CN110012488A - A kind of compressed data collection method of mobile wireless sensor network - Google Patents
A kind of compressed data collection method of mobile wireless sensor network Download PDFInfo
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- CN110012488A CN110012488A CN201910387742.7A CN201910387742A CN110012488A CN 110012488 A CN110012488 A CN 110012488A CN 201910387742 A CN201910387742 A CN 201910387742A CN 110012488 A CN110012488 A CN 110012488A
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- 239000011159 matrix material Substances 0.000 claims abstract description 31
- 230000008447 perception Effects 0.000 claims abstract description 23
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 9
- 238000013461 design Methods 0.000 claims abstract description 9
- 238000005259 measurement Methods 0.000 claims abstract description 9
- 230000002123 temporal effect Effects 0.000 claims abstract description 7
- 230000005540 biological transmission Effects 0.000 claims description 10
- 239000013598 vector Substances 0.000 claims description 9
- 238000005295 random walk Methods 0.000 claims description 8
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- 238000004364 calculation method Methods 0.000 claims description 3
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- 238000005516 engineering process Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 3
- 238000005265 energy consumption Methods 0.000 description 3
- 238000007906 compression Methods 0.000 description 2
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- H—ELECTRICITY
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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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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/02—Power saving arrangements
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- H—ELECTRICITY
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- Y02D30/00—Reducing energy consumption in communication networks
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Abstract
The invention discloses a kind of compressed data collection methods of mobile wireless sensor network, firstly, the temporal correlation using sensor node perception data designs the rarefaction representation base based on Treelet；Secondly, designing sparse random measurement matrix；Then, design Blang tree routing algorithm carries out data collection；Finally, carrying out compressed data reconstruct in Sink node according to the routing of rarefaction representation base, sparse random measurement matrix and Blang tree.The efficiency that Sensor Network data collection can be improved using the present invention, is reduced total cost of sensing network node, extends the life cycle of network to a certain extent.
Description
Technical field
The present invention relates to mobile wireless sensor network fields, and in particular to a kind of compression of mobile wireless sensor network
Method of data capture.
Background technique
In recent years, the application range of wireless sensor network is increasingly extensive, such as intelligent transportation, smart grid, intelligence
The fields such as intelligent medical treatment, safety production monitoring.If wireless sensor network in practical applications directly transmits perception data,
Very big transmission cost will be generated, will affect the performance of network, reduce the life cycle of network.2006, by David
The compressed sensing technology that Donoho, Emmanuel Candes and Tao Zhexuan et al. are proposed is received to the data of wireless sensor network
Set method provides a completely new solution.Compressed sensing technology can not only lower entire wireless sensor network significantly
Energy consumption, moreover it is possible to raw sensed signal be reconstructed with high probability in receiving end by few arbitrary measures.
Number is perceived currently, have a large amount of document report and how to complete wireless sensor network using compressed sensing technology
According to power-efficient data collect correlative study work.Have work and be broadly divided into three classes: the first kind utilizes wireless sensor network
The temporal correlation of perception data itself carries out data compression collection；Second class utilizes wireless sensor network perception data sheet
The spatial coherence of body carries out data compression collection；Third class carries out compressed data collection using effective routing algorithm.First
Class or the second class do not combine sensor node perception data time, spatial coherence；In third class scheme, has work
Make mostly research be how the Routing Protocol of design optimization, such as design tufted routing etc., there are following for these Routing Protocols
Common problem: it is unbalanced that responsible forwarding, the leader cluster node for collecting perception data are easy to happen energy consumption in whole network life cycle
Phenomenon, in addition, this kind of scheme for the transmission failure of perception data, information transmit between bring interfere the problems such as do not have it is anti-
The problem of mistake, these physical presence, will directly affect receiving end to the reconstruction accuracy of raw sensed data.On overcoming
Problem is stated, on existing compressed sensing technology basis, the present invention proposes that a kind of utilization mobile sensor node completes network
The compression collection scheme of interior perception data.The advantage of the program is, on the one hand devises a kind of based on the sparse of Treelet
Base makes full use of the temporal correlation of sensor node perception data, greatly reduces data redundancy, improves data transmission
Efficiency.On the other hand, the random walk that can use mobile node generates a Blang tree routing and carries out compressed data collection, and
And during whole network perception data is collected, without considering the connectivity and covering performance of network, reduce later maintenance
Cost.Therefore, perception data build-in attribute and compressed sensing technology is made full use of to have in mobile radio sensor net
Very big advantage.
