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Summary
This summary is machine-generated.

This study introduces a novel wolf pack optimization algorithm for movable wireless sensor networks. The algorithm enhances coverage rate and network stability while ensuring connectivity and saving energy.

Keywords:
adaptive step sizecoverage rateoptimizationprobability matrixwireless sensor networkwolf pack algorithm

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Area of Science:

  • Computer Science
  • Network Engineering
  • Optimization Algorithms

Background:

  • Wireless sensor networks (WSNs) require efficient algorithms for sustainability, connectivity, and coverage.
  • Existing algorithms face challenges in optimizing movable WSNs for maximum coverage and stability.

Purpose of the Study:

  • To propose an adaptive, discrete space oriented wolf pack optimization algorithm (DSO-WPOA) for movable WSNs.
  • To improve coverage rate and connectivity in movable WSNs.

Main Methods:

  • Developed an adaptive expansion strategy using a minimum overlapping full-coverage model.
  • Improved the adaptive shrinking grid search wolf pack optimization algorithm (ASGS-CWOA) with a target-node probability matrix and adaptive step size.
  • Optimized movable wireless sensor networks as a discrete space oriented problem.

Main Results:

  • The DSO-WPOA achieved the best coverage rate compared to PSO-WSN and classical virtual force algorithms.
  • Demonstrated superior network stability and energy savings.
  • Ensured no gaps in coverage and acceptable time performance.

Conclusions:

  • The DSO-WPOA offers significant improvements for movable WSN coverage and stability.
  • The algorithm's adaptive and discrete space oriented approach enhances convergence and global optimization.
  • This method provides a robust solution for efficient and reliable WSN deployment.