CN106572093A - Wireless sensor array data compression method and wireless sensor array data compression system - Google Patents
Wireless sensor array data compression method and wireless sensor array data compression system Download PDFInfo
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Abstract
The invention provides a wireless sensor array data compression method and a wireless sensor array data compression system. Through the method and the system, the amount of data sent can be reduced under high precision in order to reduce the cost of transmission, and the reliability of data can be ensured. The method comprises the following steps: at a sender, using a spline curve to fit original sensor array data to get an optimal control vector of a spline interpolation expression minimizing the error between the original sensor array data and the fitted value of the original sensor array data; reconstructing a spline curve according to the optimal control vector to get the fitted value of the original sensor array data; calculating and quantifying the error between the original sensor array data and the fitted value of the original sensor array data; and performing Huffman encoding on the quantified error, and sending the encoding result and the optimal control vector to a receiver. The method and the system are applicable to the technical field of data compression.
Description
Technical field
The present invention relates to technical field of data compression, particularly relates to a kind of wireless sensor array data compression method and is
System.
Background technology
With microelectric technique and MEMS (MEMS, Micro-Electro-Mechanical System) technology
Continuous progress, as the most basic and most important sensor of acquisition of information, also obtained tremendous development.Sensor array is exactly
By being deployed in what substantial amounts of microsensor node (for example, wireless sensor node) in special geological surrounding constituted, such as to
Collect hydrographic data and dispose underwater sensor array.As geographical environment is restricted, wireless sensor node typically leads to
Crossing communication carries out information transfer, reach perception, collection monitored area in perceived object information purpose.
By taking wireless sense network environmental monitoring system as an example, wireless sense network environmental monitoring system is by inside certain area
Affixing one's name to a large amount of wireless sensor nodes carries out long term monitoring to the regional environment.The sensing unit of wireless sensor node is to perception
Environmental information is acquired, and its processing unit stores and process the data of itself collection, finally enters row information biography by communication unit
It is defeated.If carrying out the monitoring of three dimensions geographical environment, such as to waters, the monitoring of soil, such sensor node cannot
Comprehensively, intactly gather information.The general single sensor by wireless sensor node is expanded as sensor array.Sensor
Array be exactly by one group in special geometric distribution the sensor group for being deployed in special geological surrounding into.For example, by sensor array
Linear deployment region under water, you can collect the hydrographic datas such as the flow velocity of different depth, direction, salinity, it is as shown in Figure 1
Wireless sense network hydrologic monitoring system.Sensor array is listed in each sampling instant and can gather one group of new sensor array columns
According to substantial amounts of sensor array data needs to be transferred to data center in time to serve phase under conditions of certain precision is ensured
Close application.Due to wireless sensor node often apart from base station farther out, more using satellite to data center's transmission data, therefore greatly
Frequently long range wireless data transmission brings huge energy expense to amount and communication spends.
Data compression refers to that on the premise of useful information is not lost reduction data volume improves which to reduce memory space
Transmission, storage and treatment effeciency, or data are reorganized according to certain algorithm, reduce redundancy and the storage of data
A kind of technical method in space.Lossless compress is compressed using the statistical redundancy of data, recovery initial data that can be complete,
Compression ratio is typically than relatively low.Compression method loses certain information during allowing compression, although can not recover completely
Initial data, but the part lost is less to the impact that understands initial data, has but brought the high compression ratio of comparison.
Huffman (Huffman) coding is a kind of typical lossless compression method, the symbol larger by will appear from probability
Represented with the shorter code word of word length, the less symbol of probability of occurrence is represented to realize compression with the longer code word of word length.In coding
The code book of symbol and code word corresponding relation be have recorded with foundation is required for when decoding.When the symbol truly probability of appearance and life
Into the symbol appearing probability that uses of code book it is consistent when, the compression effectiveness of Huffman codings is very good.If sensor array columns
Regard a vector as according to overall, code book very can be difficult to be stored in the memorizer of wireless sensor node greatly；If sensing
Device array data regards the set being made up of single sensing data scalar and the association that can ignore between adjacent sensors as.
Approximately (APCA, adaptive piecewise constant approximation) is adaptive segmentation constant
It is a kind of to be directed to seasonal effect in time series compression method, it is also possible to be applied in the spatial sequence data of sensor array generation.
