Decentralized Dynamic Data-Driven Monitoring of Dispersion Processes on Partitioned Domains

Abstract

The application of mobile sensor-carrying vehicles for online estimating dynamic dispersion processes is extremely beneficial. Based on current estimates that depend on past measurements and forecasts obtained from a discretized PDE-model, the movement of the vehicles can be adapted resulting in measurements at more informative locations. In this work, a novel decentralized monitoring approach based on a partitioning of the spatial domain into several subdomains is proposed. Each sensor is assigned to the subdomain it is located in and is only required to maintain a process and multi-vehicle model related to its subdomain. In this way, vast communication requirements of related centralized approaches and costly full model simulations are avoided making the presented approach more scalable with respect to a larger number of sensor-carrying vehicles and a larger problem domain. The approach consists of a new prediction and update method based on a domain decomposition method and a partitioned variant of the Ensemble Square Root Filter getting along with a minimum exchange of data between sensors on neighboring subdomains. Furthermore, a cooperative vehicle controller is applied in such a way that a dynamic adaption of the sensor distribution becomes possible.