CN105682171B - Spatio-temporal clustering method for compressive data gathering - Google Patents

Spatio-temporal clustering method for compressive data gathering Download PDF

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CN105682171B
CN105682171B CN201610132885.XA CN201610132885A CN105682171B CN 105682171 B CN105682171 B CN 105682171B CN 201610132885 A CN201610132885 A CN 201610132885A CN 105682171 B CN105682171 B CN 105682171B
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cluster
data
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neighborhood
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CN105682171A (en
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万江文
王东豪
聂志朋
姚雷
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Beihang University
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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
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/14—Routing performance; Theoretical aspects
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/46—Cluster building
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership
    • 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
    • H04W84/20—Master-slave selection or change arrangements
    • 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

Abstract

The invention discloses a spatio-temporal clustering method for compressive data gathering. The spatio-temporal clustering method comprises the steps of: 1, an initialization stage; 2, spatio-temporal correlation measurement; 3, network clustering; 4, compressive data gathering; 5, data reconstruction. In the network clustering step, aggregation nodes obtain clustering information according to a calculated value of spatio-temporal correlation and broadcast the information to the whole network, and intra-cluster nodes select cluster head nodes dynamically. By adopting the steps, the spatio-temporal clustering method for compressive data gathering reduces data transmission times under the condition of same reconfiguration error, balances network energy consumption, prevents a certain node from dying too early due to energy consumption, and prolongs network lifetime.

Description

Wireless sensor network compressed data collection method based on temporal correlation sub-clustering
Technical field：The present invention provides a kind of wireless sensor network compressed data based on temporal correlation sub-clustering and collects Method, it belongs to wireless communication technology field.
Background technology
Wireless sensor network (Wireless Sensor Networks, WSNs) is by a large amount of low-power consumption, low cost Sensor node deployment sets up into network in the way of wireless Ad Hoc to relevant range.These nodes can be cooperated jointly, week Phase property ground is perceived and gathered data, and is delivered to aggregation node in a multi-hop fashion.How Data Collection task is efficiently completed, I.e. under conditions of same data collection precision is ensured, maximize and extend life-span of network and be one and important study a question. Cluster Networks have topology clear, the features such as energy efficient, are widely used in compressed data and collect in agreement.Conventional wireless is sensed The problem of device network cluster dividing agreement, such as LEACH, HEED scheduling algorithm, the main selection for considering leader cluster node and sub-clustering number, and compared with The data characteristicses of monitor area are considered less.
Compressed sensing is an emerging digital signal processing method, and the method is considered as to classical Shannon- One kind of Nyqist sampling thheorems surmounts.Nyquist sampling theorem thinks that, in order to recover original signal exactly, sample frequency is extremely If 2 times of signal highest frequency less.And compressive sensing theory is thought, by obtaining a small amount of of sparse signal or compressible signal Linear projection just can reconstruct the signal exactly.
Due to there is substantial amounts of redundancy in initial data, and the number of the less consideration sensing region of traditional cluster algorithm According to temporal correlation, in same cluster, data dependence differs greatly, it is impossible to carry out rarefaction representation well, reconstructs in identical In the case of error, more observation frequencies are needed.The increase of data transmission times, wastes the network bandwidth, reduces network Life-span.
The content of the invention
For deficiency of the prior art, it is an object of the invention to provide a kind of wireless biography based on temporal correlation sub-clustering Sensor Web compression method of data capture (Spatiotemporal Clustering Method for Compressive Data Gathering, STC-CDG), a kind of method of tolerance part area data dependency is specifically proposed, and is applied to net Network sub-clustering, has considered the data dependence of the dump energy and cluster interior nodes of leader cluster node.
The problem to be solved in the present invention is illustrated visually here by an example.Assume that sensor network is responsible for monitoring The intensity of illumination in one region, the different intensity of illumination of the region representation of dotted line in Fig. 1.As shown in Fig. 1 .a, traditional net Network Clustering protocol only considers that the dump energy of communication cost and leader cluster node in cluster defines 7 clusters.Then for No. 3 clusters, No. 5 clusters For, the dependency very little of the data of cluster interior nodes, data be not it is sparse, or can not effectively rarefaction representation, thus Data collection can not be carried out using the method for compressed sensing.From unlike Fig. 1 .a, when Fig. 1 .b are according to part area data Empty dependency carries out sub-clustering, data height correlation in cluster, can be very good to carry out rarefaction representation.From compressive sensing theory, This cluster-dividing method can in significantly less cluster data pendulous frequency, reduce energy expenditure, extend network life.
