Distributed compressed sensing data classification method based on sparse representation classifier

Abstract

The invention requests protection of a distributed compressed sensing data classification method based on a sparse representation classifier, relating to the field of wireless networks. Specific to that the characteristics of overload in data transmission and processing of sending nodes and data sparseness relevancy are not fully considered when the traditional classification method is applied to a sensor network with larger scale or more applications, a classification method suitable for relevant sparse data is designed, wherein a common part and a special part of data sparse coefficients are taken as classification basis so as to process sensor data reasonably. The data classification method provided by the invention is more accurate in data classification result of the sensor network and meanwhile has the advantages of effectively reducing network energy consumption by using a sparse coefficient strategy, improving the algorithm efficiency and the data processing performance, and facilitating user management as well as network application and scale expansion.

Description

technical field [0001] The invention relates to the field of wireless networks, in particular to a data classification mechanism of a sensor network. Background technique [0002] Wireless Sensing Networks (WSNs) is an ad hoc network composed of a large number of sensor nodes. With the development of hardware and software technology, its application range has been greatly developed. However, sensor nodes have certain limitations in terms of storage space, processing power, and energy, making data fusion technology one of the research directions for large-scale sensor networks. [0003] Data fusion technology refers to the information processing technology that analyzes and synthesizes some observation information under certain criteria to complete the required decision-making and evaluation tasks, which can effectively balance and reduce the energy consumption of nodes in the sensor network. Compressive Sensing (CS) method is one of the emerging data fusion technologies. I...

AI Technical Summary

Problems solved by technology

[0005] The above classification methods include two stages of training sample learning and test sample classification, but the computational complexity is too high, and the sparsity and correlation characteristics of sensor network data are not fully considered.

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