基于数据预处理的无线气象传感网数据重构模型

针对无线气象传感网内由于节点数量大、感知数据冗余度高而导致节点通信耗能过高的问题，提出了数据联合稀疏预处理模型，利用监测区域气象要素预报值和各簇头要素值计算出一个全网公共分量并对网内数据进行预处理。将分布式压缩感知应用于簇型传感网中，对各节点感知数据进行压缩观测，在汇聚节点进行数据重构，从根本上降低节点通信量，均衡负载；同时设计了一个基于公共分量异常数据稀疏方法。仿真实验中，相对于单独使用压缩感知，数据联合稀疏预处理模型能够有效利用数据时空相关性提高数据稀疏度，压缩性能提高了25%，重构性能提高46%；同时，异常数据处理方案能够以96%的高概率恢复异常数据。因此，该数据预处理模型能够提高数据重构效率，有效降低网内数据通信量，延长网络寿命。

Data preprocessing based recovery model in wireless meteorological sensor network

To solve the problem of excessive communication energy consumption caused by large number of sensor nodes and high redundant sensor data in wireless meteorological sensor network, a Data Preprocessing Model based on Joint Sparsity (DPMJS) was proposed. By combining the meteorological forecast value with every cluster head's value in Wireless Sensor Network (WSN), DPMJS was used to compute a common portions to process sensor data. A data collection framework based on distributed compressed sensing was also applied to reduce data transmission and balance energy consumption in cluster network; data measured in common nodes was recovered in sink node, so as to reduce data communication radically. A suitable method to sparse the abnormal data was also designed. In simulation, using DPMJS can enhance the data sparsity by exploiting spatio-temporal correlation efficiently, and improve data recovery rate by 25%; compared with compressed sensing, data recovery rate is improved by 46%; meanwhile, the abnormal data processing can recovery data successfully by high probability of 96%. Experimental results indicate that the proposed data preprocessing model can increase efficiency of data recovery, reduce the amount of transmission significantly, and prolong the network lifetime.