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The data collected through high densely distributed wireless sensor networks is immense. The asymmetry between the data acquisition and information processing makes a great challenge to the restriction of energy and computation consumption of the sensor nodes, and it limits the application of wireless sensor networks. However, the recent works show that compressed sensing can break through this limitation of asymmetry. Compressed sensing is an emerging theory that is based on the fact that a signal can be recovered through a relatively small number of random projections which contain most of its salient information. In this paper, we introduce the background of compressive sensing, and then applications of compressed sensing in wireless sensor networks are presented.