CN106452666B - A kind of lightweight data compression method applied to wireless sensor network - Google Patents
A kind of lightweight data compression method applied to wireless sensor network Download PDFInfo
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- CN106452666B CN106452666B CN201610625625.6A CN201610625625A CN106452666B CN 106452666 B CN106452666 B CN 106452666B CN 201610625625 A CN201610625625 A CN 201610625625A CN 106452666 B CN106452666 B CN 106452666B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
- H04L1/0006—Systems modifying transmission characteristics according to link quality, e.g. power backoff by adapting the transmission format
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
- H04L1/0023—Systems modifying transmission characteristics according to link quality, e.g. power backoff characterised by the signalling
- H04L1/0028—Formatting
- H04L1/0029—Reduction of the amount of signalling, e.g. retention of useful signalling or differential signalling
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W52/02—Power saving arrangements
- H04W52/0209—Power saving arrangements in terminal devices
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- 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
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Abstract
The invention proposes one kind to be applied to one of wireless sensor network lightweight data compression method, carries out compression processing to redundant data at acquisition node end and aggregation node end respectively.At acquisition node end, using perception data in continually changing characteristic, proposition becomes data compression algorithm CLDCA and eliminates redundant data in sampled data.There are redundancy between aggregation node end, the perception data uploaded for acquisition node in cluster, proposes that the data anastomosing algorithm WDAA of weight coefficient compresses perception data, greatly reduce the data redundancy of aggregation node.The utilization rate of network bandwidth can be improved acquiring and converging both ends elimination redundant data, save node energy.
Description
Technical field
The present invention is a kind of lightweight data compression method applied in wireless sensor network.It is mainly used for solving nothing
The Redundancy that data are transmitted between line sensor, belongs to wireless sensor network technology field.
Background technique
With the fast development of technology of wireless sensing network, using also more and more extensive in life in wireless sense network,
Wireless sensor network be it is application oriented, close to the network system in the objective physical world, generate and development always all with answer
With being associated.Pass through the deduction of different field researcher for many years, WSN (Wireless Sensor Network) technology exists
Military field, precision agriculture, security monitoring, environment monitoring, building field, medical monitoring, industrial monitoring, intelligent transportation, logistics
The application in the fields such as management, free space exploration, smart home has obtained sufficient affirmative and displaying.With wireless sensor network
Network is greatly developed, and volume of transmitted data is increasing in wireless sensor network, wherein containing a large amount of redundant data, transmits this
A little redundant datas can consume a large amount of energy of node, shorten node lifetime, influence network performance.
The data compression method that data are transmitted in wireless sensor network can be effectively reduced the transmission of redundant data, save
Network bandwidth.Data compression is carried out to the correlation of environmental data in time at acquisition node end, at aggregation node end to cluster
The data that interior acquisition node uploads carry out fusion compression, reduce redundant data.
This method has 3 purposes:
(1) by the correlation of environmental data in time, CLDCA (Changeable Length Data is utilized
Compression Algorithm) algorithm compresses perception data, reduce upload of the acquisition node to redundant data, section
Node energy consumption is saved, service life is postponed；
(2) by WDAA (Weighting DataAggression Algorithm) algorithm to aggregation node to being adopted in cluster
The perception data that collection node uploads is merged, and can guarantee the accuracy of data, reduces redundant data；
(3) by reducing the transmission quantity of redundant data in wireless sensor network, wireless sensor network bandwidth can be improved
Utilization rate；
For a large amount of redundant data problem is transmitted in wireless sensor network, this method can acquire the method for the present invention
Node side and aggregation node end efficiently reduce the transmission quantity of redundant data, save node energy consumption, improve network performance.
Summary of the invention
Technical problem: the object of the present invention is to provide a kind of lightweight data compression sides applied to wireless sensor network
Method, this method can compress redundant data at acquisition node end and aggregation node end, reduce the transmission of node redundancy data,
Save node energy consumption.
