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Node Collaborative Strategy for 3D Coverage Based on Hopping Adaptive Grey Wolf Optimizer in Wireless Sensor Network.

Minghua Wang1,2, Zhuowen Wu1, Bo Fan1

  • 1School of Electrical Engineering, University of South China, Hengyang 421001, China.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Three-Dimensional Confident Information Coverage (3DCIC) model and a Hopping Adaptive Grey Wolf Optimizer (HAGWO) for wireless sensor networks (WSNs). These innovations significantly enhance 3D coverage performance and optimize node deployment.

Keywords:
Hopping Adaptive Grey Wolf Optimizerscheduling algorithmthree-dimensional confident information coverage modelthree-dimensional coverage

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Coverage optimization is a critical challenge in wireless sensor networks (WSNs).
  • Three-dimensional (3D) coverage models are essential for realistic applications like intelligent urban management.
  • Existing models often fall short in maximizing coverage efficiency in complex 3D environments.

Purpose of the Study:

  • To propose a novel Three-Dimensional Confident Information Coverage (3DCIC) model for enhanced WSN coverage.
  • To develop an optimized node deployment strategy using an advanced metaheuristic algorithm.
  • To improve the overall coverage performance and efficiency of 3D WSNs.

Main Methods:

  • The study introduces the Three-Dimensional Confident Information Coverage (3DCIC) model, leveraging multi-node cooperative information reconstruction.
  • A Hopping Adaptive Grey Wolf Optimizer (HAGWO), incorporating adaptive and opposition-based learning, is developed for node deployment optimization.
  • The proposed HAGWO algorithm is based on the Grey Wolf Optimizer (GWO) and applied to optimize node placement in 3D space.

Main Results:

  • The 3DCIC model demonstrated significantly improved coverage ranges compared to conventional binary spherical models.
  • Coverage ranges were 2.78x, 4.41x, and 4.00x greater under tetrahedral, hexahedral, and octahedral deployments, respectively.
  • The HAGWO algorithm proved effective in optimizing node deployment for superior coverage and performed well on classical test functions.

Conclusions:

  • The proposed 3DCIC model effectively enhances the perceptual domain of sensor nodes in 3D WSNs.
  • The HAGWO algorithm offers an efficient method for optimizing node deployment in 3D WSNs, leading to substantial coverage improvements.
  • This research provides a significant advancement in 3D WSN coverage optimization for applications like intelligent urban management.