CN105323819B - A kind of cluster-dividing method and system of sensor node - Google Patents
A kind of cluster-dividing method and system of sensor node Download PDFInfo
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- CN105323819B CN105323819B CN201410228053.9A CN201410228053A CN105323819B CN 105323819 B CN105323819 B CN 105323819B CN 201410228053 A CN201410228053 A CN 201410228053A CN 105323819 B CN105323819 B CN 105323819B
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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
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Abstract
The present invention provides a kind of cluster-dividing method of sensor node, it include: to choose cluster interior nodes, it calculates the cluster interior nodes chosen and transmits the energy consumption e generated when perception data, the perception data for calculating all cluster interior nodes in the sub-clustering where the cluster interior nodes carries out the energy consumption E transmitted again after assembly algorithms in cluster, it calculates the sub-clustering after eliminating the cluster interior nodes and the perception data of cluster interior nodes remaining in cluster is subjected to the energy consumption E ' transmitted again after assembly algorithms in cluster, judge E whether be less than E ' with e's and, if so, retaining the cluster interior nodes；If it is not, rejecting, the new sub-clustering of generation is judged whether there is, if it is not, creating new sub-clustering, the cluster interior nodes of rejecting are added in new sub-clustering；If so, the cluster interior nodes of rejecting are added in already present new sub-clustering；Obtain final sub-clustering information.The present invention also guarantees the high efficiency of the data transmission between cluster interior nodes, promotes the performance of data assembly algorithms while guaranteeing the correlation of cluster interior nodes.
Description
Technical field
The invention belongs to wireless sensor network technology network, it is related to a kind of cluster-dividing method and system, more particularly to one
The cluster-dividing method and system of kind sensor node.
Background technique
Wireless sensor network generally comprises a base station and the sensing node of several random distributions.Sensing section by
Limited resource, such as electricity, the limitation of transmission range, computing capability.In such network, sensing node needs periodically to receive
Collection sensing data is simultaneously reported to base station.Simplest method is to be forwarded the data of node to base station by multihop network,
Although this method precision is high, the node close apart from the base station node farther out that wants help carries out a large amount of data forwarding, Jin Erzeng
The power consumption for having added these nodes, cause they quickly power down and fail, largely effect on the network life of the whole network.
Data convergence technology is the key technology in wireless sensor network in terms of data acquisition applications.Its concretism is
By being pre-processed in data-gathering process to data, to achieve the purpose that reduce network communication amount.Average value, data pressure
The feasibility of the data assembly algorithms such as contracting, network code, compressed sensing be built upon between sensing node the space of perception data and
On the correlation of time dimension.Clustering strategy is proved to be able to significantly improve the effect for netting interior tidal data recovering.Reason just exists
Usually there is very strong correlation between the node of a small range.In clustering algorithm, data prediction carries out first in cluster,
Then the data packet after convergence is transmitted to base station by cluster head.Obviously, clustering algorithm determines the degree that correlation among nodes retain
Determine the superiority and inferiority of clustering algorithm.
At present in the data acquisition of sensor network, the existing data convergence technology of the overwhelming majority is to utilize sensing node
Between data dependence.Compressed sensing and Slepian-Wolf Coding technology are introduced into data respectively and adopt in the prior art
Concentrate the algorithm converged as data.Such as in " compressed sensing based data acquisition in massive wireless sensor "
In, due to not having the strategy using sub-clustering, integral energy efficiency is very low.And for example exist, " clustering wireless sensor network
In the distributed data convergence based on Slepian-Wolf coding " and a kind of " hierarchical efficient wireless sensor network of efficiency
Data assemblage method " and most other technologies, it is all based on geography information and sub-clustering is carried out to node, cause between node
Correlation is likely to be broken, and influences the performance of data assembly algorithms.If but completely with carrying out sub-clustering to node according to data dependence,
The geographical location that then may result in node is unfavorable for convergence in cluster, increases the energy consumption converged in cluster.
