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

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.