CN101932012B - Method for compressing sensor network data based on optimal order estimation and distributed clustering - Google Patents
Method for compressing sensor network data based on optimal order estimation and distributed clustering Download PDFInfo
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Abstract
The invention relates to a method for compressing sensor network data based on optimal order estimation and distributed clustering. The existed data compressing method has low efficiency. The method utilizes the space-time correlation of the data acquired by the sensing node, and determines the initial data group numbers to be transmitted by the system by introducing the optimized stage estimation, thereby not only obtaining the effective correlation factor, but also avoiding producing redundancy factor; and on the other hand, executes clustering process on the network by using cluster as a unit processing node, so that not only the node data processing efficiency of the base station can be improved, but also the capacity that the base station can rapidly locate the position with abnormalvalues or abnormal nodes can be improved. The method provided by the invention is suitable for a real time environment control system based on the wireless sensor network, and can efficiently compress the data of the wireless sensor network, and efficiently reduce the average energy consumption of the node.
Description
Technical field
The invention belongs to the data compression technique field, relate to a kind of based on the method for compressing sensor network data of optimal order estimation with distributed clustering.
Background technology
In the monitoring system based on wireless sensor network (WSNs), each sensor node is collected the local message around self, be sent to aggregation node after it is processed, the local data that aggregation node gathers all node collections obtains the Global Information of area-of-interest.In wireless sensor network, owing to being subjected to many factors to have the impact of unsteadiness and energy constraint etc. such as background noise, node failure, radio communication, usually there is certain error in the perception data information that node obtains, processes and transmits, and have to a certain degree uncertainty, yet usually allow in some applications certain error to exist.Namely under the prerequisite that guarantees application requirements, can reduce the data volume of in network, transmitting by reducing certain data precision, thereby reduce the energy consumption of nodes.Data compression algorithm in the radio sensing network is exactly to guarantee under the prerequisite of certain data precision, seeks data volume in a kind of effective minimizing transmission, thereby reduces node energy consumption, improves the method for whole network synthesis performance.In many practical applications of wireless sensor network, the probability that monitored district abnormal conditions occur is less always, in the situation that not unusual generation, same sensing node is when the continuous acquisition data, front and back continuously moment institute's image data certainly exist very large correlation, simultaneously, the different sensor node that is in adjacent area must have spatial coherence in the data of synchronization collection, if these are had the time, the data of spatial redundancy all send to the base station must expend a large amount of energy of node, therefore how effectively to eliminate the node perceived data in the time, redundancy on the space has become the critical problem that data compression will solve in the radio sensing network.How needing in the concrete application aspect the environmental monitoring for WSNs, design valid wireless sensor network data compression algorithm, is a research topic highly significant.
The wireless sensor network data compression algorithm is utilized the time that exists in the node perceived data, spatial redundancy, directly transmit first some group node perception datas, therefrom extract same node constantly different, the different internodal correlations of synchronization, again in conjunction with distributed coding, in place, base station recovery nodes raw sensed data, Nodes only has simple modulo operation, large quantity algorithm computing is transferred to the base station, so just can be in the situation that guarantee certain precision, reduce to greatest extent the energy consumption of node unit of transfer data, and also be feasible to base station periodic replacement battery or directly long-term power supply only in the practical application, therefore this kind algorithm has wider application prospect.Yet also there are some shortcomings in such algorithm, such as when estimating the prediction relative coefficient, group number to the initial data that will transmit does not define clearly, if allow node transmit too much initial data, will certainly expend a large amount of energy of node, like this not only bad for prolonging node useful life, the relative coefficient dimension that also can cause the place, base station to calculate increases, namely introduce redundant coefficient correlation, thereby made the base station amount of calculation excessive, and then had influence on response time and the data precision of system; If the initial data group numbers of node transmission very little, the base station can't calculate enough relative coefficients, the data that then dope will be difficult to satisfy required precision, causing system availability to reduce, is the problem that this algorithm values gets further investigated and solution therefore how effectively to define the original data volume that will transmit.In addition, increase along with network size, the pattern of the corresponding single base station of the multinode of this kind algorithm just is difficult to efficiently move, because the base station will receive each node compressed encoding and calculate one by one each node initial data according to relative coefficient, like this when calculating first node and last node data, certainly exist a larger time difference, therefore network size is larger, time difference is larger, this has just increased Time Delay of Systems, affected the range of application of this algorithm, based on this 2 point, optimal order distributed clustering structure tree compression algorithm is introduced on the one hand optimal order and is estimated, thereby the initial data group numbers that the system of defining will transmit, should obtain effective relative coefficient, avoid again the generation of redundant coefficient, on the other hand network being carried out sub-clustering processes, take bunch as the processed in units node data, so not only can improve the base station and process each node data efficient, can also strengthen the base station and locate rapidly the ability that produces different value or abnormal nodes occurs, thereby the whole wireless sensing network system of Effective Raise is to the comprehensively monitoring ability of monitored space.
