Decentralized Detection '

Consider a set of sensors that receive observations from the environment and transmit finite-valued messages to a fusion center that makes a final decision on one out of M alternative hypotheses. The problem is to provide rules according to which the sensors should decide what to transmit, in order to optimize a measure of organizational performance. We overview the available theory for the Bayesian formulation, and improve upon the known results for the Neyman-Pearson variant of the problem. We also discuss (i) computational issues, (ii) asymptotic results, (iii) generalizations to more complex organizations, and (iv) sequential problems.