Technical solution: method of the invention is saved using elongated data compression algorithm CLDCA to acquisition at acquisition node end
The perception data that point obtains is compressed, these perception datas have correlation in time, can be effectively compressed using CLDCA
Data are same as, the traffic volume of less redundant data saves node energy consumption.The data of data weighting coefficient are used at aggregation node end
The perception data that blending algorithm WDAA uploads acquisition node in cluster merges, which can guarantee the standard of data after fusion
True property, while can be reduced the traffic volume of redundant data, so as to save node energy consumption.
Architecture
This method is by the architecture of acquisition node and aggregation node composition one data acquisition and transmission in cluster, cluster
Interior acquisition node is mainly responsible for the acquisition of data, then carries out compression processing to the data of acquisition, reduces redundant data, then again
It carries out uploading compressed perception data.Aggregation node is mainly responsible for the perception data for receiving that acquisition node uploads in cluster, simultaneously
The data uploaded to acquisition node in cluster merge, and reduce the amount of redundant data of upload, save node energy consumption.
Method flow
In entire design method, it is broadly divided into two parts: acquisition node end, aggregation node end.Acquisition node is mainly born
The acquisition for blaming data, compresses the data of acquisition, then upload to the data after compressed.Aggregation node is mainly responsible for
The perception data uploaded to acquisition node in cluster merges, then uploads to data after fusion.The elongated data of acquisition node
Compression algorithm CLDCA executes process (such as Fig. 1):
Lightweight data compression method of the invention is at acquisition node end using elongated data compression algorithm CLDCA to use
Data are compressed, and redundant data is eliminated；At aggregation node end, using the data anastomosing algorithm WDAA of data weighting coefficient to remittance
Poly- node side carries out data fusion compression.The acquisition node data compression is specific as follows:
Step 1.1) acquisition node samples environmental data, and sampled data number adds 1；
Step 1.2) judges whether sampled data number exceeds setting value, without departing from step 1.3) is executed, beyond execution
Step 1.6)；
Step 1.3) is compared with desired value, judges whether sampled data needs to upload, and is needed to upload data and is then executed
Step 1.4, it is not necessary to which 1.1) upload thens follow the steps；
Step 1.4) will upload data and be transferred to sending module, and setting flag place value is 1, show have data to be uploaded to
Aggregation node；
Data are uploaded to aggregation node by step 1.5) sending module, are gone to step 1.1)；
Label place value is set as 0, by the statistics of sampled data if label place value is 1 by step 1.6) judge mark position
Number is also set as 0, executes step 1.1), if label place value is 0, executes step 1.7)；
Step 1.7) acquisition node is forced to upload a sampled data, counts clear 0.
The data anastomosing algorithm WDAA using data weighting coefficient carries out data fusion compression to aggregation node end
It is specific as follows:
Step 2.1) receives the perception data that acquisition node uploads；
Step 2.2) calculates the desired value E of perception data, calculates the absolute of the difference of each perception data and desired value
Value is just deleted if the absolute value of difference exceeds threshold value, realizes the perception data that optimization acquisition node uploads；
Step 2.3) calculates the standard deviation of the perception data after optimization | σ |, by perception data value Fen Bie Yi ﹙-∞, E-
2* | σ |], ﹙ E-2* | σ |, E- | σ |], ﹙ E- | σ |, E+ | σ |], ﹙ E+ | σ |, E+2* | σ |], ﹙ E+2* | σ | ,+∞ ﹚ standard is divided into 5
Level, centered on the desired value of calculating in step 2.2), standard deviation determines the amplitude of optimised rear perception data distribution；
Step 2.4) calculates the average value of perception data in each level；
Step 2.5) finds out perception data in each level and value Si, then calculate the total of perception data in all levels
And Sum, then calculate the weight coefficient S of perception data in each leveli/Sum；
Step 2.6) can calculate each level according to the product of the average value of perception data and weight coefficient in each level
Weighted value sums the weighted value of each level, and value is required fusion value；
Step 2.7) uploads data after fusion, then executes step 2.1).