Therefore, how while guaranteeing data convergence technical application effect, also guarantee that the high efficiency converged in cluster is real
The technical problem urgently to be resolved as practitioner in the art.
Summary of the invention
In view of the foregoing deficiencies of prior art, the purpose of the present invention is to provide a kind of sensor node cluster-dividing methods
And system, for solve in the prior art data converge technical application effect while not can guarantee converged in sub-clustering it is efficient
The problem of property.
In order to achieve the above objects and other related objects, one aspect of the present invention provides a kind of sub-clustering side of sensor node
Method, in the wireless sensor network applied to the sensor node for including a base station and several random distributions, the biography
The cluster-dividing method of sensor node includes: step 1, acquires n group perception data as training data；Wherein, n is more than or equal to 2；Step
Rapid two, the local correlations information in the training data is extracted using data mining algorithm to n group training data, according to institute
The local correlations information stated in training data generates initial sub-clustering information C={ c1,c2,…,cm}；Wherein, c1,c2,…,cmTable
Show m sub-clustering, m is the positive integer more than or equal to 1；Step 3 optimizes m sub-clustering one by one, a sub-clustering is chosen, in the sub-clustering
Cluster interior nodes are inside chosen one by one, and the energy consumption e generated when calculating the cluster interior nodes transmission perception data of selection calculates selection
The perception data of all cluster interior nodes carries out being transmitted to the base station again after assembly algorithms in cluster in sub-clustering where cluster interior nodes
When the energy consumption E that generates, the sub-clustering after cluster interior nodes eliminate will be chosen for the perception of cluster interior nodes remaining in cluster by calculating
Data carry out the energy consumption E ' generated when being transmitted to the base station again after assembly algorithms in cluster, judge whether E is less than E ' and e
Sum, if so, selected cluster interior nodes are retained in the sub-clustering；If it is not, then rejecting selected cluster interior nodes, sentence
The disconnected new sub-clustering generated with the presence or absence of other cluster interior nodes in the sub-clustering, if it is not, a new sub-clustering is then created, by the cluster internal segment of rejecting
Point is added in the new sub-clustering of creation；If so, directly the cluster interior nodes of rejecting are added in already present new sub-clustering；Step
Four, circulation executes step 3, until optimization finishes m sub-clustering and all newly-generated sub-clusterings, obtains final sub-clustering information X=
{x1,x2,…,xv, wherein x1,x2..., xvSub-clustering after indicating v optimization, v are the positive integer greater than 1.
Preferably, further include in the step 3 when transmission perception data is chosen in sub-clustering the consumption energy that generates it is the smallest
The perception data of nodes all in cluster is carried out assembly algorithms in cluster by leader cluster node, the sub-clustering where calculating the cluster interior nodes of selection
It is described that the energy consumption E generated when being transmitted to the base station again later refers to that all perception datas after convergence are transmitted to by calculating
Leader cluster node, the energy consumption E generated when being forwarded to base station by the leader cluster node.
It preferably, further include that successively all cluster interior nodes in each sub-clustering are optimized one by one in the step 3,
The cluster interior nodes of rejecting are placed in the new sub-clustering of creation, form new sub-clustering.
Preferably, in step 3 described in the sensor node cluster-dividing method it is geography according to the sensor node
Information to optimizing m sub-clustering one by one.
Preferably, the data mining algorithm is tree transformation algorithm.
Preferably, the cluster interior nodes chosen in each sub-clustering transmit perception data using multi-hop mode.
