CN110944373A - Wireless sensor network system, data transmission method, storage medium and terminal - Google Patents

Wireless sensor network system, data transmission method, storage medium and terminal Download PDF

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CN110944373A
CN110944373A CN201910922178.4A CN201910922178A CN110944373A CN 110944373 A CN110944373 A CN 110944373A CN 201910922178 A CN201910922178 A CN 201910922178A CN 110944373 A CN110944373 A CN 110944373A
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CN110944373B (en
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朱登军
袁海玮
马拥军
胡旭峰
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State Grid Corp of China SGCC
Xuchang Power Supply Co of Henan Electric Power Co
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0209—Power saving arrangements in terminal devices
    • H04W52/0212—Power saving arrangements in terminal devices managed by the network, e.g. network or access point is master and terminal is slave
    • H04W52/0219—Power saving arrangements in terminal devices managed by the network, e.g. network or access point is master and terminal is slave where the power saving management affects multiple terminals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses a wireless sensor network system and a data transmission method, a readable storage medium and a terminal thereof, wherein the method is suitable for data transmission among nodes of a distributed wireless sensor network and comprises the following steps: the observation node collects own original data, performs thinning processing on the collected original data, and transmits the thinned data to the corresponding management node; the management node receives the sparsified data sent by the observation node, and performs dimensionality reduction on the received sparsified data to obtain dimensionality-reduced data and sends the dimensionality-reduced data to the aggregation node; and the sink node receives the data after the dimensionality reduction sent by the management node and obtains corresponding original data through reconstruction, so that the data transmission efficiency of the wireless sensor network is improved, and energy is saved.

Description

Wireless sensor network system, data transmission method, storage medium and terminal
Technical Field
The present invention relates to the field of technologies, and in particular, to a wireless sensor network system, a data transmission method, a storage medium, and a terminal.
Background
In recent years, smart grids have received much attention due to the development of big data and AI. The smart grid is established on the basis of an integrated high-speed bidirectional communication network, and achieves the aims of safety, reliability, economy and high efficiency of the power grid through an advanced sensing and measuring technology, an advanced equipment technology, an advanced control method and a decision support system, and is widely applied to the aspects of promoting clean energy development, improving energy transmission and use efficiency, achieving bidirectional interaction between the power grid and users and the like.
Among them, wireless sensor networks play an extremely important role in smart grids. A wireless sensor network is a network of a large number of sensors densely arranged in a monitoring area, responsible for collecting data and transmitting it back to a server. From the perspective of a single sensor, the sensor has the capability of receiving and transmitting data, and in addition, a certain storage space is provided, but the sensor is limited by the size, cost, power consumption and the like, the capability of processing data of the sensor node is weak, the energy is limited, and frequent battery replacement is not possible, so that the energy consumption of the sensor node can be reduced and the transmission efficiency can be improved on the basis of not influencing the transmitted data.
In order to reduce the problem of power loss of the sensor, many researchers have conducted such studies. However, the existing data transmission method of the wireless sensor network still has the problems of low efficiency and energy waste.
Disclosure of Invention
The invention aims to provide a wireless sensor network system, a data transmission method, a storage medium and a terminal, which can improve the data transmission efficiency of the wireless sensor network and save energy.
The technical scheme adopted by the invention is as follows:
a data transmission method of a wireless sensor network is suitable for data transmission among nodes of a distributed wireless sensor network, and comprises the following steps:
s1: the observation node collects own original data, performs thinning processing on the collected original data, and transmits the thinned data to a correspondingly arranged management node;
s2: the management node receives the sparsified data sent by the observation node, and performs dimensionality reduction on the received sparsified data to obtain dimensionality-reduced data and sends the dimensionality-reduced data to the aggregation node;
s3: and the sink node receives the data after the dimensionality reduction sent by the management node and obtains corresponding original data through reconstruction.
The step S1 of performing the sparsification process on the acquired raw data includes the following steps:
step S1.1: calculating a sparse basis corresponding to the acquired original data;
step S1.2: and calculating to obtain sparse coefficient vectors of the original data projected under the sparse basis based on the calculated sparse basis, wherein the sparse coefficient vectors are used as the data corresponding to the original data after the sparse processing.
