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Data Compression optimization Strategy based on Piecewise Fitting and Matrix Completion for WSNs

The data transmitted in Wireless Sensor Networks (WSNs) have the characteristics of high redundancy and low rank. How to reduce the compression rate and computing burden of cluster-head node and improve the data reconstruction accuracy of sink node is a research hotspot in the field of data acquisition and processing technology in WSNs. For clust-head and sink node in WSNs, data compression based on piecewise fitting and data reconstruction strategy of sink node based on matrix completion were proposed respectively. Clust-head and sink node implement their respective compression strategies: Cluster head classifies and compresses the perceived data to reduce the data correlation; the sink node receives the compressed data from the cluster-heads, and then recover the data by matrix completion. Simulation results show that the proposed algorithms effectively reduce the compression ratio and improve the accuracy of data reconstruction.