Another aspect of the present invention also provides a kind of cluster system of sensor node, if be applied to including a base station and
In the wireless sensor network of the sensor node of dry random distribution, the cluster system of the sensor node includes: acquisition
Module, for acquiring n group perception data as training data；Wherein, n is more than or equal to 2；The extraction being connect with the acquisition module
Module is for extracting the local correlations information in the training data, root using data mining algorithm to n group training data
Initial sub-clustering information is generated according to the local correlations information in the training data；Wherein, c1,c2,…,cmIndicate m sub-clustering, m
For the positive integer more than or equal to 1；The sub-clustering module being connect respectively with the acquisition module and extraction module, for according to the biography
The geography information of sensor node optimizes m sub-clustering one by one, chooses a sub-clustering, chooses cluster interior nodes one by one in the sub-clustering,
It calculates the cluster interior nodes chosen and transmits the energy consumption e generated when perception data, calculate the sub-clustering where the cluster interior nodes of selection
In the perception datas of all cluster interior nodes carry out the energy consumption E generated when being transmitted to the base station again after assembly algorithms in cluster,
Calculating will choose the sub-clustering after cluster interior nodes eliminate and the perception data of remaining cluster interior nodes will carry out converging calculation in cluster in cluster
The energy consumption E ' generated when being transmitted to the base station again after method, judge E whether be less than E ' with e's and, if so, will selected by
Cluster interior nodes be retained in the sub-clustering；If it is not, then reject selected cluster interior nodes, it is judged whether there is in the sub-clustering
The cluster interior nodes of rejecting are added to the new sub-clustering of creation if it is not, then creating a new sub-clustering by the new sub-clustering that his cluster interior nodes generate
It is interior；If so, directly the cluster interior nodes of rejecting are added in already present new sub-clustering；And the sub-clustering module be also used to until
Optimization finishes m sub-clustering and all newly-generated sub-clusterings, obtains final sub-clustering information X={ x1, x2,…,xv, wherein x1,
x2..., xvSub-clustering after indicating v optimization, v are the positive integer greater than 1.
Preferably, the consumption energy that the sub-clustering module is also used to generate when transmission perception data is chosen in sub-clustering is the smallest
Leader cluster node, the energy consumption e for calculating the cluster interior nodes transmission perception data of selection refer to the cluster interior nodes transmission for calculating and choosing
The energy consumption e generated when perception data to base station.
Preferably, the sensor node cluster system further includes the selecting module connecting with the sub-clustering module, described
Selecting module is used in the sub-clustering after each optimization choose can be with the cluster head section of minimal consumption energy transmission perception data
Perception data after converging in sub-clustering after each one optimization is transmitted to the leader cluster node of the cluster so that it is by institute by point
It states data and is transmitted to the base station.
As described above, the cluster-dividing method and system of sensor of the invention node, have the advantages that
1, the cluster-dividing method of sensor node of the present invention and system ensure that the same of the correlation of cluster interior nodes
When, it also ensures the high efficiency of the data transmission between cluster interior nodes, promotes the performance of data assembly algorithms, and then save network
Energy extends network life.
2, perception data local correlations between the present invention makes data assembly algorithms preferably utilize sensor node, same
Accuracy of data recovery improves the energy efficiency of network under requiring.
Detailed description of the invention
Fig. 1 is shown as the cluster-dividing method flow diagram of sensor of the invention node.
Fig. 2 is shown as the theory structure schematic diagram of the cluster system of sensor of the invention node.
The cluster system that Fig. 3 is shown as sensor of the invention node is applied to based on compressed sensing data collection framework knot
Structure schematic diagram.
Fig. 4 is shown with cluster-dividing method and two kinds of compressed sensing based data acquisition performances without using cluster-dividing method
Comparison diagram.
Component label instructions
The cluster system of 1 sensor node
11 acquisition modules
12 extraction modules
13 sub-clustering modules
14 selecting modules
2 are based on compressed sensing data collection framework
21 signal recovery modules
S1~S10 step
Specific embodiment
Illustrate embodiments of the present invention below by way of specific specific example, those skilled in the art can be by this specification
Other advantages and efficacy of the present invention can be easily understood for disclosed content.The present invention can also pass through in addition different specific realities
The mode of applying is embodied or practiced, the various details in this specification can also based on different viewpoints and application, without departing from
Various modifications or alterations are carried out under spirit of the invention.It should be noted that in the absence of conflict, following embodiment and implementation
Feature in example can be combined with each other.
