CN110012488B - Compressed data collection method of mobile wireless sensor network - Google Patents

Compressed data collection method of mobile wireless sensor network Download PDF

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CN110012488B
CN110012488B CN201910387742.7A CN201910387742A CN110012488B CN 110012488 B CN110012488 B CN 110012488B CN 201910387742 A CN201910387742 A CN 201910387742A CN 110012488 B CN110012488 B CN 110012488B
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顾相平
常波
庄立运
王晓燕
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Huaiyin Institute of Technology
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    • H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
    • H04L69/04—Protocols for data compression, e.g. ROHC
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Abstract

The invention discloses a compressed data collection method of a mobile wireless sensor network, which comprises the following steps of firstly, designing a Treelet-based sparse representation base by utilizing the space-time correlation of sensor node sensing data; secondly, designing a sparse random measurement matrix; then, designing a Brownian routing algorithm to collect data; and finally, performing compressed data reconstruction on the Sink node according to the sparse representation base, the sparse random measurement matrix and the Brownian tree route. The invention can improve the data collection efficiency of the sensor network, reduce the total cost of the sensor network nodes and prolong the life cycle of the network to a certain extent.

Description

Compressed data collection method of mobile wireless sensor network
Technical Field
The invention relates to the field of mobile wireless sensor networks, in particular to a compressed data collection method of a mobile wireless sensor network.
Background
In recent years, the application range of wireless sensor networks has become wider, such as the fields of intelligent transportation, smart power grids, smart medical treatment, safety production monitoring and the like. If the wireless sensor network directly transmits the sensing data in practical application, a large transmission cost is generated, the performance of the network is affected, and the life cycle of the network is reduced. In 2006, the compressed sensing technology proposed by David Donoho, Emmanuel cans, and tazechen et al provided a completely new solution for the data collection method of the wireless sensor network. The compressed sensing technology not only can greatly reduce the energy consumption of the whole wireless sensor network, but also can reconstruct the original sensing signal at a receiving end with higher probability through a few random measurement values.
At present, a great deal of literature reports how to utilize the compressed sensing technology to complete the research work related to the energy-efficient data collection of the sensing data of the wireless sensor network. The existing work is mainly divided into three categories: the first type is that data compression and collection are carried out by utilizing the time correlation of the sensing data of the wireless sensor network; the second type is that the spatial correlation of the sensing data of the wireless sensor network is utilized to carry out data compression and collection; and in the third category, compressed data collection is performed by utilizing an effective routing algorithm. The first type or the second type does not give consideration to the time and space correlation of the sensing data of the sensor nodes; in the third category of schemes, most of the existing research is on how to design optimized routing protocols, such as cluster routing, and the routing protocols have the following common problems: the cluster head node which is responsible for forwarding and collecting the perception data is easy to generate the phenomenon of unbalanced energy consumption in the whole network life period, in addition, the scheme has no error resistance to the problems of transmission failure of the perception data, interference brought between information transmission and the like, and the actual problems directly influence the reconstruction precision of the receiving end on the original perception data. In order to overcome the problems, the invention provides a scheme for completing compressed collection of sensing data in a network by utilizing a mobile sensor node on the basis of the existing compressed sensing technology. The method has the advantages that on one hand, a Treelet-based sparse basis is designed, the space-time correlation of sensor node sensing data is fully utilized, data redundancy is greatly reduced, and the data transmission efficiency is improved. On the other hand, a Brownian tree route can be generated by utilizing the random walk of the mobile node to perform compressed data collection, and the connectivity and the coverage performance of the network do not need to be considered in the whole network perception data collection process, so that the cost of later maintenance is reduced. Therefore, the method has great advantages of fully utilizing the inherent property of the sensing data and the compressed sensing technology in the mobile wireless sensor network.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a compressed data collection method of a mobile wireless sensor network, which greatly enhances the efficiency of network data collection, reduces the data transmission cost, improves the reliability of data transmission and prolongs the network lifetime to a certain extent.
The technical scheme is as follows: the invention relates to a method for collecting compressed data of a mobile wireless sensor network, which comprises the following steps:
(1) designing a sparse representation base based on Treelet by utilizing the space-time correlation of sensor perception data;
(2) designing a sparse random measurement matrix;
(3) designing a Brown tree routing algorithm for data collection;
(4) and (4) performing compressed data reconstruction on the Sink node according to the sparse representation base, the sparse random measurement matrix and the Brownian tree route based on the step (3).
The step (1) comprises the following steps:
(11) at the lowest level of the tree, l is 0, assuming that the sensing data in the sensor network can be represented as x0＝[x0,1,...,x0,p]TThe corresponding Dirac group is denoted as Ψ0＝[ψ0,1,ψ0,2,...,ψ0,p]Wherein Ψ0Is a p × p unit array;
(12) calculate sample variance Σ0And a similarity matrix M0；
(13) According to the similarity matrix M0Find the most similar sum variable, assume
Figure BDA0002055408690000021
Wherein ζ belongs to {1,2,. and p }, L belongs to {1,2,. and L }, and L is the maximum number of layers of the tree;
(14) calculating Jacobi matrix of parameters alpha, beta:
Figure BDA0002055408690000022
wherein c is cos (θ)l)，s＝sin(θl)，
Figure BDA0002055408690000023
And is
Figure BDA0002055408690000024
And Σl＝JT∑l-1J；
(15) Constantly updating the base Ψl＝Ψl-1J and perceptual data xl＝JTxl-1Thereby continuously updating the similarity matrix MlA value of (d);
(16) multi-resolution algorithm: suppose that
