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Research on Coverage Optimization in Wireless Sensor Networks Based on an Improved Sparrow Search Algorithm.

Hong Kheam1, Vakhim Leang2, Chamroeun Khim1

  • 1Department of Information Technology Engineering, Faculty of Engineering, Royal University of Phnom Penh, Phnom Penh 120404, Cambodia.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

This study introduces the Density-Aware Repulsive Sparrow Search Algorithm (DAR-SSA) to improve Wireless Sensor Network (WSN) node deployment. DAR-SSA enhances coverage by dispersing nodes and overcoming limitations of traditional algorithms.

Keywords:
coverage optimizationdensity-aware repulsive sparrow search algorithmwireless sensor network

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Area of Science:

  • Computer Science
  • Network Engineering
  • Algorithm Optimization

Background:

  • Wireless Sensor Networks (WSNs) require optimal node deployment for effective monitoring coverage.
  • Traditional metaheuristics like Sparrow Search Algorithm (SSA) face challenges such as premature convergence and redundant clustering, leading to coverage gaps.
  • Existing algorithms often fail to efficiently balance exploration and exploitation in complex network environments.

Purpose of the Study:

  • To introduce a novel algorithm, the Density-Aware Repulsive Sparrow Search Algorithm (DAR-SSA), for optimizing node deployment in WSNs.
  • To address the limitations of existing algorithms, specifically premature convergence and spatial redundancy in node distribution.
  • To enhance the overall sensing coverage and efficiency of Wireless Sensor Networks.

Main Methods:

  • Developed the Density-Aware Repulsive Sparrow Search Algorithm (DAR-SSA) by integrating electrostatic principles and density-based repulsive forces.
  • Implemented a dynamic explorer-exploiter allocation rule to balance global and local search phases efficiently.
  • Evaluated DAR-SSA performance using a probabilistic sensing model and compared it against standard SSA, its variants (EFSSA, EASOA), and classical algorithms (PSO, GWO).

Main Results:

  • DAR-SSA demonstrated significantly improved effective coverage rates compared to SSA and other benchmark algorithms.
  • In high-density urban deployments, DAR-SSA achieved 95.25% coverage, a substantial increase from SSA's 76.74%.
  • In low-density environments, DAR-SSA reached an effective coverage of 97.12%, showcasing its versatility.

Conclusions:

  • DAR-SSA is a robust and efficient framework for mitigating spatial redundancy in WSN node deployment.
  • The physics-guided approach effectively disperses nodes, leading to maximized sensing coverage.
  • DAR-SSA offers a superior solution for optimizing node placement in diverse WSN deployment scenarios.