CN106341842A - Method and device for transmitting data of wireless sensor network - Google Patents

Method and device for transmitting data of wireless sensor network Download PDF

Info

Publication number
CN106341842A
CN106341842A CN201610712279.5A CN201610712279A CN106341842A CN 106341842 A CN106341842 A CN 106341842A CN 201610712279 A CN201610712279 A CN 201610712279A CN 106341842 A CN106341842 A CN 106341842A
Authority
CN
China
Prior art keywords
data
cluster
node
prediction model
matrix
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201610712279.5A
Other languages
Chinese (zh)
Other versions
CN106341842B (en
Inventor
尹长川
梁瀚樱
邓乔木
刘丹谱
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing University of Posts and Telecommunications
Original Assignee
Beijing University of Posts and Telecommunications
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing University of Posts and Telecommunications filed Critical Beijing University of Posts and Telecommunications
Priority to CN201610712279.5A priority Critical patent/CN106341842B/en
Publication of CN106341842A publication Critical patent/CN106341842A/en
Application granted granted Critical
Publication of CN106341842B publication Critical patent/CN106341842B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/06—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/147—Network analysis or design for predicting network behaviour
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/56—Provisioning of proxy services
    • H04L67/568—Storing data temporarily at an intermediate stage, e.g. caching
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/142—Network analysis or design using statistical or mathematical methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses a method and a device for transmitting data of a wireless sensor network, which are applied to a clustering wireless sensor network. A prediction model is used to perform prediction approximation on data collected by nodes, and the energy consumption of node equipment is reduced by inhibiting node data transmission. Through the scheme, under the premise of meeting application requirements, the amount of data transmitted inside a network can be reduced effectively, the energy consumption of nodes can be reduced, and the life cycle of a network can be prolonged.

