Correlation-aware sparsity-enforcing sensor placement for spatio-temporal field estimation

In this work, we propose a generalized framework for designing optimal sensor constellations for spatio-temporally correlated field estimation using wireless sensor networks. The accuracy of the field intensity estimate in every point of a given service area strongly depends upon the number and the constellation of the sensors along with the spatio-temporal statistics of the field. We formulate and solve a sparsity-enforcing optimization problem to select the best sensor locations that achieve some desired estimation performance. The sparsity-enforcing iterative selection algorithm is aware of the non-separable space-time covariance structure of the field.