Sensor networks

Data assimilation is the process by which PDE-based models use measurements to produce an optimal representation of the state of the system. Different measurements bring different contributions to reducing uncertainty in the inference results. Quantifying the impact of observations is important for data pruning and for the configuration of sensor networks. This talk discusses several adjoint-based methodologies for formal observation impact assessment and for optimal design of sensor networks.

Distributed sensing, information and computation based on sensor networks comprise a new frontier in engineering and science. Cheap, easily available sensors performing collectively complicated tasks are forthcoming, and in security and other applications involving sensor networks the coverage of a region of interest is of a high importance.