  Distributed signal processing has attracted widespread attention in the
scientific community due to its several advantages over centralized approaches.
Recently, graph signal processing has risen to prominence, and adaptive
distributed solutions have also been proposed in the area. Both in the
classical framework and in graph signal processing, sampling and censoring
techniques have been topics of intense research, since the cost associated with
measuring and/or transmitting data throughout the entire network may be
prohibitive in certain applications. In this paper, we propose a low-cost
adaptive mechanism for sampling and censoring over diffusion networks that uses
information from more nodes when the error in the network is high and from less
nodes otherwise. It presents fast convergence during transient and a
significant reduction in computational cost and energy consumption in steady
state. As a censoring technique, we show that it is able to noticeably
outperform other solutions. We also present a theoretical analysis to give
insights about its operation, and to help the choice of suitable values for its
parameters.
