Sparse dictionary-based wireless sensor network missing data reconstruction method

Abstract

The invention discloses a sparse dictionary-based wireless sensor network missing data reconstruction method. The method includes the following steps that: 1) the total number N of data frames requiring reconstruction are determined according to missing data; M data frames are selected from historical data and are adopted as training data, wherein M is an integer greater than K; 2) a K-SVD algorithm is called to obtain a dictionary D of which the size is K; 3) as for the dictionary D, an L1 norm minimization algorithm is adopted to sparse coefficients alpha i corresponding to each dictionary atoms di; 4) data frames of a current time point are reconstructed according to calculation results of the step 2) and the step 3); 5) whether dictionary update conditions are satisfied is judged, if the dictionary update conditions are satisfied, a dictionary update method is called to update data in the dictionary; 6) and data reconstruction is completed. According to the method of the invention, the influence of reconstructed data frames of the current time point on data frames to be reconstructed of a next time point is considered, the dictionary update conditions are set, and the sparse dictionary is updated adaptively, and therefore, the reconstruct data frames are closer to real data, and reconstruction precision is higher.

Description

technical field [0001] The invention relates to a sensor network data processing technology, in particular to a method for reconstructing missing data in a wireless sensor network based on a sparse dictionary. Background technique [0002] With the wide application of Wireless Sensor Networks (WSNs) in agriculture, industry, transportation, medical and other fields, the role of WSN as a data center is becoming more and more prominent. The sensing nodes in these applications are generally deployed in exposed environments. Due to weather conditions, node communication capabilities, signal strength, external faults, human interference, etc., the collected sensing data often has a large amount of data missing or abnormal. How to reconstruct these large amounts of missing perceptual data becomes the key to accurate scientific research, and to build a reasonable missing perceptual data reconstruction model, especially for large-scale missing perceptual data loss, to ensure high re...

AI Technical Summary

Problems solved by technology

However, in the general processing process, the spatio-temporal characteristics of sensory data are seldom combined, especially when a large amount of sensory data is lost, there is no design of large-scale lost data reconstruction methods combined with the characteristics of sensor networks, such as spatio-temporal correlation, low rank, or sparseness. studies, and few studies explore the impact of current reconstructed data on the reconstruction of missing data in the next moment

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