CN102752798A - Method for losslessly compressing data of wireless sensor network - Google Patents
Method for losslessly compressing data of wireless sensor network Download PDFInfo
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- CN102752798A CN102752798A CN2012102552341A CN201210255234A CN102752798A CN 102752798 A CN102752798 A CN 102752798A CN 2012102552341 A CN2012102552341 A CN 2012102552341A CN 201210255234 A CN201210255234 A CN 201210255234A CN 102752798 A CN102752798 A CN 102752798A
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Abstract
The invention relates to a method for losslessly compressing data of a wireless sensor network. The method comprises the following steps of: acquiring original data and storing the original data to an SD (Secure Digital ) card by a sensor network node by utilizing a sensor; determining a maximum data length n to be compressed and processed each time by the node according to the volume of an internal memory; reading out the original data with the length n by the node, and calculating a difference sequence between adjacent data; and carrying out self-adaptive Rice coding on the difference sequence and an original data initial value by the node, then replacing the original data in the SD card with the coded compressed data, and transmitting the compressed data to an aggregation terminal through a wireless network. According to the method, the high-efficiency lossless compression of the data is realized by utilizing the characteristics of the original data in the sensor network, and the computation complexity is low, so that the method is suitable for various kinds of data monitored by utilizing the wireless sensor networks, such as temperature, humidity and mechanical vibration signals.
Description
Technical field
The present invention relates to a kind of data compression technique field, particularly a kind of data compression method that is used for wireless sensor network.
Background technology
Along with the progress of science and technology, the development of technology such as MEMS, integrated circuit, embedded and radio communication, wireless sensor network is applied to growing field.Wireless sensor network is made up of the sensor network nodes of a large amount of self-organizings; Node generally is made up of three modules: the acquisition module that utilizes transducer that physical quantity in the real world is monitored; Carry out the communication module of wireless transmission with other nodes, and the computing module that carries out data processing and management on the sheet.Wireless sensor network node is generally by battery supplied energy non-exchange or charging, and how control energy consumption is an important problems to prolong the node service life.And in three modules of wireless sensor network node, the energy that wireless communication module consumed accounts for the overwhelming majority, therefore, needs to reduce the radio communication amount to cut down the consumption of energy, and promptly need before transfer of data, carry out processed compressed to initial data.
In data compression, compression algorithm can be divided into lossy compression method and lossless compress.Though the lossy compression method compression ratio is higher, can lose a part of raw information.The compression ratio of lossless compress is generally not high, but after convergence terminal is to the reduction of data decompress(ion), can not lose any raw information.In a lot of fields, need to guarantee the integrality of primary monitoring data, therefore tackling initial data carries out lossless compress.
When design wireless sensor network data lossless compression algorithm, not only need consider the compression ratio of initial data, also to consider limited computational power and memory headroom size on the sensor network nodes.But the lossless compression algorithm or the computation complexity that apply to wireless sensor network at present are high, or compression ratio is undesirable, or only are suitable for some specific data.To changing data slowly,, computation complexity and compression ratio have been taken into account such as a certain compression algorithm like temperature data; But to the violent data of some other variation; Such as mechanical oscillation signal, compression effectiveness is undesirable, even data become big situation on the contrary after compression occurring.
Summary of the invention
The object of the invention just provides a kind of method of wireless sensor network data lossless compress; It takes into account computation complexity and compression ratio size simultaneously; And be applicable to the lossless compress of multiple wireless sensor network data; Make it can reduce the radio communication amount, solve the high problem of transfer of data power consumption in the sensor network.
The objective of the invention is to realize that through such technical scheme concrete steps are following:
1) sensor network nodes utilizes transducer to accomplish the collection to initial data, and the initial data that collects is carried out the A/D conversion, stores in the SD card on the sensor network nodes;
2) node calculates the maximum data length n that at every turn carries out processed compressed according to the free memory size;
3) node reads the initial data that length is n from the SD card, calculates the sequence of differences between adjacent data in the initial data
;
4) node converts the sequence after merging initial value
merging of sequence of differences
with the initial data of initial data on the occasion of sequence
;
5) node utilizes self adaptation Rice coding to carry out compressed encoding to what step 4) obtained on the occasion of sequence
;
6) packed data after node will be encoded substitutes the initial data in the SD card, and arrives convergence terminal through wireless network transmissions;
7) check whether also have data to need compression, then to turn to step 3), do not finish if then compress if having.
