(RF) tomography refers to the process of inferring information about an environment by capturing and analyzing RF signals transmitted between nodes in a wireless sensor network. In the case where few available measurements are available, the inference techniques applied in previous work may not be feasible. Under certain assumptions, compressed sensing techniques can accurately infer environment characteristics even from a small set of measurements. This paper introduces Compressed RF Tomography, an approach that combines RF tomography and compressed sensing for monitoring in a wireless sensor network. We also present decentralized techniques which allow monitoring and data analysis to be performed cooperatively by the nodes. The simplicity of our approach makes it attractive for sensor networks. Experiments with simulated and real data demonstrate the capabilities of the approach in both centralized and decentralized scenarios.
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