Genetic algorithm optimization of sensor placement for CO2 concentration observation

In our previous studies, we introduced a method for determining the optimal sensor placement of wireless sensor networks for monitoring indoor carbon dioxide (CO2) concentrations. This method, based on brute force, has proven to be accurate and reliable. However, the computational complexity increases exponentially with an increase in the number of sensors. Therefore, this study proposes a novel approach for optimal sensor node placement based on a genetic algorithm (GA) that offers a more efficient alternative to the brute force method. By utilizing the GA, we achieved optimal sensor placement with reduced computational complexity. To validate the effectiveness of our GA based method, we conducted numerical experiments using observed CO2 concentration. The results demonstrate that our proposed approach not only achieves optimal sensor placement but also maintains the accuracy of the observations.