Optimal Wireless Sensor Networks Allocation for Wooded Areas Using Quantum-Behaved Swarm Optimization Algorithms

This paper aims to present a robust algorithm developed that aims to minimize the number of sensor nodes in a WSN using three quantum-behaved swarm optimization techniques based on Lorentz (QPSO-LR), Rosen-Morse (QPSO-RM), and Coulomb-like Square Root (QPSO-CS) potential fields. The algorithm aims to allocate the minimum number of wireless sensors in forested areas without losing connectivity in an environment with a high penetration of vegetation. The proposed approach incorporates a propagation model that locates the sensor nodes, calculates the approximate separation distance between each one, verifies Line of Sight (LOS) compliance, and avoids considerable intrusions in the first Fresnel zone. The results validate the robustness of the quantum-behaved swarm optimization algorithms in comparison to traditional particle swarm optimization (PSO).

Automated summary

This paper presents a robust algorithm that uses three quantum-behaved swarm optimization techniques to minimize the number of sensor nodes in a wireless sensor network (WSN). The algorithm aims to allocate a minimum number of sensors in forested areas while maintaining connectivity in highly vegetated environments. The proposed approach incorporates a propagation model to locate sensor nodes, calculate separation distances, verify line-of-sight compliance, and avoid intrusions in the first Fresnel zone. Results show the superiority of the quantum-behaved swarm optimization algorithms compared to traditional particle swarm optimization (PSO).