Further, when in the said step 1) initial data being gathered, the precision of its A/D converter is 16.
Further, the computing formula of the n of maximum data length step 2) is:
Further, length is that initial data
occupation space of n is the 512Bytes multiple.
Further; Read length in the said step 3) and be n initial data is
, sequence of differences
; Wherein
; I is i data in the initial data,
.
Further, the formula that converts into described in the step 4) on the occasion of sequence
is:
；
Further; Conversion obtains carrying out entropy coding on the occasion of sequence
to step 4) to utilize self adaptation Rice coding in the said step 5), and the initial value of auto-adaptive parameter K is made as 256 in the self adaptation Rice encoder.
Owing to adopted technique scheme, the present invention to have following advantage:
The present invention utilizes these characteristics of data in the real wireless sensor network, proposes a kind of method of wireless sensor network data lossless compress.This lossless compression method computation complexity is low, can operate in this arithmetic speed of wireless sensor network node and memory size all in the constrained environment.Simultaneously, this lossless compression method is applicable to that the multiple wireless sensor network that utilizes carries out data monitored, changes signal slowly like temperature, humidity etc., perhaps is similar to the violent signal of this variation of mechanical oscillation signal.The present invention can effectively compress node data, the purpose that reduce the radio communication amount to reach, prolongs the node service life, and the ability effective application is in fields such as environmental monitoring, structure monitoring and mechanical health monitorings.
Other advantages of the present invention, target and characteristic will be set forth in specification subsequently to a certain extent; And to a certain extent; Based on being conspicuous to those skilled in the art, perhaps can from practice of the present invention, obtain instruction to investigating of hereinafter.Target of the present invention and other advantages can realize and obtain through following specification and claims.
Description of drawings
Description of drawings of the present invention is following.
Fig. 1 is a lossless compression method FB(flow block) of the present invention;
Fig. 2 is the compression ratio comparison diagram of the present invention to mechanical oscillation data and temperature data;
Fig. 3 for the present invention and other lossless compression methods to the compression ratio of vibration signal to colon.
Embodiment
Below in conjunction with accompanying drawing and embodiment the present invention is described further.
A kind of method of wireless sensor network data lossless compress, concrete steps are following:
1) sensor network nodes utilizes transducer to accomplish the collection to initial data, and the initial data that collects is carried out the A/D conversion, stores in the SD card on the sensor network nodes;
2) node calculates the maximum data length n that at every turn carries out processed compressed according to the free memory size;
3) node reads the initial data that length is n from the SD card, calculates the sequence of differences between adjacent data in the initial data
;
4) node converts the sequence after merging initial value
merging of sequence of differences
with the initial data of initial data on the occasion of sequence
;
5) node utilizes self adaptation Rice coding to carry out compressed encoding to what step 4) obtained on the occasion of sequence
;
6) packed data after node will be encoded substitutes the initial data in the SD card, and arrives convergence terminal through wireless network transmissions;
7) check whether also have data to need compression, then to turn to step 3), do not finish if then compress if having.
The present invention utilizes these characteristics of data in the real wireless sensor network, proposes a kind of method of wireless sensor network data lossless compress.This lossless compression method computation complexity is low, can operate in this arithmetic speed of wireless sensor network node and memory size all in the constrained environment.Simultaneously, this lossless compression method is applicable to that the multiple wireless sensor network that utilizes carries out data monitored, changes signal slowly like temperature, humidity etc., perhaps is similar to the violent signal of this variation of mechanical oscillation signal.The present invention can effectively compress node data, the purpose that reduce the radio communication amount to reach, prolongs the node service life, and the ability effective application is in fields such as environmental monitoring, structure monitoring and mechanical health monitorings.
