Distributed Universal Adaptive Networks

TL;DR

This paper introduces a new distributed universal estimation framework for adaptive networks, ensuring uniform performance and robustness across nodes, with a novel cooperation protocol validated through theoretical analysis and simulations.

Contribution

It proposes the concept of distributed universal estimation and develops a new cooperation protocol that guarantees universality and improved performance in adaptive networks.

Findings

The new protocol is distributively universal and outperforms existing methods.

Analytical models match simulation results well.

Performance uniformity across network nodes is achieved.

Abstract

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.