CN105050105A - High-energy-efficiency low-information-density data collecting method based on compressed sensing - Google Patents
High-energy-efficiency low-information-density data collecting method based on compressed sensing Download PDFInfo
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- CN105050105A CN105050105A CN201510514933.7A CN201510514933A CN105050105A CN 105050105 A CN105050105 A CN 105050105A CN 201510514933 A CN201510514933 A CN 201510514933A CN 105050105 A CN105050105 A CN 105050105A
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- 238000000034 method Methods 0.000 title claims abstract description 29
- 239000011159 matrix material Substances 0.000 claims abstract description 48
- 239000013598 vector Substances 0.000 claims abstract description 33
- 238000004364 calculation method Methods 0.000 claims description 36
- 238000013481 data capture Methods 0.000 claims description 10
- 125000004122 cyclic group Chemical group 0.000 claims description 3
- 238000012217 deletion Methods 0.000 claims description 3
- 230000037430 deletion Effects 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims 2
- 238000013480 data collection Methods 0.000 abstract description 9
- 238000005259 measurement Methods 0.000 abstract description 4
- 230000005540 biological transmission Effects 0.000 abstract description 3
- 238000004891 communication Methods 0.000 abstract description 3
- 238000013461 design Methods 0.000 abstract description 3
- 238000005457 optimization Methods 0.000 abstract description 2
- 238000005070 sampling Methods 0.000 description 7
- 230000008447 perception Effects 0.000 description 6
- 230000006835 compression Effects 0.000 description 3
- 238000007906 compression Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000005265 energy consumption Methods 0.000 description 2
- 241001269238 Data Species 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000000474 nursing effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
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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
- 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
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Abstract
To solve the problem that the communication channel is not stable and the network information density is low in a wireless sensor network, the invention provides a high-energy-efficiency low-information-density data collecting method based on compressed sensing. The method uses the optimization design of a measurement matrix to remove redundant data to the largest extent to improve the quality of data collection. According to the method, firstly, whether an original signal can be reconstructed from measured observation vectors is judged, and then the measurement matrix is optimized under the premise of ensuring accurate reconstruction, so that the collected data size is reduce, and ineffective observations are removed. The method of the invention could dynamically adjust the quality of the data collection according to the transmission information of an information source, thereby being widely applicable.
Description
Technical field
The present invention relates to the low information density method of data capture of high energy efficiency based on compressed sensing, belong to the communication technology and Data Collection field.
Background technology
Wireless sensor network is a kind of brand-new information acquisition platform, its objective is the information of perceptive object in perception collaboratively, acquisition and processing network's coverage area, and send to observer, be widely used in the association areas such as military field, environmental monitoring, medical treatment and nursing.Traditional method of data capture is that all data gathered by a large amount of sensor node are transferred to base station through leader cluster node and process.But in the data acquisition of sensor network, normally multiple sensor node carries out perception compression to same event, carries a large amount of redundant datas, greatly occupies network communication bandwidth during transmission, brings unnecessary energy consumption.
Compressed sensing (
compressedSensing, CS) proposition be that wireless sensor network data collection mode opens new thinking, it breaches the restriction of conventional Nyquist sampling thheorem, provides a kind of method utilizing less sample information accurate reconstruction to go out primary signal.This theory utilizes the compressibility of signal, is realized the perception of high dimensional signal by the irrelevant observation of lower dimensional space, low resolution, Sub-nyquist sampling data.Compressive sensing theory can be divided into three processes: sampling, measurement and reconstruct.Sampling process: sparse data is sampled; Measuring process: the data obtained sampling are carried out compression and measured, and obtain observation vector; Restructuring procedure: initial data is reduced by observation vector data.Compressive sensing theory eliminates the contradiction between signals of reality process and traditional sampling theory, has vast application prospect in signal transacting field.
