Optimal scheduling of mobile sensor networks for detection and localization of stationary contamination sources

The problem of optimal detection and localization of contamination sources in a distributed parameter system is formulated as that of maximizing the power of a parametric hypothesis test which checks whether or not system parameters have nominal values. We consider a setting where mobile nodes with sensing capabilities form a network aimed at collecting the most valuable measurements for parameter estimation of a distributed system. The framework is based on the use of the D_s-optimality criterion defined on the Fisher information matrix associated with the estimated parameters as a measure of the information content in the measurements. The approach consists in converting the problem to a canonical optimal control one in Mayer form, in which the control forces of the sensors are optimized. Numerical solutions can then be obtained using widespread numerical packages dedicated to optimal control tasks. A simulation example is presented which clearly demonstrates the presented ideas.