CN109257129A - A kind of wireless sensor network - Google Patents

A kind of wireless sensor network Download PDF

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Publication number
CN109257129A
CN109257129A CN201811114748.9A CN201811114748A CN109257129A CN 109257129 A CN109257129 A CN 109257129A CN 201811114748 A CN201811114748 A CN 201811114748A CN 109257129 A CN109257129 A CN 109257129A
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China
Prior art keywords
wireless sensor
channel
data
model
sensor network
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CN201811114748.9A
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Chinese (zh)
Inventor
李晓冬
党选举
张明
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Guilin University of Electronic Technology
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Guilin University of Electronic Technology
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Priority to CN201811114748.9A priority Critical patent/CN109257129A/en
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Power Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Arrangements For Transmission Of Measured Signals (AREA)

Abstract

The present invention relates to a kind of wireless sensor networks, what is solved is to transmit the big technical problem of pressure, by using the N number of wireless sensor and central server for including distribution setting, central server includes processor and memory, memory is stored with data reconstruction program, processor is for executing data reconstruction program, to complete following steps: step 1, establishing radio network information channel estimation model；Step 2, control wireless sensor carries out time synchronization, and cycle T carries out lack sampling to logarithm accordingly, pre-processes to lack sampling data, obtains transmission vector using calculation matrix processing preprocessed data；Step 3, channel estimation is carried out to radio network information channel using residual error estimation method and calculation matrix, obtains the mutatis mutandis model of channel；Step 4, according to the mutatis mutandis model of channel, the transmission vector received is handled using solution preprocess method, the technical solution of transmission data is reconstructed, preferably resolves the problem, can be used in wireless sensor network.

