CN104703262A - Compressed sensing-based clustered data collecting method - Google Patents
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- CN104703262A CN104703262A CN201510122417.XA CN201510122417A CN104703262A CN 104703262 A CN104703262 A CN 104703262A CN 201510122417 A CN201510122417 A CN 201510122417A CN 104703262 A CN104703262 A CN 104703262A
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- 238000000034 method Methods 0.000 title claims abstract description 37
- 239000011159 matrix material Substances 0.000 claims abstract description 35
- 238000005259 measurement Methods 0.000 claims abstract description 13
- 238000004364 calculation method Methods 0.000 claims description 12
- 230000006835 compression Effects 0.000 claims description 11
- 238000007906 compression Methods 0.000 claims description 11
- 238000013481 data capture Methods 0.000 claims description 10
- 238000005070 sampling Methods 0.000 claims description 10
- 238000012546 transfer Methods 0.000 claims description 3
- 238000005265 energy consumption Methods 0.000 abstract description 18
- 238000013480 data collection Methods 0.000 abstract description 14
- 241000854291 Dianthus carthusianorum Species 0.000 abstract 3
- 230000005540 biological transmission Effects 0.000 description 11
- 230000008447 perception Effects 0.000 description 5
- 238000004891 communication Methods 0.000 description 3
- 238000013461 design Methods 0.000 description 3
- 241001269238 Data Species 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 230000002123 temporal effect Effects 0.000 description 2
- 239000006185 dispersion Substances 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000009123 feedback regulation Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
- H04L1/0014—Systems modifying transmission characteristics according to link quality, e.g. power backoff by adapting the source coding
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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
- H04W52/0222—Power saving arrangements in terminal devices managed by the network, e.g. network or access point is master and terminal is slave in packet switched networks
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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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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
The invention discloses a compressed sensing-based clustered data collecting method. The compressed sensing-based clustered data collecting method comprises that firstly, a sensor network executes an edge betweenness-based clutering algorithm and a vertex betweenness-based cluster head selecting method; secondly, a cluster head collects data of cluster member nodes and generate a random measurement matrix to compressed-sample the collected data; lastly, the cluster head transmits the compressed data to a substation along a shortest path algorithm, and the substation generates the same measurement matrix to reconstruct the compressed data, and if reconstruction errors are larger than a fixed threshold, increases the number of rows of the measurement matrix to enable the reconstructed data to meet a fixed error threshold. The compressed sensing-based clustered data collecting method reduces energy consumption of data collection and can adjust the errors in the reconstructed data to obtain reconstructed data meeting fixed errors.
Description
Technical field
The present invention relates to a kind of clustering method of data capture based on compressed sensing being used in particular for wireless sensor network, belong to communication technical field.
Background technology
Wireless sensor network (Wireless Sensor Network, WSN) is by some low-power consumption, sensor network nodes that volume is little, the self-organizing network formed in the mode of wireless multi-hop.Nodes of these a large amount of dispersions can cooperate simultaneously, monitor in real time, perception and the various data of collection to a certain region.But wireless sensor network node is densely distributed, finite energy.Especially, in the process of clustering Data Collection, a large amount of perception datas needs to be transferred to a bunch head through sensor network nodes, and then is transferred to base station.How to design efficient method of data capture and become problem demanding prompt solution.
All data that a large amount of sensor node gathers are transferred to base station through leader cluster node and process by traditional method of data capture usually.But in the data acquisition of sensor network, normally multiple sensor node carries out perception compression to a certain event, carries a large amount of redundant datas, greatly occupies network communication bandwidth, brings unnecessary energy consumption.
In recent years, along with the proposition of compressed sensing (compressive sensing, CS), new road is opened to the Data Collection of wireless sensor network.Compressive sensing theory can be divided into three processes: sampling, measures, reconstruct.Sampling: sparse data is sampled; Measure: the data obtained sampling are carried out compression and measured, and obtain measured value; Reconstruct: initial data is reduced by measured value data.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, using circulation sparse matrix and pseudorandom Bernoulli sequence, adopting structurized method construct.Have nonzero element few, good pseudo-randomness, 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, reduce transmission energy consumption.
