  Environmental disasters such as flash floods are becoming more and more
prevalent and carry an increasing burden on human civilization. They are
usually unpredictable, fast in development, and extend across large
geographical areas. The consequences of such disasters can be reduced through
better monitoring, for example using mobile sensing platforms that can give
timely and accurate information to first responders and the public. Given the
extended scale of the areas to monitor, and the time-varying nature of the
phenomenon, we need fast algorithms to quickly determine the best sequence of
locations to be monitored. This problem is very challenging: the present
informative mobile sensor routing algorithms are either short-sighted or
computationally demanding when applied to large scale systems. In this paper, a
real-time sensor task scheduling algorithm that suits the features and needs of
city-scale environmental monitoring tasks is proposed. The algorithm is run in
forward search and makes use of the predictions of an associated distributed
parameter system, modeling flash flood propagation. It partly inherits the
causal relation expressed by a search tree, which describes all possible
sequential decisions. The computationally heavy data assimilation steps in the
forward search tree are replaced by functions dependent on the covariance
matrix between observation sets. Taking flood tracking in an urban area as a
concrete example, numerical experiments in this paper indicate that this
scheduling algorithm can achieve better results than myopic planning algorithms
and other heuristics based sensor placement algorithms. Furthermore, this paper
relies on a deep learning-based data-driven model to track the system states,
and experiments suggest that popular estimation techniques have very good
performance when applied to precise data-driven models.