APCA methods have ignored a range of data fluctuations.When mark position value has reached setting with the error of present position values
Independent one section can be divided into during thresholding, just this constant is represented this section with mark position value.But the method compression ratio is higher
When, error can be very big, and precision is subject to extreme influence, is always difficult to weigh between its compression ratio and precision.
The content of the invention
The technical problem to be solved in the present invention is to provide a kind of wireless sensor array data compression method and system, to solve
Certainly the Huffman codings existing for prior art are not suitable for the application scenarios of sensor array data, adaptive segmentation constant
Approximation method cannot ensure the problem of the reliability of data.
To solve above-mentioned technical problem, the embodiment of the present invention provides a kind of wireless sensor array data compression method, bag
Include：
In transmitting terminal, using spline curve fitting original sensor array data, obtain making the original sensor array
The optimum control vector of spline interpolation expression formula when data are minimum with the match value error of the original sensor array data；
According to the optimum control vector reconstruction SPL for obtaining, the plan of the original sensor array data is obtained
Conjunction value；
Calculate the error between the match value of the original sensor array data and the original sensor array data
And quantify；
Huffman encoding is carried out to the error after quantization, coding result and the optimum control vector are sent to reception
End.
Further, the optimum control vector representation is：
Wherein,Represent optimum control vector, A+The generalized inverse matrix of A is represented, A represents the interpolation coefficient square of SPL
Battle array, Y represent the original sensor array data.
Further, the optimum control vector reconstruction SPL that the basis is obtained, obtains the raw sensory
The match value of device array data includes：
The optimum control vector to obtaining quantifies；
According to the optimum control vector reconstruction SPL after quantization；
According to the SPL of reconstruct, the match value of the original sensor array data is obtained；
Wherein, the match value of the original sensor array data is expressed as： Represent original sensor array
The match value of data, A represent the interpolation coefficient matrix of SPL,Represent optimum control vector.
Further, the error after described pair of quantization carries out Huffman encoding, and coding result and the optimum control are sweared
Amount is sent to receiving terminal to be included：
Huffman encoding is carried out to the error after quantization；
Coding result and the optimum control vector are constituted into packet；
The packet of composition is sent to receiving terminal.
Further, methods described also includes：
In receiving terminal, the packet being made up of coding result and the optimum control vector is received；
According to the optimum control vector reconstruction SPL in the packet for receiving, the raw sensor is obtained
The match value of array data；
Hafman decoding is carried out to the coding result in the packet that receives；
The match value of the original sensor array data is added with decoded result, the sensor array for decompressing out is obtained
Column data.
The embodiment of the present invention also provides a kind of wireless sensor array data compression system, including：
First determining module, in transmitting terminal, using spline curve fitting original sensor array data, obtains making institute
State original sensor array data and the original sensor array data match value error it is minimum when spline interpolation expression
The optimum control vector of formula；
Second determining module, for according to the optimum control vector reconstruction SPL for obtaining, obtaining described original
The match value of sensor array data；
3rd determining module, for calculating the original sensor array data with the original sensor array data
Error between match value simultaneously quantifies；
Coding sending module, for carrying out Huffman encoding to the error after quantization, by coding result and the optimum control
Vector processed is sent to receiving terminal.
Further, the optimum control vector representation is：
Wherein,Represent optimum control vector, A+The generalized inverse matrix of A is represented, A represents the interpolation coefficient square of SPL
Battle array, Y represent the original sensor array data.
Further, second determining module includes：
Quantifying unit, quantifies for the optimum control vector to obtaining；
Reconfiguration unit, for according to the optimum control vector reconstruction SPL after quantization；
Determining unit, for the SPL according to reconstruct, obtains the match value of the original sensor array data；
Wherein, the match value of the original sensor array data is expressed as： Represent original sensor array
The match value of data, A represent the interpolation coefficient matrix of SPL,Represent optimum control vector.
Further, the coding sending module includes：
Coding unit, for carrying out Huffman encoding to the error after quantization；
Component units, for coding result and the optimum control vector are constituted packet；
Transmitting element, for the packet of composition is sent to receiving terminal.
Further, the system also includes：
Receiver module, in receiving terminal, receiving the packet being made up of coding result and the optimum control vector；
4th determining module, the optimum control vector reconstruction SPL in the packet received for basis,
Obtain the match value of the original sensor array data；
Decoder module, carries out Hafman decoding for the coding result in the packet to receiving；
5th determining module, for the match value of the original sensor array data is added with decoded result, obtains
The sensor array data for decompressing out.