Description

A kind of transmission method of wireless sensor network data and device
Technical field
The present invention relates to the data transmission method of technical field of data transmission, more particularly, to wireless sensor network and dress Put.
Background technology
At present, wireless sensor network (wsn) is widely used to smart home, agricultural planting, environment monitoring and chemical industry The fields such as production.
Wireless sensor network (wsn) is to pass through radio communication institute by the sensor node of a large amount of spatially Discrete Distribution The self-organizing network of composition, as shown in figure 1, be traditional wireless sensor networks system structure diagram.Wherein, task management section Point is connected with aggregation node by communication network, carries out wireless between the sensor node in aggregation node and Target monitoring area Communication.
Each sensor node mutually work in coordination with to perceive, to gather, processing and transmission network institute overlay area in various mesh Mark information, is upper layer application and user's decision-making provides network connection data to support service.
In actual applications, in order to ensure service quality, wsn application purpose region is entered usually through node dense deployment Row monitoring.Therefore node institute gathered data is needed to be forwarded by some intermediate nodes and reaches destination node, and intermediate node will be at it The most suitable next-hop (next hop) node is selected among neighbor node.
At present, in wsn widely used clustering route protocol realizing the transmission of data.Make biography according to sensor in routing table Sensor
Clustering route protocol is data-centered to be divided into several clusters (cluster) by the sensor node in network. Each cluster is made up of leader cluster node (cluster head) and some cluster interior nodes (cluster member), and leader cluster node should In responsible cluster the collection of data be polymerized process, also between cluster to be undertaken data routing forwarding.Typical Clustering Routing has leach(low energy adaptive clusterig hierarchy)、gaf(geographical adaptive Fidelity), cougar agreement etc., as shown in Fig. 2 be clustering routing structural representation.
With the integrated of electronic equipment and miniaturization, using the battery power supply mode of finite energy more than sensor node, It is thus desirable to data reasonable selection to reduce network energy consumption from the path of source sensor node to the aggregation node sending data.
Powered using battery in view of sensor node it is difficult to realize the lasting supply to node energy, and each biography The storage capacity of sensor node and computing capability are also very limited, and many application scenarios need to meet by node dense distribution answers Use demand.
Studied point out sensor node by 1 bit data transmit 100 meters needed for energy about can execute 3000 meters Calculate instruction.Often there is dependency between each node institute gathered data of dense distribution, cause the redundancy of some related datas, And same node there is also certain redundancy so that data transfer energy is high according to the data that time cycle property is gathered, cause The waste of Internet resources.
Therefore, need a kind of technology badly, using clustering routing structure, can during data transfer, solve redundancy and Data transfer energy consumes high problem.
Content of the invention
The purpose of the embodiment of the present invention is to provide a kind of data transmission method of wireless sensor network and device, permissible Reducing data redundancy, reducing the energy that data transfer is consumed, thus extending Network morals.
For reaching above-mentioned purpose, the embodiment of the invention discloses a kind of transmission method of wireless sensor network data, should For the cluster interior nodes in cluster structured, described cluster structured in also include leader cluster node, wherein, comprise the steps:
S101) prediction model parameterses are determined according to historical data, skew number of times k and offset threshold j are initialized, and Transmit this prediction model parameters to leader cluster node, so that leader cluster node goes out prediction data according to prediction model parameterses and Time Calculation And be stored in the data buffer storage corresponding to this node；Described forecast model is to carry out curve plan previously according to historical data Close acquisition；
S102) gathered data, by the currently practical data collecting and the current predictive obtaining according to prediction model parameterses Data is compared；
S103) if currently practical data is less than or equal to error threshold ε with the difference ε ' of prediction data, cache this actual number According to offset threshold j adds 1, return to step s102)；
S104) if currently practical data is more than error threshold ε with the difference ε ' of prediction data, cache this real data, partially Move number of times k and add 1, offset threshold j halves, and transmits this real data to leader cluster node；And number of times k and offset threshold j will be offset It is compared, if k >=j, return to step s101)；If k < j, return to step s102).
Preferably, described method, wherein, forecast model is unitary linear prediction model: y '=α t+ β
Wherein, t represents sampling time point, and y ' is the predictive value in corresponding moment；
Cluster interior nodes are with same intervals t0Continuous n sampling is deposited in buffer queue, using the number in time serieses According to calculating parameter (α, β).
The embodiment of the present invention also provides a kind of transmitting device of wireless sensor network data, be applied to cluster structured in Cluster interior nodes, described cluster structured in also include leader cluster node, wherein, comprising:
Prediction model parameterses transport module, for determining prediction model parameterses according to historical data, to skew number of times k with partially Move threshold value j to be initialized, and transmit this prediction model parameters to leader cluster node, so that leader cluster node is according to prediction model parameterses Go out prediction data with Time Calculation and be stored in the data buffer storage corresponding to this node；Described forecast model is root in advance Carry out curve fitting acquisition according to historical data；
Data acquisition module, for gathered data, the currently practical data collecting is obtained with according to prediction model parameterses The current predictive data obtaining is compared；
First cache module, is less than or equal to the feelings of error threshold ε for the difference ε ' in currently practical data and prediction data Under condition, cache this real data, offset threshold j adds 1, trigger described data acquisition module；
Second cache module, in the case of being more than error threshold ε for the difference ε ' in currently practical data and prediction data, Cache this real data, skew number of times k adds 1, and offset threshold j halves, and transmits this real data to leader cluster node；And will offset Number of times k and offset threshold j are compared, if k >=j, trigger described prediction model parameterses transport module；If k < j, triggering is described Data acquisition module.
The embodiment of the present invention also provides a kind of method of reseptance of wireless sensor network data, be applied to cluster structured in Leader cluster node, described cluster structured in also include cluster interior nodes, including following steps:
S201) prediction model parameterses that cluster interior nodes send are received；
S202) judge currently whether receive the real data of cluster interior nodes transmission；
S203) if it is, it stores in this data buffer storage corresponding to cluster interior nodes；
S204) if it is not, then going out prediction data and as data in cluster according to prediction model parameterses and Time Calculation Store in this data buffer storage corresponding to cluster interior nodes.
Preferably, methods described, wherein, also includes polymeric compressing and processes, comprise the steps:
B1) receive the data that in cluster, each node uploads, and with independent spatial cache, data is carried out to each cluster interior nodes Caching；
B2) each being uploaded the data storage that in cycle intra-cluster, each node uploads is data matrix x, wherein x=in cluster [x1,x2,…,xn], n counts out for cluster internal segment, xi=[xi1,xi2,…,xin] for i-th cluster interior nodes according to certain time between Data every acquisition order；
B3) joint sparse process is carried out to data matrix x in cluster, and by each cluster internal segment point data be decomposed into common ground and Independent sector, obtains common ground z to after x decompositioncAnd its independent sector zn, decomposition result collection is combined into z=[zc,z1,z2,…, zn]；
B4) each component data in decomposition result set is compressed respectively with perception process, obtains final compression and gather Close results set r=[rc,r1,r2,…,rn] and calculation matrix set φ=[{ φc1,φc2},{φ11,φ12},…,{φn1, φn2}]；
B5) final compression polymerization result set is sent to aggregation node with calculation matrix set along cluster-level routing or answers Use terminal.
Preferably, methods described, wherein, independent spatial cache, is delayed with prediction model parameterses including data buffer storage space Deposit space；
If the data buffer storage space being raw sensed data, directly storing that data into corresponding node receiving In；If prediction model parameterses data, then update the prediction model parameterses caching of corresponding node, and according to the prediction after updating Model parameter and Time Calculation go out in the data buffer storage space that prediction data stores corresponding node.
Preferably, methods described, wherein, joint sparse is processed, that is, to each cluster internal segment point data xiIt is respectively adopted sparse Random matrix carries out sparse transformation.
Preferably, methods described, wherein, sparse random matrix adopts Bernoulli Jacob/Mach random matrix, and it is defined as follows:
&phi; i j = s + 1 , p = 1 2 s 0 , p = 1 - 1 2 s - 1 , p = 1 2 s
In above formula, φijRepresent the element of optional position in Bernoulli Jacob/Mach random matrix, p is that the value of this element is general Rate, i.e. the random value in { -1,0 ,+1 } of Probability p shown in element above formula in matrix, wherein parameter s control the sparse of matrix Degree, s value is bigger, and in matrix, nonzero element is less, and sparse degree is higher.
Preferably, methods described, wherein, compressed sensing is processed, and comprises the steps:
A1) according to cluster internal segment count out n, perception compression after data length m and sparse control parameter s, calculating parameter a= m/2s、Produce initially empty calculation matrix φ1、φ2；
A2 two random 1 × n dimension matrix t) are generated1=[t11,t12,…,t1n]、t2=[t21,t22,…,t2n], wherein t1nWith t2nIt is the positive integer of random value in [1, b], by matrix t1、t2Store calculation matrix respectively as data line φ1、φ2In.
A3) find out t1Intermediate value is the position of the element of i, and correspondence position element in primary signal is added；Find out t2Intermediate value is The position of the element of i, correspondence position element in primary signal is added；Deduct second value of calculation, institute with first value of calculation Obtain result and be deposited into corresponding compressed data riIn；