However, in the process by compressive sensing theory sampled data, also likely redundant data is produced.Compressive sensing theory shows: when observation frequency is
time, primary signal can be gone out by high probability Perfect Reconstruction, can think that compressed sensing is used
the event that secondary observation fully can not reconstruct signal is certain existence, and namely observation vector also exists redundancy observation, and the observation of elimination redundancy is equivalent to and reduces Data Collection amount.And the existing data collection plan in conjunction with compressed sensing, mainly undertaken by methods such as the design of calculation matrix, common sub-clustering, distributed temporal correlations.The method for designing of calculation matrix, namely design meets the matrix of certain characteristic, as designed the sparse Bernoulli Jacob's observing matrix of circulation being adapted at realizing in the limited sensor node of hardware resource, circulating, sparse Bernoulli Jacob's observing matrix has few, the good pseudo-randomness of nonzero element, hardware is easy to the advantages such as realization, under the prerequisite meeting data reconstruction error, less observation data can be obtained by compression observation, thus reach the object reducing transmission energy consumption.
In sum, realize high-quality Data Collection for how in conjunction with compressive sensing theory and reduced the collecting amount of data by calculation matrix dynamic optimization, becoming one of key technology difficult problem needing solution badly.
Summary of the invention
For the problems referred to above, propose the low information density method of data capture of high energy efficiency based on compressed sensing, concrete steps are as follows:
Step one, to determine in wireless sensor network
sinkthe position of node, utilizes the media information in the node perceived surrounding environment in the middle of wireless sensor network, passes to
sinknode, realizes primary signal
collection;
Step 2, structure sparse basis array
to primary signal
carry out rarefaction, obtain rarefaction signal
, its length is set to
,
，
；
Step 3, generation
dimension gaussian random matrix
, wherein
, be characterized in matrix
element all independently to obey average be 0, variance is
gaussian Profile, Mathematical Modeling is:
;
Step 4, pass through calculation matrix
right
carry out observation coding, obtain observation vector
, namely
, wherein
;
Step 5, to calculation matrix
carry out dimension-reduction treatment, namely delete
in
row (
), retain other row, calculation matrix becomes
, to sparse signal
again carry out observation coding can obtain
individual observation vector
;
Step 6, according to observation vector
, and obtain after dimension-reduction treatment
, select
oMPrestructing algorithm is right respectively
individual observation vector is reconstructed, and reconstructs
with
, and the error of calculation
;
If step 7
be less than given precision
, then cyclic variable is made
and go to step eight, otherwise terminate;
Step 8, successively calculating delete observation vector
in
jcalculation matrix corresponding after individual element
, wherein
, namely delete calculation matrix
?
joK;
Step 9, calculating
individual calculation matrix
maximum column correlation
, wherein
represent
column vector;
Step 10, calculating
value,
value be from observation vector
middle deletion
individual element, and obtain the minimum observing matrix of corresponding correlation
, namely
;
Step 11, according to observing matrix
, select
oMPrestructing algorithm is to observation vector
be reconstructed, obtain reconstruction signal
, the error of calculation
;
If step 12
be less than given precision
, then make
and go to step eight, otherwise terminate.
In sum, compared with the conventional method, advantage of the present invention is:
1) by the optimizing process of calculation matrix, high-quality Data Collection can be realized;
2) by the dimensionality reduction of calculation matrix, making to measure number close to theoretical value, thus can reduce the cost of signal sampling measurement;
3) quality can be collected according to the information density of information source and network bandwidth state dynamic adjusting data, there is adaptability widely.
Accompanying drawing explanation
Fig. 1 is flow chart of the present invention.
Embodiment
The present invention devises the low information density method of data capture of quality scalable based on compressed sensing, composition graphs 1, and the specific implementation method of Data Collection is as follows:
Step one, to determine in wireless sensor network
sinkthe position of node, utilizes the media information in the node perceived surrounding environment in the middle of wireless sensor network, passes to
sinknode, realizes primary signal
collection;
Step 2, structure sparse basis array
to primary signal
carry out rarefaction, obtain rarefaction signal
, its length is set to
,
，（1）
；（2）
Step 3, generation
dimension gaussian random matrix
, wherein
, be characterized in matrix
element all independently to obey average be 0, variance is
gaussian Profile, Mathematical Modeling is:
；（3）
Step 4, pass through calculation matrix
right
carry out observation coding, obtain observation vector
, namely
, wherein:
；（4）
Step 5, to calculation matrix
carry out dimension-reduction treatment, namely delete
in
row (
), retain other row, calculation matrix becomes
, to sparse signal
again carry out observation coding can obtain
individual observation vector
;
Step 6, according to observation vector
, and obtain after dimension-reduction treatment
, select
oMPrestructing algorithm is right respectively
individual observation vector is reconstructed, and reconstructs
with
, and the error of calculation:
；（5）
Table 1 represents and to use in the present invention
oMPalgorithm.
