Air pollution source estimation profiling via mobile sensor networks

The atmospheric environment is facing increasing threats from industrial pollutions. This paper presents an air pollution source estimation algorithm using mobile sensor networks. We propose a continuous point source model of pollution under windy conditions. Then we use quadrocopters which equipped with sensors that can detect pollutants to collect concentration information. Based on the collected information, we take advantage of the maximum likelihood estimation method to estimate the diffusion parameters. To improve the accuracy of the estimation of diffusion source position and make the quadrocopter approaching the source, we further propose a scheduling strategy based on the particle swarm optimizer basis. We conduct extensive experiments to show the effectiveness of our proposed approach.