Summary of the invention
The objective of the invention is for the deficiencies in the prior art, a kind of wireless sensor network (WSNs) data compression method that can be used for environmental monitoring is provided, so that by the overall target optimization of data precision and the average structure of energy consumption of node.
In order to achieve the above object, technical scheme of the present invention is achieved in that
Step (1). by the monitoring section node to base-station transmission monitor value separately, all nodes whenever transfer one group of monitor value, the base station adopts united information criterion (CIC) to carry out the optimal order judgement, if the gained exponent number is not optimum, and node passes data group number and does not surpass N/3, and node continues transmission primary monitoring value; Otherwise the base station obtains initial prediction coefficient matrix Φ by the numerical value that node transmits
m, and set up structure tree at base station place, wherein N represents the node the transmission of data group number that an algorithm cycle comprises, described acquisition initial predicted coefficient matrix method and set up the structure tree method and be mature technology;
Step (2). the base station is according to prediction coefficient matrix Φ
m, the monitored space node division is some bunches, take bunch as unit, the base station produces condensed instruction according to the node monitor value, and node produces the number of bits i that will transmit according to condensed instruction; The base station specifies a bunch interior nodes to take on a bunch head in turn, bunch head be responsible for transmitting self uncompressed monitor value and bunch in the compressed encoding of other node to the base station, bunch head receives the instruction from the base station simultaneously;
Step (3). each node obtains the base station condensed instruction successively through bunch head in bunch, and the binary value of each node after with its analog-to-digital conversion is to 2
iDelivery obtains i position binary system compressed code, and this compressed code is reached the base station through a bunch head, and bunch head is to the monitor value of its uncompressed of base-station transmission simultaneously;
Step (4). the base station take bunch as unit by the prediction coefficient matrix Φ that calculates in the step (1)
mAnd a bunch head passes the primary monitoring value, each node estimated value in can obtaining bunch
(
The estimated value of corresponding m node r group monitor value), can in structure tree, orient a subsequence by the i position compressed code of this node again, because subsequence spacing distance 2
I-1Δ with probability P greater than mean square error N
R, mTherefore, in this subsequence from
Nearest sequential value is the final estimated value of node
Simultaneously
Become again the known conditions of estimating the synchronization next node, namely upgrading Φ
mThe time to consider the numerical value that newly estimates;
Step (5). after the final estimated value that obtains all nodes of each bunch, plant the condensed instruction that the moment each bunch interior nodes needs by the base station calculating and sending, the numerical value group number that monitors when node reaches N, then jumps to step (1), otherwise jumps to step (2).
The present invention proposes based on the method for compressing sensor network data of optimal order estimation with distributed clustering.Because node energy mainly consumes on transmission primary monitoring information, estimates by introducing optimal order, has reduced the group number of node to the initial data of base-station transmission, thereby has reduced the average energy consumption of node; Calculating is concentrated on the serial nodal value that more approaches with node to be estimated on time-domain and the spatial domain, rejected the outlying node faint with correlation of nodes to be estimated, thereby effectively reduce the dimension of data that algorithm is processed, improved algorithm arithmetic speed and node data and recovered precision; If the monitored space network size is larger, after process the Area Node sub-clustering base station, if the zone occurs unusual, the base station can locate rapidly the exceptions area place bunch, and then with monitor unusual node and directly connect, compare with region-wide the search one by one, can effectively reduce Time Delay of Systems, improve the system monitoring real-time.Adopt the inventive method, under different network sizes, estimate to be better than direct distributed frame tree compression algorithm with the method for compressing sensor network data of distributed clustering in the overall target of data precision and the average structure of energy consumption of node based on optimal order.
Description of drawings
Fig. 1 is distributed coding method block diagram;
Fig. 2 is the decode structures tree;
Fig. 3 is the inventive method flow chart.
Embodiment
Core concept of the present invention is: introduce on the one hand optimal order and estimate, thereby the initial data group numbers that the system of defining will transmit, should obtain effective relative coefficient, avoid again the generation of redundant coefficient, on the other hand network being carried out sub-clustering processes, take bunch as the processed in units node data, so not only can improve the base station and process each node data efficient, can also strengthen the base station and locate rapidly the ability that produces different value or abnormal nodes occurs, thereby the whole wireless sensing network system of Effective Raise is to the monitoring capacity of monitored space.
Below in conjunction with accompanying drawing the present invention is described in further detail.
