In this paper, we investigate the problem of jointly selecting a predefined number of energy-
harvesting (EH) sensors and computing the optimal power allocation. The ultimate goal is to …

The problem of sparse linear regression is relevant in the context of linear system
identification from large datasets. When data are collected from real-world experiments …

Optimal sensor selection for source parameter estimation in energy harvesting Internet of
Things (IoT) networks is studied in this paper. Specifically, the focus is on the selection of the …

We propose a new approach for the recovery of binary signals in compressed sensing,
based on the local minimization of a non-convex cost functional. The desired signal is …

Iterative l 1 reweighting algorithms are very popular in sparse signal recovery and
compressed sensing, since in the practice they have been observed to outperform classical …

The sparse linear regression problem is difficult to handle with usual sparse optimization
models when both predictors and measurements are either quantized or represented in low …

In this paper, we bring together two trends that have recently emerged in sparse signal
recovery: the problem of sparse signals that stem from finite alphabets and the techniques …

In this paper, the focus is on optimal sensor placement and power rating selection for
parameter estimation in wireless sensor networks (WSNs). We take into account the amount …

We consider the problem of the recovery of a k-sparse vector from compressed linear
measurements when data are corrupted by a quantization noise. When the number of …

The optimal sensor selection for scalar state parameter estimation in wireless sensor
networks is studied in the paper. A subset of N candidate sensing locations is selected to …