A Sampling Algorithm for Diffusion Networks

TL;DR

This paper introduces an adaptive sampling algorithm for diffusion networks that reduces computational costs and energy consumption by dynamically adjusting sampled nodes based on local error, with proven bounds on sampling size.

Contribution

It presents a novel adaptive sampling mechanism that improves efficiency and energy savings in diffusion networks, supported by theoretical bounds.

Findings

Fast convergence during transient phase

Significant reduction in sampled nodes at steady state

Theoretical bounds for sampling in steady state

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.