A kind of wireless sensor network compressed data collection method based on temporal correlation sub-clustering of the present invention, it comprising with Lower step：
Step one, initial phase：
In this stage, each sensor acquisition data, and the initial data for collecting is sent in a multi-hop fashion Aggregation node；Aggregation node preserves sampled data of each sensor node in each time slot；When aggregation node receives data Afterwards, carry out the temporal correlation tolerance stage of data；During specific use, as aggregation node preserves each node Historical data, when data dependency change less than setting threshold value when, sub-clustering information need not change, and directly be compressed Perception data is collected；
Step 2, temporal correlation tolerance：
Aggregation node according to the data collected in step one, according to area data relativity measurement side proposed by the present invention Method, calculates the data temporal correlation between any two sensor node.
Make x(k)∈RN,xi∈RTRespectively the data vector and i-th sensor of network kth time sampling is in T time slot Vector of samples, N for nodes number.It is the ultimate range in neighborhood between two nodes that we define Gmax_dst, Or maximum hop count, when the distance in net between any two node is less than Gmax_dst, a neighborhood N can be classified asc.We are only Consider in neighborhood NcCorrelation calculations of the interior joint to data, had so both reduced the computation complexity of aggregation node, had avoided again There is distant company side, be more conform with practical situation.
It is specific as follows,
Step 2.1：Calculate the temporal correlation of two nodes in any neighborhood, if the arbitrary node in neighborhood to (i, J) existence time dependency, then need to meet
ρm(i,j)>ρm_min,
Wherein ρm_minFor self-defining temporal correlation threshold value,
In formula, NcNeighborhood defined in expression is above-mentioned,WithRepresent i-th node respectively in kth time and kth+m Secondary sampled value, xiRepresent vector of samples of i-th sensor in T time slot, E [xi] andI-th sensing is represented respectively Sampling average and sample variance of the device in T time slot, are understood in the same mannerE[xj] andExpression implication.
Step 2.2：Calculate the spatial coherence of two nodes in any neighborhood, if the arbitrary node in neighborhood to (i, J) Existential Space dependency, then need to meet
ρs(i,j)>ρs_min,
Wherein ρs_minFor self-defining spatial coherence threshold value,
In formula, NcNeighborhood defined in expression is above-mentioned,Represent sampled value of i-th node in kth time, xiRepresent i-th Vector of samples of the individual sensor in T time slot, E [xi] andRepresent that i-th sensor is equal in the sampling of T time slot respectively Value and standard for manual sampling are poor, understand in the same mannerE[xj] andExpression implication.
Step 2.3：Calculate the temporal correlation of two nodes in any neighborhood, if the arbitrary node in neighborhood to (i, J) there is temporal correlation, then need to meet
ρms(i,j)>ρmsmin,
Wherein ρmsminFor self-defining spatial coherence threshold value,
ρms(i, j)=ρm(i,j)*ρs(i,j)
In formula, ρm(i, j) and ρsThe implication of (i, j) is given in step 2.1 and step 2.2 respectively.
Step 3, network cluster：Aggregation node obtains sub-clustering information backward according to the temporal correlation value of calculation between node The whole network broadcast message, cluster interior nodes dynamically select leader cluster node again.
It is specific as follows,
Step 3.1：According to the temporal correlation data that step 2 is obtained, given threshold thresholding ρmsminIf, neighborhood interior nodes Data dependence is more than threshold value thresholding, is considered as the presence of company side (u, v) between the two nodes, constructs graph model G, figure Summit is exactly sensor node in network, figure while be defined as it is above-mentioned even while (u, v)；
Step 3.2：Sub-clustering and leader cluster node are determined according to graph model G interior joints angle value and residue energy of node；
Step 3.3：Aggregation node confirms the position that cluster internal segment is counted out with leader cluster node, and is determined by compressive sensing theory In cluster, compressed sensing number of samples is no less than Cklog (n/k), and wherein C is decimation factor, and k is the degree of rarefication of signal in cluster, and n is Cluster internal segment is counted out.
