This paper describes a prototype application to use different algorithms for creating optimal evacuation routes in the presence of a wildfire with a dynamic event-based update. The application uses a meteorological API that obtains real-time temperature, atmospheric pressure, humidity, speed, and wind direction of each location within an area using geographic coordinates (latitude and longitude) for creating a sensor network. The data are stored in a database for monitoring and visualization using the open-source platform Grafana, which includes an early warning mechanism that sends messages when it detects a temperature outside the normal range. Three different simulation scenarios were evaluated, varying the fire’s starting point coordinates and the evacuation route. The results show that the algorithm reacts to the presence of fire, maximizing safety margins even on longer evacuation routes. The prototype can be used to create an application to fight forest fires and safeguard rescue agents’ lives.
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
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