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An Optimal WSN Node Coverage Based on Enhanced Archimedes Optimization Algorithm.

Thi-Kien Dao1, Shu-Chuan Chu2, Trong-The Nguyen1,3,4

  • 1Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China.

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Summary
This summary is machine-generated.

This study introduces an enhanced Archimedes optimization algorithm (EAOA) to improve node coverage in wireless sensor networks (WSNs). The EAOA effectively optimizes node distribution for better monitoring capacity in complex environments.

Keywords:
coverage optimizationenhanced Archimedes optimization algorithmoptimization approachwireless sensor network

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Node coverage is critical for wireless sensor network (WSN) quality of service, impacting monitoring capacity.
  • Optimizing node coverage is challenging due to limited node computation, network scale, and dynamic environments.
  • Existing methods struggle with unbalanced WSN distributions from random deployment.

Purpose of the Study:

  • To propose an enhanced Archimedes optimization algorithm (EAOA) for optimal node coverage in WSNs.
  • To address the limitations of the standard Archimedes optimization algorithm (AOA) in complex and unbalanced network scenarios.
  • To improve the efficiency and effectiveness of node deployment for enhanced monitoring capabilities.

Main Methods:

  • Developed an enhanced Archimedes optimization algorithm (EAOA) by integrating reverse learning and multidirection techniques into the AOA.
  • Applied EAOA to optimize node coverage for unbalanced WSN distributions resulting from random deployment.
  • Combined optimal coverage findings from multiple sub-areas using the EAOA.

Main Results:

  • The EAOA demonstrated superior performance in optimizing WSN node coverage compared to existing algorithms.
  • The enhanced algorithm showed increased feasible range and faster convergence speed.
  • Testing on benchmark functions confirmed the EAOA's effectiveness in complex optimization scenarios.

Conclusions:

  • The EAOA provides an effective solution for achieving optimal node coverage in WSNs with unbalanced distributions.
  • The proposed algorithm enhances network monitoring capacity and overcomes limitations of the original AOA.
  • EAOA offers improved convergence and a wider feasible range for WSN optimization problems.