Step 4, compressed data are collected：
Step 4.1：Cluster interior nodes and leader cluster node use identical pseudorandom number generator, generate measurement coefficient Φi, institute Need not be transmitted in a network with measurement coefficient.Cluster interior nodes using compressed sensing data collection method, by initial data with The linear operation result of measurement coefficient passes to leader cluster node；
Step 4.2：Calculation matrix ΦiFor the submatrix of i-th cluster of correspondence, CHiThe leader cluster node of i-th cluster is represented, is responsible for Collect all data x in clusteri, then in single cluster, data collection is expressed as yi,yi=Φixi；
Step 4.3：Leader cluster node CHiUsing minimum range square spanning tree algorithm, by measured value yiIt is transferred to convergence section Point.
Step 5, data reconstruction：Aggregation node receives the data from cluster head, constitutes measured value vectorThen converge Poly- node produces identical random matrix Φ, and primary collection data are reconstructed, it is assumed that is divided into 5 clusters in network, then weighs Structure formula meets：
In formula, measurement coefficient ΦiFor the submatrix of i-th cluster of correspondence, xiRepresent the vector of samples in i-th cluster, yiRepresent The compressed sensing measured value of i-th cluster of correspondence.
By above step, based on the wireless sensor network compressed data collection method of temporal correlation sub-clustering, in phase In the case of with reconstructed error, data transmission times are reduced, balanced network energy consumption extends network lifetime.
Compare with the radio sensor network data collection method of compressive sensing theory with existing combination network cluster dividing, this The advantage of invention is：
1st, temporal correlation measure proposed by the present invention, in can effectively calculating network, data dependence is larger Neighbor node, and the dump energy of the temporal correlation and node for considering data carries out sub-clustering, the cluster interior nodes for obtaining Data height correlation, can effectively carry out rarefaction representation, and then in the case of identical reconstructed error, reduce data transfer time Number.
2nd, due to having considered the dump energy of node during cluster, the balanced network energy consumption of this method, it is to avoid Certain node extends network lifetime because of energy consumption problem premature death.
Description of the drawings
Fig. 1 a are the cluster-dividing method of the present invention and traditional cluster-dividing method effect diagram.
Fig. 1 b are the cluster-dividing method of the present invention and traditional cluster-dividing method effect diagram.
Fig. 2 is the Hierarchical network topological model schematic diagram of the present invention.
Fig. 3 is the methods described flow chart of the present invention.
Fig. 4 is the cluster-dividing method and random cluster-dividing method data collection observation frequency of the present invention and Between Signal To Noise Ratio contrast Figure.
Fig. 5 is the method for data capture of the present invention and shortest path method of data capture energy consumption comparison figure.
Specific embodiment
See Fig. 1 a- Fig. 5, a kind of wireless sensor network compressed data based on temporal correlation sub-clustering proposed by the present invention Collection method, with reference to specific implementation method, the present invention is described in detail.
The network model of the lower present invention is introduced first.When network size is larger, Node distribution interior in a big way, we Network can be layered, set up meromixis center.As shown in Fig. 2 if network is divided into each sub-network, each subnet Network has respective local aggregation node, and final each local aggregation node is directly transmitted to data the sink centers of network again.I Consider the data collection situation of single localized network here, and single localized network is made the following assumptions：
(1) node is all that isomorphism, i.e. node have identical communication capacity and primary power；
(2) net interior nodes periodically collect same physical message, and the data of diverse location different time there may be Larger difference；
(3) in positive direction region of the length for L, N number of sensor node is evenly distributed on equally spaced grid, is converged Outside monitor area, all nodes keep fixed position motionless after placing to node；
(4) cluster interior nodes and leader cluster node direction communication, leader cluster node adopt minimum range square tree algorithm (SSDST) and Cluster head communicates；
(5) in net, each sensor node and Sink node have identical pseudorandom number generator, produce random matrix Coefficient, so projection matrix need not be transmitted in a network.
Aggregation node has the energy supply of abundance compared with the ordinary node in network, and computing capability is stronger.Commonly Node receives the scheduling of aggregation node, performs and perceives and data transfer task.Aggregation node is responsible for whole network data and collects, performs net The reconstruct of network Clustering protocol and perception data.
A kind of wireless sensor network compressed data collection method based on temporal correlation sub-clustering of the present invention, such as Fig. 3 institutes Show, specifically comprise the steps of：
Step one, initial phase：
In this stage, each sensor acquisition data, and the initial data for collecting is sent in a multi-hop fashion Aggregation node.Aggregation node possesses larger memory space, saves hits of each sensor node in each time slot According to.When aggregation node receives enough data, the temporal correlation tolerance stage of data is carried out.Process is being used specifically In, as aggregation node preserves the historical data of each node, when the dependency change of data is less, sub-clustering information is not required to Change, can directly be compressed perception data collection as needed.
