Spatial function estimation using Gaussian process with sparse history data in mobile sensor networks
A distributed approach to monitoring the environmental field function with mobile sensor networks is presented in this paper. With this approach, a mobile sensor network is capable to estimate a model of field functions in real-time. This approach consists of two stages, a field function learning stage and a locational optimising stage. A distributed least square support vector regression (LS-SVR) is developed for the field function learning stage. On the locational optimising stage, a gradient based method: centroidal Voronoi tessellation (CVT) is used to allocate each sensor node's position. These two stages are running alternately in a loop so that the field function learning stage can keep updating the field function with new sensor readings resulted from the locational optimising stage, and simultaneously, the locational optimising stage can relocate sensor nodes according to a more accurate field function model. Eventually, the field function is estimated and the sensor nodes are distributed based on the estimated model. The simulation results given in this paper show the effectiveness of this approach.