The utility model has the advantages that the method for the present invention proposes one of wireless sensor network lightweight data compression method, it should
Method respectively compresses data at acquisition node end and aggregation node end.It is calculated at acquisition node end using elongated data compression
Method CLDCA compresses perception data, and the sequence which can change calculating desired value according to the transformation of environmental data is long
Degree, can constantly adapt to the continually changing demand of environment, compressing at acquisition node end to data can be with less aggregation node end
To the reception amount of redundant data, in this way the reception energy consumption of the less aggregation node of energy.The number of weight coefficient is used at aggregation node end
Data fusion is carried out according to the data characteristic that blending algorithm WDAA can be uploaded for acquisition node, guarantees the accurate of fused data
Property, at the same can significantly less redundant data upload amount.
Combination: in the present invention, forming an architecture for acquisition node and aggregation node, since collection terminal pair
Redundant data is compressed, meanwhile, also redundant data is compressed when data are transferred to aggregation node, is reduced at both ends superfluous
Remainder evidence.
High efficiency: the present invention can effectively in less wireless sensor network redundant data transmission quantity, save node energy
Source extends node service life, improves network bandwidth and utilizes interest rate.
Detailed description of the invention
Fig. 1 is that the elongated data compression algorithm CLDCA of the method for the present invention executes flow diagram.
Fig. 2 is that the data anastomosing algorithm WDAA of the method for the present invention weight coefficient executes flow diagram.
Fig. 3 is that redundant data process schematic diagram is eliminated in the CLDCA of the method for the present invention.
Fig. 4 is the aggregation node optimization perception data flow diagram of the method for the present invention.
Fig. 5 is that each state levels data and Sum are calculated in the WDAA of the method for the present inventioni, data amount check miFlow diagram.
Fig. 6 is that weight coefficient p is calculated in the WDAA of the method for the present inventioniWith the average value w of data in each state levelsiProcess is shown
It is intended to.
Fig. 7 is that the method for the present invention realizes schematic diagram.
Specific embodiment
For the convenience of description, it is assumed that there is following application example:
Definition: the data set S, S of wireless sensor network interior joint acquisition are in a cycle of acquisition node storage
Perception data collection, S={ di(i ∈ [1, n]), diFor the perception data of acquisition node.
Primary variables and its definition
Variable name meaning
TpAcquisition node uploads the data period
diThe perception data of acquisition
xiAggregation node terminates the data received
λ user's acceptable error threshold
N n is the length of the data sequence of node acquisition
E length is the desired value of n perception data
μ error amount μ=| E-d'|
piWeight coefficient
Flag acquisition node uploads data markers position, and Flag==1 indicates data upload；Flag==0, indicate without
Data upload
NTpAcquisition node is in cycle TpThe maximum length N of interior acquisition perception data sequenceTp
Sum counting variable, for judging whether more than NTp
One, the acquisition node of deployment in the environment
Acquisition node obtains environmental data, while compressing to environmental data, less redundant data, and be uploaded to convergence
Node.
Acquisition node compressed data detailed process is as follows:
Step 1: acquisition node samples environment, perception data sequence S={ d is initializedi(i ∈ [1, n]):
1: initialization perception data length n；
2: acquisition node samples environment, and memory length is the perception data of n.
Step 2: calculating desired value:
Calculate the desired value of perception data；
Step 3: carrying out elimination redundant data (such as Fig. 3)；
The minimum value of perception data sequence length is 1 in CLDCA algorithm, maximum value TPPerception data is long in time cycle
The value of degree.Perception data length can be adjusted with environmental change and adaptively, constantly meet the change of environment.
It is mainly that response environment data fluctuations are quite acute that CLDCA algorithm, which sets 1 for perception data sequence length minimum value,
Strong situation, collected data may have great disparity with the data of preceding one acquisition to acquisition node each time, in order to guarantee number
According to validity, need to upload data in real time.
