Implementing the Particle Swarm Optimization Algorithm to enhance the positioning of sensors in an indoor CO2 monitoring system using wireless sensor networks

This work focuses on advancing the usage of a specific optimization algorithm called Particle Swarm Optimization (PSO) for optimizing the placement of sensor nodes for the WSN system to continuously measure CO2 concentration in indoor spaces. The main aim is to achieve the good placement of the sensory nodes covering all the area of interest in detecting CO2 with less energy and cost is hope for achieving the best placing of the sensor nodes that cover all the area of interest for detection of CO2 using minimal energy and cost. With regards to the optimization of the locations of the sensor nodes, the PSO algorithm is incorporated into the system as it simulates swarms while aiming to enhance the quality of the network and the dependability of the data gathered. This is achieved using simulations and evaluations that show how effective the proposed PSO-based is in comparison to the traditional placement methods. This study presents innovative strategies to solve the difficulties of selecting the optimal spots to deploy the sensors in WSNs, which will help in the development of improved indoor air quality monitoring systems.