The above-mentioned technical proposal of the present invention has the beneficial effect that：
In such scheme, in transmitting terminal, using spline curve fitting original sensor array data, obtain making described original
Spline interpolation expression formula when sensor array data is minimum with the match value error of the original sensor array data is most
Excellent control vector；According to the optimum control vector reconstruction SPL for obtaining, the original sensor array data are obtained
Match value；Calculate the error between the match value of the original sensor array data and the original sensor array data
And quantify；Huffman encoding is carried out to the error after quantization, coding result and the optimum control vector are sent to receiving terminal.
So, by spline curve fitting original sensor array data of the employing with high compression rate and with high encoding precision
The Mixing compression algorithm of Huffman codings, can effectively reduce the data volume for sending, under degree of precision to reduce transmitting into
This, while ensure that the reliability of data.
Description of the drawings
Fig. 1 is the structural representation of wireless sense network hydrologic monitoring system provided in an embodiment of the present invention；
Fig. 2 is the schematic flow sheet of wireless sensor array data compression method provided in an embodiment of the present invention；
Fig. 3 represents original sensor array data for utilization Catmull-Rom SPLs provided in an embodiment of the present invention
Schematic diagram；
Fig. 4 is control point p provided in an embodiment of the present inventioni-1,pi,pi+1And pi+2Position view；
Fig. 5 is dummy control point schematic diagram provided in an embodiment of the present invention；
Fig. 6 is the idiographic flow schematic diagram of wireless sensor array data compression method provided in an embodiment of the present invention；
Fig. 7 is direct quantization provided in an embodiment of the present invention, the schematic diagram of the pack arrangement of tri- kinds of compression algorithms of APCA, CSA；
Fig. 8 is the distribution schematic diagram of error provided in an embodiment of the present invention；
Fig. 9 is E provided in an embodiment of the present inventionmaxWhen=8, size of data schematic diagram after compression；
Figure 10 is E provided in an embodiment of the present inventionmaxWhen=32, the schematic diagram of size of data after compression；
Figure 11 is the structural representation of wireless sensor array data compression system provided in an embodiment of the present invention.
Specific embodiment
To make the technical problem to be solved in the present invention, technical scheme and advantage clearer, below in conjunction with accompanying drawing and tool
Body embodiment is described in detail.
The present invention is not suitable for the application scenarios of sensor array data, self adaptation point for existing Huffman codings
Section constant approximation method cannot ensure data reliability problem, there is provided a kind of wireless sensor array data compression method and
System.
Embodiment one
Referring to shown in Fig. 2, wireless sensor array data compression method provided in an embodiment of the present invention, including：
S101, in transmitting terminal, using spline curve fitting original sensor array data, obtains making the raw sensor
The optimum control of spline interpolation expression formula when array data is minimum with the match value error of the original sensor array data
Vector；
S102, according to the optimum control vector reconstruction SPL for obtaining, obtains the original sensor array number
According to match value；
S103, calculates between the original sensor array data and the match value of the original sensor array data
Error simultaneously quantifies；
S104, carries out Huffman encoding to the error after quantization, by coding result and the optimum control vector send to
Receiving terminal.
Wireless sensor array data compression method described in the embodiment of the present invention, in transmitting terminal, is intended using SPL
Original sensor array data are closed, obtains making the plan of the original sensor array data and the original sensor array data
The optimum control vector of spline interpolation expression formula when conjunction value error is minimum；According to the optimum control vector reconstruction sample for obtaining
Bar curve, obtains the match value of the original sensor array data；Calculate the original sensor array data and the original
Error between the match value of beginning sensor array data simultaneously quantifies；Huffman encoding is carried out to the error after quantization, will coding
As a result send to receiving terminal with the optimum control vector.So, by adopting the spline curve fitting with high compression rate former
The Mixing compression algorithm of beginning sensor array data and the codings of the Huffman with high encoding precision, can be under degree of precision
The data volume for sending is reduced effectively, to reduce transmission cost, while ensure that the reliability of data.
In the present embodiment, using the spline curve fitting original sensor array data with high compression rate and with high volume
The Mixing compression algorithm of the Huffman codings of code precision, can be prevented effectively from exclusive use spline curve fitting method compression ratio
The excessive application for not being suitable for sensor array data of code book of height and the inadequate shortcoming of precision and exclusive use Huffman codings
The problem of scene.