Common cluster-dividing method, namely by performing certain cluster algorithm to network, then generating stochastical sampling sequence and being distributed to a bunch member, then in bunch member, carrying out low speed stochastical sampling, finally in bunch head, carrying out signal reconstruction in bunch head.The method can reduce certain transmission energy consumption, but, the quality reconstructed can not be ensured.
Distributed temporal correlation method, namely in the process of Data Collection, for at present only carrying out accidental projection operation to spatial perception data, and the compression performance of space perception data bad in live network, thus cause that date restoring is of poor quality and transmission cost that is packed data is large.Propose a kind of distributed space-time data collection method, can effectively reduce the number of measurements transmitted in network.
In sum, the data collection strategy of existing compressed sensing combined sensor network all can reduce the measured value of some, thus reduce transmission energy consumption, but, energy consumption is transmitted to reduce Internet Transmission total energy consumption in how reducing bunch, obtain the reconstruct data meeting certain error value simultaneously, still there is no suitable solution at present.
Summary of the invention
For the problems referred to above and the sensor network with certain institutional framework, a kind of clustering method of data capture based on compressed sensing is proposed.By sensor network nodes is performed the cluster algorithm based on limit betweenness and bunch head system of selection based on a betweenness, and the feedback of reconstructed error, solve in data-gathering process and transmit the data problem that energy consumption is large, can not be met certain error requirement.The present invention, by node is performed the cluster algorithm based on limit betweenness and bunch head system of selection based on a betweenness, obtains optimum sub-clustering, transmits energy consumption in reducing bunch, thus reduce transmission total energy consumption as far as possible, meanwhile, by feedback reconstruction error, be met the reconstruct data of certain error requirement.
The present invention, first performs the cluster algorithm based on limit betweenness and bunch head system of selection based on a betweenness to sensor network.Then, bunch head collects the data of bunch interior nodes, and produces random measurement matrix and carry out compression sampling to the data of collecting.Finally, data after compression are transferred to base station along shortest path first by bunch head, and base station produces identical calculation matrix and is reconstructed packed data, if reconstructed error is greater than certain threshold value, then increase calculation matrix line number, make reconstruct data meet certain error threshold value.Present invention reduces the energy consumption of Data Collection, and the error of reconstruct data can be regulated in real time, be met the reconstruct data of certain error.
Concrete steps of the present invention are as follows:
Step one, adjacency matrix according to node
, the cluster algorithm based on limit betweenness and bunch head system of selection based on a betweenness are performed to sensor network nodes, obtain network cluster dividing
and bunch head that each sub-clustering is corresponding
;
Step 2, bunch interior nodes by the transfer of data that perceives to corresponding bunch head
;
Step 3, leader cluster node
produce random measurement matrix
, and to the data of collecting
carry out compression sampling, obtain measured value matrix
in
row
,
;
Step 4, leader cluster node
through shortest path first by measured value
be transferred to base station;
Step 5, base station receive the data from bunch head, form measured value matrix
, then base station produces identical random measurement matrix
, and to primary collection data
be reconstructed, restructuring procedure meets following formula:
(1)
Step 6, to reconstruct data out
, calculate its mean square error, then, perform feedback algorithm, finally, be met the reconstruct data of error threshold;
Step 7, end.
Compared with the existing radio sensor network data collection method in conjunction with compressed sensing, the invention has the advantages that:
What 1, the present invention proposed carries out the cluster algorithm based on limit betweenness and bunch head system of selection based on a betweenness by node, can obtain more reasonably clustering architecture, the energy consumption of transmission in effectively reducing bunch, thus reduces Internet Transmission total energy consumption as far as possible;
2, the present invention calculates reconstructed error according to reconstruct data, carries out Real-time Feedback, under the condition not increasing redundant communication energy consumption, can be met the reconstruct data of certain error requirement.