Table 1
oMPalgorithm
OrthogonalMatchingPursuit
Input: perceptual signal
, perception matrix
Initialization: surplus
support set
, cycle-index
Process:
(1) select an element of inner product maximum absolute value in surplus and perception matrix, be designated as corresponding to the atom in perception matrix
, upgrade support set
;
(2) least square method is utilized to calculate restoring signal
, and upgrade surplus
;
(3) if
, exit circulation; Not so cycle-index
if,
then go to step (1), otherwise end loop;
Export:
If step 7
be less than given precision
, then cyclic variable is made
and go to step eight, otherwise terminate;
Step 8, successively calculating delete observation vector
in
jcalculation matrix corresponding after individual element
, wherein
, namely delete calculation matrix
?
joK;
Step 9, calculating
individual calculation matrix
maximum column correlation
:
，（6）
Wherein
represent
column vector;
Step 10, calculating
value,
value be from observation vector
middle deletion
individual element, and obtain the minimum observing matrix of corresponding correlation
, wherein:
；（7）
Step 11, according to observing matrix
, select
oMPrestructing algorithm is to observation vector
be reconstructed, obtain reconstruction signal
, the error of calculation:
；（8）
If step 12
be less than given precision
, then make
and go to step eight, otherwise terminate.
Claims (5)
1. based on the low information density method of data capture of high energy efficiency of compressed sensing, it is characterized in that, first generate gaussian random matrix and be used as calculation matrix, then judge the observation vector that records
primary signal can be reconstructed, then to calculation matrix
be optimized, finally reject invalid observation data, at least further comprising the steps of,
Step one, collection primary signal
, right
carry out sparse transformation and obtain sparse signal
;
Step 2, generation
dimension gaussian random matrix
(
), pass through calculation matrix
right
carry out observation coding, obtain observation vector
;
Step 3, right
carry out dimensionality reduction, observation vector of resurveying to obtain
, with suitable restructing algorithm, according to
with
reconstruct signal respectively
with
, the error of calculation
;
If step 4
be less than given precision
, then cyclic variable is made
and go to step five, otherwise terminate;
Step 5, calculating are deleted
correlation size after any a line, determines to delete calculation matrix
?
oK, the observing matrix that correlation is minimum is obtained
;
Step 6, use
with corresponding observation vector
the signal reconstructed
, the error of calculation
;
If step 7
be less than given precision
, then make
and go to step five, otherwise terminate.
2. the low information density method of data capture of the high energy efficiency based on compressed sensing according to claim 1, is characterized in that, collects primary signal
, right
carry out sparse transformation and obtain sparse signal
, it is at least further comprising the steps of,
Step one, to determine in wireless sensor network
sinkthe position of node, utilizes the media information in the node perceived surrounding environment in the middle of wireless sensor network, passes to
sinknode, realizes primary signal
collection;
Step 2, structure sparse basis array
to primary signal
carry out rarefaction, obtain rarefaction signal
, its length is set to
,
，
。
3. the low information density method of data capture of the high energy efficiency based on compressed sensing according to claim 1, is characterized in that, right
carry out dimensionality reduction, observation vector of resurveying to obtain
, with suitable restructing algorithm, according to
with
reconstruct signal respectively
with
, the error of calculation
, it is at least further comprising the steps of,
Step one, to calculation matrix
carry out dimension-reduction treatment, namely delete
in
row (
), retain other row, calculation matrix becomes
, to sparse signal
again carry out observation coding can obtain
individual observation vector
;
Step 2, according to observation vector
, and obtain after dimension-reduction treatment
, select
oMP(
orthogonalMatchingPursuit) restructing algorithm is right respectively
individual observation vector is reconstructed, and reconstructs
with
;
Step 3, the error of calculation
.