A4) repeat step a2) and step a3) a time；
A5) make b=m-a × b, repeat step a3), a4), obtain final compression polymerization result data riAnd calculation matrix
The embodiment of the present invention also provides a kind of reception device of wireless sensor network data, be applied to cluster structured in Leader cluster node, described cluster structured in also include cluster interior nodes, this data sink includes:
Receiver module, with receiving the prediction model parameterses that cluster interior nodes send；
Judge module, for judging currently whether receive the real data of cluster interior nodes transmission；
First memory module, for when judged result is to be, by actual data storage to corresponding to this cluster interior nodes In data buffer storage；
Second memory module, for judged result for no when, go out to predict number according to prediction model parameterses and Time Calculation According to and as in the data buffer storage corresponding to this cluster interior nodes of data Cun Chudao in cluster.
Therefore, the present invention program is directed to the resource-constrained feelings of many application of higher wireless sensor network scene interior joint Condition, based on existing common clustering routing structure it is proposed that a kind of data transmission mechanism of Energy Efficient.This mechanism is directed to node The feature of energy constraint, in cluster, member node is predicted approaching to node data using forecast model, suppresses node data Transmission is thus reduce energy expenditure.And, on leader cluster node, by improved compression sensing method, data in cluster is joined Combined pressure contracting is processed, the volume of transmitted data of compression cluster head.This programme can effectively reduce in net under the premise of meeting application demand Volume of transmitted data, thus reducing node energy consumption, extends network lifecycle.
Brief description
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing Have technology description in required use accompanying drawing be briefly described it should be apparent that, drawings in the following description be only this Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, acceptable Other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 is the structural representation of traditional wireless sensor networks application system；
Fig. 2 is the clustering routing structural representation of existing wireless sensor network；
The compressed sensing measurement process schematic diagram that Fig. 3 adopts for technical solution of the present invention；
The data acquisition transfer process figure of the cluster interior nodes that Fig. 4 provides for the embodiment of the present invention 1；
The data transfer of the cluster interior nodes that Fig. 5 provides for the embodiment of the present invention 1 adjusts schematic diagram with model；
Fig. 6 is a kind of structural representation of data transmission device provided in an embodiment of the present invention；
The data receiver flow chart of the leader cluster node that Fig. 7 provides for the embodiment of the present invention 2；
Fig. 8 is a kind of structural representation of data sink provided in an embodiment of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete Site preparation describes it is clear that described embodiment is only a part of embodiment of the present invention, rather than whole embodiments.Based on this Embodiment in invention, the every other reality that those of ordinary skill in the art are obtained under the premise of not making creative work Apply example, broadly fall into the scope of protection of the invention.
The present invention is the wireless sensor network based on clustering routing structure, and data compression perception theory passes to data The improvement that defeated process is carried out.
Clustering route protocol plane road by the basis of node division is member node in leader cluster node and cluster, and bright The really respective division of labor.Cluster head is responsible for and member communication in cluster, collects information about firms data and is responsible for data forwarding；Become in cluster Member gathers the target information in monitored area and is sent to cluster head.Typically carry out equilibrium energy using periodicity cluster head rotation mechanism to disappear Consumption.This clustering algorithm splits the network into different logical subfield domains, has more preferable extensibility, can be to data in cluster Carry out further dispatch deal.
Node energy mainly consumes on data transmit-receive, so the embodiment of the present invention is by being compressed locating to data in net Manage and to realize energy efficient utilization.Compressive sensing theory is a kind of Signal Compression and high accuracy recovery technology, is initially mainly used in Process the digital signal of discrete domain.Compressive sensing theory is pointed out: if signal x=[x1,x2,…,xn] a certain conversion base ψ= [ψ1,ψ2,…,ψn] under can carry out k rarefaction representation, that is, meet element number in x=ψ s and vectorial s and be much smaller than x, if element in s Number is that the energy of k then primary signal x all concentrates on this k nonzero element, and s is called the k rank rarefaction representation of signal x.Compression Perception is as shown in Figure 3.When signal x meets above-mentioned condition, by signal vector y=[y1,y2,…,ym] can recover X, wherein y=φ x=φ ψ s, the length of signal vector y are much smaller than original signal x, and that is, y is that x is compressed with the knot after processing Really, and claim φ ∈ rm×nObserving matrix for x.
In extensive time multiplexed signal, can be by the convex optimization problem of solution:
m i n s &element; r n | | s | | 0 s . t . y = &phi; &psi; s
Obtain optimal solution s, then utilize x=ψ s, realize the Exact recovery to primary signal x.Solving above formula needs to travel through All nonzero value combinations in s, so above formula 0- norm can be converted into 1- norm approximate solution, obtain as drag:
m i n s &element; r n | | s | | 1 s . t . y = &phi; &psi; s
Consider error and noise factor, also above formula can be changed further:
m i n s &element; r n | | s | | 1 s . t . | | y - &phi; &psi; s | | &le; &epsiv;
On stricti jurise, most of signal is not sparse, but signal can be made dilute under transform domain by suitable conversion base ψ Relieving the exterior syndrome shows.Gaussian random matrix is frequently as calculation matrix；Common sparse alternative approach has Fourier transformation, wavelet transformation.To survey Amount signaling protein14-3-3 algorithm has orthogonal matching pursuit (omp) algorithm, base to follow the trail of (bp) algorithm, Adaptive matching tracing algorithm etc., Respectively there is quality.
The present invention is based on clustering routing structured radio sensor network, for member node in leader cluster node and cluster not With division characteristic it is proposed that a kind of data aggregate transmission of energy efficient is tactful.In this data transmission policies, member and cluster in cluster Head node can carry out polymeric compressing process respectively according to application demand to transmission data, reduces the transmission of redundant data in network, Save energy thus extending network normal working hourss.It is emphasized that the present invention program passes mainly for the data in cluster Defeated processing procedure, is not related to cluster-level routing and sets up and data transfer.Particular content can be divided into cluster internal segment by the type according to node Point treatment mechanism and leader cluster node treatment mechanism two parts.It is described in detail by way of example separately below.
Embodiment 1
The present embodiment provides a kind of cluster interior nodes data transmission method based on cluster structured wireless sensor network, such as Shown in Fig. 4:
S101) prediction model parameterses are determined according to historical data, skew number of times k and offset threshold j are initialized, and Transmit this prediction model parameters to leader cluster node, so that leader cluster node goes out prediction data according to prediction model parameterses and Time Calculation And be stored in the data buffer storage corresponding to this node；Described forecast model is to carry out curve plan previously according to historical data Close acquisition；
S102) gathered data, by the currently practical data collecting and the current predictive obtaining according to prediction model parameterses Data is compared；
S103) if currently practical data is less than or equal to error threshold ε with the difference ε ' of prediction data, cache this actual number According to offset threshold j adds 1, return to step s102)；
S104) if currently practical data is more than error threshold ε with the difference ε ' of prediction data, cache this real data, partially Move number of times k and add 1, offset threshold j halves, and transmits this real data to leader cluster node；And number of times k and offset threshold j will be offset It is compared, if k >=j, return to step s101)；If k < j, return to step s102).
Specifically, in common clustering routing structured radio sensor network, cluster interior nodes are led to as network tip Often only it is responsible for the object information data in collection monitored area the node that continues thereto transmission, cluster interior nodes only use self-responsibility The adjacent cluster interior nodes in the domain of same district are not needed to communicate.By being analyzed to environmental objects such as common temperature, humidity, This kind of environmental data typically exhibits cyclically-varying feature, and presents linear variability law in certain period of time, that is, save Put gathered environmental data and there is between the time linear relationship.Cluster interior nodes a period of time in gathered environmental data and when Between linear correlation, then the time can be regarded as independent variable, corresponding data as dependent variable, using linear regression model (LRM) come structure Make anticipation function to simulate the relation of both dynamic changes.
Define time serieses for cluster interior nodes sample in chronological order, have intervals time data to collection Close, be designated as s={ (t1,y1),(t2,y2),…,(tn,yn)}.Wherein (tn,yn) represent in time tiMoment collection data be yi, n is sequence length.Set up the piecewise linearity forecast model of dynamic adjustment using the calculating storage capacity of node come to adopting data Number carries out approximation timates.
Initial phase includes the process of setting up of network cluster dividing routing infrastructure, and cluster interior nodes are in this stage and cluster head section Point establishes a connection, and receives control information, including but not limited to error threshold ε, offset threshold j, buffer storage length, collection mesh Mark, collection period, data upload cycle etc..
Subsequently cluster interior nodes enter into the data acquisition transmission stage, and this stage can be refined as building forecast model, data Transmission and model set-up procedure.
Forecast model is set up
Consider that node calculates limited by storage capacity, set up unitary linear prediction model in cluster interior nodes:
Y '=α t+ β
Wherein, t represents sampling time point, and y ' is the predictive value in corresponding moment, and (α, β) is constant.
