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Node Deployment Optimization for Wireless Sensor Networks Based on Virtual Force-Directed Particle Swarm Optimization

Liangshun Wu1,2, Junsuo Qu1,3, Haonan Shi1

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Summary
This summary is machine-generated.

This study optimizes wireless sensor network deployment for better coverage using a novel D-S evidence theory approach. The proposed method enhances node sensing probability and uses virtual forces for efficient optimization.

Keywords:
D-S evidenceparticle swarm optimizationvirtual forcewireless sensor network

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless sensor networks (WSNs) require optimized deployment for maximum network coverage.
  • Dempster-Shafer (D-S) evidence theory offers robust information fusion for uncertain and inconsistent data.
  • Traditional D-S evidence theory aggregation rules can yield inaccurate results in complex scenarios.

Purpose of the Study:

  • To develop an optimized node sensing probability model using D-S evidence theory.
  • To address limitations in traditional D-S evidence theory aggregation rules.
  • To propose a virtual force-directed particle swarm optimization (VF-PSO) approach for WSN deployment.

Main Methods:

  • A node sensing probability model based on D-S evidence theory was developed.
  • A priority factor was introduced to handle major evidence disputes in D-S theory.
  • A virtual force-directed particle swarm optimization (VF-PSO) algorithm was proposed, utilizing sensing probability for virtual forces.

Main Results:

  • The proposed D-S evidence-based model effectively fuses sensing probabilities, even with disputes.
  • The VF-PSO approach successfully optimizes WSN node deployment to maximize network coverage.
  • Simulation results demonstrated improved network coverage and reduced deployment time compared to traditional methods.

Conclusions:

  • The enhanced D-S evidence theory with a priority factor improves sensing probability modeling.
  • The VF-PSO approach provides an effective and efficient method for WSN deployment optimization.
  • This work contributes to maximizing network coverage in wireless sensor networks through advanced fusion and optimization techniques.