Compressive sensing (CS) is a new technology in digital signal processing capable of high-resolution capture of physical signals from few measurements, which promises impressive improvements in the field of wireless sensor networks (WSNs). In this work, we extensively investigate the effectiveness of compressive sensing (CS) when real COTSresource-constrained sensor nodes are used for compression, evaluating how the different parameters can affect the energy consumption and the lifetime of the device. Using data from a real dataset, we compare an implementation of CS using dense encoding matrices, where samples are gathered at a Nyquist rate, with the reconstruction of signals sampled at a sub-Nyquist rate. The quality of recovery is addressed, and several algorithms are used for reconstruction exploiting the intra- and inter-signal correlation structures. We finally define an optimal under-sampling ratio and reconstruction algorithm capable of achieving the best reconstruction at the minimum energy spent for the compression. The results are verified against a set of different kinds of sensors on several nodes used for environmental monitoring.
Efficient data aggregation and compression in sensor networks is becoming fundamental with the increase of the number of nodes in the network. Although several data aggregation and compression techniques have been proposed in the literature only few of them can perform in-network compression and can extend lifetime without prior knowledge of the sensed data or without a central coordination. In this paper we consider a scenario where a wireless sensor network (WSN) exploits ZigBee protocols for smart building application. We study a classical gathering scheme and a distributed compressive sampling approach. We discuss limitations and we propose a new distributed mixed algorithm for in-network compression. With this algorithm each node takes a decision about which scheme to adopt aiming at the reducing the number of packets to transmit. We are interested in scalability of this new method and lifetime of the system with respect to the increase of network dimension. Simulations are performed using real data sets and results show that the use of this algorithm permits to obtain longer network lifetime with small computational complexity. The performances of the algorithm are also investigated when some sensor parameters are modified and sporadic readings rise in the network.
A key design challenge for successful wireless sensor network (WSN) deployment is a good balance between the collected data resolution and the overall energy consumption. In this paper, we present a WSN solution developed to efficiently satisfy the requirements for long-term monitoring of a historical building. The hardware of the sensor nodes and the network deployment are described and used to collect the data. To improve the network's energy efficiency, we developed and compared two approaches, sharing similar sub-sampling strategies and data reconstruction assumptions: one is based on compressive sensing (CS) and the second is a custom data-driven latent variable-based statistical model (LV). Both approaches take advantage of the multivariate nature of the data collected by a heterogeneous sensor network and reduce the sampling frequency at sub-Nyquist levels. Our comparative analysis highlights the advantages and limitations: signal reconstruction performance is assessed jointly with network-level energy reduction. The performed experiments include detailed performance and energy measurements on the deployed network and explore how the different parameters can affect the overall data accuracy and the energy consumption. The results show how the CS approach achieves better reconstruction accuracy and overall efficiency, with the exception of cases with really aggressive sub-sampling policies.
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