  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.
