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  3. Coverage Optimization Strategy For Wireless Sensor Networks Based On Improved Northern Goshawk Optimization Algorithm.
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  3. Coverage Optimization Strategy For Wireless Sensor Networks Based On Improved Northern Goshawk Optimization Algorithm.

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Coverage Optimization Strategy for Wireless Sensor Networks Based on Improved Northern Goshawk Optimization

Shuxin Wang1, Yonglong Deng2, Nuomei Lan3

  • 1School of Intelligent Manufacturing, Shanghai Zhongqiao Vocational and Technical University, Shanghai 201514, China.

Biomimetics (Basel, Switzerland)
|June 25, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an improved Northern Goshawk Optimization (INGO) algorithm for wireless sensor networks (WSNs). INGO significantly enhances coverage optimization, achieving higher rates and reducing blind spots in 2D and 3D scenarios.

Keywords:
Gaussian–Lévy mutationTent chaotic mapcoverage optimizationnorthern goshawk optimizationwireless sensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Optimization Algorithms

Background:

  • Wireless sensor networks (WSNs) face coverage challenges due to uneven node distribution and blind spots.
  • Existing optimization algorithms may struggle with WSN coverage optimization efficiency and robustness.

Purpose of the Study:

  • To enhance the Northern Goshawk Optimization (NGO) algorithm for improved WSN coverage optimization.
  • To address limitations of existing methods in achieving uniform node distribution and minimizing coverage gaps.

Main Methods:

  • Introduced an improved NGO algorithm (INGO) incorporating a Logistic chaotic map for population initialization.
  • Integrated a nonlinear dynamic weight for balancing exploration and exploitation.
  • Incorporated a Gaussian-Lévy hybrid mutation mechanism to escape local optima.

Main Results:

  • INGO demonstrated stable convergence and superior performance on standard test functions compared to NGO.
  • INGO achieved significantly higher coverage rates in 2D (98.32%) and 3D (72.32%) WSN scenarios.
  • INGO resulted in more uniform node deployment and reduced coverage blind spots with improved stability.

Conclusions:

  • The enhanced INGO algorithm provides a robust and efficient solution for WSN coverage optimization.
  • INGO offers a reliable technical approach for deploying WSNs effectively in complex environments.
  • INGO outperforms the original NGO algorithm in terms of coverage rate, uniformity, and stability.