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Coverage and connectivity maximization for wireless sensor networks using improved chaotic grey wolf optimization.

Muhammad Suhail Shaikh1,2, Chang Wang1, Senlin Xie1

  • 1School of Physics and Electronic Engineering, Hanshan Normal University, Chaozhou, 521000, Guangdong, China.

Scientific Reports
|May 5, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an Improved Chaotic Grey Wolf Optimization (ICGWO) algorithm for optimal wireless sensor network (WSN) node placement. The ICGWO algorithm significantly enhances network coverage and connectivity, offering a cost-effective solution for data-driven applications.

Keywords:
CEC_22Chaotic mapConnectivityCoverageImproved grey WolfOptimizationWireless sensor network

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Optimization

Background:

  • Efficient network coverage and connectivity are crucial for wireless sensor networks (WSNs) in data-driven applications.
  • Optimal sensor node placement is a key challenge impacting WSN performance and deployment costs.
  • Existing methods face limitations in achieving high coverage, connectivity, and cost-efficiency.

Purpose of the Study:

  • To develop an Improved Chaotic Grey Wolf Optimization (ICGWO) algorithm for enhancing WSN coverage and connectivity.
  • To address challenges of high deployment costs, limited coverage, and insufficient connectivity in WSNs.
  • To provide an optimized solution for sensor node placement in diverse WSN scenarios.

Main Methods:

  • Development of a mathematical model for WSN coverage and connectivity optimization.
  • Enhancement of the Grey Wolf Optimizer (GWO) using a chaotic map to create the ICGWO algorithm.
  • Evaluation of ICGWO performance using CEC_22 benchmark functions and comparison with other optimization methods.

Main Results:

  • The ICGWO algorithm demonstrated superior performance in finding optimal solutions and achieving faster convergence.
  • Practical WSN deployment cases showed significant improvements in coverage rates, reaching up to 99.4940% with 50 nodes.
  • Average coverage improvements ranged from 2.18% to 16.41% compared to state-of-the-art methods across different node counts.

Conclusions:

  • The ICGWO algorithm is an effective and reliable solution for optimizing sensor node placement in WSNs.
  • ICGWO significantly enhances network coverage and connectivity, addressing critical deployment challenges.
  • The proposed method contributes to the advancement of WSN technology by maximizing network performance and efficiency.