Compressive sensing (CS) has been widely used in wireless sensor networks (WSNs). In WSNs, the sensors are battery-powered and hence their communication and processing powers are limited. One of the dominant features of the CS is its complex recovery phase. Thus, great care should be taken into account when designing the CS recovery algorithm for WSNs. In this paper, we propose a distributed and cooperative recovery algorithm for two different cooperation modes of sensor networks including incremental and diffusion. The theoretical performance analysis of the proposed algorithms in both exact and noisy measurements is investigated. The obtained results show the superiority of the proposed method in terms of convergence rate and steady-state error compared with the non-cooperative scenario and the well-known distributed least absolute shrinkage and selection operator (D-LASSO) approach. Furthermore, the proposed structure requires much fewer measurements for exact recovery.

压缩感测（CS）已广泛用于无线传感器网络（WSN）。在WSN中，传感器由电池供电，因此它们的通信和处理能力受到限制。CS的主要特征之一是其复杂的恢复阶段。因此，在设计WSN的CS恢复算法时应格外小心。在本文中，我们针对传感器网络的两种不同协作模式（包括增量和扩散）提出了一种分布式协作恢复算法。研究了所提出算法在精确和噪声测量中的理论性能分析。所得结果表明，与非合作方案和著名的分布式最小绝对收缩与选择算子（D-LASSO）方法相比，该方法在收敛速度和稳态误差方面具有优势。此外，所提出的结构需要少得多的测量来精确地恢复。