Covariogram-Based Compressive Sensing for Environmental Wireless Sensor Networks

Abstract

In this paper, we propose covariogram-based compressive sensing (CB-CS), a spatio-temporal compression algorithm for environmental wireless sensor networks. CB-CS combines a novel sampling mechanism along with an original covariogram-based approach for the online estimation of the covariance structure of the signal and leverages the signal’s spatio-temporal correlation structure through the Kronecker CS framework. CB-CS’s performance is systematically evaluated in the presence of synthetic and real signals, comparing it against a number of compression methods from the literature, based on linear approximations, Fourier transforms, distributed source coding, and against several approaches based on CS. CB-CS is found superior to all of them and able to effectively and promptly adapt to changes in the underlying statistical structure of the signal, while also providing compression versus energy tradeoffs that approach those of idealized CS schemes (where the signal correlation structure is perfectly known at the receiver)

Image Transmission Model with Quality of Service and Energy Economy in Wireless Multimedia Sensor Network

“…In WMSN, the lossy compression is often preferred for lowspeed transmissions; it loses details in the image but within acceptable limits. Using this type of compression to data transmission in WMSN [10][11][12] allow energy saving visible, which leads to prolong the lifetime of the network, in contrast, www.ijacsa.thesai.org the transmission the natural image that consumes seven times the energy, energy consumption are the additive value [23]. As prove it in this article.…”

Energy Efficient Data Gathering Schema for Wireless Sensor Network: A Matrix Completion Based Approach

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A hybrid adaptive spatiotemporal compression scheme in cluster‐based wireless sensor networks

“…In this paper work, we consider in the first simulation synthetic signals that are already proposed in Zordan et al 38 and used in Hooshmand et al 14 and Rossi et al 16 These signals have been approved and verified against real‐world datasets, providing the generation of varying signal in space and time with tunable correlation parameters. In this simulation, the Gaussian function is used as a spatial correlation function 39 and is given by the following: where α is a scaling factor that depends on the field size and γ is a free parameter used to control the spatial correlation.…”

“…This work presents also a logical classification of these techniques, considering the compression of sampling, data, and communication. Furthermore, the research work in Hooshmand et al 14 adopts another classification of data compression considering temporal, spatial, and spatio‐temporal methods. Temporal approaches exploit the incurred temporal correlation in the sensed time series to discard redundant information, 15 while the spatial approaches are designed to eliminate repetitious information gathered from sensor nodes exploiting the spatial correlation induced by the density of the network 16 .…”

“…As the sensed time series of each node presents generally a temporal dependency, and the observed signal of neighboring sensors at the same time slot is highly related for monitoring the same area, the collected data are highly correlated temporally and spatially 17 . Effectively, lossy compression approaches take advantage of these redundancies in space and time to discard additional information without affecting the data reliability 14 . Some of these methods have proven remarkable results in terms of energy saving owing to their efficient data reduction but at the cost of some information loss.…”