Compressed sensing (CS) is an emerging sampling technique by which the data sampling and aggregating can be done simultaneously, which can be applied to many fields, including data processing in wireless sensor networks (WSNs). In WSNs, data aggregating can reduce data transmission cost and improve energy efficiency. Existing CS-based data gathering work in WSNs utilizes the centralized method to process the data by a sink node, which causes the load imbalance and "coverage hole" problems, and

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... o forth. In this paper, we propose an energy-balanced data gathering and aggregating (EDGA) scheme that integrates a clustering hierarchical structure with the CS to optimize and balance the amount of data transmitted. We also design a data reconstruction algorithm to perform data recovery tasks by utilizing the orthogonal matching pursuit theory, which helps to reconstruct the original data accurately and effectively at sink node. The advantages of the proposed scheme compared with other state-of-the-art related methods are measured on the metrics of data recovery ratio and energy efficiency. We implement our scheme on a simulation platform using a real dataset from Intel lab. Simulation results demonstrate that the proposed data gathering and aggregating scheme guarantees accurate data reconstruction performance and obtains energy efficiency significantly compared to existing methods.