Step 2, temporal correlation tolerance：
Aggregation node according to the data collected in step one, according to area data relativity measurement side proposed by the present invention Method, calculates the data temporal correlation between any two sensor node.
Make x(k)∈RN,xi∈RTRespectively the data vector and i-th sensor of network node kth time sampling is at T Between groove vector of samples, N for nodes number.We define Gmax_dst for the maximum between two nodes in neighborhood away from From, or maximum hop count, when the distance in net between any two node is less than Gmax_dst, a neighborhood N can be classified asc.I Only consider in neighborhood NcSimilarity Measure of the interior joint to data, had so both reduced the computation complexity of aggregation node, and Avoid the presence of distant company side, be more conform with practical situation.
It is specific as follows,
Step 2.1：Calculate the temporal correlation of two nodes in any neighborhood, if the arbitrary node in neighborhood to (i, J) existence time dependency, then need to meet
ρm(i,j)>ρm_min,
Wherein ρm_minFor self-defining temporal correlation threshold value,
In formula, NcNeighborhood defined in expression is above-mentioned,WithRepresent i-th node respectively in kth time and kth+m Secondary sampled value, xiRepresent vector of samples of i-th sensor in T time slot, E [xi] andI-th sensing is represented respectively Sampling average and sample variance of the device in T time slot, are understood in the same mannerE[xj] andExpression implication.
Step 2.2：Calculate the spatial coherence of two nodes in any neighborhood, if the arbitrary node in neighborhood to (i, J) Existential Space dependency, then need to meet
ρs(i,j)>ρs_min,
Wherein, ρs_minFor self-defining spatial coherence threshold value,
In formula, NcNeighborhood defined in expression is above-mentioned,Represent sampled value of i-th node in kth time, xiRepresent i-th Vector of samples of the individual sensor in T time slot, E [xi] andRepresent that i-th sensor is equal in the sampling of T time slot respectively Value and standard for manual sampling are poor, understand in the same mannerE[xj] andExpression implication.
Step 2.3：Calculate the temporal correlation of two nodes in any neighborhood, if the arbitrary node in neighborhood to (i, J) there is temporal correlation, then need to meet
ρms(i,j)>ρmsmin,
Wherein, ρmsminFor self-defining spatial coherence threshold value,
ρms(i, j)=ρm(i,j)*ρs(i,j)
In formula, ρm(i, j) and ρsThe implication of (i, j) is given in step 2.1 and step 2.2 respectively.
Step 3, network cluster：Aggregation node obtains sub-clustering information backward according to the temporal correlation value of calculation between node The whole network broadcast message, cluster interior nodes dynamically select leader cluster node again.
It is specific as follows,
Step 3.1：According to the temporal correlation data that step 2 is obtained, given threshold thresholding ρmsminIf, neighborhood interior nodes Data dependence is more than threshold value thresholding, is considered as the presence of company side (u, v) between the two nodes, constructs graph model G, figure Summit is exactly sensor node in network, figure while be defined as it is above-mentioned even while (u, v)；
Step 3.2：Sub-clustering and leader cluster node are determined according to graph model G interior joints angle value and residue energy of node；
Step 3.3：Aggregation node confirms the position that cluster internal segment is counted out with leader cluster node, and is determined by compressive sensing theory In cluster, compressed sensing number of samples is no less than Cklog (n/k), and wherein C is decimation factor, and k is the degree of rarefication of signal in cluster, and n is Cluster internal segment is counted out.The network Clustering Algorithm of this step describes false code, referring to table 1.
1 STC Clustering Algorithms of table
Step 4, compressed data are collected：
Step 4.1：Cluster interior nodes and leader cluster node use identical pseudorandom number generator, generate measurement coefficient Φi, institute Need not be transmitted in a network with measurement coefficient.Cluster interior nodes using compressed sensing data collection method, by initial data with The linear operation result of measurement coefficient passes to leader cluster node；
Step 4.2：Measurement coefficient ΦiFor the submatrix of i-th cluster of correspondence, CHiThe leader cluster node of i-th cluster is represented, is responsible for Collect all data x in clusteri, then in single cluster, data collection is expressed as yi,yi=Φixi；
Step 4.3：Leader cluster node CHiUsing minimum range square spanning tree algorithm, by measured value yiIt is transferred to convergence section Point.