Summary of the invention
Goal of the invention: the present invention provides a kind of compressed data collection method of mobile wireless sensor network, greatly enhances
The efficiency of network data collection, reduces data transmission cost, improves the reliability of data transmission, extends to a certain extent
Network lifetime.
Technical solution: a kind of compressed data collection method of mobile wireless sensor network of the present invention, including with
Lower step:
(1) the rarefaction representation base based on Treelet is designed using the temporal correlation of sensor perception data；
(2) sparse random measurement matrix is designed；
(3) design Blang tree routing algorithm carries out data collection；
(4) according to the rarefaction representation base, sparse random measurement matrix and based on step (3) Blang tree route exist
Sink node carries out compressed data reconstruct.
The step (1) the following steps are included:
(11) in the bottom l=0 of tree, it is assumed that the perception data in Sensor Network can be expressed as x0=[x0,1,...,
x0,p]T, corresponding Dirac base is denoted as Ψ0=[ψ0,1,ψ0,2,...,ψ0,p], wherein Ψ0It is the unit matrix of a p × p；
(12) sample variance ∑ is calculated0With similar matrix M0；
(13) according to similar matrix M0Search most like and variable, it is assumed thatWherein ζ ∈ 1,
2 ..., p }, l ∈ { 1,2 ..., L }, L are the maximum number of plies of tree；
(14) the Jacobi matrix of calculating parameter α, β:
Wherein c=cos (θl), s=sin (θl),AndAnd ∑l=JT∑l-1J；
(15) substrate Ψ is constantly updatedl=Ψl-1J and perception data xl=JTxl-1, to constantly update similar matrix Ml's
Value；
(16) multi-resolution algorithm: assuming thatWherein index α and β be respectively first principal component and second it is main at
Point；Define l layers and variable and difference variable be respectivelyWithBasic matrix ΨlScaling function and details
Function is respectively φlAnd ψl, by with after the difference variable deletion in variables collection, obtain a new set be denoted as ζ=ζ { β }；
L layers of orthogonal Treelet is decomposed intoThe new scaling vector set wherein generatedThen
For vector φlWith the scaling function { φ on upper layerl-1jJ ≠ α, and the cascade of β, newly-generated and variableIt is then initial data
Projection on these vectors.
Sparse random measurement matrix Φ described in the step (2) are as follows:
Wherein, Φi,jFor the i-th row of calculation matrix, j column element, BriIt is i-th of section carried out data transmission in Blang tree
Point.
The step (3) the following steps are included:
(31) it initializes, in area to be monitored a2It is interior, random placement n (N=10n) a sensor node, wherein being monitored
The total sensor node number in region is N；
(32) n sensor node random walk in monitoring region, when some sensor node random walk is to other
Just stop moving when near nodal, until the wholly off movement of all n sensor nodes, these sensor nodes since then
Just a stalk Blang tree is formd；
(33) n sensor node is generated at random, repeats the process of step (32), until N number of sensing in area to be monitored
Until device node all becomes a part of Blang tree.
Sink node described in step (4) is located at area to be monitored center.
The utility model has the advantages that compared with prior art, beneficial effects of the present invention: 1, the performance that can preferably enhance network refers to
Mark, for example, reduce energy consumption, reduce transmission cost etc.；2, using mobile network without considering the performances such as network connectivty and covering,
The efficiency for not only increasing compressed data collection, also extends the service life of wireless sensor network.