In the step S1.1, a sparse basis corresponding to the acquired raw data is calculated by using a principal component analysis method.
In step S2, a preset number of row vectors in a preset observation matrix are used to perform observation and dimensionality reduction on the thinned data.
The observation matrix is a random Gaussian measurement matrix.
In step S3, the step of reconstructing to obtain corresponding original data includes the following steps:
s3.1: converting the reconstruction problem of the data after dimensionality reduction into the problem of the minimum first norm, and solving to obtain an estimated value of a sparse coefficient vector;
s3.2: and obtaining corresponding original data through inverse transformation based on the estimated value of the sparse coefficient vector obtained through solving.
A wireless sensor network system comprises an observation node, a management node and a sink node; the aggregation nodes are respectively coupled with one or more than one management node, and the management nodes are respectively coupled with two or more than two observation nodes which are correspondingly arranged;
the observation node is suitable for acquiring own original data, performing thinning processing on the acquired original data and transmitting the thinned data to the correspondingly arranged management node;
the management node is suitable for receiving the sparsified data sent by the observation node, reducing the dimension of the received sparsified data, obtaining the dimension-reduced data and sending the dimension-reduced data to the sink node;
the sink node is suitable for receiving the data after the dimensionality reduction sent by the management node and obtaining corresponding original data through reconstruction.
The observation node of the wireless sensor network system is suitable for calculating a sparse basis corresponding to the acquired original data by adopting a principal component analysis method, and calculating a sparse coefficient vector of the original data projected on the sparse basis based on the calculated sparse basis to serve as data after sparse processing corresponding to the original data;
the management node is suitable for observing, reducing the dimension of the thinned data by adopting a preset number of row vectors in a preset random Gaussian measurement matrix;
the sink node is suitable for converting the reconstruction problem of the sparse signal into the problem of the minimum first norm and solving the estimation value of the sparse coefficient vector; and obtaining a corresponding original signal through inverse transformation based on the estimated value of the sparse coefficient vector obtained through the solution.
A computer readable storage medium having stored thereon computer instructions which, when executed, perform the steps of the data transmission method of a wireless sensor network.
The wireless sensor network data transmission system comprises a memory and a processor, wherein the memory is stored with computer instructions capable of running on the processor, and the processor executes the steps of the data transmission method of the wireless sensor network when executing the computer instructions.
The wireless sensor network system adopts the observation nodes to collect the own original data and carry out sparse processing on the collected original data, transmits the thinned data to the corresponding management nodes, receives the thinned data sent by the observation nodes by the management nodes, reduces the dimension of the received thinned data, obtains the dimension-reduced data and sends the dimension-reduced data to the aggregation nodes, and can reduce the transmission quantity of the data, improve the data transmission efficiency and save energy.
According to the wireless sensor network system and the data transmission method thereof, the collected original data are subjected to thinning processing, the thinned data are transmitted to the corresponding management nodes, the management nodes receive the thinned data sent by the observation nodes, the received thinned data are subjected to dimensionality reduction, the dimensionality reduced data are obtained and sent to the aggregation nodes, and therefore the data transmission quantity can be reduced, the data transmission efficiency is improved, and energy is saved.
The readable storage medium operates the data transmission method of the wireless sensor network, improves the data transmission efficiency and saves energy.
The terminal stores the computer instructions and operates the data transmission method, so that the data transmission efficiency is improved and the energy is saved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for a person skilled in the art to obtain other drawings without inventive exercise from these drawings.
FIG. 1 is a schematic diagram of a wireless sensor network system of the present invention;
FIG. 2 is a flow chart illustrating a data transmission method according to the present invention;
FIG. 3 is a schematic diagram of a data transmission method of a wireless sensor network according to the present invention;
fig. 4 is a schematic diagram of a comparison result of normalization of a reconstruction error between a data transmission method of a wireless sensor network and a compression algorithm using a fourier transform matrix as a sparse matrix according to an embodiment of the present invention.