Step 5, data reconstruction：Aggregation node receives the data from cluster head, constitutes measured value vectorThen converge Poly- node produces identical random matrix Φ=[Φ1 Φ2 Φ3 Φ4], and primary collection data are reconstructed, reconstruct formula Meet (in assuming network, be divided into 5 clusters)：
In formula, measurement coefficient ΦiFor the submatrix of i-th cluster of correspondence, xiRepresent the vector of samples in i-th cluster, yiRepresent The compressed sensing measured value of i-th cluster of correspondence.
For checking the effectiveness of the method, the present invention carries out emulation experiment using Matlab emulation platforms, by 20 nodes In the region of 100m × 100m, aggregation node is located at outside monitor area random placement.The energy that node is consumed is according to the following formula Calculate：
Wherein ETxThe energy that 1 bit data is consumed, E are sent by transtation mission circuitRx1 bit data is received for receiving circuit The energy for being consumed, EAmpFor sending the energy consumption of amplifying circuit.Energy parameter used in emulation is shown in Table 2：
2 STC-CDG method data collection simulation parameters of table
For convenience of contrasting, we carry out sub-clustering using following two methods：First, the random sub-clustering of network；Second, using this The sub-clustering based on temporal correlation that invention is proposed.Test every time, it is ensured that observation frequency equally, each test repeats 50 times, In two kinds of cluster-dividing methods of contrast, the relation of observation frequency and signal to noise ratio.Signal to noise ratio (the Signal Noise of reconstruction signal Ratio, SNR) it is defined as follows：
In formula, | | x | |2With | | e | |2The l of primary signal and noise signal is represented respectively2Norm.
From Fig. 4, it is apparent that under same observation frequency, being recovered using the signal of the cluster-dividing method in the present invention Precision is significantly larger than random cluster-dividing method.
In network node normal working hourss, the analysis of experiments method of the present invention and shortest path data collection is based on The power consumption of method, as a result as shown in Figure 5.As can be seen from Figure 5, under same data reconstruction precision, dividing based on temporal correlation Cluster data collection method, hence it is evident that reduce network energy consumption.

Claims (1)

1. a kind of wireless sensor network compressed data collection method based on temporal correlation sub-clustering, it is characterised in that：It wraps Containing following steps：
Step one, initial phase：
In this stage, each sensor acquisition data, and the initial data for collecting is sent to convergence in a multi-hop fashion Node；Aggregation node preserves sampled data of each sensor node in each time slot；
Step 2, temporal correlation tolerance：
After aggregation node receives data, the temporal correlation tolerance stage of data is carried out；Aggregation node is received according in step one The data for collecting, according to the number of regions that the wireless sensor network compressed data collection method based on temporal correlation sub-clustering is proposed According to relativity measurement method, the data temporal correlation between any two sensor node is calculated；
Make x(k),xiRespectively vector of samples of the data vector and i-th sensor of network kth time sampling in T time slot, N For the number of nodes；It is the ultimate range in neighborhood between two nodes, i.e. maximum hop count to define Gmax_dst, is appointed in net When distance between two nodes of meaning is less than Gmax_dst, a neighborhood N is classified asc；Only consider in neighborhood NcPhase of the interior joint to data Closing property is calculated, to tally with the actual situation；
The area data that the wireless sensor network compressed data collection method based on temporal correlation sub-clustering is proposed is related Property measure, it is specific as follows,
Step 2.1：The temporal correlation of two nodes in any neighborhood is calculated, if the arbitrary node in neighborhood is deposited to (i, j) In temporal correlation,
ρm(i,j)>ρm_min,
ρm(i, j) represents the temporal correlation of two nodes in neighborhood；
Wherein ρm_minFor self-defining temporal correlation threshold value,
ρ m ( i , j ) = ρ m ( i ) + ρ m ( ( j ) = 1 2 T ( Σ k = 1 T ( x i ( k ) - E [ x i ] ) ( x i ( k + m ) - E [ x i ] ) σ x i 2 + Σ k = 1 T ( x j ( k ) - E [ x j ] ) ( x j ( k + m ) - E [ x j ] ) σ x j 2 ) , ( i , j ) ∈ N c
In formula, NcNeighborhood defined in expression is above-mentioned,WithRepresent i-th node adopting in kth time and kth+m time respectively Sample value, xiRepresent vector of samples of i-th sensor in T time slot, E [xi] andRepresent i-th sensor at T respectively The sampling average of time slot and sample variance, are understood in the same mannerE[xj] andExpression implication；
