This chapter presents the optimal scheduling strategy and the framework for dynamic sensor deployment. It discusses the different objective functions for the deployment. The scheduling of dynamic sensors is intended to actively respond to precipitation events. The differences in discharge vary greatly, especially after high precipitation events, indicating that higher variations tend to decrease in time, but are conditioned to the initial conditions of the system. Typically, precipitation models are data-driven, so several models can describe the process with similar accuracy. The experiments aim to explore the optimal deployment of dynamic sensors under different time-windows and different artificial perturbations in the baseline precipitation field. The chapter explores the effect of the deployment of dynamic sensors in cases where perturbations are added. This perturbation is artificially introduced, aiming to explore the effect unknown processes in the precipitation field. In non-stationary Kriging-type interpolation, ‘hot’ areas for dynamic sensors are identified, and trend to enlarge the coverage of the network.