Tracking and modeling of spatio-temporal fields with a mobile sensor network

This paper presents an approach to modeling and tracking spatio-temporal field functions by using a mobile sensor network. The modeling tool used is the Gaussian process regression (GPR) technique characterized by a spatial kernel function. Due to the dynamic nature of spatio-temporal fields, the sampled data points have to be selected to remove the outdated data points before they are used for modeling. Less data points also reduces the computational complexity of GPR. The data selection is conducted via an information entropy based selection criteria. With the selected data points and the estimated GPR model, the mobile sensor nodes are controlled to cover the interested region and track the field function. The coverage and tacking control are implemented by using the centroidal Voronoi tessellation (CVT) method with a constraint of limited communication range. The algorithms are verified by using simulation and real robot experiments. The environmental field in the practical experiment is a moving light intensity distribution. The experimental results show the robots are able to model and track the moving field