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

This paper aims to present a robust algorithm developed that minimize the number of sensor nodes in 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 allocate minimum wireless sensors forested areas without losing connectivity an environment with high penetration vegetation. proposed approach incorporates propagation model locates nodes, calculates approximate separation distance between each one, verifies Line Sight (LOS) compliance, avoids considerable intrusions first Fresnel zone. results validate robustness algorithms comparison traditional particle (PSO).