It should be noted that illustrating the basic structure that only the invention is illustrated in a schematic way provided in following embodiment
Think, only shown in schema then with related component in the present invention rather than component count, shape and size when according to actual implementation
Draw, when actual implementation kenel, quantity and the ratio of each component can arbitrarily change for one kind, and its assembly layout kenel
It is likely more complexity.
Embodiment one
The present embodiment provides a kind of cluster-dividing method of sensor node, being applied to includes a base station and several are random
In the wireless sensor network of the sensor node of distribution, sensor node cluster-dividing method described in the present embodiment is based on sensing
The geographical location information of the local correlations of perception data and sensor node between device node, referring to Fig. 1, being shown as sensor
The cluster-dividing method of the flow chart of node clustering method, the sensor node includes:
S1 acquires n group perception data as training data；Wherein, n is more than or equal to 2.N is at least 2, perception collected
The group number of data is more, and precision is higher.
S2 extracts the local correlations information in the training data using data mining algorithm to n group training data,
Initial sub-clustering information C={ c is generated according to the local correlations information in the training data1, c2..., cm}；Wherein, c1,
c2..., cmIndicate m sub-clustering, m is the positive integer more than or equal to 1.In the present embodiment, the data mining algorithm is tree-like change
Change (Treelets) algorithm.
S3 optimizes m sub-clustering one by one according to the geography information of the sensor node, chooses a sub-clustering, exist at random
Cluster interior nodes are chosen one by one in each sub-clustering, the energy consumption generated when calculating the cluster interior nodes transmission perception data of selection
E, the perception data for calculating all cluster interior nodes in the sub-clustering where the cluster interior nodes of selection pass again after assembly algorithms in cluster
The energy consumption E generated when transporting to the base station, the sub-clustering after cluster interior nodes eliminate will be chosen for cluster remaining in cluster by calculating
The perception data of interior nodes carries out the energy consumption E ' generated when being transmitted to the base station again after assembly algorithms in cluster.
S4, judge choose cluster interior nodes where sub-clustering in all cluster interior nodes perception data carry out cluster in convergence calculate
Whether the energy consumption E generated when being transmitted to the base station again after method, which is less than, is chosen the sub-clustering after cluster interior nodes eliminate for cluster
The perception data of interior residue cluster interior nodes carries out the energy consumption generated when being transmitted to the base station again after assembly algorithms in cluster
E ' and the cluster interior nodes chosen transmit energy consumption e, i.e. E < E '+e generated when perception data, if so, thening follow the steps S5, i.e.,
Selected cluster interior nodes are retained in the sub-clustering；If it is not, then follow the steps S6, that is, judge whether there is in the sub-clustering other
The cluster interior nodes of rejecting are then directly added to already present new by the new sub-clustering that cluster interior nodes generate if so, thening follow the steps S7
In sub-clustering；If it is not, thening follow the steps S8, a new sub-clustering is created, the cluster interior nodes of rejecting are added in the new sub-clustering of creation；
In the present embodiment, with sub-clustering c1For, at random in sub-clustering c1Middle selection cluster interior nodes vi, i is cluster interior nodes mark
Number.Step S3-S4 is specifically included:
The first step, in sub-clustering c1The smallest leader cluster node of consumption energy generated when choosing transmission perception data.
Second step calculates the cluster interior nodes v of selectioniThe energy consumption generated when transmitting perception data to the leader cluster node
e。
Third step calculates the cluster interior nodes v of selectioniThe sub-clustering c at place1In all cluster interior nodes perception data carry out cluster
The energy consumption E generated when being transmitted to the base station again after interior assembly algorithms and calculating will choose cluster interior nodes viAfter eliminating
Sub-clustering c1' assembly algorithms in the perception data progress cluster of cluster interior nodes remaining in cluster are lagged to production when being transmitted to the base station again
Raw energy consumption E '.