In the present embodiment, it is assumed that have N number of sensor on an array of sensors, N number of sensor is designated as S1,S2,...,SN, it is N number of
Sensor is deployed at equal intervals from x1To xNDepth bounds in, then between sensor at intervals of (xN-x1)/(N-1)。
In certain specific sampling instant, sensor SiThe data for collecting are yi, in order to represent whole sensor array data, define N
Dimension column vector Y, Y are expressed as formula (1)：
Y=[y1,y2,...,yN]T (1)
In formula (1), Y represents sensor array S1,S2,...,SNIn the sensor array columns that a certain sampling instant is collected
According to, Y is referred to as original sensor array data,TRepresent transposition.
In the present embodiment, it is possible to use Catmull-Rom SPLs as accurately as possible will be original using a small amount of numerical value
Sensor array data Y is expressed, as shown in figure 3, wherein, p1,p2,...,pMControl point is represented, SPL control point
Number is total M, and i-th control point is designated as pi, control point note piCan be with a two-dimensional columns vector representationIts
In, 1≤i≤M, i-th on curve section, i.e., positioned at control point piAnd pi+1Between one section, can be calculated by four control point
Come, i.e. pi-1,pi,pi+1And pi+2, as shown in Figure 4.The wireless biography based on Catmull-Rom SPLs described in the present embodiment
Sensor array data compression method is alternatively referred to as based on Catmull-Rom spline approxmations (CSA, Catmull-Rom spline
The compression method of wireless sensor array data approximation).
In the present embodiment, if control point position is spacedly distributed in x-axis, then control point piPosition p in x-axisi x
Formula (2) can be expressed as：
It follows that Catmull-Rom SPLs have been divided into M-1 sections by control point, put in x-axis in i-th segmentation
Position meetAs Catmull-Rom SPLs have seriality, so waypoint is divided into left section also
It is right section of no impact.
By the value at control pointThe M dimensional vectors of composition are designated as B, then B can be expressed as formula (3)：
In other words, control vectors of the B for Catmull-Rom SPLs.
For the first section and rear of Catmull-Rom battens, as no preposition control point and subsequent control point can not enter
Row data interpolating, is its dummy two extra control point, as shown in figure 5, dummy control point meets formula (4)：
If j-th sensor belongs to the kth of Catmull-Rom SPLsjSection, can obtain kjMeet formula (5)：
In the present embodiment, can be according to the variable coefficient parameter { b of Catmull-Rom SPLsiReconstruct SPL.
If sensor SjBelong to the kth of SPLjSection, then the interpolation parameter of Catmull-Rom spline interpolation expression formulas can be represented
For formula (6)：
In formula (6), tjThe interpolation parameter of j-th sensor is represented, x is also illustrated thatjIn kthjRelative position in section；xjTable
Show the position of j-th sensor；Represent kthjPosition of the individual control point in x-axis,Represent kthj+1Individual control point is in x-axis
On position, kthjThe two ends of section are control pointAnd control point
In the present embodiment, the Catmull-Rom SPLs are a kind of C1Continuous cubic spline curve, therefore,
Catmull-Rom spline interpolations formula can be expressed as multinomial p (t)=c0+c1+c2t2+c3t3, wherein, c0、c1、c2、c3Table
Show polynomial coefficient, the interpolation parameter of t representative polynomials, t is normalized between 0-1.
As shown in figure 4, multinomial p (t)=c0+c1+c2t2+c3t3Constraints be：
P (0)=pi
P (1)=pi+1
According to multinomial p (t)=c0+c1+c2t2+c3t3With multinomial p (t)=c0+c1+c2t2+c3t3Constraints, can
Obtain Catmull-Rom spline interpolation formula：
In formula (7), pi-1,pi,pi+1And pi+2Represent control point.
According to formula (7), it is obtained in xjThe match value at placeFor：
In formula (8), tjThe interpolation parameter of j-th sensor is represented, x is also illustrated thatjIn kthjRelative position in section, such as Fig. 4
Shown, per section needs four control point to calculate, 4 control parameters of per section of needs,Represent
KthjThe corresponding control parameter of section.
According to formula (8), if defining aj,iFor
In formula (9), aj,iRepresent the value positioned at the row of interpolation coefficient matrix jth row i-th；Formula (9) is illustrated with formula
The interpolation coefficient that any one section of Catmull-Rom SPLs.
According to formula (8) and formula (9), can obtain
Further, formula (10) can be equivalent to：
In formula (11), B represents the control vector of Catmull-Rom SPLs, and the element in B is control parameter, that is, control
The value of system point, A represent the interpolation coefficient matrix of Catmull-Rom SPLs,Represent the plan of original sensor array data
Conjunction value.
