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

TL;DR

This paper introduces a low-cost adaptive sampling and censoring algorithm for diffusion networks that improves efficiency and performance by adjusting node participation based on network error levels.

Contribution

It presents a novel adaptive mechanism for sampling and censoring in diffusion networks, reducing costs while maintaining fast convergence and outperforming existing methods.

Findings

Fast convergence during transient phases

Significant reduction in computational cost and energy consumption

Outperforms other censoring solutions

Abstract

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.