4th step, judge E whether be less than E ' with e's and, if being less than, execution the 5th step, i.e., by selected cluster internal segment
Point viIt is retained in sub-clustering c1In, if being not less than, the 6th step is executed,
6th step, by selected cluster interior nodes viEliminate c1, judge whether there is sub-clustering c1Other interior cluster interior nodes
The new sub-clustering generated, if it is not, one new sub-clustering c of creationNewly -1={ vi, by the cluster interior nodes v of rejectingiIt is added to the new sub-clustering of creation
It is interior；If so, the cluster interior nodes of rejecting are directly added to already present new sub-clustering cNewly -1It is interior.
7th step continues to optimize sub-clustering c1, to sub-clustering c1In all cluster interior nodes detected, repeat the step first step
To the 6th step.
S9, circulation execute step S4-S8, until optimization finishes new sub-clustering (the i.e. above-mentioned steps of m sub-clustering and all generations
The new sub-clustering of middle generation), obtain final sub-clustering information X={ x1, x2..., xv, wherein x1,x2,…,xvAfter indicating v optimization
Sub-clustering, v are the positive integer greater than 1.With sub-clustering c1For when, if cNewly -1When not being empty set, updated sub-clustering information C'=
{ c1, c2..., cm, cNewly -1, cNewly -2..., cNewly-j, wherein j is the positive integer greater than 1.
S10, choosing in the sub-clustering after each optimization can be with the cluster head section of minimal consumption energy transmission training data
Training data after converging in sub-clustering after each one optimization is transmitted to the leader cluster node all so that it will be described by point
Training data is transmitted to the base station.
Sensor node cluster-dividing method described in the present embodiment also guarantees while ensure that the correlation of cluster interior nodes
The high efficiency of data transmission between cluster interior nodes, promotes the performance of data assembly algorithms, and then save network energy, extends
Network life.
Embodiment two
The present embodiment provides a kind of cluster system 1 of sensor node, be applied to including a base station and several with
In the wireless sensor network of the sensor node of machine distribution, referring to Fig. 2, the principle of the cluster system of display sensor node
Structure chart, the sensor node cluster system include acquisition module 11, extraction module 12, sub-clustering module 13 and selecting module
14.As described in Figure 1, the acquisition module 11 is in the sensitive zones of the sensor node including several random distributions, described
Extraction module 12, sub-clustering module 13 and selecting module 14 are located in base station side.
The acquisition module 11 is for acquiring n group perception data as training data；Wherein, n is more than or equal to 2.N is at least
2, the group number of perception data collected is more, and precision is higher.
The extraction module 12 connecting with the acquisition module 11 is for extracting n group training data using data mining algorithm
Local correlations information in the training data out generates initial point according to the local correlations information in the training data
Cluster information；Wherein, c1, c2..., cmIndicate m sub-clustering, m is the positive integer more than or equal to 1.In the present embodiment, the data
Mining algorithm is tree transformation (Treelets) algorithm.
The sub-clustering module 13 connecting respectively with the acquisition module 11 and extraction module 12 is used for according to the sensor section
The geography information of point optimizes m sub-clustering one by one, chooses a sub-clustering, chooses cluster interior nodes one by one in the sub-clustering and chooses one,
It calculates the cluster interior nodes chosen and transmits the energy consumption e generated when perception data, calculate the sub-clustering where the cluster interior nodes of selection
In the perception datas of all cluster interior nodes carry out the energy consumption E generated when being transmitted to the base station again after assembly algorithms in cluster,
The sub-clustering after cluster interior nodes eliminate will be chosen by, which calculating, converge in cluster by the perception data of cluster interior nodes remaining in cluster
The energy consumption E ' generated when being transmitted to the base station again after algorithm, judge choose cluster interior nodes where sub-clustering in all clusters
Whether the energy consumption E that the perception data of interior nodes generate when being transmitted to the base station again after assembly algorithms in cluster is less than choosing
Sub-clustering after taking cluster interior nodes to eliminate carries out the perception data of cluster interior nodes remaining in cluster in cluster after assembly algorithms again
The energy consumption E ' generated when being transmitted to the base station and the cluster interior nodes chosen transmit the energy consumption generated when perception data
E, i.e. E < E '+e, if so, selected cluster interior nodes are retained in the sub-clustering；If it is not, then by selected cluster interior nodes
It rejects, continues to determine whether that there are the new sub-clusterings that other cluster interior nodes in the sub-clustering generate, if it is not, a new sub-clustering is then created, it will
The cluster interior nodes of rejecting are added in the new sub-clustering of creation；If so, directly the cluster interior nodes of rejecting are added to already present
In new sub-clustering；Until optimization finishes m sub-clustering and all new sub-clusterings, final sub-clustering information X={ x is obtained1, x2..., xv, wherein
x1, x2..., xvSub-clustering after indicating v optimization, v are the positive integer greater than 1.