Figure BDA0002055408690000031
Wherein the indices α and β are the first principal component and the second principal component, respectively; the sum variable and the difference variable defining the l-th layer are respectively
Figure BDA0002055408690000032
And
Figure BDA0002055408690000038
basis matrix ΨlRespectively, is philAnd psilDeleting the difference variables in the sum variable set to obtain a new set, namely ζ \ β; orthogonal Treelet decomposition of layer I
Figure BDA0002055408690000034
Wherein a new set of scale vectors is generated
Figure BDA0002055408690000035
Is the vector philScale function with upper layer [ phi ]l-1jCascade of j ≠ α, β, newly generated sum variable
Figure BDA0002055408690000036
Then the projection of the raw data onto these vectors.
The sparse random measurement matrix phi in the step (2) is as follows:
Figure BDA0002055408690000037
wherein phii,jTo measure the ith row, j column element, Br of the matrixiIs the ith node in the Brown tree for data transmission.
The step (3) comprises the following steps:
(31) initialization in the monitored area a2In the method, N (N is 10N) sensor nodes are randomly deployed, wherein the total number of the sensor nodes in a monitored area is N;
(32) the n sensor nodes randomly walk in the monitoring area, and stop moving when a certain sensor node randomly walks to the vicinity of other nodes until all the n sensor nodes stop moving, so that the sensor nodes form a sub-Brown tree;
(33) and (4) randomly generating N sensor nodes, and repeating the process of the step (32) until all the N sensor nodes in the monitored area become a part of the Brown tree.
And (4) the Sink node in the step (4) is positioned in the central position of the monitored area.
Has the advantages that: compared with the prior art, the invention has the beneficial effects that: 1. the performance index of the network can be better enhanced, for example, the energy consumption is reduced, the transmission cost is reduced, and the like; 2. by adopting the mobile network, the network connectivity, coverage and other performances do not need to be considered, the energy efficiency of compressed data collection is improved, and the service life of the wireless sensor network is prolonged.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of a method for collecting perception data based on Brownian tree routing according to the present invention;
fig. 3 is a Treelet sparse representation of five different energies of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, the present invention specifically includes the following steps:
1. and designing a sparse representation base based on Treelet by utilizing the space-time correlation of the sensor node perception data.
(1) At the lowest level of the tree, l is 0, assuming that the sensing data in the sensor network can be represented as x0＝[x0,1,...,x0,p]TThe corresponding Dirac group is denoted as Ψ0＝[ψ0,1,ψ0,2,...,ψ0,p]Wherein Ψ0Is a p x p unit array, and then the sample variance sigma is calculated0And a similarity matrix M0。
(2) According to the similarity matrix M0Find the most similar sum variable, assume
Figure BDA0002055408690000041
Where ζ is in {1,2,. and p }, L is in {1,2,. and L }, and L is the maximum number of layers in the tree.
(3) Jacobi matrices of the parameters α, β are calculated.
Figure BDA0002055408690000042
Wherein c is cos (θ)l)，s＝sin(θl)，
Figure BDA0002055408690000043
And is
Figure BDA0002055408690000044
And Σl＝JT∑l-1J。
By converting the above equation, the base Ψ can be continuously updatedl＝Ψl-1J and perceptual data xl＝JTxl-1Thereby continuously updating the similarity matrix MlThe value of (c).
(4) Multi-resolution algorithm: for ease of labeling, assume
Figure BDA0002055408690000045
Wherein the indices α and β are the first principal component and the second principal component, respectively; the sum variable and the difference variable defining the l-th layer are respectively
Figure BDA0002055408690000046
And
Figure BDA0002055408690000047
defining a base matrix ΨlRespectively, is philAnd psil(ii) a Deleting the difference variables in the sum variable set to obtain a new set, namely ζ ═ ζ \ β }; thus, the orthogonal Treelet of the l-th layer can be decomposed into
Figure BDA0002055408690000048
Wherein a new set of scale vectors is generated
Figure BDA0002055408690000049
Is the vector philScale function with upper layer [ phi ]l-1,jJ ≠ α, β concatenation, and newly generated sum variable
Figure BDA0002055408690000051
Then the projection of the raw data onto these vectors.
FIG. 2 shows Treelet sparse representation bases of five different energies, with energy magnitudes T in sequence1＞T2＞T3＞T4＞T5. The horizontal axis in fig. 2 represents the coefficient based on the Treelet sparse representation basis, and the vertical axis represents the energy level. In the actual method for collecting compressed data of the wireless sensor network, the expression base with the maximum energy can be selected, and the optimal sparse expression performance can be obtained.
2. Designing a sparse random measurement matrix:
Figure BDA0002055408690000052
wherein phii,jTo measure the ith row, j column element, Br of the matrixiIs the ith node in the Brown tree for data transmission.
3. And designing a Brownian routing algorithm for data collection.
(1) Initialization in the monitored area a2And N (N is 10N) sensor nodes are randomly deployed, wherein the total number of the sensor nodes in the monitored area is N, and the Sink node is positioned in the central position of the monitored area.
(2) The method comprises the steps that n sensor nodes carry out random walk in a monitored area, when the n sensor nodes walk to a Sink node, the n sensor nodes become a part of the Brown tree, then the n sensor nodes are generated randomly, the random walk process is repeated, and the generated Brown tree is larger and larger.
(3) And (3) randomly generating N sensor nodes, and repeating the process of the step (2) until all the N sensor nodes in the monitored area become a part of the Brown tree.
In fig. 3, a block □ represents a Sink node in a monitored area of the wireless sensor network, a circle o represents a sensor node, and a line with an arrow → represents a path along which the sensor node randomly walks. In the network, a Brown tree is finally formed due to the random walk of the sensor nodes, the sensing data is subjected to compressed data transmission along the Brown tree, and the original sensing data is finally reconstructed at the Sink node.
4. And (3) reconstructing the collected sensing data of the wireless sensor network according to the sparse basis based on the space-time correlation designed in the step (1), the sparse measurement matrix designed in the step (2) and the Brownian tree routing algorithm designed in the step (3).
The above description is only a preferred embodiment of the present invention, and is not intended to limit the technical scope of the present invention, so that any minor modifications, equivalent changes and modifications made to the above embodiment according to the technical spirit of the present invention are within the technical scope of the present invention.