Formula (11) is also denoted as：
In formula (11), A can be expressed as：
A=[aj,i]N×M (13)
It is intended to ask the optimum control vector for representing Catmull-Rom SPLs to cause original sensor array data Y
With match valueError is minimum, i.e.,：
If the generalized inverse matrix of matrix A is A+, then optimum control vectorAs：
Due to matrix A it is separate with matrix Y, matrix A+Can calculate in advance and store, be that follow-up calculating is won conveniently.
In the present embodiment, to the optimum control vector for obtainingQuantified, according to the optimum control vector after quantization
Reconstruct SPL；According to the SPL of reconstruct, the match value of the original sensor array data is obtained, wherein, it is described
The match value of original sensor array data is：
In the present embodiment, original sensor array data are remained based on the match value of Catmull-Rom SPLs
Major trend, especially when control point is reduced, error can not just have ignored.Then also need to encode error, because
Wireless sensor array data compression method described in this present embodiment is a kind of compression algorithm of mixing.Remember the raw sensory
Error between the match value of device array data and the original sensor array data is：
Error delta is quantified, be designated as Δ '.Using Huffman encode to the result Δ after quantization ' carry out Huffman
Coding, is designated as E, and coding result E and the optimum control vector are constituted packet；The packet of composition is sent to reception
End；Wherein, the code book of Huffman codings can be calculated in advance according to historical data.
Most termination in the present embodiment, after the wireless sensor array data compression method compression described in the present embodiment
Fruit can be expressed as：
As shown in fig. 6, in the present embodiment, based on the compression method of the wireless sensor array data of CSA, needing in transmitting terminal
Will be through following 6 steps：
Step1:Using Catmall-Rom spline curve fitting original sensor array data, obtain making the original biography
The optimum control vector of spline interpolation expression formula of the sensor array data with match value error when minimum
Step2:To the optimum control vector for obtainingQuantified；
Step3:Using optimum control vectorReconstruct Catmall-Rom SPLs, obtain the original sensor array
The match value of data
Step4:Calculate between original sensor array data and the match value of the original sensor array data
Error simultaneously quantifies；
Step5:Huffman codings are carried out to the quantized result of Step4 and obtains E, willPacket is constituted with E；
Step6:Send byThe packet constituted with E is to receiving terminal.
In the specific embodiment of aforementioned wireless sensor array data compression system, further, methods described is also
Including：
In receiving terminal, the packet being made up of coding result and the optimum control vector is received；
According to the optimum control vector reconstruction SPL in the packet for receiving, the raw sensor is obtained
The match value of array data；
Hafman decoding is carried out to the coding result in the packet that receives；
The match value of the original sensor array data is added with decoded result, the sensor array for decompressing out is obtained
Column data.
As shown in fig. 6, in the present embodiment, based on the compression method of the wireless sensor array data of CSA, needing in receiving terminal
Will be through following 4 steps：
Step1:Receive byThe packet constituted with E；
Step2:According to the optimum control vector in the packet for receivingReconstruct Catmall-Rom SPLs, obtain
The match value of the original sensor array data
Step3:Huffman decodings are carried out to the E in the packet that receives；
Step4:By the match value of the original sensor array dataIt is added with the Huffman decoded results of Step4,
Obtain the sensor array data Y' for decompressing out.
In order to verify the effectiveness of the compression method of the wireless sensor array data based on CSA described in the present embodiment,
By the compression method (abbreviation CSA compression algorithms) of the wireless sensor array data based on CSA described in the present embodiment with it is direct
Quantify, two kinds of Lossy Compression Algorithms of APCA are contrasted, the corresponding pack arrangement of three kinds of algorithms is as shown in Figure 7.
The actual sensor array data that three kinds of compression algorithms are collected with Qinhuangdao harbour is as compressed object.Given one
The individual sensor array with 15 sensors, i.e. N=15.Sensor is dilute to collect the flow velocity of different depth water, its data
- 4096 are distributed in thinly between 4096mm/s, that is, needing 13bits represent.So one sensor of each sampling instant
The data volume that array is produced is 13 × 15=195bits.Through CSA compression algorithms emulation also find, range of error -600 to
Between 600mm/s, wherein having 70% to be confined to -128 between 128mm/s, its distribution is as shown in Figure 8.According to the distribution of error
Understand, the Huffman codings in CSA compression algorithms will be highly effective, encodes code book also very little.
