This paper summarizes the results obtained by Daniel G. Tiglea during the period he was working to obtain the M.S. Degree.

Here, we propose a low-cost adaptive mechanism for sampling over diffusion networks. The scheme uses information from more nodes when the error in the network is high and from less nodes otherwise. It presents fast convergence during transient and a significant reduction in computational cost in steady state.

Besides that, we propose a censoring technique that presents a significant reduction in energy consumption in comparison with other solutions in the literature.

The PDF file can be obtained in this page.