In the present embodiment, the sub-clustering module 13 carries out sub-clustering first with sub-clustering c1For, at random in sub-clustering c1It is middle to choose one
Cluster interior nodes vi, i is cluster internal segment piont mark.The detailed process of sub-clustering includes:
The first step, the smallest leader cluster node of consumption energy generated when transmission perception data is chosen in sub-clustering.
Second step, the energy consumption e generated when calculating cluster interior nodes transmission perception data to the leader cluster node of selection.
Third step, the perception data for calculating all cluster interior nodes in the sub-clustering where the cluster interior nodes of selection converge in cluster
The energy consumption E generated when being transmitted to the base station again after poly- algorithm and calculating will choose the sub-clustering after cluster interior nodes eliminate
The perception data of cluster interior nodes remaining in cluster is carried out to the energy generated when assembly algorithms lag is transmitted to the base station again in cluster
Consume E '.
4th step, judge E whether be less than E ' with e's and, if being less than, execution the 5th step, i.e., by selected cluster internal segment
Point is retained in the sub-clustering, if being not less than, executes the 6th step,
6th step eliminates selected cluster interior nodes, judges whether there is other cluster interior nodes in the sub-clustering and generates
New sub-clustering, if it is not, creation one new sub-clustering, the cluster interior nodes of rejecting are added in the new sub-clustering of creation；If so, will directly pick
The cluster interior nodes removed are added in already present new sub-clustering.
7th step continues to optimize sub-clustering, detect to all cluster interior nodes in sub-clustering, repeats the step first step to the
Six steps.
The circulation of sub-clustering module 13 executes above-mentioned steps, until optimization finishes m sub-clustering and all new sub-clusterings, obtains most
Whole sub-clustering information X={ x1, x2..., xv, wherein x1, x2..., xvSub-clustering after indicating v optimization, v are the positive integer greater than 1.
With sub-clustering c1For when, if cNewly -1When not being empty set, updated sub-clustering information C '={ c1, c2..., cm, cNewly -1, cNewly -2...,
cNewly-j, wherein j is the positive integer greater than 1.
The selecting module 14 connecting with the sub-clustering module 13 is used in the sub-clustering after each optimization choose can be with
The leader cluster node of minimal consumption energy transmission perception data, after in the sub-clustering after each one optimization using assembly algorithms in cluster
Perception data is transmitted to the leader cluster node all so that the training data is transmitted to the base station by it after convergence.
The sensor node cluster system is applied to based on compressed sensing data collection framework 2, as shown in figure 3, also
Using compressed sensing data assembly algorithms are based in each sub-clustering for needing to have optimized by the sub-clustering module 13, obtain each
Perception measured value corresponding with each sub-clustering is sent by the perception measured value in sub-clustering, the cluster head chosen by selecting module 14
Signal recovery module 21 carries out signal recovery.
Cluster-dividing method will be used and carried out without using two kinds of compressed sensing based data acquisition technologys of cluster-dividing method
Comparison.Emulation has used the collected data of true Sensor Network, and 83 nodes comprising being distributed in a campus exist
Collected environmental information in several weeks.Use Matlab as emulation tool, be arranged according to the wireless parameter of node, introduces decline
Channel model, and simulate compressed sensing based data transmission procedure.Compressed sensing is implemented in cluster in clustering algorithm, regardless of
Cluster strategy compressed sensing is implemented in whole net.4 are please referred to, cluster-dividing method is shown with and is based on without using two kinds of cluster-dividing method
The data acquisition performance comparison diagram of compressed sensing uses this patent institute by analogous diagram it is found that under identical energy consumption grade
The data assemblage method of the clustering algorithm of proposition can significantly improve accuracy of data recovery.