It is a kind of basic compression method directly to quantify.Assume maximum error thresholding Emax=8, Q-8 is designated as, that is, is measured
It is 16 to change step-length, then represent that the data of a sensor need 9bits.Therefore one sensor array of a sampling instant is produced
Raw data volume is 9 × 15=135bits.In the same manner, maximum error thresholding Emax=16, Q-16 is designated as, data volume is 120bits.
A kind of limited Lossy Compression Algorithm of error of APCA algorithms.In the algorithm compress after data can with (x, y) this
The array representation that sample occurs in pairs, the i.e. value (data that sensor acquisition is arrived) of the position of sensor and sensor.Sensor
Position needs 4bits to represent, the value of sensor needs 13bits to represent.Number K of data pair needs 4bits.Note APCA-
8 is that the maximum error thresholding of APCA algorithms takes 8, i.e. Emax=8.Can obtain in the same manner, APCA-16 is Emax=16.
Data after the compression of CSA compression algorithms mainly have two parts, and a part is determined for expression Catmull-Rom battens
Long parameter, another part are the elongated data for representing error.The number at control point is set as 4, the step-length for quantifying fixed length parameter is
64, then after quantization, the total amount of data of fixed length argument section is 28bits.The maximum error thresholding of CSA compression algorithms depends on table
Show the quantization step of the elongated data of error, the step-length for quantifying elongated parameter is 16, then maximum error thresholding is Emax=8, note
For CSA-8.Can obtain in the same manner, CSA-16 is Emax=16.
The data that 100 sampling instants are gathered are compressed using different compression algorithms, maximum error thresholding is respectively 8
With 32, as a result as shown in Figure 9, Figure 10.Statistics compression ratio is as shown in the table.
The compression ratio of 1 different compression algorithms of table
| Compression algorithm | Size of data (bit) | Relative size |
| Initial data | 195 | 100 |
| APCA-8 | 238.2 | 122.2 |
| Q-8 | 135 | 69.2 |
| CSA-8 | 114.4 | 58.7 |
| APCA-32 | 192.6 | 98.8 |
| Q-32 | 105 | 53.9 |
| CSA-32 | 77.4 | 39.7 |
Experimental result understands that CSA compression algorithms have some superiority with respect to other algorithms, in EmaxOriginal is compressed to when=8
58.7% for coming, in EmaxOriginal 39.7% is compressed to when=32.The compression effectiveness of CSA compression algorithms is than directly quantization
Method well at least 10%, than APCA compression algorithm at least 60%.
Embodiment two
The present invention also provides a kind of specific embodiment of wireless sensor array data compression system, as the present invention is carried
For wireless sensor array data compression system and aforementioned wireless sensor array data compression method specific embodiment
Corresponding, the wireless sensor array data compression system can pass through to perform the flow process step in said method specific embodiment
Suddenly realizing explaining in the purpose of the present invention, therefore above-mentioned wireless sensor array data compression method specific embodiment
It is bright, the specific embodiment of the wireless sensor array data compression system of present invention offer is also applied for, below the present invention
Specific embodiment in will not be described in great detail.
Referring to shown in Figure 11, the embodiment of the present invention also provides a kind of wireless sensor array data compression system, including：
First determining module 11, in transmitting terminal, using spline curve fitting original sensor array data, being made
Spline interpolation table when the original sensor array data are minimum with the match value error of the original sensor array data
Up to the optimum control vector of formula；
Second determining module 12, for according to the optimum control vector reconstruction SPL for obtaining, obtaining the original
The match value of beginning sensor array data；
3rd determining module 13, for calculating the original sensor array data with the original sensor array data
Match value between error and quantify；
Coding sending module 14, for carrying out Huffman encoding to the error after quantization, by coding result and the optimum
Control vector is sent to receiving terminal.
Wireless sensor array data compression system described in the embodiment of the present invention, in transmitting terminal, is intended using SPL
Original sensor array data are closed, obtains making the plan of the original sensor array data and the original sensor array data
The optimum control vector of spline interpolation expression formula when conjunction value error is minimum；According to the optimum control vector reconstruction sample for obtaining
Bar curve, obtains the match value of the original sensor array data；Calculate the original sensor array data and the original
Error between the match value of beginning sensor array data simultaneously quantifies；Huffman encoding is carried out to the error after quantization, will coding
As a result send to receiving terminal with the optimum control vector.So, by adopting the spline curve fitting with high compression rate former
The Mixing compression algorithm of beginning sensor array data and the codings of the Huffman with high encoding precision, can be under degree of precision
The data volume for sending is reduced effectively, to reduce transmission cost, while ensure that the reliability of data.
