WSN无线链路不可靠,分组丢失现象普遍存在,且基于压缩感知(CS)数据收集算法对分组丢失十分敏感.首先,通过实验对分组丢失率和基于CS数据重构精度关系进行定量研究,提出极稀疏块观测矩阵,在降低每轮数据采集能耗的同时,也保持观测矩阵的近似低秩性质.其次,结合矩阵补全(MC)技术与CS 技术,提出基于极稀疏块观测矩阵的压缩感知数据收集算法,在一个采集周期内进行数据收集,利用 MC 技术恢复丢失数据,减少分组丢失对数据收集的影响;利用CS技术重构全网数据,减少数据收集量,降低节点在数据收集时所需能耗,延长网络寿命.仿真分析表明,所提算法在分组丢失率小于50%的情况下能够保证高精度重构全网数据,抵抗不可靠链路.%The unreliable links and packet losing are ubiquitous in WSN. The performance of data collection algorithm based on compressive sensing is sensitive to packet losing. Firstly, the relationship between packet loss rate and CS-based reconstruction precision was analyzed, and the sparsest block measurement (SBM) matrix was formulated to keep the da-ta gathering consumption smallest and make sure the low-rank property of measurements. Then, combined with the ma-trix completion (MC) and compressive sensing (CS), the CS data gathering algorithm based on sparsest block measure-ment matrix (CS-SBM) algorithm was proposed. CS-SBM gathered data in a period and recovered the loss data based on MC to weaken the impact of packet loss on data gathering. CS-SBM reconstructed data based on CS to reduce measure-ment number and energy consumption and prolong the network lifetime. Simulation analysis indicates that the proposed algorithm reconstruct the whole data with high-accuracy under 50% packet loss rate, resisting unreliable links effectively.
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