论文标题
用于自适应抽样和审查扩散网络中的低成本算法
A Low-Cost Algorithm for Adaptive Sampling and Censoring in Diffusion Networks
论文作者
论文摘要
分布式信号处理因其在集中式方法上的几个优势而引起了科学界的广泛关注。最近，图形信号处理已上升到突出，并且在该地区也提出了自适应分布式溶液。在经典框架和图形信号处理中，采样和审查技术都是激烈研究的主题，因为在整个网络中与测量和/或传输数据相关的成本在某些应用中可能会令人难以置信。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，否则较小的节点时，该扩散网络使用了更多节点。它显示了瞬态期间快速收敛，并且在稳定状态下的计算成本和能源消耗大大降低。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。我们还提出了理论分析，以洞悉其操作，并帮助选择合适的值的参数。
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.