Accompanying drawing explanation
Fig. 1 is the flow chart realizing Data Collection of the present invention;
Fig. 2 is the 20 meshed network schematic diagrames with certain institutional framework;
Fig. 3 is 20 meshed network division result schematic diagrames;
Fig. 4 is the energy consumption result figure of contrast shortest path Data Collection and the clustering Data Collection based on compressed sensing.
specific implementation method
The present invention devises the clustering method of data capture based on compressed sensing, composition graphs 1, and the specific implementation method of Data Collection is as follows:
For the wireless sensor network with certain institutional framework, the error threshold of setting base station reconfiguration data
, be example with 20 meshed networks (Fig. 2, node serial number is from 1 to 20, and coordinate (0,0) point is base-station node) and image data.Concrete steps are as follows:
Mean square error threshold value after step one, base station sets data reconstruction
;
Step 2, the cluster algorithm based on limit betweenness and bunch head system of selection based on a betweenness are carried out to sensor network nodes, obtain network cluster dividing
and bunch head that each sub-clustering is corresponding
,
,
,
(Fig. 3, its interior joint 1,2,3,4,5 is one bunch, node 6,7,8,9,10 is one bunch, node 11,12,13,14,15 is one bunch, node 16,17,18,19,20 is one bunch, all indicate with difformity for each bunch, five-pointed star node is corresponding bunch intra-cluster head);
Step 3, bunch interior nodes by the transfer of data that perceives to corresponding bunch head
;
Step 4, leader cluster node
,
,
,
produce random measurement matrix respectively
,
,
,
, and to the data of collecting
,
carry out compression sampling, obtain measured value matrix
in
row
,
;
Step 5, leader cluster node
through shortest path first by measured value
be transferred to base station;
Step 6, base station receive the data from bunch head, form measured value matrix
, then base station produces random measurement matrix
, and to primary collection data
be reconstructed, restructuring procedure meets following formula:
(1)
Step 7, to reconstruct data out
, calculate mean square error according to the following formula:
(2)
Step 8, contrast reconstructed error
and error threshold
if error is more than or equal to error threshold, namely
, go to step nine, otherwise go to step ten;
Step 9, increase calculation matrix
number of lines, re-start reconstruct, obtain reconstruct data, if the mean square error of reconstruct data is greater than mean square error threshold value, namely
, continue to increase calculation matrix
number of lines, until mean square error is less than mean square error threshold value, go to step ten;
Step 10, end.
For verifying the validity of the method, this method is tested by Matlab emulation platform, and 20 meshed networks are deployed in 100m
in the monitored area of 100m, base station coordinates is (0,0).Node has identical primary power, and the energy of node consumption calculates according to the following formula:
(3)
In formula
represent the distance between two nodes,
for the proportionality coefficient of distance energy consumption,
for regulatory factor,
, work as node
and node
between have limit to connect, namely
, transmit one at the two ends of this edge and wrap the energy that consumes and be
; And limit is not had between two nodes, namely
, the energy that transmission packet consumes between these two limits is
.Transmission range is set to 40m.
Transmit data 50 round in a network, now also do not have node energy to exhaust in network, contrast the clustering Data Collection based on compressed sensing and shortest path method of data capture, result is as Fig. 4.From figure, the clustering method of data capture based on compressed sensing significantly reduces network energy consumption.
Then, in base station, feedback processing is carried out to view data, when the line number of calculation matrix
set gradually when being 10,20,30,40,50, every round feedback numerically increases by two row at original row, namely
, the tolerance interval of the mean square error threshold value of setting view data to be 200,150-250 be mean square error, find through test, the calculation matrix of different line numbers all can pass through feedback regulation line number, is met the mean square error of condition.
In sum, based on the clustering method of data capture of compressed sensing, can not only effectively reduce transmission total energy consumption, and, by being fed back by reconstructed error, the reconstruct data of certain error requirement can be met.