When in the said step 1) initial data being gathered, the precision of its A/D converter is 16.The response speed of system when gathering in order to improve node guarantees the sampling precision of node, and the initial data that node collects is also handled without compressed encoding, but directly stores in the SD card on the node.The mode of operation of SD card should be the SDIO pattern, rather than the SPI pattern.
Said step 2) calculating the maximum data length of at every turn carrying out data compression process according to the internal memory available size in is n:
Wherein, the size of the maximum free memory of application program on
expression node;
is binary file cache size in the self adaptation Rice encoder; The data block of what SD cards of presentation code device buffer memory; Its value is set by initial parameter more than or equal to 1; The value of n should be 256 multiple; Be that length is that initial data
the occupation space size of n should be the multiple of 512 Bytes; This is because in the SD card of original data storage on node; And the least unit that SD card data transmit is to be unit with the piece; Default block size is 512 Bytes; The data that at every turn read the monoblock size can improve the reading speed of node initial data, and can make full use of internal memory limited on the node.
Node is that the initial data of n is carried out processed compressed to data length, and cache size is:
Wherein
is binary file cache size in the self adaptation Rice encoder; The data block of what SD cards of presentation code device buffer memory; Its value is set by initial parameter more than or equal to 1;
should be 512 multiple, and promptly n should be 256 multiple.
Can find out; The spatial cache that the compression algorithm of the present invention's design takies is very little; As
; When
; The cache size that takies is merely 1 KB, and this only considers that also the monoblock readwrite performance that can improve the SD card that reads and writes data limits.Simultaneously; The total length that can find out cache size and initial data is irrelevant; The total length that is the needed memory headroom of lossless compression method that designs of the present invention and initial data is irrelevant, and this makes it be applicable to this memory headroom constrained environment of wireless sensor network node.
Node reads the initial data that length is n
from the SD card in the said step 3); Be saved in the metadata cache
; Calculate the sequence of differences
of adjacent data in the initial data then; Computing formula is
,
.Node is for accomplishing the calculating and the preservation of sequence of differences; The initial value of buffer memory initial data
at first; From
, take out adjacent 2 initial data
and
then successively; Wherein
; Calculate difference
; Then with
in difference
replacement
; Node does not stop from
, to take out initial data; All calculate and preserve until all differences and finish, last.
The main purpose of said step 4) is that sequence is converted on the occasion of sequence, carries out early-stage preparations for self adaptation Rice encoder carries out encoding compression to data.Wireless sensor network node at first with during the initial data initial value
of buffer memory in the step 3) substitutes
not by the initial data
of difference replacement, make the data sequence in the metadata cache become
.Then data sequence
is converted on the occasion of sequence
, conversion formula is:
That utilizes self adaptation Rice coding in the said step 5) conversion obtains to step 4) carries out compressed encoding on the occasion of sequence
, and the initial value of auto-adaptive parameter k is made as 256 in the self adaptation Rice encoder.
The compression ratio of lossless compression algorithm and the characteristic of data are closely related, have no a kind of lossless compression algorithm applicable to all types of data.For the data in the wireless sensor network, generally obtain through transducer continuous sampling by node, difference is less between adjacent data, has certain correlation.For utilizing wireless sensor network to carry out the data of continuous monitoring; No matter be to change data slowly in time; Like temperature, moisture signal; Still change violent data in time; Like mechanical oscillation signal; The sequence that difference in the initial data between the adjacent data is formed all shows identical rule: the absolute value of each difference in the sequence of differences
is little more a lot of than the absolute value
of each value in the original data sequence; Simultaneously; The frequency that the data that absolute value is more little in the sequence of differences occur is high more, and its distribution is close with laplacian distribution.
The compressed file that node obtains after step 5) is encoded in the said step 6) is saved in the SD card again, and alternative initial data, to reduce the storage size of data, improves the space availability ratio of SD card.Simultaneously, node is transferred to convergence terminal through wireless communication module with the data file after the compression, has reduced the radio communication amount, when improving the wireless bandwidth utilance, has reduced the energy consumption of node.
