Abstract
In this paper, we consider the problem of reconstructing the temporal and spatial profile of some physical phenomena monitored by large-scale Wireless Sensor Networks (WSNs) in an energy efficient manner. Compressive sensing is one of the popular choices to reduce the energy consumption of the data collection in WSNs. The existing solutions only consider sparsity of sensors’ data from either temporal or spatial dimensions. In this paper, we propose a novel data collection strategy, CS2-collector, for WSNs based on the theory of Two Dimensional Compressive Sensing (2DCS). It exploits both temporal and spatial sparsity, i.e., 2D-sparsity of WSNs and achieves significant gain on the tradeoff between the compression ratio and reconstruction accuracy as the numerical simulations and evaluations on different types of sensors’ data. More intuitively, with the same given energy budget, CS2-collector provides significantly more accurate reconstruction of the profile of the physical phenomena that are temporal-spatially sparse.
Highlights
The recent technological evolution of sensing devices has significantly broadened the applications of Wireless Sensor Networks (WSNs)
To further improve the performance of WSNs on energy consumption and signal reconstruction accuracy, we propose a new data collection strategy, CS2 -collector, for WSNs based on the theory of two-dimensional Compressive Sensing (CS) (2DCS) by exploiting the two-Dimensional sparsity (2D-sparsity), i.e., the temporal and spatial sparsity, existing in most of WSNs
In order to improve the efficiency of wireless sensor networks, numerous prior works have been done to investigate the availability of compressive sensing
Summary
The recent technological evolution of sensing devices has significantly broadened the applications of Wireless Sensor Networks (WSNs). To further improve the performance of WSNs on energy consumption and signal reconstruction accuracy, we propose a new data collection strategy, CS2 -collector, for WSNs based on the theory of two-dimensional CS (2DCS) by exploiting the two-Dimensional sparsity (2D-sparsity), i.e., the temporal and spatial sparsity, existing in most of WSNs. Like our evaluations on different types of real world sensors’ data, CS2 -collector produces significant performance gain on signal reconstruction accuracy compared with the traditional one-dimensional CS (1DCS) based approaches with the same compression ratio or energy consumption budget. With the same goal of reconstruction accuracy, CS2 -collector requires a significantly less amount of data transmitted through the network to the base station so that the energy consumption can be reduced.
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