In conclusion sensor node cluster-dividing method of the present invention and system are in the correlation that ensure that cluster interior nodes
While, it also ensures the high efficiency of the data transmission between cluster interior nodes, promotes the performance of data assembly algorithms, and then save
Network energy extends network life.And the present invention so that data assembly algorithms is preferably utilized sensor node between perception data office
Portion's correlation is restored to improve the energy efficiency of network under required precision in coordinates data.
So the present invention effectively overcomes various shortcoming in the prior art and has high industrial utilization value.
The above-described embodiments merely illustrate the principles and effects of the present invention, and is not intended to limit the present invention.It is any ripe
The personage for knowing this technology all without departing from the spirit and scope of the present invention, carries out modifications and changes to above-described embodiment.Cause
This, institute is complete without departing from the spirit and technical ideas disclosed in the present invention by those of ordinary skill in the art such as
At all equivalent modifications or change, should be covered by the claims of the present invention.
Claims (8)
1. a kind of cluster-dividing method of sensor node, applied to the sensor section for including a base station and several random distributions
In the wireless sensor network of point, which is characterized in that the cluster-dividing method of the sensor node includes:
Step 1 acquires n group perception data as training data；Wherein, n is more than or equal to 2；
Step 2 extracts the local correlations information in the training data using data mining algorithm to n group training data,
Initial sub-clustering information C={ c is generated according to the local correlations information in the training data1,c2,…,cm}；Wherein, c1,
c2,…,cmIndicate m sub-clustering, m is the positive integer more than or equal to 1；
Step 3 optimizes m sub-clustering one by one according to the geography information of the sensor node, a sub-clustering is chosen, in this point
Cluster interior nodes are chosen in cluster one by one, the energy consumption e generated when calculating the cluster interior nodes transmission perception data of selection is calculated and chosen
Cluster interior nodes where sub-clustering in the perception datas of all cluster interior nodes carry out being transmitted to the base again after assembly algorithms in cluster
The energy consumption E generated when standing, the sub-clustering after cluster interior nodes eliminate will be chosen for the sense of cluster interior nodes remaining in cluster by calculating
Primary data carries out the energy consumption E ' generated when being transmitted to the base station again after assembly algorithms in cluster, judges whether E is less than E '
With e's and, if so, selected cluster interior nodes are retained in the sub-clustering；If it is not, then selected cluster interior nodes are picked
It removes, judges whether there is the new sub-clustering that other cluster interior nodes generate in the sub-clustering, if it is not, a new sub-clustering is then created, by rejecting
Cluster interior nodes are added in the new sub-clustering of creation；If so, the cluster interior nodes of rejecting are directly added to already present new sub-clustering
It is interior；
Step 4, circulation execute step 3, until optimization finishes m sub-clustering and all newly-generated sub-clusterings, obtain final sub-clustering
Information X={ x1,x2,…,xv, wherein x1,x2,…,xvSub-clustering after indicating v optimization, v are the positive integer greater than 1.
2. the cluster-dividing method of sensor node according to claim 1, it is characterised in that: further include in the step 3
The smallest leader cluster node of consumption energy generated when transmission perception data is chosen in sub-clustering, calculates point where the cluster interior nodes of selection
The energy that cluster generates when carrying out by the perception data of nodes all in cluster and be transmitted to the base station again after assembly algorithms in cluster disappears
Consumption E refers to that all perception datas after convergence are transmitted to the leader cluster node by calculating, is forwarded to base by the leader cluster node
The energy consumption E generated when standing.
3. the cluster-dividing method of sensor node according to claim 2, it is characterised in that: further include in the step 3 according to
Secondary all cluster interior nodes in each sub-clustering are optimized one by one, and the cluster interior nodes of rejecting are placed in the new sub-clustering of creation,
Form new sub-clustering.