According to buffer storage length, node is with same intervals t0Continuous n sampling is deposited in caching sequence, using caching sequence Data in row calculates parameter (α, β), and parameter is sent to leader cluster node by cluster interior nodes, and the data that it is subsequently gathered should edge Time shafts are distributed near anticipation function.
Data transfer is adjusted with model
Referring to Fig. 5, in each data acquisition transmitting procedure, node calculates actual acquired data y and predicted estimate value y ' Between difference ε ', it is compared with default error threshold ε, when gathered data change slow when, i.e. ε ' < ε, cluster interior nodes Do not transmit this secondary data, cluster head calculates the approximate data of this time point according to existing prediction model parameterses；When perception data becomes When changing violent, i.e. ε ' > ε, then in real time this secondary data is sent to cluster head.
Node can monitor its data situation of change while data transfer, if the long-term deviation of forecast model is truly adopted Sample, illustrates that parameter is inapplicable, need to be adjusted.Error ε ' is exceeded the number of times of threshold value ε and offset threshold is compared to Determine parameter the need of adjustment.Offset threshold is not definite value, if predictive value meets requirement when being compared every time, will Offset threshold adds 1, otherwise halves offset threshold.
In order to prevent the generation of " seemingly-dead node ", in time cycle t (t=t0× n) in, cluster interior nodes need to be forced to cluster head Node sends a secondary data.In order that the new parameter calculating every time meets current state, need time serieses are carried out in real time Update operation, so in node software design, maintaining a spatial cache storage time sequence.Entering with data acquisition It is assumed that the model parameter of current time is (α, β), caching sequence is sc to row, and model offset number of times is k, and offset threshold is j, number As follows with model method of adjustment according to transmitting:
Step 1:
Node gathered data yn, according to time point tnCalculate prediction data y with parameter (α, β)n' and forecast error ε '
if(ε′<ε)
By ynIt is added to queue sc tail of the queue, if sc length is full, remove queue heads；
J=j+1；
Go to step 1
if(ε′>ε)
By ynIt is added to queue sc tail of the queue, if sc length is full, remove queue heads；
K=k+1；
J=j/2；
Node sends perception data；
if(k≥j)
Go to step 2；
Step 2:
K=0；
J=1；(certainly, this offset threshold j can also be initialized as any be not 0 constant)
According to method of least square, calculate new parameter (α, β) using the data in sc, and send it to leader cluster node, turn To step 1.
By the present invention program accompanying method, cluster interior nodes can be predicted to gathered data simulating, and need not send all Gathered data, thus effectively reducing data is activation amount, reducing energy expenditure, extend node working life.
Below according to Fig. 7, a kind of data transmission device of wireless sensor network of the present invention is illustrated, this data Transmitting device be applied to cluster structured in cluster interior nodes, described cluster structured in also include leader cluster node.This data transfer fills Put including prediction model parameterses transport module 101, data acquisition module 102, the first cache module 103 and the second cache module 104.
Wherein, prediction model parameterses transport module 101, for determining prediction model parameterses according to historical data, to skew Number of times k and offset threshold j are initialized, and transmit this prediction model parameters to leader cluster node, so that leader cluster node is according to pre- Survey model parameter and Time Calculation goes out prediction data and is stored in the data buffer storage corresponding to this node；Described prediction mould Type is to carry out curve fitting acquisition previously according to historical data；
Wherein, data acquisition module 102, for gathered data, by the currently practical data collecting and according to prediction mould The current predictive data that shape parameter obtains is compared；
Wherein, the first cache module 103, is less than or equal to error door for the difference ε ' in currently practical data and prediction data In the case of limit ε, cache this real data, offset threshold j adds 1, trigger described data acquisition module；
Wherein, the second cache module 104, is more than error threshold ε for the difference ε ' in currently practical data and prediction data In the case of, cache this real data, skew number of times k adds 1, and offset threshold j halves, and transmits this actual number to leader cluster node According to；And skew number of times k is compared with offset threshold j, if k >=j, trigger described prediction model parameterses transport module；If k < J, then trigger described data acquisition module.
Embodiment 2
The present embodiment is directed to the data transmission method of the cluster interior nodes that embodiment 1 provides, and provides one kind based on cluster structured The leader cluster node of wireless sensor network data receiver method, as shown in Figure 6:
S201) prediction model parameterses that cluster interior nodes send are received；
S202) judge currently whether receive the real data of cluster interior nodes transmission；
S203) if it is, it stores in this data buffer storage corresponding to cluster interior nodes；
S204) if it is not, then going out prediction data and as data in cluster according to prediction model parameterses and Time Calculation Store in this data buffer storage corresponding to cluster interior nodes.
Specifically, based in cluster structured wireless sensor network, each cluster is equivalent to an independent network Node region, as the manager in this region and coordinator, the data forwarding amount being undertaken is than member node in cluster for leader cluster node More, energy consumption is bigger.
In order to reduce the load pressure of leader cluster node, reduce the energy expenditure that data transfer is brought, the technology of the present invention Scheme carries out further polymeric compressing process in leader cluster node to data.As shown in fig. 6, the method also includes step s205) Polymeric compressing process is carried out to the data of each the predetermined period memory storage in data cached.
In initial phase, leader cluster node needs the foundation completing cluster-level routing to be connected and distribute with the foundation of cluster interior nodes Time slot, also will receive application or the control information of aggregation node simultaneously, including but not limited to upload cycle, purpose data, application Tolerance thresholding etc..Work and concrete application scene close association is completed needed for this stage.After completing node initializing, cluster head Node can start waiting for receiving data in cluster.
From embodiment 1, cluster interior nodes are predicted approaching to gathered data by forecast model, reduce data is activation Amount.Cluster head node needs the model parameter being sent by each node offer spatial cache in this cluster in order to memory node respectively Data and perception data, cluster head needs and cluster interior nodes keep clock synchronous simultaneously.
Leader cluster node receives each node data in cluster, if receive is raw sensed data, directly deposits this data Store up in the data buffer storage of corresponding node；If prediction model parameterses data, leader cluster node updates this cluster interior nodes pair first The parameter cache answered, subsequently goes out prediction data according to new parameter and Time Calculation and is stored to the data corresponding to this node In caching.In each uplink time point, leader cluster node needs the data buffer storage of all nodes to be sent to aggregation node, clearly simultaneously Empty caching.
In cluster, each node has certain association on geographical position, and its gathered data there is also certain dependency, this Bright embodiment is directed to cluster head and sends the excessive problem of data volume it is proposed that new compression processing method carries out joint to data in cluster Compression polymerization, thus reduce leader cluster node and sending data volume, saving energy expenditure.
Improved data compressing method
Compressed sensing may insure the reconstruction primary signal of high probability in the case of minimizing hits, can be used for wireless sensing In device network, data is compressed.But because sensor node is resource-constrained, being compressed perception needs to process calculation matrix With the multiplying of initial data, the storage of sensor node and computing capability are required higher.The present invention program is with regard to this problem Propose and be suitably modified work.
At present, gaussian random calculation matrix is widely applied in compressive sensing theory, but it realizes process needs Random number generator meets, to produce, the m × n random number requiring.Produce the mistake that calculation matrix and matrix are multiplied with initial data Journey complexity is higher, is not suitable for resource-constrained sensor node.
This programme adopts sparse random matrix to obtain the main information of compressible data, to make up the deficiency of this respect, primary As common sparse matrix, it is defined as follows Nu Li/Mach random matrix:
&phi; i j = s + 1 , p = 1 2 s 0 , p = 1 - 1 2 s - 1 , p = 1 2 s
In above formula, φijRepresent the element of optional position in Bernoulli Jacob/Mach random matrix, p is that the value of this element is general Rate, i.e. the random value in { -1,0 ,+1 } of Probability p shown in element above formula in matrix, wherein parameter s control the sparse of matrix Degree, s value is bigger, and in matrix, nonzero element is less, and sparse degree is higher.If data length is respectively after initial data and compression For n, m, then still need to produce and store the sparse matrix of m × n using said method.
Sparse random matrix is split by the present invention program according to element is positive and negative, then produced by way of piecemeal with Machine number is representing the coordinate of nonzero element in calculation matrix, then is combined into improved sparse random measurement matrix by each coordinate block φ1、φ2Two matrixes, φ1、φ2Do not store true calculation matrix element data, but store the position of its nonzero element. Meanwhile, matrix multiplication is reduced to additive operation, completes the compressed sensing to primary signal while generating calculation matrix and survey Amount process, reduces the complexity of data processing, improves the real-time performance of wireless sensor network system.
It is assumed that to length, data x for n is compressed the data length after perception is processed is m, then improved compressed sensing Processing procedure is as follows:
(1) ignore normalization operator, the positive element of each column and negative element value ± 1, number is a, each column is divided into a group, Meet a=m/2s, calculateInitial φ1、φ2It is empty matrix.
(2) two random 1 × n dimension matrix t are generated1=[t11,t12,…,t1n]、t2=[t21,t22,…,t2n], t1With t2Represent element position in calculation matrix.Wherein t1nWith t2nIt is the positive integer of random value in [1, b], need to make t1nWith t2nDo not take identical value it is ensured that the positive element of every string only one of which of each matrix in block form and negative element.Then, by matrix t1、t2 Store matrix φ respectively as data line1、φ2Among.