Detailed Description
As shown in fig. 1, the wireless sensor network system of the present invention includes an observation node, a management node, and a sink node; the aggregation nodes are respectively coupled with the n management nodes, and the management nodes are respectively coupled with the m observation nodes correspondingly arranged; n is an integer greater than or equal to 1, m is an integer greater than or equal to 1;
the observation node is suitable for acquiring own original data, performing thinning processing on the acquired original data and transmitting the thinned data to the correspondingly arranged management node;
the management node is suitable for receiving the sparsified data sent by the observation node, reducing the dimension of the received sparsified data, obtaining the dimension-reduced data and sending the dimension-reduced data to the sink node;
the sink node is suitable for receiving the data after the dimensionality reduction sent by the management node and obtaining corresponding original data through reconstruction.
The observation node is suitable for calculating a sparse basis corresponding to the acquired original data by adopting a principal component analysis method, and calculating to obtain a sparse coefficient vector of the original data projected under the sparse basis based on the calculated sparse basis, wherein the sparse coefficient vector is used as data corresponding to the original data after sparse processing;
the observation node is suitable for calculating a sparse basis corresponding to the acquired original data by adopting a principal component analysis method;
the management node is suitable for observing, reducing the dimension of the thinned data by adopting a preset number of row vectors in a preset random Gaussian measurement matrix;
the observation matrix adopted by the management node is a random Gaussian measurement matrix;
the sink node is suitable for converting the reconstruction problem of the sparse signal into the problem of the minimum first norm and solving the estimation value of the sparse coefficient vector; and obtaining a corresponding original signal through inverse transformation based on the estimated value of the sparse coefficient vector obtained through the solution.
In a specific implementation, the observation nodes can be classified according to different collected data. For example, in this embodiment, the observation node respectively measures data such as temperature, structural deformation, water accumulation, and gas flow rate by using a distributed optical fiber temperature sensing technology, a distributed optical fiber dependent sensing technology, and a distributed optical fiber vibration technology. Therefore, the observation nodes respectively comprise different types for acquiring temperature data, structural deformation data, water accumulation data, gas flow velocity data and the like, so that the real-time dynamic collection of the underground cable from temperature, pressure, vibration and other omnibearing data can be realized. Wherein the observation nodes of the same type are all coupled with the same management node.
The wireless sensor network system in the embodiment of the invention adopts a distributed compressed sensing technology to reduce the calculation storage pressure of a single node, sends the original data collected by the observation nodes of the same type to the corresponding management node for compression and sparse processing, and finally transmits the compressed data to the sink node by the management node for reconstruction and restoration to obtain complete signal data, thereby reducing the transmission quantity of the data, improving the data transmission efficiency and saving energy.
As shown in fig. 2, a data transmission method of a wireless sensor network is suitable for data transmission between nodes of a distributed wireless sensor network, and includes the following steps:
s1: the observation node collects own original data, performs thinning processing on the collected original data, and transmits the thinned data to a correspondingly arranged management node; in specific implementation, the observation node firstly compresses the acquired data of the sensor to obtain corresponding original data, and then sparsizes the obtained original data.
When the acquired original data is thinned, a proper orthogonal basis needs to be selected to represent the original data.
In this embodiment, the thinning of the raw data includes the following steps:
step S1.1: and calculating a sparse basis corresponding to the acquired original data by adopting a principal component analysis method.
Suppose for a real number set RNCan be represented by a column vector of dimension N x 1, and a real number set RNCan use vector base satisfying orthogonal condition
Figure BDA0002217915250000051
According to a linear combination of the basis matrices
Figure BDA0002217915250000052
Then any one of the signals, i.e. the raw data X ∈ RNCan be expressed as:
Figure BDA0002217915250000053
wherein X represents original data, psi represents sparse basis corresponding to the original data X,
Figure BDA0002217915250000054
represents the sparse coefficient vector of the raw data X-projection at the sparse basis Ψ.
Then, one can get:
Θ＝ΨTX (2)
therein, ΨTA transposed matrix representing the sparse basis Ψ.
As can be seen from the above description, "raw data X" and "sparse coefficient vector Θ of raw data X projected under sparse basis Ψ" can be regarded as representations of the same signal in different domains. When the number of the nonzero coefficients in the sparse coefficient vector theta is much smaller than N, the signal is compressible. For example, if the number of non-zero coefficients in the sparse coefficient vector Θ is k and all other coefficients are zero, the signal is said to be k-sparse.
Next, an appropriate sparse basis Ψ corresponding to the original data X is calculated.