Detailed description of the invention
Fig. 1 is flow chart of the invention；
Fig. 2 is to route to collect perception data method schematic diagram the present invention is based on Blang tree；
Fig. 3 is the Treelet rarefaction representation base of five kinds of different-energies of the invention.
Specific embodiment
The present invention is described in further detail below in conjunction with the accompanying drawings.
As shown in Figure 1, the present invention specifically includes following steps:
1, the rarefaction representation base based on Treelet is designed using the temporal correlation of sensor node perception data.
(1) in the bottom l=0 of tree, it is assumed that the perception data in Sensor Network can be expressed as x0=[x0,1,...,x0,p
]T, corresponding Dirac base is denoted as Ψ0=[ψ0,1,ψ0,2,...,ψ0,p], wherein Ψ0It is the unit matrix of a p × p, next
Calculate sample variance ∑0With similar matrix M0。
(2) according to similar matrix M0Search most like and variable, it is assumed thatWherein ζ ∈ 1,
2 ..., p }, l ∈ { 1,2 ..., L }, L are the maximum number of plies of tree.
(3) the Jacobi matrix of calculating parameter α, β.
Wherein c=cos (θl), s=sin (θl),AndAnd ∑l=JT∑l-1J。
By the conversion of above-mentioned formula, substrate Ψ can be constantly updatedl=Ψl-1J and perception data xl=JTxl-1, thus
Constantly update similar matrix MlValue.
(4) multi-resolution algorithm: for the ease of label, it is assumed thatWherein index α and β be respectively first it is main at
Point and Second principal component,；Define l layers and variable and difference variable be respectivelyWithDefine basic matrix Ψl
Scaling function and Detailfunction be respectively φlAnd ψl；By with after the difference variable deletion in variables collection, to obtain one new
Set be denoted as ζ=ζ { β }；Then, l layers of orthogonal Treelet can be decomposed intoWherein give birth to
At new scaling vector setIt is then vector φlWith the scaling function { φ on upper layerl-1,jJ ≠ α, and the cascade of β, and it is new
Generate and variableIt is then projection of the initial data on these vectors.
Fig. 2 indicates the Treelet rarefaction representation base of five kinds of different-energies, and energy size is followed successively by T1> T2> T3> T4
> T5.Horizontal axis in Fig. 2 indicates that the coefficient based on Treelet rarefaction representation base, the longitudinal axis indicate energy size.Actual wireless
In sensor network compressed data collection method, the expression base with ceiling capacity can choose, it is available optimal sparse
Indicate performance.
2, sparse random measurement matrix is designed:
Wherein, Φi,jFor the i-th row of calculation matrix, j column element, BriIt is i-th of section carried out data transmission in Blang tree
Point.
3, design Blang tree routing algorithm carries out data collection.
(1) it initializes, in area to be monitored a2It is interior, random placement n (N=10n) a sensor node, wherein monitored district
The total sensor node number in domain is N, and Sink node is located at area to be monitored center.
(2) n sensor node carries out random walk in area to be monitored, when this n sensor node migration extremely
When Sink node, a part of Blang tree will be become, then generate n sensor node at random again, repeat above-mentioned random walk
Process, the Blang tree generated in this way are increasing.
(3) it generates n sensor node at random again, repeats the process of step (2), until N number of sensing in area to be monitored
Until device node all becomes a part of Blang tree.
In Fig. 3, box indicates the Sink node in wireless sensor network area to be monitored, and circlec method indicates sensor
Node, straight line with the arrow → expression sensor node random walk path.Due to the random trip of sensor node in network
It walks, ultimately forms a Blang tree, perception data will carry out compressed data transmission along Blang tree, finally at Sink node
Carry out the reconstruct of raw sensed data.
4, the sparseness measuring matrix and step that the sparse basis based on temporal correlation, the step 2 designed according to step 1 designs
The reconstruct for the wireless sensor network perception data that Blang's tree routing algorithm of rapid 3 design is collected.
