Identity-Aware Decision Network Communication Budgeting: Is Who as Important as What?

All practical sensing operations must work with quantized data. In some settings, “high-resolution” uniform quantization is used, and data are treated as approximately continuous. The aim of this article is to facilitate “cheap” central decision making by considering extremely low resolution quantization of data sent from distributed sensors. Along with measurement data, each sensor is assumed to have some label value that is relevant to its stochastic measurement model. All measurement and label data are transmitted to the decision maker in the form of discrete “types.” Censoring is also used to control the expected communication cost—each sensor decides locally whether or not to send its data to the decision center based on the value of its label as well as the value of its measurement. In this article, we formalize the test statistic based on censored and quantized data. We also form a metric that is predictive of decision performance. This performance metric can be maximized to obtain optimal censoring and quantization rules. This optimization is demonstrated for a model that assumes passive sensors uniformly distributed in space.