4. the low information density method of data capture of the high energy efficiency based on compressed sensing according to claim 1, is characterized in that, calculates and deletes
correlation size after any a line, determines to delete calculation matrix
?
oK, the observing matrix that correlation is minimum is obtained
, it is at least further comprising the steps of,
Step one, successively calculating delete observation vector
in
jcalculation matrix corresponding after individual element
, wherein
;
Step 2, calculating
individual calculation matrix
maximum column correlation
, wherein
represent
column vector;
Step 3, calculating
value,
value be from observation vector
middle deletion
individual element, and obtain the minimum observing matrix of corresponding correlation
.
5. the low information density method of data capture of the high energy efficiency based on compressed sensing according to claim 1, is characterized in that, uses
with corresponding observation vector
reconstruct signal
, the error of calculation
, it is at least further comprising the steps of,
Step one, according to observing matrix
, select
oMPrestructing algorithm is to observation vector
be reconstructed, obtain reconstruction signal
;
Step 2, the error of calculation
.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105530012A (en) * | 2015-11-18 | 2016-04-27 | 北京理工大学 | Compressed sensing based wavelet domain sparse one-dimensional oil well data compression and reconstruction method |
| CN107196738A (en) * | 2017-04-25 | 2017-09-22 | 湘潭大学 | A kind of compressed sensing method of data capture based on the dynamic norms of L_p |
| CN107743302A (en) * | 2017-10-27 | 2018-02-27 | 南京航空航天大学 | Rate-allocation and route combined optimization algorithm in wireless sensor network based on compressed sensing |
| WO2020020002A1 (en) * | 2018-07-26 | 2020-01-30 | 深圳大学 | Sensing matrix construction method and system for multi-measurement compressed sensing, and storage medium |
| CN111314875A (en) * | 2020-02-28 | 2020-06-19 | 西安交通大学 | Selection method of energy perception sampling set in signal reconstruction of Internet of things |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120250748A1 (en) * | 2011-04-04 | 2012-10-04 | U.S. Government As Represented By The Secretary Of The Army | Apparatus and method for sampling and reconstruction of wide bandwidth signals below nyquist rate |
| CN104618947A (en) * | 2015-02-03 | 2015-05-13 | 中国人民解放军信息工程大学 | Compressive sensing based dynamic clustering wireless sensor network data collecting method and device |
-
2015
- 2015-08-21 CN CN201510514933.7A patent/CN105050105B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120250748A1 (en) * | 2011-04-04 | 2012-10-04 | U.S. Government As Represented By The Secretary Of The Army | Apparatus and method for sampling and reconstruction of wide bandwidth signals below nyquist rate |
| CN104618947A (en) * | 2015-02-03 | 2015-05-13 | 中国人民解放军信息工程大学 | Compressive sensing based dynamic clustering wireless sensor network data collecting method and device |
Non-Patent Citations (1)
| Title |
|---|
| 裴廷睿等: "压缩感知中迂回式匹配追踪算法", 《计算机研究与发展》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105530012A (en) * | 2015-11-18 | 2016-04-27 | 北京理工大学 | Compressed sensing based wavelet domain sparse one-dimensional oil well data compression and reconstruction method |
| CN105530012B (en) * | 2015-11-18 | 2019-02-26 | 北京理工大学 | The compressed sensing based sparse one-dimensional well data of wavelet field compresses and reconstructing method |
| CN107196738A (en) * | 2017-04-25 | 2017-09-22 | 湘潭大学 | A kind of compressed sensing method of data capture based on the dynamic norms of L_p |
| CN107743302A (en) * | 2017-10-27 | 2018-02-27 | 南京航空航天大学 | Rate-allocation and route combined optimization algorithm in wireless sensor network based on compressed sensing |
| CN107743302B (en) * | 2017-10-27 | 2020-07-24 | 南京航空航天大学 | Rate allocation and routing combined optimization method based on compressed sensing |
| WO2020020002A1 (en) * | 2018-07-26 | 2020-01-30 | 深圳大学 | Sensing matrix construction method and system for multi-measurement compressed sensing, and storage medium |
| CN111314875A (en) * | 2020-02-28 | 2020-06-19 | 西安交通大学 | Selection method of energy perception sampling set in signal reconstruction of Internet of things |
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