Abstract
A Distributed Compressed Sensing-based Algorithm for the Joint Recovery of Signal Ensemble
Highlights
The advances in the field of telecommunication and newly developed applications have increased the need for deploying distributed wireless sensor networks (WSNs), which of multiple sensors for monitoring a specific phenomenon both in the time and space of an area of interest
In a typical distributed compressive sensing (DCS) setting with a joint sparsity model (JSM), each sensor compresses its signal independently by projecting the signal onto an incoherent basis and transmitting the compressed information to the fusion center (FC)
Suppose N is the length of the original signal satisfying Nyquist rate needed to sample a signal x, M is the CS measurement samples, and k is the sparsity order of the original signal. the compressed sensing measures only M data samples, which is M = O(klog (N/k)), where k
Summary
The advances in the field of telecommunication and newly developed applications have increased the need for deploying distributed wireless sensor networks (WSNs), which of multiple sensors for monitoring a specific phenomenon both in the time and space of an area of interest. There are three main challenges in WSNs, i.e., network lifetime, computational ability and bandwidth constraints [1]. In this respect, the theory of distributed compressive sensing (DCS) has been used to exploit inter- and intra-signal correlations [2]. In a typical DCS setting with a joint sparsity model (JSM), each sensor compresses its signal independently by projecting the signal onto an incoherent basis and transmitting the compressed information to the fusion center (FC). Obtaining the required number of M data points needs some prior knowledge of the signal, which is not applicable in this case. We derive an appropriate sparsity measure which utilizes the efficient GINI index (GI) introduced in [4]
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