Resource-Constrained Sensor Routing for Parameter Estimation of Distributed Systems

We consider a setting where mobile nodes with sensing capacity form a network whose mission is to collect measurements for parameter estimation of a distributed parameter system (DPS). Two techniques to optimize node motions are presented which constitute a tradeoff between the achievable accuracy of parameter estimates and limited motion resources of sensor network nodes. The framework is based on the use of the D-optimality criterion defined on the Fisher information matrix associated with the estimated parameters as a measure of the information content in the measurements. Restrictions on maximal distances traveled by sensor nodes are imposed so as to guarantee realizable solutions. The approach is to convert the problem to a canonical optimal control one in Mayer form, in which both the control forces of the sensors and the initial sensor positions are optimized. Numerical solutions are then obtained using the Matlab PDE toolbox and the RIOTS 95 optimal control toolbox which handles various constraints imposed on the node motions. Illustrative numerical experiments with the proposed techniques are presented.