Virtual Force-Guided Particle Swarm Optimization for Sector Sensor Area Coverage Enhancement

Sector sensor area coverage optimization represents a critical challenge in wireless sensor networks, where traditional methods struggle to address the directional characteristics of sector-shaped sensing regions. The virtual force-guided particle swarm algorithm achieves superior coverage efficiency by integrating the advantages of both mechanisms through intelligent code implementation.

The virtual force model employs physics-inspired attraction and repulsion forces between sensor nodes to optimize positioning. For sector coverage scenarios, the force calculation algorithm must incorporate directional parameters including sensor orientation angles and unique perception ranges. The particle swarm component implements collective intelligence where each node dynamically adjusts its position and orientation based on both personal best (pBest) and global best (gBest) solutions using velocity update equations.

The hybrid algorithm initialization involves setting sensor positions and orientations through randomized coordinate generation. The core iterative process executes these key steps: calculating virtual forces based on coverage gaps using vector operations, evaluating individual and swarm optimal solutions through fitness functions, and updating velocities/positions with weighted combinations. For sector-specific optimization, the algorithm implements an angle adjustment mechanism that dynamically optimizes sensor orientations to maximize effective coverage area through trigonometric computations.

Compared to conventional approaches, this algorithm better accommodates directional characteristics of sector sensors while leveraging swarm intelligence to escape local optima. Practical implementations allow tuning of virtual force parameters and PSO weights to balance exploration-exploitation trade-offs, making it suitable for directional sensing applications like environmental monitoring and security surveillance systems through configurable threshold settings.