Specifically, for a set of signal sample sets, i.e., raw data X:
X＝{X1，X2，...，Xi，...，XN},Xi＝{Xi1，Xi2，...，Xij，...，XiN}∈Rd，i＝1,2,...,N (3)
wherein, XiRepresenting the ith piece of data, R, in the original data XdRepresenting a real number set of dimensions d, Xij(j ═ 1, 2.. times.d) denotes XiThe j-th dimension of (1).
The sample matrix of the original data X is S, and S belongs to RN×dThen the covariance matrix C of the original data X can be expressed as:
Figure BDA0002217915250000062
wherein S isTA transposed matrix, R, representing a matrix S of samples of the original data XN×dRepresenting a real number set of dimensions N x d.
The covariance matrix C is then diagonalized, since the covariance matrix C is a symmetric matrix, there is an orthogonal matrix P, such that:
PTCP＝Λ∈Rd×d(5)
wherein R isd×dThe real number set with the dimension of d multiplied by d is represented, and Λ represents a diagonal matrix, which represents that the original matrix is subjected to linear transformation so that the correlation among all dimensions is reduced to 0. Meanwhile, the part with smaller coefficient on the diagonal is noise, so that the first K characteristic values with larger coefficient can be selected and recorded as
Figure BDA0002217915250000063
The corresponding feature matrix is noted
Figure BDA0002217915250000064
After the sample matrix S of the original data X is subjected to denoising processing, a corresponding denoised sample matrix is obtained
Figure BDA0002217915250000065
Hypothesis denoised sample matrix
Figure BDA0002217915250000066
Uncorrelated and noise free between the dimensions, then:
Figure BDA0002217915250000067
Figure BDA0002217915250000068
from the above formula, one can obtain:
Figure BDA0002217915250000071
by mapping a sample matrix S of raw data X to a feature matrix
Figure BDA0002217915250000072
Obtaining a matrix of denoised samples
Figure BDA0002217915250000073
And the mapped sample data is subjected to redundancy removal and decorrelation, and a diagonal matrix is formed according to the maximum characteristic values
Figure BDA0002217915250000074
The obtained feature vector is a unit vector, and the dimension of the original data can be reduced, so that the obtained feature matrix
Figure BDA0002217915250000075
I.e. the sparse matrix to be obtained, i.e. the sparse basis Ψ.
Step S1.2: and calculating to obtain sparse coefficient vectors of the original data projected under the sparse basis based on the calculated sparse basis, wherein the sparse coefficient vectors are used as the data corresponding to the original data after the sparse processing.
When the adaptive sparse basis Ψ corresponding to the original data is obtained through calculation, a sparse coefficient vector Θ of the original data X projected under the sparse basis Ψ, that is, data after the sparse processing corresponding to the original data X, can be obtained through calculation.
Because the sensors, namely observation nodes, are designed based on the distributed technology, the calculation storage pressure of a single node can be reduced, the workload of the sensors for calculating and storing data is reduced, the positions of the sensors are reasonably arranged according to the type of the data to be measured, and the feasibility and the stability of the whole scheme are enhanced.
S2: and the management node receives the sparsified data sent by the observation node, reduces the dimension of the received sparsified data, obtains the dimension-reduced data and sends the dimension-reduced data to the aggregation node.
In this embodiment, a preset number of row vectors in a preset observation matrix are used to perform observation projection dimension reduction on the sparsely processed data. The measurement matrix is a random Gaussian measurement matrix.
In this embodiment, when receiving data of the thinned data sent by the corresponding observation node, the management node performs dimensionality reduction on the thinned data to project and reduce the dimensionality of the sparse coefficient vector Θ in the high-dimensional space to the low-dimensional vector space. In particular, M row vectors of an M × N measurement matrix are used
Figure BDA0002217915250000076
The raw data X are subjected to an observation projection and their inner product is calculated, i.e.:
Figure BDA0002217915250000077
wherein, Y represents the measured value after the X-ray linear projection of the original data, the dimension is M, and phi represents the measurement matrix.
For the measurement matrix, it needs to satisfy the condition:
Figure BDA0002217915250000078
wherein, deltakE (0,1) represents a preset constant, and k represents that the signal is consistent with k-order sparsity.