Claims (2)
1. based on a clustering method of data capture for compressed sensing, it is characterized in that, first, sensor network performs the cluster algorithm based on limit betweenness and bunch head system of selection based on a betweenness; Then, bunch head collects the data of bunch interior nodes, and produces random measurement matrix and carry out compression sampling to the data of collecting; Finally, data after compression are transferred to base station along shortest path first by bunch head, and base station produces identical calculation matrix and is reconstructed packed data, if reconstructed error is greater than certain threshold value, then increase calculation matrix line number, make reconstruct data meet certain error threshold value; Described method at least comprises following prerequisite and step:
Prerequisite:
Sensor network nodes distribution has certain texture characteristic;
The mean square error threshold value of base station sets reconstruct data
;
Step:
Step one, adjacency matrix according to node
, the cluster algorithm based on limit betweenness and bunch head system of selection based on a betweenness are performed to sensor network nodes, obtain network cluster dividing
and bunch head that each sub-clustering is corresponding
;
Step 2, bunch interior nodes by the transfer of data that perceives to corresponding bunch head
;
Step 3, leader cluster node
produce random measurement matrix
, and to the data of collecting
carry out compression sampling, obtain measured value matrix
in
row
,
;
Step 4, leader cluster node
through shortest path first by measured value
be transferred to base station;
Step 5, base station receive the data from bunch head, form measured value matrix
, then base station produces identical random measurement matrix
, and to primary collection data
be reconstructed, restructuring procedure meets following formula:
(1)
Step 6, to reconstruct data out
, calculate its mean square error, then, perform feedback algorithm, finally, be met the reconstruct data of error threshold;
Step 7, end.
2. the method for claim 1, is characterized in that described feedback algorithm, at least also comprises:
1) calculate the mean square error of reconstruct data, use
represent, contrast reconstructed error
and error threshold
if error is greater than error threshold, namely
, go to step 2), otherwise go to step 3);
2) in order to improve reconstruction accuracy, calculation matrix is increased
number of lines, regenerate the random measurement matrix of more higher-dimension, then, re-start reconstruct, obtain reconstruct data, if reconstruct after mean square error be still greater than error threshold, namely
, continue to increase calculation matrix
number of lines, until error is less than error threshold, go to step 3);
3) terminate.
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Cited By (6)
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| CN106851767A (en) * | 2016-09-22 | 2017-06-13 | 华东理工大学 | A kind of radio sensing network node fused data collection method |
| CN109587651A (en) * | 2018-12-26 | 2019-04-05 | 中国电建集团河南省电力勘测设计院有限公司 | A kind of collecting network data of wireless sensor algorithm |
| CN109743708A (en) * | 2018-11-07 | 2019-05-10 | 湘潭大学 | A kind of car networking collecting method based on vehicle density |
| CN110311687A (en) * | 2019-07-09 | 2019-10-08 | 南京天数智芯科技有限公司 | A kind of time series data lossless compression method based on Integrated Algorithm |
| CN116527525A (en) * | 2023-05-05 | 2023-08-01 | 国网湖南省电力有限公司 | Equipment data acquisition method and system based on edge calculation |
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105120469A (en) * | 2015-07-06 | 2015-12-02 | 湘潭大学 | Method for collecting low information density data with scalable quality based on compressed sensing |
| CN106851767A (en) * | 2016-09-22 | 2017-06-13 | 华东理工大学 | A kind of radio sensing network node fused data collection method |
| CN106851767B (en) * | 2016-09-22 | 2021-01-19 | 华东理工大学 | Method for collecting node fusion data of wireless sensor network |
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| CN109587651A (en) * | 2018-12-26 | 2019-04-05 | 中国电建集团河南省电力勘测设计院有限公司 | A kind of collecting network data of wireless sensor algorithm |
| CN109587651B (en) * | 2018-12-26 | 2021-11-02 | 中国电建集团河南省电力勘测设计院有限公司 | Wireless sensor network data aggregation method |
| CN110311687A (en) * | 2019-07-09 | 2019-10-08 | 南京天数智芯科技有限公司 | A kind of time series data lossless compression method based on Integrated Algorithm |
| CN110311687B (en) * | 2019-07-09 | 2022-10-04 | 上海天数智芯半导体有限公司 | Time sequence data lossless compression method based on integration algorithm |
| CN116527525A (en) * | 2023-05-05 | 2023-08-01 | 国网湖南省电力有限公司 | Equipment data acquisition method and system based on edge calculation |
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