Embodiment:
The processor adopting of node is based on the microprocessor STM32F103RET6 of 32 ARM Cortex M3 kernel frameworks; The running frequency maximum can reach 72MHz; Have the Flash space of 256KB and the internal RAM of 64KB, the high speed SD card storage size of expanding on the node is 4GB.
According to node memory space size;
is set to 12;
size that is self adaptation Rice encoder is 6KB; It is 5120 that the maximum data length n that at every turn carries out data compression process is set; Promptly
size is 10KB, carries out the needed cache size of data compression and is total up to 16KB.
The performance of data compression method can be passed judgment on through compression ratio, and the computing formula of compression ratio CR is:
Instance is as shown in Figure 2 to the compression ratio contrast of mechanical oscillation data and temperature data; From experimental result, can find out; On instance, 7 groups of different mechanical oscillation experimental datas are carried out data compression process, the mean pressure shrinkage that obtains is 84.08%, promptly can compress the data about 16%.On instance, temperature data is carried out compression experiment, obtain good compression effectiveness, compression ratio is 37.96%, promptly compressible about 63% data.
Lossless compression method of the present invention and other lossless compression methods are as shown in Figure 3 to the compression ratio contrast of vibration data; Find the compression method that other are famous; Such as WinRAR and Gzip; Also can only compress the data about 5% to the mechanical oscillation data, and compression method of the present invention can compress the data about 16%.
The compression ratio size of lossless compression method receives the influence of data characteristic, and changing size, signal amplitude distribution situation etc. between adjacent data all has very big influence to compression ratio.The compression method of the present invention's design like temperature, moisture signal, has compression effectiveness preferably, compressible 63% data to tempolabile signal.Because variation is bigger between the vibration signal adjacent data, relevance is lower between adjacent data, and the compression method compression ratio of the present invention's design is not high, can only compress the data about 16%.But to mechanical oscillation signal, other classical compression methods also can only compress the data about 5%.Simultaneously, the compression method and the data by MoM and MEI of the present invention's design, the memory headroom that needs is littler, and computation complexity is lower.Therefore, a kind of wireless sensor network data lossless compression method provided by the invention has bigger advantage, can be applied to a plurality of fields that utilize wireless sensor network to monitor, like environmental monitoring, structure monitoring and mechanical health monitoring etc.
Explanation is at last; Above embodiment is only unrestricted in order to technical scheme of the present invention to be described; Although with reference to preferred embodiment the present invention is specified, those of ordinary skill in the art should be appreciated that and can make amendment or be equal to replacement technical scheme of the present invention; And not breaking away from the aim and the scope of present technique scheme, it all should be encompassed in the middle of the claim scope of the present invention.
Claims (6)
1. the method for a wireless sensor network data lossless compress is characterized in that, concrete steps are following:
1) sensor network nodes utilizes transducer to accomplish the collection to initial data, and the initial data that collects is carried out the A/D conversion, stores in the SD card on the sensor network nodes;
2) node calculates the maximum data length n that at every turn carries out processed compressed according to the free memory size;
3) node reads the initial data that length is n from the SD card, calculates the sequence of differences between adjacent data in the initial data
;
4) node converts the sequence after merging initial value
merging of sequence of differences
with the initial data of initial data on the occasion of sequence
;
5) node utilizes self adaptation Rice coding to carry out compressed encoding to what step 4) obtained on the occasion of sequence
;
6) packed data after node will be encoded substitutes the initial data in the SD card, and arrives convergence terminal through wireless network transmissions;
7) check whether also have data to need compression, then to turn to step 3), do not finish if then compress if having.
2. the method for a kind of wireless sensor network data lossless compress as claimed in claim 1 is characterized in that: when in the said step 1) initial data being gathered, the precision of its A/D converter is 16.
3. the method for a kind of wireless sensor network data lossless compress as claimed in claim 1 is characterized in that step 2) described in the computing formula of maximum data length n be:
4. like the method for claim 1 or 3 described a kind of wireless sensor network data lossless compress, it is characterized in that: length is that initial data
occupation space of n is the 512Bytes multiple.