In this embodiment, since the random gaussian measurement matrix is uncorrelated with most of the orthogonal bases or the orthogonal dictionaries and the number of measurements required for accurate reconstruction is small, the random gaussian measurement matrix is selected as the observation matrix, and the design method thereof is as follows: constructing a matrix phi of size M × N such that each element phi in the matrix phiuvIndependent Gaussian distribution with mean 0 and variance 1/M, i.e.:
Figure BDA0002217915250000081
s3: and the sink node receives the data after the dimensionality reduction sent by the management node and obtains corresponding original data through reconstruction.
Obtaining corresponding original data through reconstruction comprises the following steps:
s3.1: when the management node transmits the obtained measurement data to the sink node, the sink node converts the reconstruction problem of the data after dimensionality reduction into a problem of solving a minimum L0 norm, and solves the problem to obtain an estimated value of a sparse coefficient vector;
X＝argmin||X||0subject to Y＝ΦX (12)
in general, the solution method of the L0 norm problem is mainly convex relaxation, greedy pursuit and iterative thresholding. However, the L0 norm is often equivalent to the L0 norm to solve, that is, the sink node converts the reconstruction problem of the data after dimensionality reduction into a problem of solving the minimum first norm.
Because the L1 norm solves a convex optimization problem, while the L0 norm solves an NP-hard problem. After transformation, the problem can be solved by using a linear programming and polynomial method, and the error of the reconstructed value and the original value is controlled at each iteration, namely:
X＝argmin||X||1subject to ||Y-ΦX||2＜ε (13)
where epsilon represents the reconstruction accuracy of the signal. Clearly, a smaller epsilon indicates a more accurate result.
S3.2: and obtaining corresponding original data through inverse transformation based on the estimated value of the sparse coefficient vector obtained through solving.
As shown in fig. 3, in equation (12), the measurement values after the linear projection of the raw data X, i.e., the measurement data Y and the measurement matrix Φ, are known to the sink node:
Y＝ΦΘ＝ΦΨTX (14)
therefore, by the above formula (14), the estimation value of the sparse coefficient vector Θ can be obtained by using the compressive sampling matching pursuit algorithm, and then inverse transformation is performed on the estimation value, so that the reconstructed original data X can be obtained according to the formula (1).
The experiment of the invention is based on a matlab simulation platform, the measurement signal accords with K-sparsity, the measurement data of the node is transmitted to the management node firstly, then transmitted to the sink node and reconstructed, and a random Gaussian orthogonal matrix is selected as the measurement matrix.
And normalizing and comparing the reconstruction errors of the compression algorithm adopting the Fourier transform matrix as the sparse matrix and the distributed compressed sensing algorithm based on principal component analysis in the embodiment of the invention. As shown in fig. 4, when the observed quantity is 100, the reconstruction error of the former is about 0.5, and the observed error of the latter is about 0.1, it is obvious that the reconstruction completion degree of the latter is much higher than that of the former; when the reconstruction error is 0.2, the former needs about 120 observations, and the latter needs about 90 observations, so that the latter can reconstruct a more accurate signal by using fewer observations; the former needs about 170 observed values to complete the approximate accurate reconstruction of the signal, while the latter needs only 120 observed values to achieve the approximate accurate reconstruction, and the two are different by 50 measured values, namely, the latter can save about 20% of energy. Therefore, the effect of removing redundancy of data is achieved by utilizing the principal component analysis technology, the energy consumption is effectively reduced, the purpose of the invention is finally achieved, the transmission quantity of data is reduced, and the energy consumption of the sensor is reduced.
The invention also provides a computer readable storage medium, on which computer instructions are stored, which when executed perform the steps of the data transmission method of the wireless sensor network. For a data transmission method of the wireless sensor network, please refer to the detailed description of the foregoing parts, which are not described again.
The invention also provides a terminal, which comprises a memory and a processor, wherein the memory is stored with computer instructions capable of running on the processor, and the processor executes the steps of the data transmission method of the wireless sensor network when running the computer instructions. For a data transmission method of the wireless sensor network, please refer to the detailed description of the previous section, which is not described again.
