Performance Optimization and Design of a Fire Extinguisher Wireless Sensor Drone System Using Petri Nets Modeling and PSO Algorithm

Fire safety appears to be an important global concern due to the limitations of traditional systems in successfully detecting and fighting fires, particularly in critical situations. This study focuses on improving the effectiveness and performance of a fire extinguisher wireless sensor drone (FEWSD) system through advanced optimization techniques such as particle swarm optimization (PSO) algorithm and petri nets (PN) approach to address these challenges. The main goal is to guarantee the quick and efficient response of the sensor-based drone system in firefighting emergencies. The PN simulation modeling has been used to analyze the behavior of each subsystem in the system. Based on this, which subsystem is more critical and assigns a higher maintenance priority is determined. This work also examines how variations in different subsystem parameters affect and compute the system’s availability. It also analyzes the number of repairmen needed in case of system failure for the FED system. Enhancing the dependability and availability of essential devices like FEWSDs is crucial for aiding firefighters in addressing situations in inaccessible locations; thus, optimizing the system’s availability is essential. The FEWSD system’s availability has been optimized by utilizing the PSO algorithm, which assesses the best-fit value of the failure and repair rate (FRR) parameters to optimize system performance. It is established that the system’s availability in the Petri net modeling is analyzed at 0.9883 and that the PSO has optimized the system’s availability by 0.9937. An analysis reveals that the sensor subsystem is more crucial and requires greater maintenance.