Compressed-sensing-based signal sampling method for distributed wireless sensor network nodes

Abstract

The invention relates to a method for simultaneously acquiring a plurality of voice signals by using a DCS (distributed compressed sensing) technology. According to the method, the requirements of low hardware cost and high transmission rate of a plurality of sensor acquisition nodes in the agricultural Internet of Things can be met, the power consumption of a plurality of sensors in a communication process is reduced, and the sensors cooperatively work to prevent damage to the whole network structure due to the fact that the energy of certain nodes is excessively fast consumed. A JSM-1-model-based joint sparsity model for the voice signals is established by using a DCS theory, a plurality of pieces of sensing data are simultaneously compressed at a coding end, and the signals are precisely recovered by using a DCS-theory-based joint reconstruction algorithm at a decoding end to implement a complete signal acquisition process.

Description

technical field [0001] The invention relates to the application field of signal sampling, in particular to a method for sampling and transmitting voice signals by a plurality of wireless sensor networks in the agricultural internet of things. Background technique [0002] As one of the most cutting-edge development fields of modern agriculture, agricultural Internet of Things technology is the key and core technology for developing agricultural informatization and realizing sustainable agricultural development in the world today. Agricultural Internet of Things information technology mainly includes three levels of agricultural information perception, transmission and information application. However, traditional agricultural information acquisition faces several major technical bottlenecks: first, the sensor technology is backward; second, traditional agricultural information monitoring is only a single point, static timing measurement, which cannot realize real-time dynami...

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Problems solved by technology

[0007] In order to overcome the problem of wasting storage space caused by a large amount of redundant data generated in the process of collecting data by the traditional sampling method following the Shannon sampling theorem, the present invention provides a distributed wireless sensor network node signal sampling method based on compressed sensing. Distributed compressed sensing technology establishes a joint sparse model based on the JSM-1 model for multiple speech signals, compresses the perceived data at the encoding end, and uses the DCS theory joint reconstruction algorithm at the decoding end to accurately restore the signal to achieve complete data collection process

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