Description

A kind of wireless sensor network
Technical field
The present invention relates to network fields, and in particular to a kind of wireless sensor network.
Background technique
Wireless sensor network (Wireless SensorNetworks, WSN) is a kind of distributed sensor, it Tip is the sensor that can perceive and check the external world.Sensor in WSN wirelessly communicates, therefore network is set It sets flexibly, device location can be changed at any time, and the connection of wired or wireless way can also be carried out with internet.Pass through channel radio The multihop self-organizing network that letter mode is formed.
Existing wireless sensor network, in transmission of large capacity data, because bandwidth is qualitative, so that the pressure of transmission is non- Chang great, therefore a kind of small wireless sensor network of data transmission pressure is provided with regard to necessary.
Summary of the invention
The technical problem to be solved by the present invention is to the big technical problems of data existing in the prior art transmission pressure.It mentions For a kind of new wireless sensor network, which has the characteristics that data transmission pressure is small.
In order to solve the above technical problems, the technical solution adopted is as follows:
A kind of wireless sensor network, the wireless sensor network includes N number of wireless sensor of distribution setting, with N number of nothing For line sensor commonly through the central server of wireless network connection, the central server includes processor and memory, institute It states memory and is stored with data reconstruction program, the processor is for executing the data reconstruction program, to complete following steps:
Step 1, radio network information channel estimation model is established；
Step 2, control wireless sensor carries out time synchronization, and cycle T carries out lack sampling to logarithm accordingly, to lack sampling number According to being pre-processed, transmission vector is obtained using calculation matrix processing preprocessed data；
Step 3, channel estimation is carried out to radio network information channel using residual error estimation method and calculation matrix, obtains channel standard Use model；
Step 4, according to the mutatis mutandis model of channel, the transmission vector received is handled using solution preprocess method, reconstructs biography Transmission of data.
The working principle of the invention: the present invention is by the estimation for wireless channel model and for the pre- of wireless signal Processing realizes highdensity data transmission, alleviates the transmission pressure of wireless network.Improvement of the invention is mainly Estimation for wireless sensor network and the processing for transmitting signal, rest part and existing wireless sensor network phase Together, the present invention repeats no more.
In above scheme, for optimization, further, the lack sampling data carry out pretreatment and include:
Step A1 establishes sparse matrix ψ=[ψ using discrete sine transform method1, ψ2..., ψp]T；
Step A2 carries out data fusion according to sampling perception matrix and sparse matrix, obtains preprocessed signal:
Wherein, sampling perception matrix is φ={ φJ, i}；
Step A3 defines interfering with each other as ω between N number of wireless sensor, calculates YM×1=ΦM×NXN×1+YωM×1:
Wherein,Matrix is used for channel.
Further, step 3 includes:
Step C1, definition n are signal length, and the atom vector forward less than the S priority of n is chosen according to priority Collection, definition transmission vector are Initial residuls, and Initial residuls are projected on calculation matrix, relevance degree is compared, according to the degree of correlation It is worth size sequence, the maximum S corresponding index numerical value of atom vector set of relevance degree is included into set S1；
Step C2 reconstructs sparse signal using algorithm of support vector machine, and updates residual values using the sparse signal of reconstruct；
The Initial residuls in new residual values and step C1 in step C3, comparison step C2, such as new residual values are greater than residual error Initial value thens follow the steps C5；C4 is thened follow the steps as new residual values are less than or equal to Initial residuls；
Step C4, circulation execute step C1- step C3；
Step C5 exports the mutatis mutandis model of channel.
Further, the S=n/10.
Further, the solution preprocess method includes:
Asymptotic expansion solution is carried out to the signal Y received using gradient method, recycles the reduction of discrete sine inverse transformation, The mutatis mutandis model of channel and the reduction result of discrete sine inverse transformation are subjected to interpolation processing, reconstruct transmission data.
Further, the gradient method includes:
Step B1, defining the initial vector that length is n is x0The initial value of=zeros (N, 1), the number of iterations k are 1；
Step B2 is defined and is inputted, yM×1=ΦM×NxN×1+ωM×1, N < M, sampling perception matrix φ={ φJ, i, transmission Vector y, step-length t, weight λ restrain threshold values ε, the mutatis mutandis model of channel；Define x'sFor the transmission data reconstructed；
Step B3, calculates
Ek=xk-1-2tkΦT(Φxk-1- y),
The step-length x of kth step is calculated using linear contraction operatork=λ × shrink (βj, tkλ)；
Step B4 is calculated | | xk-xk-1||2, compare | | xk-xk-1||2With convergence threshold values ε size, if | | xk-xk-1||2 < ε thens follow the steps B5, otherwise k=k+1 is enabled to return to step B3；
Step B5 exports reconstruction signal.