The above is only present pre-ferred embodiments, is not intended to limit the scope of the present invention, therefore
Any subtle modifications, equivalent variations and modifications to the above embodiments according to the technical essence of the invention, still belong to
In the range of technical solution of the present invention.
Claims (5)
1. a kind of compressed data collection method of mobile wireless sensor network, which comprises the following steps:
(1) the rarefaction representation base based on Treelet is designed using the temporal correlation of sensor perception data；
(2) sparse random measurement matrix is designed；
(3) design Blang tree routing algorithm carries out data collection；
(4) according to the rarefaction representation base, sparse random measurement matrix and based on step (3) Blang tree routing in Sink
Node carries out compressed data reconstruct.
2. a kind of compressed data collection method of mobile wireless sensor network according to claim 1, which is characterized in that
The step (1) the following steps are included:
(11) in the bottom l=0 of tree, it is assumed that the perception data in Sensor Network can be expressed as x0=[x0,1,...,x0,p]T,
Corresponding Dirac base is denoted as Ψ0=[ψ0,1,ψ0,2,...,ψ0,p], wherein Ψ0It is the unit matrix of a p × p；
(12) sample variance ∑ is calculated0With similar matrix M0；
(13) according to similar matrix M0Search most like and variable, it is assumed thatWherein ζ ∈ 1,
2 ..., p }, l ∈ { 1,2 ..., L }, L are the maximum number of plies of tree；
(14) the Jacobi matrix of calculating parameter α, β:
Wherein c=cos (θl), s=sin (θl),AndAnd ∑l=JT∑l-1J；
(15) substrate Ψ is constantly updatedl=Ψl-1J and perception data xl=JTxl-1, to constantly update similar matrix MlValue；
(16) multi-resolution algorithm: assuming thatWherein index α and β is respectively first principal component and Second principal component,；It is fixed
L layer of justice is respectively with variable and difference variableWithBasic matrix ΨlScaling function and Detailfunction
Respectively φlAnd ψl, by with after the difference variable deletion in variables collection, obtain a new set be denoted as ζ=ζ { β }；L
The orthogonal Treelet of layer is decomposed intoThe new scaling vector set wherein generatedThen it is
Vector φlWith the scaling function { φ on upper layerL-1, jJ ≠ α, and the cascade of β, newly-generated and variableIt is then that initial data exists
Projection on these vectors.
3. a kind of compressed data collection method of mobile wireless sensor network according to claim 1, which is characterized in that
Sparse random measurement matrix Φ described in the step (2) are as follows:
Wherein, Φi,jFor the i-th row of calculation matrix, j column element, BriIt is i-th of node carried out data transmission in Blang tree.
4. a kind of compressed data collection method of mobile wireless sensor network according to claim 1, which is characterized in that
The step (3) the following steps are included:
(31) it initializes, in area to be monitored a2Interior, random placement n (N=10n) a sensor node, wherein area to be monitored is total
Sensor node number be N；
(32) n sensor node random walk in monitoring region, when some sensor node random walk to other nodes
Just stop moving when nearby, until the wholly off movement of all n sensor nodes, these sensor nodes just shape since then
At a stalk Blang tree；
(33) n sensor node is generated at random, repeats the process of step (32), until N number of sensor section in area to be monitored
Point all as a part of Blang tree until.
5. a kind of compressed data collection method of mobile wireless sensor network according to claim 1, which is characterized in that
Sink node described in step (4) is located at area to be monitored center.
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| CN116033380A (en) * | 2023-03-28 | 2023-04-28 | 华南理工大学 | Data collection method of wireless sensor network under non-communication condition |
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Application publication date: 20190712 Assignee: TIANJIN BOTING OPTOELECTRONICS TECHNOLOGY CO.,LTD. Assignor: HUAIYIN INSTITUTE OF TECHNOLOGY Contract record no.: X2023980053320 Denomination of invention: A compressed data collection method for mobile wireless sensor networks Granted publication date: 20220311 License type: Common License Record date: 20231221 |
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