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

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