摘要：Wireless sensor network (WSN) consisting of a large number of nodes, are usually deployed in a large region for environmental monitoring, security and surveillance. The data collected through high densely distributed WSN is immense. To improve measure accuracy and prolong network lifetime, reducing data traffic is needed. Compressive sensing (CS) is a novel approach to achieve much lower sampling rate for sparse signals. In order to reduce the number of data transmissions and save more energy, we apply CS theory to gather and reconstruct the sparse signals in energy-constrained large-scale WSN. Instead of sending full pair-wise measurement data to a sink, each sensor transmits only a small number of compressive measurements. The processes of CS aggregation in WSN are given, including sparse presentation of signal，observation matrix and reconstruction algorithm design. The relation-ship between observations and reconstruct MSE are also discussed. Simulation result shows that our scheme can recovery the unknown data with acceptable accuracy as well as reduce global scale cost.