(3) subsequently carry out initial data x and be circulated perception process.In processing each time, find out t first1Intermediate value is i unit The position of element, the value of relevant position element in primary signal x is added, subsequently equally finds out t2Intermediate value is the position of i element, will In primary signal x, the value of relevant position element is added, and is multiplied by after finally subtracting each other above-mentioned two result againObtain result ri, And by result riIt is stored in last compressed data r.I initial value is 1, and after each circulation, cumulative 1, i is equal to b is last locating Reason.
(4) process 2 and process 3 are repeated a time；
(5) finally make b=m-a × b, carry out once-through operation according to process (3), process (4) and result is stored in riIn.
By said process, data r after initial data x is compressed and corresponding improvement are just obtained Calculation matrix
From said process, node only needs uniformly random number generator and the memory space of 2 (a+1) × n, spatial complex Degree reduces；Also by matrix multiplication, addition is reduced to the measurement process of primary signal, time complexity reduces；Product by calculation matrix The raw process with data perception measurement combines, in hgher efficiency.
Cluster head handling process
Distributed compression perception theory is thought to multiple signals or data, their time domain can be utilized openness and spatial domain Dependency tectonic syntaxis sparse model, is carried out combined coding and is recovered with combined decoding, so than independent, signal can be processed There are more excellent energy consumption characteristics, also can preferably recover primary signal.
Because node each in cluster has seriality on geographical position, it can thus be assumed that the gathered target data of each node There is certain dependency.Node data each in cluster is decomposed into the common ground that all nodes all comprise and each by the present invention program From distinctive independent sector.In the cluster of supposition cluster head caching within each upload cycle, the data of i-th sensor node is xi=[xi1,xi2,…,xin], xi=[xi1,xi2,…,xin] it is i-th cluster interior nodes according to intervals acquisition order Data, then in this time period, all data of cluster head caching are x=[x1,x2,…,xn], n counts out for cluster internal segment.Due to Not each cluster interior nodes can send perception data to cluster head in each sampling period, and this just correspondingly reduces communication in cluster and opens Pin.
The data aggregate process of haptophore interior nodes, the data processing of leader cluster node, it is described in detail below:
(1) leader cluster node remains working condition and prepares to receive cluster internal segment point data, and is each cluster interior nodes Distribute corresponding data buffer storage space.Cluster interior nodes send data and are divided into prediction model parameterses and true perception data two Class.
(2) the prediction model parameterses caching of corresponding node, meter when cluster head receives data, if supplemental characteristic, are then updated The prediction data calculating the corresponding moment is deposited into data buffer storage；If perception data, then it is directly stored in corresponding node Data buffer storage.
(3) often through upload cycle t, it data cached is generated as signal vector x according to cluster interior nodes id by cluster headi= [x1i,x2i,…,xni]tIf cluster internal segment is counted as n, the data matrix of cluster head storage is x=[x1,x2,…,xn].
(4) utilize distributed compression perception theory, by common sparse transformation method described previously, this cluster head is stored Data matrix x carry out joint sparse process.The purpose entering to exercise joint sparse is so that the data after conversion is always sparse Degree is minimum, so needing to meet optimal condition:
min(||zc||0+||z1||0+||z2||0+…+||zn||0) i=1,2 ... n
By above-mentioned condition to xiCarrying out the data after sparse transformation is ai=[a1i,a2i,…,ani]t, ai=zc+zn, wherein zcFor the common ground of all data, znFor aiDistinctive independent sector.
(5) common ground of each signal and independent sector can be combined and be expressed as z=[zc,z1,z2,…,zn], each component root Determine the data length after perception compression according to its degree of rarefication.Using previously described data compressing method, sparse data is entered Obtaining last compression aggregated data after row compression is r=[rc,r1,r2,…,rn] and corresponding improvement calculation matrix φ=[{ φc1,φc2},{φ11,φ12},…,{φn1,φn2}].
Leader cluster node uploads at each and will compress aggregated data in cycle t with calculation matrix data is activation to convergence section Point, then data recovery is carried out by compressed sensing recovery algorithms by aggregation node.
Embodiment 3
The present embodiment is so that agriculture Internet of Things is applied as a example.In large-scale field application scene, need to be grasped the life of crops Long ambient condition is realizing more scientific breed of crop.Typically temperature, humidity, illumination need to be collected by wireless sensor node Etc. data for applying fertilizers scientifically, high-efficient irrigation provide data support, realize green agriculture.
Will not be too high to the required precision of the ambient parameter datas such as humiture in this kind of application scenarios, can there is certain mistake Difference tolerance.Due to the sensor node that is positioned in farmland using battery or solar powered by the way of, and node cost is unsuitable Too high, therefore its energy and calculating storage capacity are limited.Using the present invention program, can on the premise of meeting application demand, Reduce the energy expenditure that data transfer is brought, extend the working time of each node in farmland, save production cost.
User determines the destination object needing monitoring by terminal, and obtains the target farmland being gathered by sensor node Interior object data, these destination objects include but is not limited to temperature, humidity, intensity of illumination, something concentration etc. in soil.Tool Body application process is as follows:
1. the sensor node in target area completes cluster structured construction process, and completes cluster-level routing foundation；Pass Sensor node reports the positional information of oneself, id etc. to aggregation node
2. each leader cluster node is all joint structure spatial caches in this cluster.
3. user sends control information to aggregation node, including but not limited to collection target, acquisition time interval, upload week Phase, error in data thresholding, leader cluster node rotational cycle, node time sequence length etc..
4. control information is sent to each sensor node by aggregation node, and to user terminal feedback node information.
5. sensor node starts data acquisition, and each node utilizes initial gathered data structure according to length of time series Make prediction model parameterses, parameter is sent to leader cluster node.Subsequently according to cluster interior nodes treatment mechanism described previously, according to default Error threshold examination data collection send work.
6. leader cluster node keeps receiving data in cluster, and when each uploads the moment in cycle and reaches, according to cluster head datatron System carries out polymeric compressing process to data in cluster, and it is sent to aggregation node along cluster-level routing.
7. after aggregation node receives data, both can be immediately sent to user side, carry out reducing by user side extensive Multiple；User side can also be sent it to again after aggregation node carries out decompression reduction.
8. often through a cluster head rotational cycle, the wireless sensor network of target area needs to carry out cluster head again to select Lift so that energy uniformly consumes.
9. user can adjust the control parameters such as error threshold according to real needs.
Below according to Fig. 8, a kind of data transmission device of wireless sensor network of the present invention is illustrated, this data Reception device be applied to cluster structured in leader cluster node, described cluster structured in also include cluster interior nodes.This data transfer fills Put including receiving module 201, judge module 202, the first memory module 203 and the second memory module 204.
Wherein, receiver module 201, with receiving the prediction model parameterses that cluster interior nodes send；
Wherein, judge module 202, for judging currently whether receive the real data of cluster interior nodes transmission；
Wherein, the first memory module 203, for when judged result is to be, by actual data storage to this cluster interior nodes In corresponding data buffer storage；
Wherein, the second memory module 204, for judged result for no when, according to prediction model parameterses and Time Calculation Go out prediction data and as in the data buffer storage corresponding to this cluster interior nodes of data Cun Chudao in cluster.
As seen from the above-described embodiment, the present invention program is directed to the resource-constrained feelings of application of higher wireless sensor network interior joint Condition, based on existing clustering routing structure it is proposed that a kind of data transmission method of Energy Efficient.For member node in cluster, Using forecast model, node data is predicted approaching, suppression node data transmits thus reducing energy expenditure.For cluster head Node, carries out joint compression by improved compression sensing method and processes, reduce the volume of transmitted data of cluster head to data in cluster.This Scheme can effectively reduce volume of transmitted data in net under the premise of meeting application demand, thus reducing node energy consumption, prolongs Long network lifecycle.
It should be noted that herein, such as first and second or the like relational terms are used merely to a reality Body or operation are made a distinction with another entity or operation, and not necessarily require or imply these entities or deposit between operating In any this actual relation or order.And, term " inclusion ", "comprising" or its any other variant are intended to Comprising of nonexcludability, wants so that including a series of process of key elements, method, article or equipment and not only including those Element, but also include other key elements being not expressly set out, or also include for this process, method, article or equipment Intrinsic key element.In the absence of more restrictions, the key element that limited by sentence "including a ..." it is not excluded that Also there is other identical element including in the process of described key element, method, article or equipment.
Each embodiment in this specification is all described by the way of related, identical similar portion between each embodiment Divide mutually referring to what each embodiment stressed is the difference with other embodiment.Real especially for system For applying example, because it is substantially similar to embodiment of the method, so description is fairly simple, referring to embodiment of the method in place of correlation Part illustrate.
The foregoing is only presently preferred embodiments of the present invention, be not intended to limit protection scope of the present invention.All Any modification, equivalent substitution and improvement made within the spirit and principles in the present invention etc., are all contained in protection scope of the present invention Interior.

