  Adaptive networks (ANs) are effective real time techniques to process and
track events observed by sensor networks and, more recently, to equip Internet
of Things (IoT) applications. ANs operate over nodes equipped with
collaborative adaptive filters that solve distributively an estimation problem
common to the whole network. However, they do not guarantee that nodes do not
lose from cooperation, as compared to its non-cooperative operation; that poor
nodes are rejected and exceptional nodes estimates reach the entire network;
and that performance is uniform over all nodes. In order to enforce such
properties, this work introduces the concept of distributed universal
estimation, which encompasses the new concepts of local universality, global
universality and universality with respect to the non-cooperative operation. We
then construct a new cooperation protocol that is proven to be distributively
universal, outperforming direct competitors from the literature, as shown by
several simulations. Mean and mean-square analytical models are developed, with
good agreement between theory and simulations.