4. the cluster-dividing method of sensor node according to claim 1, it is characterised in that: the data mining algorithm is tree
Deformation scaling method.
5. the cluster-dividing method of sensor node according to claim 1, it is characterised in that: the cluster chosen in each sub-clustering
Interior nodes transmit perception data using multi-hop mode.
6. a kind of cluster system of sensor node, applied to the sensor section for including a base station and several random distributions
In the wireless sensor network of point, which is characterized in that the cluster system of the sensor node includes:
Acquisition module, for acquiring n group perception data as training data；Wherein, n is more than or equal to 2；
The extraction module connecting with the acquisition module is for extracting the instruction using data mining algorithm to n group training data
Practice the local correlations information in data, initial sub-clustering information is generated according to the local correlations information in the training data；
Wherein, c1,c2,…,cmIndicate m sub-clustering, m is the positive integer more than or equal to 1；
The sub-clustering module being connect respectively with the acquisition module and extraction module, for being believed according to the geography of the sensor node
Breath optimizes m sub-clustering one by one, chooses a sub-clustering, chooses cluster interior nodes one by one in the sub-clustering, calculates the cluster internal segment of selection
The energy consumption e that generates when point transmission perception data calculates all cluster interior nodes in the sub-clustering where the cluster interior nodes of selection
Perception data carries out the energy consumption E generated when being transmitted to the base station again after assembly algorithms in cluster, and cluster internal segment will be chosen by calculating
Point eliminate after sub-clustering will in cluster the perception data of remaining cluster interior nodes be transmitted to again after assembly algorithms in cluster it is described
The energy consumption E ' generated when base station, judge E whether be less than E ' with e's and, if so, selected cluster interior nodes are retained in
In the sub-clustering；If it is not, then rejecting selected cluster interior nodes, judge whether there is what other cluster interior nodes in the sub-clustering generated
The cluster interior nodes of rejecting are added in the new sub-clustering of creation by new sub-clustering if it is not, then creating a new sub-clustering；
If so, directly the cluster interior nodes of rejecting are added in already present new sub-clustering；And the sub-clustering module is also used to directly
M sub-clustering and all newly-generated sub-clusterings are finished to optimization, obtains final sub-clustering information X={ x1,x2,…,xv, wherein x1,
x2,…,xvSub-clustering after indicating v optimization, v are the positive integer greater than 1.
7. the cluster system of sensor node according to claim 6, it is characterised in that: the sub-clustering module is also used to
The smallest leader cluster node of consumption energy generated when transmission perception data is chosen in sub-clustering, calculates the cluster interior nodes transmission perception of selection
The energy consumption e of data refers to the energy consumption e generated when calculating cluster interior nodes transmission perception data to the base station chosen.
8. the cluster system of sensor node according to claim 6, it is characterised in that: sensor node sub-clustering system
System further includes the selecting module connecting with the sub-clustering module, and the selecting module is used in the sub-clustering after each optimization select
Taking can be with the leader cluster node of minimal consumption energy transmission perception data, by the sense after converging in the sub-clustering after each one optimization
Primary data is transmitted to the leader cluster node of the cluster so that the data are transmitted to the base station by it.
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Non-Patent Citations (4)
| Title |
|---|
| Cluster-Based Energy-Efficient Data Collection in Wireless Sensor Networks utilizing Compressive Sensing;Minh Tuan Nguyen等;《Military Communications Conference, MILCOM 2013-2013 IEEE》;20131120;全文 * |
| IN-NETWORK AGGREGATION TECHNIQUES FOR WIRELESS SENSOR NETWORKS: A SURVEY;ELENA FASOLO等;《IEEE Wireless Communications》;20070430;全文 * |
| 基于位置信息的分簇传感网数据汇聚方案;陈德礼等;《嘉应学院学报（自然科学）》;20131130;第31卷(第11期);全文 * |
| 无线传感器网络分簇路由协议综述;王镇;《电脑知识与技术》;20110331;第7卷(第8期);全文 * |
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