New paper: An Adaptive Algorithm for Sampling over Diffusion Networks with Dynamic Parameter Tuning and Change Detection Mechanisms

Recently, we proposed a sampling algorithm for diffusion networks that locally adapts the number of nodes sampled according to the estimation error. In this paper, we extend the results, proposing some improvements to the algorithm.

Here, we propose a different solution, which automatically adjusts its own parameters based on the noise power estimation. Although its computational cost is slightly increased, this modification removes the need for a priori knowledge of the noise variance across the network, and increases its robustness to the presence of noisy nodes in the network. Furthermore, by implementing an adaptive reset system for the sampling mechanism, we are able to significantly improve the tracking capability of the original algorithm.

The PDF file can be obtained in this page.


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