A Clustered Space-Time Compression Method for Sensor Networks Based on Network Coding and Compressed Sensing

Abstract

The invention relates to a sensing network clustering type space time compression method based on network coding and compression sensing. Targeted at problems of performance defects of reconstruction errors and computing complexities which are not low enough in existing research schemes during exploration of correlation of time and space of sensing data of a wireless sensor network, the invention brings forward a clustering type space time compression method with reference to network coding and a compression sensing theory; time space correlation of sensing data is deeply excavated; through design of appropriate network coding coefficients and observation matrix elements, network coding and the compression sensing theory are fused and unified in a real number domain; data reconstruction is ensured to be feasible and a high success rate is ensured; through construction of sensor node (cluster head node) independent codes and combination with a node combination decoding idea, reconstruction of compressed data of the method is enabled to have lower reconstruction errors. Meanwhile, exploration is carried out on the correlation between the time and the space step by step to guarantee low complexity of the reconstruction process.

Description

technical field [0001] The invention relates to a sensor network clustering space-time compression method based on network coding and compressed sensing, and belongs to the technical fields of wireless communication networks and wireless sensor networks. Background technique [0002] In wireless sensor networks, sensory nodes perceive data (time series) that often have time correlation, and at the same time, adjacent nodes often monitor the same phenomenon in the same area so that the observed data has spatial correlation. Mining the time and space correlation properties of sensor network data can effectively reduce the number of redundant data transmissions in the network, which can effectively reduce network energy consumption and prolong the life cycle of the network. Therefore, how to mine the space-time correlation of sensor network data has become a research topic. hotspot. However, due to the particularity of sensor nodes with limited energy and computing power, it i...

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Problems solved by technology

[0004] The first type of scheme only considers the spatial correlation or temporal correlation of mining sensor data. Although this type of scheme reduces the number of network transmissions to a certain extent, and has the characteristics of simplicity and high efficiency, it only considers the space-time correlation after all. A class of attributes that fail to fully mine all the correlations of the data

[0005] The second type of scheme studies the space-time correlation based on the joint sparsity model (Joint Sparsity Model-JSM). into a one-dimensional vector, although the spatial-temporal correlation of the sensory data has been effectively mined, the computational complexity in the reconstruction process of the sampling data of this type of scheme is very high

The feature of this type of scheme is that it is simple and easy to implement, but the reconstruction accuracy of the compressed sampling data of this type of scheme is not high enough, especially when the perceived data deviation between nodes is far away, the reconstruction accuracy is even more unsatisfactory

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