Step 2.2：The spatial coherence of two nodes in any neighborhood is calculated, if the arbitrary node in neighborhood is deposited to (i, j) In spatial coherence, then need to meet
ρs(i,j)>ρs_min,
ρs(i, j) represents the spatial coherence of two nodes in neighborhood；
Wherein ρs_minFor self-defining spatial coherence threshold value,
ρ S ( i , j ) = 1 T Σ k = 1 T ( x i ( k ) - E [ x i ] ) ( x j ( k ) - E [ x j ] ) σ x i σ x j , ( i , j ) ∈ N c
In formula, NcNeighborhood defined in expression is above-mentioned,Represent sampled value of i-th node in kth time, xiRepresent i-th biography Vector of samples of the sensor in T time slot, E [xi] andRepresent respectively i-th sensor T time slot sampling average and Standard for manual sampling is poor, understands in the same mannerE[xj] andExpression implication；
Step 2.3：The temporal correlation of two nodes in any neighborhood is calculated, if the arbitrary node in neighborhood is deposited to (i, j) In temporal correlation, then need to meet
ρms(i,j)>ρmsmin,
ρms(i, j) represents the temporal correlation of two nodes in neighborhood；
Wherein ρmsminFor self-defining spatial coherence threshold value,
ρms(i, j)=ρm(i,j)*ρs(i,j)
In formula, ρm(i, j) and ρsThe implication of (i, j) is given in step 2.1 and step 2.2 respectively；
Step 3, network cluster：Aggregation node obtains sub-clustering according to the data temporal correlation between any two sensor node To the whole network broadcast message after information, cluster interior nodes dynamically select leader cluster node again；
It is specific as follows,
Step 3.1：According to self-defining spatial coherence threshold value ρ of settingmsminWith the temporal correlation number obtained in step 2 According to, if neighborhood interior nodes data dependence is more than threshold value thresholding, it is considered as between the two nodes, the presence of company side (u, v), Construction graph model G, the summit of figure is exactly sensor node in network, figure when above-mentioned company is defined as (u, v)；
Step 3.2：Sub-clustering and leader cluster node are determined according to graph model G interior joints angle value and residue energy of node；According to step 3.1 Frontier juncture system of company in the graph model G for obtaining, if all nodes are all connected in region, is divided in a cluster, cluster with probability Pch= Eresidual/EmaxSelect leader cluster node, wherein EresidualRepresent the dump energy of node, EmaxRepresent the primary power of node, Pch Represent that node is elected as the probability of cluster head；
Step 3.3：Aggregation node confirms the position that cluster internal segment is counted out with leader cluster node, and is determined in cluster by compressive sensing theory Compressed sensing number of samples is no less than C kln (n/k), and wherein C is decimation factor, and k is the degree of rarefication of signal in cluster, and n is in cluster Interstitial content；C kln (n/k) show the index with natural number e as bottom；
Step 4, compressed data are collected：
As aggregation node preserves the historical data of each node, when the dependency change of data is less than the threshold value for setting, Sub-clustering information need not change, and directly be compressed perception data collection；The compressed data collection method is specific as follows：
Step 4.1：Cluster interior nodes and leader cluster node use identical pseudorandom number generator, generate measurement coefficient Φi, so surveying Coefficient of discharge need not be transmitted in a network；Method of the cluster interior nodes using compressed sensing data collection, by initial data and measurement The linear operation result of coefficient passes to leader cluster node；Cluster interior nodes carry out data transfer according to step 4.2；Cluster interior nodes are adopted Compressed sensing method of data capture specifically as described in step 4.2；
Step 4.2：Calculation matrix ΦiFor the submatrix of i-th cluster of correspondence, CHiThe leader cluster node of i-th cluster is represented, is responsible for collection All data x in clusteri, then in single cluster, data collection is expressed as yi,yi=Φixi；
Step 4.3：Leader cluster node CHiUsing minimum range square spanning tree algorithm, by measured value yiIt is transferred to aggregation node；
Step 5, data reconstruction：
Aggregation node receives the data from cluster head, constitutes measured value vectorThen aggregation node produces the random square of identical Battle array Φ, and primary collection data are reconstructed, it is assumed that it is divided into 5 clusters in network, then reconstructs formula and meet：
In formula, measurement coefficient ΦiFor the submatrix of i-th cluster of correspondence, xiRepresent the vector of samples in i-th cluster, yiRepresent correspondence The compressed sensing measured value of i-th cluster.
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