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Distributed signal processing has attracted widespread attention in thescientific community due to its several advantages over centralized approaches.Recently, graph signal processing has risen to prominence, and adaptivedistributed solutions have also been proposed in the area. Both in theclassical framework and in graph signal processing, sampling and censoringtechniques have been topics of intense research, since the cost associated withmeasuring and/or transmitting data throughout the entire network may beprohibitive in certain applications. In this paper, we propose a low-costadaptive mechanism for sampling and censoring over diffusion networks that usesinformation from more nodes when the error in the network is high and from lessnodes otherwise. It presents fast convergence during transient and asignificant reduction in computational cost and energy consumption in steadystate. As a censoring technique, we show that it is able to noticeablyoutperform other solutions. We also present a theoretical analysis to giveinsights about its operation, and to help the choice of suitable values for itsparameters.

A Low-Cost Algorithm for Adaptive Sampling and Censoring in Diffusion Networks