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Measurements from sensor networks consisting of thousands of nodes are often correlated, since nearby sensors observe the same phenomenon. Using Compressed Sensing, that data can be reconstructed with a high probability from a small collection of random linear combinations of those measurements. This opens a new approach to simultaneously extract, transmit and distribute information in wireless sensor networks. Efficient communication schemes well matched to compressive sensing are, nonetheless, needed to realize the full benefits of this approach. We present a simple, practical scheme, called NetCompress, using a novel form of Network Coding. It preserves the reconstruction conditions required for Compressed Sensing and also overcomes the high link-failure rate in wireless sensor networks. NetCompress simultaneously transmits packets of sensor measurements and encodes them to form random projections for Compressed Sensing recovery. A recent result in Compressed Sensing guarantees that the data at all nodes can be accurately recovered with a high probability from a small number of projections, which is much less than the total number of nodes in the network. NetCompress demonstrates this result on both the TOSSIM simulation platform and a testbed comprising 20 micaz and tmote sensor nodes. Our experimental results show that the number of packets that is needed to reconstruct light intensity measurements with reasonable quality is just half the number of nodes in the network.