Selection method of matrix wireless sensor network-oriented sectioned random router

Abstract

The invention discloses a selection method of a matrix wireless sensor network-oriented sectioned random router. The method comprises the following steps that: (1), a sensor node in a network is randomly selected as a route starting node according to a set probability, and the route starting node selects a corresponding route terminating node according to the node number thereof; (2), the route starting node serves as a node ready for sending packet and performs the subsequent steps according to the number of the corresponding route terminating node; (3), the node ready for sending packet judges whether to select a convergent node as a next-hop node receiving the packet thereof so as to finish random router selection or select other next-hop node to receive the packet thereof according tothe node number thereof; and (4), the next-hop node judges whether the node number of the next-hop node is in the number information in the received packet or not so as to select the updated packet or the received packet as the packet to be sent by the next-hop node in the selection of the next route and return to perform the step (3).

Description

technical field [0001] The invention relates to a block routing selection method for a matrix wireless sensor network. Background technique [0002] Compressed Sensing (Compressed Sensing) is an emerging signal processing technology in recent years. Its core idea is to combine data sampling and compression. The algorithm recovers the signal from the measured values. Compressed sensing has two basic requirements: the sparsity of the signal, and the non-correlation between the observation base (observation matrix) and the transformation base (transformation matrix). For any signal in nature, there is a specific representation space, which makes the signal sparse in this space. It has been proved by relevant theories that a random matrix, that is, a matrix whose elements are random numbers, has a good non-correlation with a fixed transformation basis. [0003] The mathematical principle of compressed sensing is as follows: Let the transformation coefficients of the vector si...

AI Technical Summary

Problems solved by technology

[0011] However, there are significant shortcomings in the current research on the use of compressive sensing for data collection in wireless sensor networks

The application of traditional compressed sensing algorithms will lead to the generation of dense observation matrix, which greatly increases the loss in the data collection process

Although, in the compressed sensing theory, the sparse observation matrix that meets the specific requirements can also ensure the validity and reliability of data reconstruction in compressed sensing, but the existing routing technology cannot stably meet the characteristics of the sparse observation matrix. requirements

The main performance is that the number of non-zero values ​​in the observation matrix formed by the existing routing technology is insufficient, and the position distribution of non-zero values ​​is not uniform enough, so the performance of signal reconstruction in compressed sensing is greatly reduced

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