According to the data processing method of the wireless transmission network, the observation node is adopted to collect the own original data and carry out sparse processing on the collected original data, the thinned data are transmitted to the corresponding management node, the management node receives the thinned data sent by the observation node and carries out dimensionality reduction on the received thinned data, the dimensionality reduced data are obtained and sent to the aggregation node, and therefore the transmission amount of the data can be reduced, the data transmission efficiency is improved, and energy is saved.

Claims (10)

1. A data transmission method of a wireless sensor network is suitable for data transmission among nodes of a distributed wireless sensor network, and is characterized in that: the method comprises the following steps:
s1: the observation node collects own original data, performs thinning processing on the collected original data, and transmits the thinned data to a correspondingly arranged management node;
s2: the management node receives the sparsified data sent by the observation node, and performs dimensionality reduction on the received sparsified data to obtain dimensionality-reduced data and sends the dimensionality-reduced data to the aggregation node;
s3: and the sink node receives the data after the dimensionality reduction sent by the management node and obtains corresponding original data through reconstruction.
2. The data transmission method of the wireless sensor network according to claim 1, wherein: the steps are
The step S1 of performing the thinning process on the acquired raw data includes the steps of:
step S1.1: calculating a sparse basis corresponding to the acquired original data;
step S1.2: and calculating to obtain a sparse coefficient vector of the original data projected under the sparse basis based on the calculated sparse basis, wherein the sparse coefficient vector is used as the data corresponding to the original data after the sparse processing.
3. The data transmission method of the wireless sensor network according to claim 2, wherein: in the step S1.1, a sparse basis corresponding to the acquired raw data is calculated by using a principal component analysis method.
4. The data transmission method of the wireless sensor network according to any one of claims 1 to 3, wherein: in step S2, a preset number of row vectors in a preset observation matrix are used to perform observation and dimensionality reduction on the thinned data.
5. The data transmission method of the wireless sensor network according to claim 4, wherein: the observation matrix is a random Gaussian measurement matrix.
6. The data transmission method of the wireless sensor network according to claim 2, wherein: in step S3, the step of reconstructing to obtain corresponding original data includes the following steps:
s3.1: converting the reconstruction problem of the data after dimensionality reduction into the problem of the minimum first norm, and solving to obtain an estimated value of a sparse coefficient vector;
s3.2: and obtaining corresponding original data through inverse transformation based on the estimated value of the sparse coefficient vector obtained through solving.
7. A wireless sensor network system, characterized by: the system comprises observation nodes, management nodes and sink nodes; the aggregation nodes are respectively coupled with one or more than one management node, and the management nodes are respectively coupled with two or more than two observation nodes which are correspondingly arranged;
the observation node is suitable for acquiring own original data, performing thinning processing on the acquired original data and transmitting the thinned data to the correspondingly arranged management node;
the management node is suitable for receiving the sparsified data sent by the observation node, reducing the dimension of the received sparsified data to obtain the dimension-reduced data and sending the dimension-reduced data to the sink node;
the sink node is suitable for receiving the data after the dimensionality reduction sent by the management node and obtaining corresponding original data through reconstruction.
8. The wireless sensor network system according to claim 7, wherein: the observation node is suitable for calculating a sparse basis corresponding to the acquired original data by adopting a principal component analysis method, and calculating to obtain a sparse coefficient vector of the original data projected under the sparse basis based on the calculated sparse basis, wherein the sparse coefficient vector is used as data corresponding to the original data after sparse processing;
the management node is suitable for observing, reducing the dimension of the thinned data by adopting a preset number of row vectors in a preset random Gaussian measurement matrix;
the sink node is suitable for converting the reconstruction problem of the sparse signal into the problem of the minimum first norm and solving the estimation value of the sparse coefficient vector; and obtaining a corresponding original signal through inverse transformation based on the estimated value of the sparse coefficient vector obtained through solving.
9. A computer-readable storage medium having stored thereon computer instructions, characterized in that: the computer instructions when executed perform the steps of the method of data transmission of a wireless sensor network of any of claims 1 to 6.
10. A terminal, characterized by: the wireless sensor network data transmission system comprises a memory and a processor, wherein the memory is stored with computer instructions capable of running on the processor, and the processor executes the computer instructions to execute the steps of the data transmission method of the wireless sensor network according to any one of claims 1 to 6.
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