As shown in Figure 1, distributed coding is that monitored space is when having simultaneously perception of two or more nodes initial data, between node, adopt asymmetric coded system, set up the wireless-sensor network distribution type pressure texture, when the data sequence of node collection is the relevant independent same distribution discrete series of Gauss, experimental results show that, Node B is when the compression its data, no matter whether it knows current time A, the relative coefficient of B node data, adopt above-mentioned distributed compression pattern, all can obtain the compression performance of equivalence.Block diagram is expanded to each node of n by two nodes, compressed code according to image data compression current data gained before self, by correlation between base station calculating and definite each node data, and to each node transmission compressed code number of bits separately, restore at last each node primary monitoring value.
Based on above-mentioned distributed coding basic principle, distributed frame tree compression algorithm is at first organized original value by all nodes to base-station transmission N/3, the large I of N is definite according to concrete application, and a structure tree is set up according to these original values in the base station, and calculates prediction coefficient matrix Φ
mThe base station also need calculate the condensed instruction corresponding with each node, be next gather each node constantly need be with the figure place after monitor value separately (through behind the A/D) compression, meanwhile, the base station will specify a node to transmit its unpressed monitor value, is consumed by equilibrium for making each node energy of monitored space, and this node can be specified in turn by the base station, node carries out 2 by the instruction of receiving separately to the primary monitoring value
iModulo operation, thereby image data is compressed to the i position separately, the place, base station can orient one than the fractional value sequence after receiving each node i position coding in the structure tree of setting up before, pass the primary monitoring value in conjunction with specified node and reach the prediction coefficient matrix Φ that calculates before
mRecover primary monitoring value corresponding to this i position compressed code, all nodal values of current time are recovered, and prediction coefficient matrix and next figure place of constantly encoding of each node will be upgraded in the base station, this moves in circles class, and the value of each node of current time is predicted estimating constantly.Be necessary structure tree that above algorithm is related to, predictive coefficient and i value be tailor-made further instruction really, structure tree at first is to organize the average of initial data as starting point take aforementioned N/3, expand to two ends as the interval take Δ, the Δ size determines arithmetic accuracy, spreading range is determined by concrete application, above-mentioned sequence spreading take Δ as the interval is carried out the sequence of parity division, can obtain spacing is two groups of subsequences of 2 Δs, can divide subsequence more in the same way, the division number of times is by the figure place decision of i, and after i the division, each subsequence adjacent node spacing is 2
iΔ, the i position coding that each subsequence of i layer is corresponding unique, can set up as shown in Figure 2 decode structures tree according to above statement, when encoding by the i position that obtains certain node transmission in the base station, can orient corresponding subsequence, calculate an estimated value by predictive coefficient again, by searching and the immediate sequential value of estimated value in the subsequence of location, thereby recover this node monitor value.
This method is followed following process in the compression of above-mentioned two-dimensional space realization wireless sensor network data, sees Fig. 3:
Step (1). by the monitoring section node to base-station transmission monitor value separately, all nodes whenever transfer one group of monitor value, the base station adopts united information criterion (CIC) to carry out the optimal order judgement, if the gained exponent number is not optimum, and node passes data group number and does not surpass N/3, and node continues transmission primary monitoring value; Otherwise the base station obtains initial prediction coefficient matrix Φ by the numerical value that node transmits
m, and set up structure tree at base station place, wherein N represents the node the transmission of data group number that an algorithm cycle comprises, and obtains the initial predicted coefficient method and set up the structure tree method to be mature technology;
Step (2). the base station is according to prediction coefficient matrix Φ
m, the monitored space node division is some bunches, take bunch as unit, the base station produces condensed instruction according to the node monitor value, and node produces the number of bits i that will transmit according to condensed instruction; The base station specifies a bunch interior nodes to take on a bunch head in turn, bunch head be responsible for transmitting self uncompressed monitor value and bunch in the compressed encoding of other node to the base station, bunch head receives the instruction from the base station simultaneously, wherein the i value is according to formula
Try to achieve, wherein p is probability, and Δ is sequence spacing in Fig. 2 structure tree,
It is variance;
Step (3). each node obtains the base station condensed instruction successively through bunch head in bunch, and the binary value of each node after with its analog-to-digital conversion is to 2
iDelivery obtains i position binary system compressed code, and this compressed code is reached the base station through a bunch head, and simultaneously, bunch head is to the monitor value of its uncompressed of base-station transmission;
Step (4). the base station take bunch as unit by the prediction coefficient matrix Φ that calculates in the step (1)
mAnd a bunch head passes the primary monitoring value, each node estimated value in can obtaining bunch
(estimated value of corresponding m node r group monitor value) can orient a subsequence by the i position compressed code of this node, again because subsequence spacing distance 2 in structure tree
I-1Δ with probability P greater than mean square error N
R, mTherefore, in this subsequence from
Nearest sequential value is the final estimated value of node
, simultaneously,
Become again the known conditions of estimating the synchronization next node, namely upgrading Φ
mThe time to consider the numerical value that newly estimates;
Step (5). after the final estimated value that obtains all nodes of each bunch, plant the condensed instruction that the moment each bunch interior nodes needs by the base station calculating and sending, the numerical value group number that monitors when node reaches N, then jumps to step (1), otherwise jumps to step (2).