5. the method for a kind of wireless sensor network data lossless compress as claimed in claim 1; It is characterized in that; Read length in the said step 3) and be n initial data is
, sequence of differences
; Wherein
; I is i data in the initial data,
.
6. the method for a kind of wireless sensor network data lossless compress as claimed in claim 1; It is characterized in that the formula that converts into described in the step 4) on the occasion of sequence
is:
The method of a kind of wireless sensor network data lossless compress as claimed in claim 1; It is characterized in that: conversion obtains carrying out entropy coding on the occasion of sequence
to step 4) to utilize self adaptation Rice coding in the said step 5), and the initial value of auto-adaptive parameter K is made as 256 in the self adaptation Rice encoder.
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| CN103390339A (en) * | 2013-07-30 | 2013-11-13 | 国家电网公司 | Coordinator for temperature acquisition of substation equipment |
| CN105992154A (en) * | 2015-02-02 | 2016-10-05 | 酷派软件技术（深圳）有限公司 | Position data transmission method and device and positioning system |
| CN103944580B (en) * | 2014-04-14 | 2017-07-04 | 天津普仁万合信息技术有限公司 | A kind of lossless compression method of physiology condition sensor continuous collecting signal |
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| CN103390339A (en) * | 2013-07-30 | 2013-11-13 | 国家电网公司 | Coordinator for temperature acquisition of substation equipment |
| CN103944580B (en) * | 2014-04-14 | 2017-07-04 | 天津普仁万合信息技术有限公司 | A kind of lossless compression method of physiology condition sensor continuous collecting signal |
| CN105992154A (en) * | 2015-02-02 | 2016-10-05 | 酷派软件技术（深圳）有限公司 | Position data transmission method and device and positioning system |
| CN108267510A (en) * | 2018-02-08 | 2018-07-10 | 南京林业大学 | Wooden component health monitoring systems and method |
| CN108267510B (en) * | 2018-02-08 | 2023-10-24 | 南京林业大学 | Wood member health monitoring system and method |
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| CN110233626A (en) * | 2019-07-05 | 2019-09-13 | 重庆邮电大学 | Mechanical oscillation signal edge data lossless compression method based on two-dimensional adaptive quantization |
| CN111757017B (en) * | 2020-06-08 | 2021-12-24 | 深圳市哈博森科技有限公司 | Method for lossless storage of Raw image |
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| CN112003624A (en) * | 2020-08-05 | 2020-11-27 | 北京有生志广科技有限公司 | Lossless compression and online decompression method and device for radio monitoring data |
| CN112003624B (en) * | 2020-08-05 | 2024-01-26 | 北京有生志广科技有限公司 | Radio monitoring data lossless compression and online decompression method and device |
| 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 |
| CN112533164A (en) * | 2020-10-26 | 2021-03-19 | 中国人民解放军92942部队 | Method for improving transmission bandwidth of vibration data in reliability test |
| CN112525450B (en) * | 2020-10-26 | 2022-09-20 | 中国人民解放军92942部队 | Method for reducing occupancy rate of vibration data storage space in reliability test |
| CN112533164B (en) * | 2020-10-26 | 2023-10-10 | 中国人民解放军92942部队 | Method for improving transmission bandwidth of vibration data in reliability test |
| CN112525450A (en) * | 2020-10-26 | 2021-03-19 | 中国人民解放军92942部队 | Method for reducing occupancy rate of vibration data storage space in reliability test |
| CN112867020B (en) * | 2021-02-24 | 2022-04-19 | 中南大学 | Wireless sensor network transmission method |
| CN112867020A (en) * | 2021-02-24 | 2021-05-28 | 中南大学 | Wireless sensor network transmission method |
| CN113740066A (en) * | 2021-11-08 | 2021-12-03 | 中国空气动力研究与发展中心设备设计与测试技术研究所 | Early fault detection method for compressor bearing |
| CN113740066B (en) * | 2021-11-08 | 2022-02-08 | 中国空气动力研究与发展中心设备设计与测试技术研究所 | Early fault detection method for compressor bearing |
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