Beneficial effects of the present invention: the present invention establishes letter by first estimating the network channel of wireless sensor network Road model pre-processes data, lack sampling in data transmission, reduces the scale of construction of data.Simultaneously in wireless sensor network Central node carry out reef knot calculation, be sequentially to settle accounts pretreated data on the basis of settling accounts channel model, by two kinds The interpolation processing of checkout result reduces evaluated error.It is able to use the transmission that smaller transmission cost completes big data quantity.
Detailed description of the invention
Present invention will be further explained below with reference to the attached drawings and examples.
Fig. 1, the wireless sensor network schematic diagram in embodiment 1.
Fig. 2, the data reconstruction program step schematic diagram in embodiment 1.
Fig. 3, the gradient method and step schematic diagram in embodiment 1.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to embodiments, to the present invention It is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, is not used to limit The fixed present invention.
Embodiment 1
The present embodiment provides a kind of wireless sensor network, such as Fig. 1, the wireless sensor network includes the N number of of distribution setting Wireless sensor, with N number of wireless sensor commonly through the central server of wireless network connection, the central server includes Processor and memory, the memory are stored with data reconstruction program, and the processor is for executing the data reconstruction journey Sequence, such as Fig. 2, to complete following steps:
Step 1, radio network information channel estimation model is established；
Step 2, control wireless sensor carries out time synchronization, and cycle T carries out lack sampling to logarithm accordingly, to lack sampling number According to being pre-processed, transmission vector is obtained using calculation matrix processing preprocessed data；
Step 3, channel estimation is carried out to radio network information channel using residual error estimation method and calculation matrix, obtains channel standard Use model；
Step 4, according to the mutatis mutandis model of channel, the transmission vector received is handled using solution preprocess method, reconstructs biography Transmission of data.
Specifically, the lack sampling data, which pre-process, includes:
Step A1 establishes sparse matrix ψ=[ψ using discrete sine transform method1, ψ2..., ψp]T；
Step A2 carries out data fusion according to sampling perception matrix and sparse matrix, obtains preprocessed signal:
Wherein, sampling perception matrix is φ={ φJ, i}；
Step A3 defines interfering with each other as ω between N number of wireless sensor, calculates YM×1=ΦM×NXN×1+YωM×1:
Wherein,Matrix is used for channel.
Specifically, step 3 includes:
Step C1, definition n are signal length, and the atom vector forward less than the S priority of n is chosen according to priority Collection, definition transmission vector are Initial residuls, and Initial residuls are projected on calculation matrix, relevance degree is compared, according to the degree of correlation It is worth size sequence, the maximum S corresponding index numerical value of atom vector set of relevance degree is included into set S1；
Step C2 reconstructs sparse signal using algorithm of support vector machine, and updates residual values using the sparse signal of reconstruct；
The Initial residuls in new residual values and step C1 in step C3, comparison step C2, such as new residual values are greater than residual error Initial value thens follow the steps C5；C4 is thened follow the steps as new residual values are less than or equal to Initial residuls；
Step C4, circulation execute step C1- step C3；
Step C5 exports the mutatis mutandis model of channel.
The degree of rarefication S of pre-estimation is less than actual signal degree of rarefication, to reduce the number of iterations, is set as in general algorithm / 10th of signal length, the value of degree of rarefication are generally higher than the value initialized.The S=n/10.
Specifically, the solution preprocess method includes:
Asymptotic expansion solution is carried out to the signal Y received using gradient method, recycles the reduction of discrete sine inverse transformation, The mutatis mutandis model of channel and the reduction result of discrete sine inverse transformation are subjected to interpolation processing, reconstruct transmission data.
Specifically, such as Fig. 3, the gradient method includes:
Step B1, defining the initial vector that length is n is x0The initial value of=zeros (N, 1), the number of iterations k are 1；
Step B2 is defined and is inputted, yM×1=ΦM×NxN×1+ωM×1, N < M, sampling perception matrix, φ={ φJ, i, it passes The amount of transferring to y, step-length t, weight λ restrain threshold values ε, the mutatis mutandis model of channel；Define x'sFor the transmission data reconstructed；
Step B3, calculates
Ek=xk-1-2tkΦT(Φxk-1- y),
The step-length x of kth step is calculated using linear contraction operatork=λ × shrink (βj, tkλ)；
Step B4 is calculated | | xk-xk-1||2, compare | | xk-xk-1||2With convergence threshold values ε size, if | | xk-xk-1||2 < ε thens follow the steps B5, otherwise k=k+1 is enabled to return to step B3；
Step B5 exports reconstruction signal.
Although the illustrative specific embodiment of the present invention is described above, in order to the technology of the art Personnel are it will be appreciated that the present invention, but the present invention is not limited only to the range of specific embodiment, to the common skill of the art For art personnel, as long as long as various change the attached claims limit and determine spirit and scope of the invention in, one The innovation and creation using present inventive concept are cut in the column of protection.

