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A Low-Cost Algorithm for Adaptive Sampling and Censoring in Diffusion Networks
IEEE Transactions on Signal Processing ( IF 5.4 ) Pub Date : 2020-01-01 , DOI: 10.1109/tsp.2020.3037404
Daniel G. Tiglea , Renato Candido , Magno T. M. Silva

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.

中文翻译:

一种用于扩散网络中自适应采样和删失的低成本算法

分布式信号处理由于其优于集中式方法的几个优点而在科学界引起了广泛关注。最近,图信号处理已经崭露头角,该领域也提出了自适应分布式解决方案。在经典框架和图形信号处理中,采样和审查技术一直是深入研究的主题,因为与在整个网络中测量和/或传输数据相关的成本在某些应用中可能会令人望而却步。在本文中,我们提出了一种低成本的自适应机制,用于对扩散网络进行采样和审查,当网络中的误差较高时使用来自更多节点的信息,否则使用来自较少节点的信息。它在瞬态期间表现出快速收敛,并在稳定状态下显着降低计算成本和能耗。作为一种审查技术,我们表明它能够明显优于其他解决方案。我们还提出了一个理论分析,以深入了解其操作,并帮助为其参数选择合适的值。
更新日期:2020-01-01
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