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

Abstract

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). [8]. In a study by the World Wildlife Fund (WWF) and Boston Consulting Group (BCG) in April 2020, fire alarms

Accurate Range Free Location based Partial Derivative and Stochastic Feedforward Neural Network Hyperbolic-based Agriculture Sensor Network Formation

“…With this type of design, the network lifetime was said to be improved significantly. Yet another quantum based swarm optimization algorithm was presented by Velasquez et al, [8] that with the aid of approximate separation distance ensured robustness with improved network lifetime.…”