Claims (6)

1. a kind of wireless sensor network, it is characterised in that: the wireless sensor network includes N number of wireless sensing of distribution setting Device, with N number of wireless sensor commonly through the central server of wireless network connection, the central server include processor with Memory, the memory are stored with data reconstruction program, and the processor is for executing the data reconstruction program, to complete Following steps:
Step 1, radio network information channel estimation model is established；
Step 2, control wireless sensor carry out time synchronization, and logarithm accordingly cycle T carry out lack sampling, to lack sampling data into Row pretreatment obtains transmission vector using calculation matrix processing preprocessed data；
Step 3, channel estimation is carried out to radio network information channel using residual error estimation method and calculation matrix, obtains the mutatis mutandis mould of channel Type；
Step 4, according to the mutatis mutandis model of channel, the transmission vector received is handled using solution preprocess method, reconstructs transmission number According to.
2. wireless sensor network according to claim 1, it is characterised in that: the lack sampling data carry out pretreatment packet It includes:
Step A1 establishes sparse matrix ψ=[ψ using discrete sine transform method1, ψ2..., ψp]T；
Step A2 carries out data fusion according to sampling perception matrix and sparse matrix, obtains preprocessed signal:
Wherein, sampling perception matrix is φ={ φJ, i}；
Step A3 defines interfering with each other as ω between N number of wireless sensor, calculates YM×1=ΦM×NXN×1+YωM×1:
Wherein,Matrix is used for channel.
3. wireless sensor network according to claim 2, it is characterised in that: step 3 includes:
Step C1, definition n are signal length, and the atom vector set forward less than the S priority of n is chosen according to priority, fixed Adopted transmission vector is Initial residuls, and Initial residuls are projected on calculation matrix, relevance degree is compared, according to relevance degree size The maximum S corresponding index numerical value of atom vector set of relevance degree is included into set S1 by sequence；
Step C2 reconstructs sparse signal using algorithm of support vector machine, and updates residual values using the sparse signal of reconstruct；
The Initial residuls in new residual values and step C1 in step C3, comparison step C2, such as new residual values are greater than Initial residuls Then follow the steps C5；C4 is thened follow the steps as new residual values are less than or equal to Initial residuls；
Step C4, circulation execute step C1- step C3；
Step C5 exports the mutatis mutandis model of channel.
4. wireless sensor network according to claim 3, it is characterised in that: the S=n/10.
5. wireless sensor network according to claim 3 or 4, it is characterised in that: the solution preprocess method includes:
Asymptotic expansion solution is carried out to the signal Y received using gradient method, the reduction of discrete sine inverse transformation is recycled, will believe The mutatis mutandis model in road and the reduction result of discrete sine inverse transformation carry out interpolation processing, reconstruct transmission data.
6. wireless sensor network according to claim 5, it is characterised in that: the gradient method includes:
Step B1, defining the initial vector that length is n is x0The initial value of=zeros (N, 1), the number of iterations k are 1；
Step B2 is defined and is inputted, yM×1=ΦM×NxN×1+ωM×1, N < M, sampling perception matrix, φ={ φJ, i, transmit to Y, step-length t, weight λ are measured, threshold values ε, the mutatis mutandis model of channel are restrained；Define x'sFor the transmission data reconstructed；
Step B3, calculates
Ek=xk-1-2tkφT(φxk-1- y),
The step-length x of kth step is calculated using linear contraction operatork=λ × shrink (βj, tkλ)；
Step B4 is calculated | | xk-xk-1||2, compare | | xk-xk-1||2With convergence threshold values ε size, if | | xk-xk-1||2< ε B5 is thened follow the steps, otherwise k=k+1 is enabled to return to step B3；
Step B5 exports reconstruction signal.
CN201811114748.9A 2018-09-25 2018-09-25 A kind of wireless sensor network Pending CN109257129A (en)

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Publication number Priority date Publication date Assignee Title
US20110298610A1 (en) * 2010-06-02 2011-12-08 Raul Hernan Etkin Compressing data in a wireless network
CN102497337A (en) * 2011-12-11 2012-06-13 天津大学 Compressed sensing wireless communication channel estimation method based on sparsity self-adapting
CN102594515A (en) * 2012-03-30 2012-07-18 清华大学 Node data transmitting method and device of sensor network and node data reconfiguring method and device of sensor network

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110298610A1 (en) * 2010-06-02 2011-12-08 Raul Hernan Etkin Compressing data in a wireless network
CN102497337A (en) * 2011-12-11 2012-06-13 天津大学 Compressed sensing wireless communication channel estimation method based on sparsity self-adapting
CN102594515A (en) * 2012-03-30 2012-07-18 清华大学 Node data transmitting method and device of sensor network and node data reconfiguring method and device of sensor network

Non-Patent Citations (3)

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Title
M. JULIA FERNÁNDEZ-GETINO GARCÍA等: "Support Vector Machines for Robust Channel Estimation in OFDM", 《IEEE SIGNAL PROCESSING LETTERS》 *
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Application publication date: 20190122