Two, aggregation node carries out data fusion
Aggregation node receives the perception data that acquisition node uploads in cluster, to carry out to data before data are merged excellent
Change, handle " noise point ", " noise point " is abnormal data, and optimization data are the accuracy in order to improve fused data.WDAA
It is as follows to execute process:
Step 1: optimization perception data:
1: aggregation node stores perception data, statistical data number N；
2: calculating the desired value of perception data；
3: processing " noise point " (such as Fig. 4):
Step 2: relevant parameter is calculated:
1: calculate average value and data summation:
2: calculate standard deviation:
3: calculating each state levels data and Sumi, data amount check mi(such as Fig. 5)；
Step 3: weight coefficient p is calculatediWith the average value w of data in each state levelsi(such as Fig. 6)；
Step 4: fused data is calculated:
Step 5: fused data are uploaded；
W is that the perception data that aggregation node uploads acquisition node is merged as a result, WDAA algorithm can be for number
Data fusion is carried out according to the specific gravity occupied in data set, can be improved the accuracy of data in this way.Aggregation node can simultaneously
It reduces and uploads redundant data, improve network bandwidth utilization factor, save energy consumption.
Claims (1)
1. a kind of lightweight data compression method applied to wireless sensor network, which is characterized in that the lightweight data pressure
Contracting method compresses the data of acquisition using elongated data compression algorithm CLDCA at acquisition node end, eliminates redundant data；
At aggregation node end, data fusion compression is carried out to aggregation node end using the data anastomosing algorithm WDAA of data weighting coefficient；
Described is specific as follows to compress using data using elongated data compression algorithm CLDCA at acquisition node end:
Step 1.1) acquisition node samples environmental data, and sampled data number adds 1；
Step 1.2) judges whether sampled data number exceeds setting value, without departing from step 1.3) is executed, beyond execution step
1.6)；
Step 1.3) is compared with desired value, judges whether sampled data needs to upload, and is needed to upload data and is thened follow the steps
1.4, it is not necessary to which 1.1) upload thens follow the steps；
Step 1.4) will upload data and be transferred to sending module, and setting flag place value is 1, show have data to be uploaded to convergence
Node；
Data are uploaded to aggregation node by step 1.5) sending module, are gone to step 1.1)；
Label place value is set as 0, by the statistical number of sampled data if label place value is 1 by step 1.6) judge mark position
It is set as 0, executes step 1.1), if label place value is 0, executes step 1.7)；
Step 1.7) acquisition node is forced to upload a sampled data, counts clear 0；
It is specific that the data anastomosing algorithm WDAA using data weighting coefficient carries out data fusion compression to aggregation node end
It is as follows:
Step 2.1) receives the perception data that acquisition node uploads；
Step 2.2) calculates the desired value E of perception data, calculates the absolute value of the difference of each perception data and desired value, such as
The absolute value of fruit difference exceeds threshold value, is just deleted, and realizes the perception data that optimization acquisition node uploads；
Step 2.3) calculates the standard deviation of the perception data after optimization | σ |, by perception data value Fen Bie Yi ﹙-∞, E-2* | σ
|], ﹙ E-2* | σ |, E- | σ |], ﹙ E- | σ |, E+ | σ |], ﹙ E+ | σ |, E+2* | σ |], ﹙ E+2* | σ | ,+∞ ﹚ standard is divided into 5 layers
Secondary, centered on the desired value of calculating in step 2.2), standard deviation determines the amplitude of optimised rear perception data distribution；
Step 2.4) calculates the average value of perception data in each level；
Step 2.5) finds out perception data in each level and value Si, then the summation Sum of perception data in all levels is calculated,
The weight coefficient S of perception data in each level is calculated againi/Sum；
Step 2.6) can calculate the weight of each level according to the product of the average value of perception data and weight coefficient in each level
Value, the weighted value of each level is summed, and value is required fusion value；
Step 2.7) uploads data after fusion, then executes step 2.1).
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| CN103346864A (en) * | 2013-07-05 | 2013-10-09 | 哈尔滨工业大学深圳研究生院 | Data processing method and system suitable for wireless distributed perception system |
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| CN103346864A (en) * | 2013-07-05 | 2013-10-09 | 哈尔滨工业大学深圳研究生院 | Data processing method and system suitable for wireless distributed perception system |
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