In the specific embodiment of aforementioned wireless sensor array data compression system, further, the optimum control
Vector representation processed is：
Wherein,Represent optimum control vector, A+The generalized inverse matrix of A is represented, A represents the interpolation coefficient square of SPL
Battle array, Y represent the original sensor array data.
In the specific embodiment of aforementioned wireless sensor array data compression system, further, described second is true
Cover half block includes：
Quantifying unit, quantifies for the optimum control vector to obtaining；
Reconfiguration unit, for according to the optimum control vector reconstruction SPL after quantization；
Determining unit, for the SPL according to reconstruct, obtains the match value of the original sensor array data；
Wherein, the match value of the original sensor array data is expressed as： Represent original sensor array
The match value of data, A represent the interpolation coefficient matrix of SPL,Represent optimum control vector.
In the specific embodiment of aforementioned wireless sensor array data compression system, further, the coding is sent out
Module is sent to include：
Coding unit, for carrying out Huffman encoding to the error after quantization；
Component units, for coding result and the optimum control vector are constituted packet；
Transmitting element, for the packet of composition is sent to receiving terminal.
In the specific embodiment of aforementioned wireless sensor array data compression system, further, the system is also
Including：
Receiver module, in receiving terminal, receiving the packet being made up of coding result and the optimum control vector；
4th determining module, the optimum control vector reconstruction SPL in the packet received for basis,
Obtain the match value of the original sensor array data；
Decoder module, carries out Hafman decoding for the coding result in the packet to receiving；
5th determining module, for the match value of the original sensor array data is added with decoded result, obtains
The sensor array data for decompressing out.
The above is the preferred embodiment of the present invention, it is noted that for those skilled in the art
For, on the premise of without departing from principle of the present invention, some improvements and modifications can also be made, these improvements and modifications
Should be regarded as protection scope of the present invention.
Claims (10)
1. a kind of wireless sensor array data compression method, it is characterised in that include：
In transmitting terminal, using spline curve fitting original sensor array data, obtain making the original sensor array data
The optimum control vector of spline interpolation expression formula when minimum with the match value error of the original sensor array data；
According to the optimum control vector reconstruction SPL for obtaining, the fitting of the original sensor array data is obtained
Value；
Calculate the error between the match value of the original sensor array data and the original sensor array data and measure
Change；
Huffman encoding is carried out to the error after quantization, coding result and the optimum control vector are sent to receiving terminal.
2. wireless sensor array data compression method according to claim 1, it is characterised in that the optimum control arrow
Amount is expressed as：
Wherein,Represent optimum control vector, A+The generalized inverse matrix of A is represented, A represents the interpolation coefficient matrix of SPL, Y
Represent the original sensor array data.
3. wireless sensor array data compression method according to claim 1, it is characterised in that what the basis was obtained
The optimum control vector reconstruction SPL, the match value for obtaining the original sensor array data include：
The optimum control vector to obtaining quantifies；
According to the optimum control vector reconstruction SPL after quantization；
According to the SPL of reconstruct, the match value of the original sensor array data is obtained；
Wherein, the match value of the original sensor array data is expressed as： Represent original sensor array data
Match value, A represents the interpolation coefficient matrix of SPL,Represent optimum control vector.
4. wireless sensor array data compression method according to claim 1, it is characterised in that after described pair quantifies
Error carries out Huffman encoding, and coding result and the optimum control vector are sent to receiving terminal and included：
Huffman encoding is carried out to the error after quantization；
Coding result and the optimum control vector are constituted into packet；
The packet of composition is sent to receiving terminal.
5. wireless sensor array data compression method according to claim 1, it is characterised in that methods described is also wrapped
Include：
In receiving terminal, the packet being made up of coding result and the optimum control vector is received；
According to the optimum control vector reconstruction SPL in the packet for receiving, the original sensor array is obtained
The match value of data；
Hafman decoding is carried out to the coding result in the packet that receives；
The match value of the original sensor array data is added with decoded result, the sensor array columns for decompressing out is obtained
According to.
