Capacity Aware Optimal Activation of Sensor Nodes Under Reproduction Distortion Measures

We consider a sensor network involving sensors that are placed in specific locations. A point phenomenon is being detected and tracked by the activated sensors. The latter collect data characterizing parameters of the phenomenon; possibly compress it and transport it to a central node. The central node processes the received data to derive an estimate of the phenomenon´s parameters. It is essential that the estimate reproduced at the center reflects the parameters characterizing the phenomenon at a sufficiently high fidelity level. Our sensing stochastic process models account for distance dependent observation noise perturbations as well as location dependent correlations between observation noise components, and assume sample mean estimates to be employed at the processing center. As such, they are distinctly different than corresponding models presented in the literature. We develop computationally efficient algorithms for determining the specific set of sensors to be activated, so that a sufficiently low reproduction distortion level can be attained. Corresponding algorithms are also derived for a system that operates under communications capacity constraints. For those sensor selection problems that are NP-hard, we introduce computationally efficient heuristic algorithms. We use our algorithms to present illustrative system performance results. We demonstrate that the activation of sensors that belong to a critical set of sensors can provide a distinct reduction in the distortion measure, while the activation of additional sensors outside such a set may not lead to further distinct estimate fidelity improvements