BCAM Scientific Seminar: Multidimensional Stochastic Sampling, a new look to wireless sensor network design

With the advent of the Internet-of-Things, spatiotemporal signal reconstruction from samples stochastically gathered in a multidimensional space with uncertainty became a crucial problem for a variety of applications including crowd-sensing. Such a problem generalizes the reconstruction of deterministic signals and one-dimensional stationary random processes, first addressed by Whittaker, Kotelnikov, and Shannon. This talk aims to present recent results on multidimensional stochastic sampling with uncertainties jointly accounting for signal properties (signal spectrum and spatial correlation) and for sampling properties (sample spatial distribution, sample availability, and non-ideal knowledge of sample positions). A framework is developed for wireless sensor network design where the accuracy of signal reconstruction is accounted jointly with network lifetime.