A Sampling Algorithm for Diffusion Networks

Daniel Gilio Tiglea, Renato Candido, Magno T. M. Silva. A Sampling Algorithm for Diffusion Networks. In: European Signal Processing Conference, 2020, Amsterdam. Proceedings of EUSIPCO’2020, 2020.


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.


Diffusion strategies, energy efficiency, adaptive networks, distributed estimation, convex combination.