In this survey paper, our goal is to discuss recent advances of compressive sensing (CS)
based solutions in wireless sensor networks (WSNs) including the main ongoing/recent …

The problem of the distributed recovery of jointly sparse signals has attracted much attention
recently. Let us assume that the nodes of a network observe different sparse signals with …

In this paper, we tackle the in-network recovery of sparse signals with innovations. We
assume that the nodes of the network measure a signal composed by a common component …

Compressive sensing (CS) has been shown to be promising in a wide variety of applications
including compressive imaging, video processing, communication, and radar to name a few …

Compressed sensing is a new technique for nonadaptive compressed acquisition, which
takes advantage of signal sparsity and allows signal recovery starting from few linear …

We consider the problem of decentralized estimation of multiple joint sparse vectors by a
network of nodes from locally acquired noisy and underdetermined linear measurements …

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 …

Time-varying systems are a challenge in many scientific and engineering areas. Usually,
estimation of time-varying parameters or signals must be performed online, which calls for …

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 …

Distributed collaborative spectrum sensing has been considered for Cognitive Radio (CR) in
order to cope with fading and shadowing effects that affect a single CR performance, without …