A Sampling Algorithm for Diffusion Networks
Signal Processing
2020-07-16 v2
Abstract
In this paper, we propose a sampling mechanism for adaptive diffusion networks that adaptively changes the amount of sampled nodes based on mean-squared error in the neighborhood of each node. It presents fast convergence during transient and a significant reduction in the number of sampled nodes in steady state. Besides reducing the computational cost, the proposed mechanism can also be used as a censoring technique, thus saving energy by reducing the amount of communication between nodes. We also present a theoretical analysis to obtain lower and upper bounds for the number of network nodes sampled in steady state.
Cite
@article{arxiv.2007.06456,
title = {A Sampling Algorithm for Diffusion Networks},
author = {Daniel Gilio Tiglea and Renato Candido and Magno T. M. Silva},
journal= {arXiv preprint arXiv:2007.06456},
year = {2020}
}
Comments
Change from previous version: included a header in the first page regarding the paper's acceptance at EUSIPCO