Clustering Data Collection Method Based on Compressed Sensing

Abstract

The invention discloses a compressed sensing-based clustered data collecting method. The compressed sensing-based clustered data collecting method comprises that firstly, a sensor network executes an edge betweenness-based clutering algorithm and a vertex betweenness-based cluster head selecting method; secondly, a cluster head collects data of cluster member nodes and generate a random measurement matrix to compressed-sample the collected data; lastly, the cluster head transmits the compressed data to a substation along a shortest path algorithm, and the substation generates the same measurement matrix to reconstruct the compressed data, and if reconstruction errors are larger than a fixed threshold, increases the number of rows of the measurement matrix to enable the reconstructed data to meet a fixed error threshold. The compressed sensing-based clustered data collecting method reduces energy consumption of data collection and can adjust the errors in the reconstructed data to obtain reconstructed data meeting fixed errors.

Description

Technical field [0001] The invention relates to a clustered data collection method based on compressed sensing, which is particularly used for wireless sensor networks, and belongs to the field of communication technology. Background technique [0002] Wireless Sensor Network (Wireless Sensor Network, WSN) is a self-organizing network formed by some low-power, small-sized sensor network nodes in a wireless multi-hop manner. These large numbers of scattered nodes can collaborate at the same time to monitor, sense and collect various data in a certain area in real time. However, wireless sensor network nodes are densely distributed and energy is limited. Especially in the process of clustered data collection, a large amount of sensing data needs to be transmitted to the cluster head through the sensor network node, and then to the base station. How to design an efficient data collection method has become an urgent problem to be solved. [0003] Traditional data collection methods ...

AI Technical Summary

Problems solved by technology

By implementing the clustering algorithm based on the edge betweenness and the cluster head selection method based on the point betweenness on the sensor network nodes, as well as the feedback of the reconstruction error, it solves the problem of high energy consumption during data collection and the inability to obtain data that meets certain error requirements. question

Images