Claims (10)

1. a kind of transmission method of wireless sensor network data, be applied to cluster structured in cluster interior nodes, described sub-clustering knot Also leader cluster node is included it is characterised in that comprising the steps: in structure
S101) prediction model parameterses are determined according to historical data, skew number of times k and offset threshold j are initialized, and to cluster Head node transmits this prediction model parameters, so that leader cluster node goes out prediction data according to prediction model parameterses and Time Calculation and incites somebody to action It stores in the data buffer storage corresponding to this node；Described forecast model is to carry out curve fitting previously according to historical data to obtain ?；
S102) gathered data, by the currently practical data collecting and the current predictive data obtaining according to prediction model parameterses It is compared；
S103) if currently practical data is less than or equal to error threshold ε with the difference ε ' of prediction data, cache this real data, partially Move threshold value j and add 1, return to step s102)；
S104) if currently practical data is more than error threshold ε with the difference ε ' of prediction data, this real data, skew time are cached Number k adds 1, and offset threshold j halves, and transmits this real data to leader cluster node；And skew number of times k is carried out with offset threshold j Relatively, if k >=j, return to step s101)；If k < j, return to step s102).
2. method according to claim 1 is it is characterised in that described forecast model is unitary linear prediction model: y '=α t+β
Wherein, t represents sampling time point, and y ' is the predictive value in corresponding moment；
Cluster interior nodes are with same intervals t0Continuous n sampling is deposited in buffer queue, is calculated using the data in time serieses Go out parameter (α, β).
3. a kind of transmitting device of wireless sensor network data, be applied to cluster structured in cluster interior nodes, described sub-clustering knot Also leader cluster node is included it is characterised in that including in structure:
Prediction model parameterses transport module, for determining prediction model parameterses according to historical data, to skew number of times k and skew threshold Value j is initialized, and transmits this prediction model parameters to leader cluster node so that leader cluster node according to prediction model parameterses and when Between calculate prediction data and be stored in the data buffer storage corresponding to this node；Described forecast model is previously according to going through History data carries out curve fitting acquisition；
Data acquisition module, for gathered data, by the currently practical data collecting and according to prediction model parameterses acquisition Current predictive data is compared；
First cache module, in the case of being less than or equal to error threshold ε for the difference ε ' in currently practical data and prediction data, Cache this real data, offset threshold j adds 1, trigger described data acquisition module；
Second cache module, in the case of being more than error threshold ε for the difference ε ' in currently practical data and prediction data, caching This real data, skew number of times k adds 1, and offset threshold j halves, and transmits this real data to leader cluster node；And number of times will be offset K is compared with offset threshold j, if k >=j, triggers described prediction model parameterses transport module；If k < j, trigger described data Acquisition module.
4. a kind of method of reseptance of wireless sensor network data, be applied to cluster structured in leader cluster node, described sub-clustering knot Cluster interior nodes are also included it is characterised in that comprising the steps: in structure
S201) prediction model parameterses that cluster interior nodes send are received；
S202) judge currently whether receive the real data of cluster interior nodes transmission；
S203) if it is, it stores in this data buffer storage corresponding to cluster interior nodes；
S204) if it is not, then going out prediction data and as data storage in cluster according to prediction model parameterses and Time Calculation In data buffer storage corresponding to this cluster interior nodes.
5. method according to claim 4 is processed it is characterised in that also including polymeric compressing, comprises the steps:
B1) receive the data that in cluster, each node uploads, and with independent spatial cache, data buffer storage is carried out to each cluster interior nodes；
B2) each being uploaded the data storage that in cycle intra-cluster, each node uploads is data matrix x, wherein x=[x in cluster1, x2,…,xn], n counts out for cluster internal segment, xi=[xi1,xi2..., xin] suitable according to intervals for i-th cluster interior nodes The data of sequence collection；
B3) joint sparse process is carried out to data matrix x in cluster, and each cluster internal segment point data is decomposed into common ground and independence Part, obtains common ground z to after x decompositioncAnd its independent sector zn, decomposition result collection is combined into z=[zc,z1,z2,…,zn]；
B4) each component data in decomposition result set is compressed respectively with perception process, obtains final compression polymerization knot Fruit set r=[rc,r1,r2,…,rn] and calculation matrix set φ=[{ φc1,φc2},{φ11,φ12},…,{φn1, φn2}]；
B5) final polymeric compressing results set is sent to aggregation node with calculation matrix set along cluster-level routing or application is whole End.
6. method according to claim 5 is it is characterised in that described independent spatial cache, including data buffer storage space With prediction model parameterses spatial cache；
If receive is raw sensed data, directly store that data in the data buffer storage space of corresponding node；As Fruit is prediction model parameterses data, then update the prediction model parameterses caching of corresponding node, and according to the forecast model after updating Parameter and Time Calculation go out in the data buffer storage space that prediction data stores corresponding node.
7. method according to claim 5, it is characterised in that described joint sparse is processed, is counted to each cluster internal segment According to xiIt is respectively adopted sparse random matrix and carry out sparse transformation.
8. method according to claim 7 is it is characterised in that described sparse random matrix adopts Bernoulli Jacob/Mach random Matrix, it is defined as follows:
&phi; i j = s + 1 , p = 1 2 s 0 , p = 1 - 1 2 s - 1 , p = 1 2 s
In above formula, φijRepresent the element of optional position in Bernoulli Jacob/Mach random matrix, p is the probability of this element, that is, Probability p shown in element above formula in matrix random value in { -1,0 ,+1 }, wherein parameter s control the sparse degree of matrix, S value is bigger, and in matrix, nonzero element is less, and sparse degree is higher.
9. method according to claim 5, it is characterised in that described compressed sensing is processed, comprises the steps:
A1) according to cluster internal segment count out n, perception compression after data length m and sparse control parameter s, calculating parameter a=m/ 2s、Produce initially empty calculation matrix φ1、φ2；
A2 two random 1 × n dimension matrix t) are generated1=[t11,t12,…,t1n]、t2=[t21,t22,…,t2n], wherein t1nWith t2nIt is the positive integer of random value in [1, b], by matrix t1、t2Store calculation matrix φ respectively as data line1、 φ2In.
A3) find out t1Intermediate value is the position of the element of i, and correspondence position element in primary signal is added；Find out t2Intermediate value is i's The position of element, correspondence position element in primary signal is added；Deduct second value of calculation with first value of calculation, gained is tied Fruit is deposited into corresponding compressed data riIn；
A4) repeat step a2) and step a3) a time；
A5) make b=m-a × b, repeat step a3), a4), obtain final compression polymerization result data riAnd calculation matrix
10. a kind of reception device of wireless sensor network data, be applied to cluster structured in leader cluster node, described sub-clustering knot Cluster interior nodes are also included it is characterised in that including in structure:
Receiver module, with receiving the prediction model parameterses that cluster interior nodes send；
Judge module, for judging currently whether receive the real data of cluster interior nodes transmission；
First memory module, for judged result be when, by actual data storage to this cluster interior nodes corresponding to data In caching；
Second memory module, for judged result for no when, go out prediction data simultaneously according to prediction model parameterses and Time Calculation As in the data buffer storage corresponding to this cluster interior nodes of data Cun Chudao in cluster.
CN201610712279.5A 2016-08-23 2016-08-23 Method and device for transmitting wireless sensor network data Active CN106341842B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201610712279.5A CN106341842B (en) 2016-08-23 2016-08-23 Method and device for transmitting wireless sensor network data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201610712279.5A CN106341842B (en) 2016-08-23 2016-08-23 Method and device for transmitting wireless sensor network data

