless sensor networks.