Ricerc@Sapienza

We study decentralized estimation of time-varying signals at a fusion center (FC), when energy harvesting sensors transmit sampled data over rate-constrained links. We propose a dynamic strategy based on stochastic optimization for selecting radio parameters, sampling set, and harvested energy at each node, with the aim of estimating a time-varying signal with guaranteed performance while ensuring stability of the batteries around a prescribed operating level. Numerical results validate the proposed approach for dynamic signal estimation under communication and energy constraints.

In this paper, we introduce a novel adaptive method for distributed recovery of graph processes, which are observed over a dynamic set of vertices. The proposed algorithm hinges on proximal gradient optimization techniques, while leveraging in-network projections as a mechanism to enforce graph bandwidth constraints in a cooperative and distributed fashion, and thresholding operators to identify anomalous sparse components hidden in the signals. The theoretical analysis illustrates the mean-square stability of the proposed adaptive method.