Claims (3)

1. A compressed data collection method of a mobile wireless sensor network is characterized by comprising the following steps:
(1) designing a sparse representation base based on Treelet by utilizing the space-time correlation of sensor perception data;
(2) designing a sparse random measurement matrix;
(3) designing a Brown tree routing algorithm for data collection;
(4) performing compressed data reconstruction on the Sink node according to the sparse representation base, the sparse random measurement matrix and the Brownian tree route based on the step (3);
the step (1) comprises the following steps:
(11) at the lowest level of the tree, l is 0, assuming that the sensing data in the sensor network can be represented as x0＝[x0,1,...,x0,p]TCorresponding Dira thereofRadical c as Ψ0＝[ψ0,1,ψ0,2,...,ψ0,p]Wherein Ψ0Is a p × p unit array;
(12) calculate sample variance Σ0And a similarity matrix M0；
(13) According to the similarity matrix M0Find the most similar sum variable, assume
Figure FDA0003393333130000011
Wherein ζ belongs to {1,2,. and p }, L belongs to {1,2,. and L }, and L is the maximum number of layers of the tree;
(14) calculating Jacobi matrix of parameters alpha, beta:
Figure FDA0003393333130000012
wherein
Figure FDA0003393333130000013
And is
Figure FDA0003393333130000014
And Σl＝JT∑l-1J；
(15) Constantly updating the base Ψl＝Ψl-1J and perceptual data xl＝JTxl-1Thereby continuously updating the similarity matrix MlA value of (d);
(16) multi-resolution algorithm: suppose that
Figure FDA0003393333130000015
Wherein the indices α and β are the first principal component and the second principal component, respectively; the sum variable and the difference variable defining the l-th layer are respectively
Figure FDA0003393333130000016
And
Figure FDA0003393333130000017
basis matrix ΨlRespectively, is philAnd psilDeleting the difference variables in the sum variable set to obtain a new set, namely ζ \ β; orthogonal Treelet decomposition of layer I
Figure FDA0003393333130000021
Wherein a new set of scale vectors is generated
Figure FDA0003393333130000022
Is the vector philScale function with upper layer [ phi ]l-1,jCascade of j ≠ α, β, newly generated sum variable
Figure FDA0003393333130000023
Then the projection of the original data on these vectors;
the sparse random measurement matrix phi in the step (2) is as follows:
Figure FDA0003393333130000024
wherein phii,jTo measure the ith row, j column element, Br of the matrixiIs the ith node in the Brown tree for data transmission.
2. The method for collecting compressed data of a mobile wireless sensor network according to claim 1, wherein the step (3) comprises the following steps:
(31) initialization in the monitored area a2In the method, N (N is 10N) sensor nodes are randomly deployed, wherein the total number of the sensor nodes in a monitored area is N;
(32) the n sensor nodes randomly walk in the monitoring area, and stop moving when a certain sensor node randomly walks to the vicinity of other nodes until all the n sensor nodes stop moving, so that the sensor nodes form a sub-Brown tree;
(33) and (4) randomly generating N sensor nodes, and repeating the process of the step (32) until all the N sensor nodes in the monitored area become a part of the Brown tree.
3. The method for collecting compressed data of a mobile wireless sensor network according to claim 1, wherein the Sink node in step (4) is located at a central location of the monitored area.
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