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A data aggregation scheme based on multi-resolution with compressed sensing in wireless sensor networks is proposed. Firstly, the network is configured to achieve the multiple-level and the different types of cluster structure for intermediate data collection, on this structure, the leaf nodes in the lowest level only transmit the raw data. The collecting clusters in other levels perform the compressed sampling and then transmit them to their parent cluster heads. When parent collecting clusters receive random measurements, they use inverse DCT transformation and DCT model based CoSaMP algorithm to recover the original data. We implement the proposed scheme on a SIDnet-SWANS simulation platform and test different sizes of two-dimensional randomly deployed sensor network. The experiment results show that the substantial energy savings are reported for a large portion of sensors on the different hierarchical positions, ranging from 50% to 77% when compared with NCS, and from 37% to 70% when compared with HCS.