CN110944373B - 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 PDFInfo
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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
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- 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
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- 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
Abstract
The application 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 acquires own original data, performs sparsification processing on the acquired original data, and transmits the sparsified data to a corresponding management node; the management node receives the sparsified data sent by the observation node, performs dimension reduction on the received sparsified data, obtains dimension reduced data and sends the dimension reduced data to the sink node; the sink node receives the reduced-dimension data 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
Technical Field
The present application relates to the technical field, 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 a great deal of attention because of the development of big data and AI. The intelligent power grid is established on the basis of an integrated high-speed two-way 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 conveying and using efficiency, achieving two-way interaction between the power grid and a user and the like.
Among them, the wireless sensor network plays an extremely important role in the smart grid. A wireless sensor network is a network in which a large number of sensors are densely arranged in a monitoring area and are responsible for collecting data and transmitting the data back to a server. In view of a single sensor, the sensor node has the capability of receiving and transmitting data, and a certain storage space, but is limited by volume, cost, power consumption and the like, the sensor node has weak capability of processing the data and limited energy, and the battery cannot be replaced frequently, so that the energy consumption of the sensor node is reduced and the transmission efficiency is improved on the basis of not influencing the data transmission.
Many students have conducted this study in order to reduce the energy loss problem of the sensor. 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 application 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 application adopts the technical scheme that:
a data transmission method of a wireless sensor network, suitable for data transmission between nodes of a distributed wireless sensor network, comprising the steps of:
s1: the observation node acquires own original data, performs sparsification processing on the acquired original data, and transmits the sparsified data to a corresponding management node;
s2: the management node receives the sparsified data sent by the observation node, performs dimension reduction on the received sparsified data, obtains dimension reduced data and sends the dimension reduced data to the sink node;
s3: and the sink node receives the reduced-dimension data sent by the management node and obtains corresponding original data through reconstruction.
The step S1 of thinning the acquired original data comprises the following steps:
step S1.1: calculating a sparse basis corresponding to the acquired original data;
step S1.2: based on the calculated sparse basis, a sparse coefficient vector of the original data projected under the sparse basis is calculated and obtained and used as data after the sparse processing corresponding to the original data.
In the step S1.1, a sparse basis corresponding to the acquired original data is calculated by adopting a principal component analysis method.
In the step S2, a preset number of row vectors in a preset observation matrix are adopted to perform video watching and dimension reduction on the data after the thinning processing.
The observation matrix is a random Gaussian measurement matrix.
In the step S3, the obtaining the corresponding original data through reconstruction includes the following steps:
s3.1: converting the reconstruction problem of the dimensionality reduced data into a problem of a 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 an aggregation node; the sink 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 original data of the observation node and carrying out sparsification processing on the acquired original data, and the data after the sparsification processing is transmitted to a corresponding management node;
the management node is suitable for receiving the thinned data sent by the observation node, carrying out dimension reduction on the received thinned data, obtaining dimension-reduced data and sending the dimension-reduced data to the sink node;
the sink node is suitable for receiving the reduced-dimension data 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 a sparse coefficient vector of the original data projected under the sparse basis based on the calculated sparse basis to serve as data after the sparse processing corresponding to the original data;
the management node is suitable for performing video watching and dimension reduction on the data subjected to the sparsification processing 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 signals into a problem of a minimum first norm and solving an estimated value of the sparse coefficient vector; based on the estimated value of the sparse coefficient vector obtained by solving, the corresponding original signal is obtained by inverse transformation.
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 method comprises a memory and a processor, wherein the memory stores computer instructions capable of running on the processor, and the processor executes the steps of the wireless sensor network data transmission method when running the computer instructions.
The wireless sensor network system of the application adopts the observation node to collect the original data of the wireless sensor network system and carry out the sparsification processing on the collected original data, the sparsification processing data is transmitted to the corresponding management node, the management node receives the sparsification processing data transmitted by the observation node, and the received sparsification processing data is subjected to dimension reduction, so that the dimension reduction data is obtained and transmitted to the aggregation node, thereby reducing the transmission quantity of the data, improving the data transmission efficiency and saving energy.
According to the wireless sensor network system and the data transmission method thereof, the acquired original data are subjected to sparsification processing, the sparsified data are transmitted to the corresponding management node, the management node receives the sparsified data transmitted by the observation node, and the received sparsified data are subjected to dimension reduction, so that the dimension reduced data are obtained and transmitted to the sink node, the transmission quantity of the data can be reduced, the data transmission efficiency is improved, and the energy is saved.
The readable storage medium of the application runs the data transmission method of the wireless sensor network, improves the data transmission efficiency and saves energy.
The terminal of the application stores the computer instruction, runs the data transmission method, improves the data transmission efficiency and saves the energy.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is a schematic structural diagram of a wireless sensor network system of the present application;
FIG. 2 is a flow chart of a data transmission method of the present application;
fig. 3 is a schematic diagram of a data transmission method of a wireless sensor network according to the present application;
fig. 4 is a schematic diagram of a comparison result of normalization of a reconstruction error of a data transmission method of a wireless sensor network according to an embodiment of the present application and a compression algorithm using a fourier transform matrix as a sparse matrix.
Detailed Description
As shown in fig. 1, the wireless sensor network system of the present application includes an observation node, a management node and a convergence node; the sink nodes are respectively coupled with n management nodes, and the management nodes are respectively coupled with m observation nodes which are 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 original data of the observation node and carrying out sparsification processing on the acquired original data, and the data after the sparsification processing is transmitted to a corresponding management node;
the management node is suitable for receiving the thinned data sent by the observation node, carrying out dimension reduction on the received thinned data, obtaining dimension-reduced data and sending the dimension-reduced data to the sink node;
the sink node is suitable for receiving the reduced-dimension data 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 a sparse coefficient vector of the original data projected under the sparse basis based on the calculated sparse basis to serve as data after the sparse processing corresponding to the original data;
the observation node is suitable for calculating sparse bases corresponding to the acquired original data by adopting a principal component analysis method;
the management node is suitable for performing video watching and dimension reduction on the data subjected to the sparsification processing 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 machine random Gaussian measurement matrix;
the sink node is suitable for converting the reconstruction problem of the sparse signals into a problem of a minimum first norm and solving an estimated value of the sparse coefficient vector; based on the estimated value of the sparse coefficient vector obtained by solving, the corresponding original signal is obtained by inverse transformation.
In a specific implementation, the observation nodes may be classified according to the data they collect. For example, in this embodiment, the observation node adopts a distributed optical fiber temperature sensing technology, a distributed optical fiber strain sensing technology, and a distributed optical fiber vibration technology, and respectively measures data such as temperature, structural variables, water accumulation amount, and gas flow rate. Therefore, the observation nodes respectively comprise different types for collecting temperature data, structural deformation data, ponding data, gas flow rate data and the like, so that the real-time dynamic collection of the underground cable from the omnibearing data such as temperature, pressure, vibration and the like 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 application adopts a distributed compressed sensing technology to reduce the calculation and storage pressure of a single node, transmits the original data acquired by the same type of observation nodes to the corresponding set management nodes for centralized compression and sparsification treatment, and finally transmits the data to the aggregation node for reconstruction and restoration by the management nodes 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 acquires own original data, performs sparsification processing on the acquired original data, and transmits the sparsified data to a corresponding management node; in specific implementation, the observation node firstly compresses the acquired data of the sensor to obtain corresponding original data, and then sparsifies the obtained original data.
When the acquired original data is subjected to sparsification, a proper orthogonal basis is selected to display the original data.
In this embodiment, the thinning processing of the original data includes the following steps:
step S1.1: and calculating sparse bases corresponding to the acquired original data by adopting a principal component analysis method.
Suppose for real set R N Can be represented by a column vector of dimension N1, and the real set R N Each of which may be used with a vector base satisfying the orthogonality conditionIs expressed in terms of a linear combination of +.>Then any one signal, i.e. the original data X.epsilon.R N Can be expressed as:
wherein X represents original data, ψ represents sparse basis corresponding to the original data X,the sparse coefficient vector of the original data X projection under the sparse basis ψ is represented.
Then, it is possible to obtain:
Θ＝Ψ T X (2)
wherein ψ is T Represents the transpose of the sparse basis ψ.
From the above, it can be seen that the "original data X" and the "sparse coefficient vector Θ of the original data X projected under the sparse basis ψ" can be regarded as representations of the same signal in different domains. Wherein when the number of non-zero coefficients in the sparse coefficient vector Θ is much smaller than N, it is stated that the signal is compressible. For example, if the number of non-zero coefficients in the sparse coefficient vector Θ is k and the other coefficients are zero, the signal is said to be k-sparse.
Next, a suitable sparse basis ψ corresponding to the original data X is calculated.
Specifically, for a set of signal sample sets, namely raw data X:
X＝{X 1 ，X 2 ，...，X i ，...，X N },X i ＝{X i1 ，X i2 ，...，X ij ，...，X iN }∈R d ，i＝1,2,...,N (3)
wherein X is i Representing the originalIth data in data X, R d Representing a real set of dimensions d, X ij (j=1, 2,., d) represents X i Data of the j-th dimension in (a).
The sample matrix of the original data X is S, and S epsilon R N×d The covariance matrix C of the original data X can be expressed as:
wherein S is T Transposed matrix R representing sample matrix S of original data X N×d Representing a real set of dimensions N x d.
Then, the covariance matrix C is diagonalized, and since the covariance matrix C is a symmetric matrix, there is an orthogonal matrix P such that:
P T CP＝Λ∈R d×d (5)
wherein R is d×d Representing a real set of dimensions d x d, Λ represents a diagonal matrix, representing that the original matrix undergoes linear transformation such that the correlation between the dimensions is reduced to 0. At the same time, 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 asThe corresponding feature matrix is marked +.>
After denoising the sample matrix S of the original data X, obtaining a corresponding denoised sample matrixSuppose that the denoise sample matrix +.>Uncorrelated and noise free from each dimension, then one can get:
from the above formula:
by mapping the sample matrix S of the raw data X to a feature matrixObtaining a denoised sample matrix->And the mapped sample data is redundancy-removed and decorrelated, and diagonal matrix formed according to maximum several eigenvalues ++>The obtained feature vector is a single bit vector, and the original data can be reduced in dimension, so that the obtained feature matrix +.>Namely, the sparse matrix to be obtained, namely, the sparse basis ψ.
Step S1.2: based on the calculated sparse basis, a sparse coefficient vector of the original data projected under the sparse basis is calculated and obtained and used as data after the sparse processing corresponding to the original data.
When the self-adaptive sparse base psi corresponding to the original data is obtained through calculation, a sparse coefficient vector theta of the original data X projected under the sparse base psi, namely the data subjected to the sparse processing corresponding to the original data X, can be obtained through calculation.
Because the sensor, namely the observation node, is designed based on the distributed technology, the calculation storage pressure of a single node can be reduced, the workload of the sensor for calculating and storing data is lightened, the positions of the sensor 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: the management node receives the sparsified data sent by the observation node, performs dimension reduction on the received sparsified data, obtains dimension reduced data and sends the dimension reduced data to the sink node.
In this embodiment, a preset number of row vectors in a preset observation matrix are adopted to observe, project and reduce dimensions of the data after the sparsification processing. The measurement matrix is a random Gaussian measurement matrix.
In this embodiment, when receiving the data of the thinned data transmitted by the corresponding observation node, the management node reduces the dimension of the thinned data, and then projects the sparse coefficient vector Θ in the high-dimension space to the low-dimension vector space. In particular, M row vectors of an M N measurement matrix are utilizedThe raw data X are observed projected and their inner products are calculated, namely:
wherein Y represents the measured value of the original data X after linear projection, the dimension of the measured value is M, and phi represents a measurement matrix.
For the measurement matrix, it needs to satisfy the condition:
wherein delta k E (0, 1), representing a preset constant, k representing that the signal is consistent with k-order sparsity.
In this embodiment, since the random gaussian measurement matrix is not related to most of orthogonal basis or orthogonal dictionary, and the number of measurements required for accurate reconstruction is small, the method for designing the random gaussian measurement matrix is as the observation matrix, which comprises the following steps: constructing a matrix Φ of m×n size such that each of the matrices ΦIndividual element phi uv Independent gaussian distribution with mean 0 and variance 1/M, i.e.:
s3: and the sink node receives the reduced-dimension data sent by the management node and obtains corresponding original data through reconstruction.
The corresponding original data obtained 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 reduced-dimension data into a problem of solving the minimum L0 norm, and solves to obtain an estimated value of the sparse coefficient vector;
X＝argmin||X|| 0 subject to Y＝ΦX (12)
generally, the solution method of the L0 norm problem is mainly convex relaxation, greedy tracking and iteration threshold. However, the L0 norm is often equivalently referred to as the L0 norm to be solved, that is, the convergence node converts the reconstruction problem of the reduced-dimension data into a problem of solving the minimum first norm.
Because the L1 norm solves for a convex optimization problem, and the L0 norm solves for an NP-difficult problem. After transformation, the problem can be solved by using a linear programming and polynomial method, and the error between the reconstructed value and the original value in each iteration is controlled, namely:
X＝argmin||X|| 1 subject to ||Y-ΦX|| 2 ＜ε (13)
where ε 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 the formula (12), the measurement values after linear projection of the raw data X, that is, the measurement data Y and the measurement matrix Φ, are known to the sink node:
Y＝ΦΘ＝ΦΨ T X (14)
therefore, through the above formula (14), an estimated value of the sparse coefficient vector Θ can be obtained by adopting a compressive sampling matching pursuit algorithm, and then the estimated value is inversely transformed, so that the reconstructed original data X can be obtained according to the formula (1).
The experiment is based on a matlab simulation platform, measurement signals conform to K-sparseness, measurement data of nodes are transmitted to a management node first, then transmitted to a sink node, reconstruction is completed, and a random Gaussian orthogonal matrix is selected as a measurement matrix.
And (3) carrying out normalization comparison on a compression algorithm adopting a Fourier transformation matrix as a sparse matrix and a reconstruction error of a distributed compressed sensing algorithm based on principal component analysis in the embodiment of the application. As shown in fig. 4, when the observed quantity is 100, the reconstruction error of the former is about 0.5, and the observation error of the latter is about 0.1, and it is obvious that the reconstruction completion degree of the latter is far higher than that of the former; when the reconstruction error is 0.2, the former requires about 120 observables and the latter requires about 90 observables, it is seen that the latter can reconstruct a more accurate signal with fewer observables; the former requires about 170 observations to accomplish an approximately exact reconstruction of the signal, while the latter requires only 120 observations, which differ by 50 measurements, i.e. the latter saves about 20% of the energy. Therefore, the main component analysis technology can be used for achieving the effect of removing redundancy of data, effectively reducing energy consumption, finally achieving the aim of the application, reducing the transmission quantity of the data and reducing the energy consumption of the sensor.
The application also provides a computer readable storage medium, on which computer instructions are stored, which when run perform the steps of the data transmission method of the wireless sensor network. The data transmission method of the wireless sensor network is described in detail in the foregoing sections, and will not be described in detail.
The application also provides a terminal, which comprises a memory and a processor, wherein the memory stores 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. The data transmission method of the wireless sensor network is described in detail in the foregoing sections, and will not be repeated.