Publications (2)

Publication Number Publication Date
CN106341842A true CN106341842A (en) 2017-01-18
CN106341842B CN106341842B (en) 2020-05-01

Family

ID=57824975

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201610712279.5A Active CN106341842B (en) 2016-08-23 2016-08-23 Method and device for transmitting wireless sensor network data

Country Status (1)

Country Link
CN (1) CN106341842B (en)

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106961656A (en) * 2017-02-23 2017-07-18 南京邮电大学 A kind of wireless sensor network data Forecasting Methodology
CN107071800A (en) * 2017-03-01 2017-08-18 北京邮电大学 A kind of cluster wireless sensor network method of data capture and device
CN107105394A (en) * 2017-05-11 2017-08-29 江苏食品药品职业技术学院 Building safety monitoring system based on wireless sensor network
CN108347709A (en) * 2017-01-21 2018-07-31 中山大学 A kind of system adaptive recognition method and system of super low-power consumption traceability system
CN110505597A (en) * 2019-07-31 2019-11-26 北京邮电大学 A kind of data transmission method of wireless sensor network
CN110996304A (en) * 2019-12-04 2020-04-10 深圳信可通讯技术有限公司 Method and system for inquiring and calculating edge data of low-power-consumption Internet of things
CN111010704A (en) * 2019-12-03 2020-04-14 沈阳化工大学 Underwater wireless sensor network data prediction optimization method based on exponential smoothing
CN111262948A (en) * 2020-02-18 2020-06-09 广东大橘果业有限公司 Planting intelligent management system based on internet
CN111311427A (en) * 2020-02-18 2020-06-19 广东大橘果业有限公司 Planting intelligent management system based on internet
CN111726768A (en) * 2020-06-16 2020-09-29 天津理工大学 Edge-oriented computation reliable data collection method based on compressed sensing
CN112039934A (en) * 2019-06-03 2020-12-04 大唐移动通信设备有限公司 Information feedback method, feedback information processing method and device
CN115695564A (en) * 2022-12-30 2023-02-03 深圳市润信数据技术有限公司 Efficient transmission method for data of Internet of things
CN115835060A (en) * 2023-02-17 2023-03-21 云南瀚哲科技有限公司 Data acquisition method for industrial and agricultural Internet of things

