Research On Sparse Sampling And Approximate Reconstruction For Internet Of Things

Wireless sensor network technology in the Internet of Things has always been one of the research hotspots.There are a lot of redundant data in the network,and sometimes there is the possibility of anomaly or loss.How to reconstruct the original data by collecting only part of the valid data,how to improve the reconstructing accuracy and how to reduce the sampling rate are the focus of the research.Firstly,the data matrix models of different scales with spatial-temporal correlation are constructed and simulated based on the random non-uniform sampling mechanism in sparse sampling.In the experiment,many different parameters are set up,such as the spatial-temporal correlation coefficient,the number of sensors,sampling time,sampling ratio,etc.The simulation results show that an effective matrix with spatial-temporal correlation can be established.For the reconstruction of sampled data,compressed sensing and other commonly used data recovery algorithms are usually used.There is no research on approximate reconstruction of spatial-temporal correlation data matrix using matrix completion.Under the premise of matrix reconstruction,this paper proposes sparse sampling of spatially correlated data matrix models of different scales at different sampling ratios,and then uses SVT algorithm to approximate the original matrix to obtain different reconstruction accuracy.The simulation also proves that the iteration time of the algorithm increases with the increase of matrix size and sampling ratio.Based on the in-depth study of sparse sampling and approximate reconstruction of sparse data matrix with spatial-temporal correlation,it is found that there is a certain relationship between spatial-temporal correlation and reconstruction feasibility,that is,it can change the reconstruction accuracy and sampling ratio.Based on the relationship between spatial-temporal correlation and data redundancy,this paper proposes a PCA algorithm for the data matrix that has been constructed.Under the condition of spatial-temporal correlation,the number of retained principal components is studied,which proves that there will be a lot of redundancy between data with high spatial-temporal correlation.Therefore,the spatial-temporal correlation has an impact on the feasibility of reconstruction-sampling ratio and reconstruction accuracy.The simulation results show that high spatial-temporal correlation can improve the reconstruction accuracy and reduce the sampling rate.