A large scale analysis of a classification algorithm over sensor networks

This paper is devoted to study an iterative estimation/classification algorithm over a sensor network with faulty units recently appeared in the literature. We here present a complete analysis of the performance of the algorithm when the number of units goes to infinity both in terms of estimation and of classification error. In particular it is shown that the algorithm solution converges to the optimal Maximum Likelihood estimator.