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130097128A1 (en) * 2010-04-26 2013-04-18 Shoji Suzuki Time-series data diagnosing/compressing method
CN104469797A (en) * 2014-11-28 2015-03-25 北京农业信息技术研究中心 Method for generating sequence prediction on basis of farmland wireless network intra-cluster data sparsity

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130097128A1 (en) * 2010-04-26 2013-04-18 Shoji Suzuki Time-series data diagnosing/compressing method
CN104469797A (en) * 2014-11-28 2015-03-25 北京农业信息技术研究中心 Method for generating sequence prediction on basis of farmland wireless network intra-cluster data sparsity

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
WEI CHEN AND IAN J.WASSELL: "energy efficient signal acquisition via compressive sensing in wireless sensor networks", 《INTERNATIONAL SYMPOSIUM ON WIRELESS AND PERVASIVE COMPUTING》 *
刘少强等: "基于节点间输出比值稳定的WSN分布式数据压缩", 《PROCEEDINGS OF THE 32ND CHINESE CONTROL CONFERENCE》 *

Cited By (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108347709B (en) * 2017-01-21 2020-07-24 中山大学 Self-adaptive data transmission method and system of ultra-low power consumption traceability system
CN108347709A (en) * 2017-01-21 2018-07-31 中山大学 A kind of system adaptive recognition method and system of super low-power consumption traceability system
CN106961656A (en) * 2017-02-23 2017-07-18 南京邮电大学 A kind of wireless sensor network data Forecasting Methodology
CN106961656B (en) * 2017-02-23 2020-04-07 南京邮电大学 Wireless sensor network data prediction method
CN107071800B (en) * 2017-03-01 2019-10-18 北京邮电大学 A kind of cluster wireless sensor network method of data capture and device
CN107071800A (en) * 2017-03-01 2017-08-18 北京邮电大学 A kind of cluster wireless sensor network method of data capture and device
CN107105394B (en) * 2017-05-11 2020-06-30 江苏食品药品职业技术学院 Building safety monitoring system based on wireless sensor network
CN107105394A (en) * 2017-05-11 2017-08-29 江苏食品药品职业技术学院 Building safety monitoring system based on wireless sensor network
CN112039934A (en) * 2019-06-03 2020-12-04 大唐移动通信设备有限公司 Information feedback method, feedback information processing method and device
CN110505597A (en) * 2019-07-31 2019-11-26 北京邮电大学 A kind of data transmission method of wireless sensor network
CN111010704A (en) * 2019-12-03 2020-04-14 沈阳化工大学 Underwater wireless sensor network data prediction optimization method based on exponential smoothing
CN110996304A (en) * 2019-12-04 2020-04-10 深圳信可通讯技术有限公司 Method and system for inquiring and calculating edge data of low-power-consumption Internet of things
CN111262948A (en) * 2020-02-18 2020-06-09 广东大橘果业有限公司 Planting intelligent management system based on internet
CN111311427A (en) * 2020-02-18 2020-06-19 广东大橘果业有限公司 Planting intelligent management system based on internet
CN111726768A (en) * 2020-06-16 2020-09-29 天津理工大学 Edge-oriented computation reliable data collection method based on compressed sensing
CN115695564A (en) * 2022-12-30 2023-02-03 深圳市润信数据技术有限公司 Efficient transmission method for data of Internet of things
CN115695564B (en) * 2022-12-30 2023-03-10 深圳市润信数据技术有限公司 Efficient transmission method of Internet of things data
CN115835060A (en) * 2023-02-17 2023-03-21 云南瀚哲科技有限公司 Data acquisition method for industrial and agricultural Internet of things
CN115835060B (en) * 2023-02-17 2023-05-05 云南瀚哲科技有限公司 Data acquisition method of industrial and agricultural Internet of things

Also Published As

Publication number Publication date
CN106341842B (en) 2020-05-01

Similar Documents

Publication Publication Date Title
CN106341842A (en) Method and device for transmitting data of wireless sensor network
CN106304191B (en) A kind of data receiver method and device based on cluster structured radio sensor network
Cambra et al. An IoT service-oriented system for agriculture monitoring
CN107871164B (en) Fog computing environment personalized deep learning method
Li et al. Minimizing packet expiration loss with path planning in UAV-assisted data sensing
CN100581119C (en) Method for recognizing distributed amalgamation of wireless sensor network
CN107786959B (en) Compressed data collection method in wireless sensor network based on adaptive measuring
CN105959987A (en) Data fusion algorithm for improving energy utilization rate and service performance of wireless sensor network
Li et al. INCOME: Practical land monitoring in precision agriculture with sensor networks
CN103957582B (en) Wireless sensor network self-adaptation compression method
CN112804107A (en) Layered federal learning method for energy consumption adaptive control of equipment of Internet of things
Geng et al. An Agricultural Monitoring System Based on Wireless Sensor and Depth Learning Algorithm.
CN102244882A (en) Mobility-agent-based intelligent data acquisition method for wireless sensor network
CN102612080B (en) Minimum-energy-consumption self-adaption convergent routing method on basis of second-generation wavelet zero tree encoding
CN103210817A (en) Automatic farmland irrigation system
Shouyi et al. Design of wireless multi-media sensor network for precision agriculture
CN106375940A (en) Agricultural perception data sparse vector acquiring and space coupling method
CN104486807A (en) Routing method of small-scale wireless sensor network
CN107332631A (en) A kind of method of use multi-attribute group decision making theoretical evaluation link-quality assessment models
CN115530054A (en) Irrigation control method and device, electronic equipment and storage medium
Sinha et al. A multi-level strategy for energy efficient data aggregation in wireless sensor networks
CN105007599A (en) Data compression collection method based on connectivity clustering
CN102164395A (en) Method for locally acquiring overall information of wireless sensor network based on compressed sensing
Yao et al. Research and development precision irrigation control system in agricultural
CN103593435A (en) Approximate treatment system and method for uncertain data PT-TopK query

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant