Data Modelling with Gaussian Process in Sensor Networks for Urban Environmental Monitoring

In this paper, the multidimensional output Gaussian process (GP) is applied to model urban environmental data collected by sensor networks. Measurements from sensors at different locations are correlated. Moreover, we observe that the pollution level in urban area is highly coupled with human activities and shows periodic patterns accordingly. Based on these observations, we discuss the design of mean and kernel functions with two approaches: (1) composed kernel and maximum likelihood estimation of hyper-parameters, (2) Wiener-Khinchin theorem based approximation of sample covariances. To validate the models, the accuracy of interpolations given by different approaches are compared. The experimental results show that, for the application of interpolation, the dependent GP with the approximated sample covariances as kernels can provide better performance than the independent GP model with composed kernels.