关键词: 无线传感器网络, 数据采集, 压缩感知, 智能优化, 稀疏重构算法

Abstract: In order to improve the data processing efficiency and reduce network energy consumption for Wireless Sensor Networks（WSNs）, a WSNs sparse data acquisition scheme based on adaptive intelligent optimization and cluster compressed sensing is proposed. Firstly, a clustering WSNs sparse data communication model is established. Based on the quantitative analysis of the relationship between node density and total number of hops, the adaptive clustering results are given. Also the data acquisition using observation matrix in cluster and multi hop communication between clusters is used to complete the WSNs compressed sensing data acquisition. Secondly, the StOMP algorithm is used to reconstruct sparse signal. For the network node data packet loss and other unreliable links, the correlation matrix transformation strategy is introduced to reduce the impact of error data transmission on data reconstruction. Aiming at the problem that the data sparsity is unknown and the parameters of StOMP algorithm are difficult to configure, an Improved Adaptive Intelligent Optimization（IAIO） algorithm is introduced to StOMP algorithm. On the basis of theoretical analysis of the global optimization ability of IAIO, reliable reconstruction of sparse data is realized. Finally, the simulation results show that, the scheme can realize the accurate reconstruction of sparse signals, reduce the total amount of network communications, and improve the network lifetime.

Key words: Wireless Sensor Networks（WSNs）, data acquisition, Compressed Sensing（CS）, intelligent optimization, sparse reconstruction algorithm