6. a kind of wireless sensor array data compression system, it is characterised in that include：
First determining module, in transmitting terminal, using spline curve fitting original sensor array data, obtains making the original
Spline interpolation expression formula when beginning sensor array data is minimum with the match value error of the original sensor array data
Optimum control vector；
Second determining module, for according to the optimum control vector reconstruction SPL for obtaining, obtaining the raw sensory
The match value of device array data；
3rd determining module, for calculating the fitting of the original sensor array data and the original sensor array data
Error between value simultaneously quantifies；
Coding sending module, for carrying out Huffman encoding to the error after quantization, coding result and the optimum control is sweared
Amount is sent to receiving terminal.
7. wireless sensor array data compression system according to claim 6, it is characterised in that the optimum control arrow
Amount is expressed as：
Wherein,Represent optimum control vector, A+The generalized inverse matrix of A is represented, A represents the interpolation coefficient matrix of SPL, Y
Represent the original sensor array data.
8. wireless sensor array data compression system according to claim 6, it is characterised in that described second determines mould
Block includes：
Quantifying unit, quantifies for the optimum control vector to obtaining；
Reconfiguration unit, for according to the optimum control vector reconstruction SPL after quantization；
Determining unit, for the SPL according to reconstruct, obtains the match value of the original sensor array data；
Wherein, the match value of the original sensor array data is expressed as： Represent original sensor array data
Match value, A represents the interpolation coefficient matrix of SPL,Represent optimum control vector.
9. wireless sensor array data compression system according to claim 6, it is characterised in that the coding sends mould
Block includes：
Coding unit, for carrying out Huffman encoding to the error after quantization；
Component units, for coding result and the optimum control vector are constituted packet；
Transmitting element, for the packet of composition is sent to receiving terminal.
10. wireless sensor array data compression system according to claim 6, it is characterised in that the system is also wrapped
Include：
Receiver module, in receiving terminal, receiving the packet being made up of coding result and the optimum control vector；
4th determining module, for according to the optimum control vector reconstruction SPL in the packet for receiving, obtaining
The match value of the original sensor array data；
Decoder module, carries out Hafman decoding for the coding result in the packet to receiving；
5th determining module, for the match value of the original sensor array data is added with decoded result, is decompressed
Contract the sensor array data for.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112153034A (en) * | 2020-09-17 | 2020-12-29 | 青岛海研电子有限公司 | Data lossless compression method applied to sea surface drifting observation body |
| CN114584150A (en) * | 2022-03-04 | 2022-06-03 | 南京邮电大学 | Sensor network signal completion method realized by utilizing deep neural network |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001035534A2 (en) * | 1999-11-09 | 2001-05-17 | Nokia Corporation | Variable length encoding of compressed data |
| CN101064850A (en) * | 2006-04-24 | 2007-10-31 | 富士通株式会社 | Image compression apparatus, image compression program and image compression method |
| CN101925091A (en) * | 2010-07-29 | 2010-12-22 | 中国地质大学（武汉） | Data compression method of wireless sensor network nodes based on non-threshold |
| CN103812509A (en) * | 2014-01-20 | 2014-05-21 | 北京科技大学 | Marine linear sensor array data compression method based on discrete cosine transformation |
| CN105592313A (en) * | 2014-10-21 | 2016-05-18 | 广东中星电子有限公司 | Grouped adaptive entropy coding compression method |
-
2016
- 2016-10-31 CN CN201610930928.9A patent/CN106572093B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001035534A2 (en) * | 1999-11-09 | 2001-05-17 | Nokia Corporation | Variable length encoding of compressed data |
| CN101064850A (en) * | 2006-04-24 | 2007-10-31 | 富士通株式会社 | Image compression apparatus, image compression program and image compression method |
| CN101925091A (en) * | 2010-07-29 | 2010-12-22 | 中国地质大学（武汉） | Data compression method of wireless sensor network nodes based on non-threshold |
| CN103812509A (en) * | 2014-01-20 | 2014-05-21 | 北京科技大学 | Marine linear sensor array data compression method based on discrete cosine transformation |
| CN105592313A (en) * | 2014-10-21 | 2016-05-18 | 广东中星电子有限公司 | Grouped adaptive entropy coding compression method |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112153034A (en) * | 2020-09-17 | 2020-12-29 | 青岛海研电子有限公司 | Data lossless compression method applied to sea surface drifting observation body |
| CN114584150A (en) * | 2022-03-04 | 2022-06-03 | 南京邮电大学 | Sensor network signal completion method realized by utilizing deep neural network |
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|---|---|
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