By adopting the data processing method of the wireless transmission network, the observation node is adopted to collect the original data of the wireless transmission network and carry out the sparsification processing on the collected original data, the sparsified data is transmitted to the corresponding management node, the management node receives the sparsified data transmitted by the observation node, and the received sparsified data is subjected to dimension reduction to obtain the dimension reduced data and is transmitted to the sink node, so that the transmission quantity of the data can be reduced, the data transmission efficiency is improved, and the energy is saved.
Claims (3)
1. The data transmission method of the wireless sensor network is suitable for data transmission among nodes of the distributed wireless sensor network, and is characterized in that: the method comprises the following steps:
s1: the observation node collects original data of the observation node and performs thinning processing on the collected original data, the thinned data is transmitted to a corresponding management node, and the thinning processing on the collected original data comprises the following steps:
s1.1: calculating sparse bases corresponding to the acquired original data by adopting a principal component analysis method, wherein the method comprises the following specific steps of;
s1.1.1: solving a covariance matrix C of the original data X, wherein the covariance matrix C has the expression of;
wherein S is T Transposed matrix R representing sample matrix S of original data X N×d Representing a real set of dimensions N x d;
s1.1.2: performing opposite-angle treatment on the covariance matrix C to obtain a diagonal matrix Λ, wherein the expression of the diagonal matrix Λ is;
P T CP＝Λ∈R d×d
wherein R is d×d A real number set with dimension d multiplied by d is represented, Λ represents a diagonal matrix, and the original matrix is represented by linear transformation so that the correlation among the dimensions is reduced to 0; meanwhile, the part with smaller coefficient on the diagonal is noise;
s1.1.3: selecting the first K eigenvalues with larger coefficients on the diagonal matrix lambda asThe corresponding feature matrix is marked +.> Namely, the sparse basis ψ corresponding to the original data X is obtained;
step S1.2: based on the calculated sparse basis, calculating a sparse coefficient vector theta of the original data projected under the sparse basis, wherein the sparse coefficient vector theta is used as data after the corresponding sparsification processing of the original data, and the expression is as follows;
Θ＝Ψ T X
wherein, ψ is T Representing a transposed matrix of the sparse basis ψ, wherein X is original data;
s2: the management node receives the sparsified data sent by the observation node, adopts a preset number of row vectors in a preset measurement matrix to perform video watching and dimension reduction on the sparsified data, obtains dimension reduced data and sends the dimension reduced data to the sink node, and the specific steps are as follows;
s2.1: the method for designing the random Gaussian measurement matrix is characterized by selecting the random Gaussian measurement matrix as an observation matrix, and comprises the following steps:
constructing a matrix Φ of m×n size such that each element Φ in the matrix Φ uv Independent gaussian distribution with mean 0 and variance 1/M, i.e
S2.2: calculating a measured value Y of the original data X after linear projection in a low-dimensional vector space, wherein the Y is the data after dimension reduction, and the expression is as follows;
Y＝<Θ,Φ>，
wherein Y represents a measurement value obtained after linear projection of original data X, the dimension of the measurement value is M, phi represents a measurement matrix, and theta is a sparse coefficient vector;
s3: the sink node receives the reduced-dimension data sent by the management node and obtains corresponding original data through reconstruction;
s3.1: when the management node transmits the obtained measurement data to the sink node, the sink node converts the reconstruction problem of the reduced-dimension data into a problem of solving the minimum L0 norm, and solves to obtain an estimated value of the sparse coefficient vector;
step S3.2: and obtaining corresponding original data through inverse transformation based on the estimated value of the sparse coefficient vector obtained through solving.
2. A computer-readable storage medium having stored thereon computer instructions, characterized by: the computer instructions, when executed, perform the steps of the data transmission method of the wireless sensor network of claim 1.
3. A terminal, characterized by: the wireless sensor network comprises a memory and a processor, wherein the memory stores computer instructions capable of being executed on the processor, and the processor executes the steps of the data transmission method of the wireless sensor network according to claim 1 when the processor executes the computer instructions.
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