In a word, the method for compressing sensor network data that is based on optimal order estimation and distributed clustering that the present invention proposes: network node is in the monitored space random distribution, after optimal order is estimated, this method can will have the node division of spatial coherence at same bunch, by set up prediction coefficient matrix and decode structures tree in the base station, realization is to recovery and the reduction of monitoring nodes value coding, node is transmission of monitoring value in the predictive coefficient initialization procedure only, then only need make simple modulo operation and realize the coding of monitoring value coding and transmitted data amount less, the power consumption of node average energy enough is greatly reduced, and then Effective Raise sensor network comprehensively monitoring performance.Should be noted that the method such as the mode that base station place utilizes optimal order to estimate different (as are applied in the spatial coherence rank are estimated or the temporal correlation rank are estimated) all is the spirit and scope that do not break away from technical solution of the present invention.
Claims (1)
1. estimate and the method for compressing sensor network data of distributed clustering based on optimal order, it is characterized in that the method comprises the steps:
Step (1). by the monitoring section node to base-station transmission monitor value separately, all nodes whenever transfer one group of monitor value, the base station adopts the united information criterion to carry out the optimal order judgement, do not pass data group number above N/3 if the gained exponent number is not optimum and node, then node continues transmission primary monitoring value; If the gained exponent number is optimum, then the base station obtains initial prediction coefficient matrix Φ by the numerical value that node transmits
m, and set up structure tree at base station place, wherein N represents the node the transmission of data group number that an algorithm cycle comprises;
Step (2). the base station is according to prediction coefficient matrix Φ
m, the monitored space node division is some bunches; Take bunch as unit, the base station produces condensed instruction according to the node monitor value, and node produces the number of bits i that will transmit according to condensed instruction; The base station specifies a bunch interior nodes to take on a bunch head in turn, bunch head be responsible for transmitting self uncompressed monitor value and bunch in the compressed encoding of other node to the base station, bunch head receives the instruction from the base station simultaneously;
Step (3). each node obtains the base station condensed instruction successively through bunch head in bunch, and the binary value of each node after with its analog-to-digital conversion is to 2
iDelivery obtains i position binary system compressed code, and this compressed code is reached the base station through a bunch head, and bunch head is to the monitor value of its uncompressed of base-station transmission simultaneously;
Step (4). the base station is take bunch as unit, by the prediction coefficient matrix Φ that calculates in the step (1)
mAnd a bunch head pass the primary monitoring value obtain bunch in each node estimated value
In structure tree, orient a subsequence by the i position compressed code of this node again, in this subsequence from
Nearest sequential value is the final estimated value of node
Simultaneously
Become again the known conditions of estimating the synchronization next node, namely upgrading Φ
mThe time consider the numerical value newly estimate;
Step (5). after the final estimated value that obtains all nodes of each bunch, plant the condensed instruction that the moment each bunch interior nodes needs by the base station calculating and sending, if the numerical value group number that node monitors reaches N, then jump to step (1); If the numerical value group number that node monitors does not reach N, then jump to step (2);
Wherein r represents to organize number, and m represents node serial number, Φ
mThe prediction coefficient matrix of expression m node,
The final estimated value that represents m node r group,
The estimated value that represents m node r group monitor value.
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| CN102202349B (en) * | 2011-05-18 | 2013-08-07 | 杭州电子科技大学 | Wireless sensor networks data compression method based on self-adaptive optimal zero suppression |
| CN102868488A (en) * | 2012-09-13 | 2013-01-09 | 中国人民解放军理工大学 | Space time diversity-based reliable transmission method for low-spell wireless sensor |
| CN103974276B (en) * | 2013-01-29 | 2018-04-17 | 中国人民解放军总参谋部第六十一研究所 | A kind of wireless-sensor network distribution type signal characteristic parameter extracting method |
| CN103957582B (en) * | 2014-05-17 | 2017-05-24 | 浙江大学宁波理工学院 | Wireless sensor network self-adaptation compression method |
| CN106604211B (en) * | 2016-12-19 | 2019-07-12 | 南京邮电大学 | Compression method when a kind of hierarchical self-adaptive sky based on sensor network |
| CN107222551B (en) * | 2017-06-23 | 2020-03-17 | 东软集团股份有限公司 | Data transmission and processing method, equipment and information processing center |
| CN109756860A (en) * | 2018-12-20 | 2019-05-14 | 深圳高速工程顾问有限公司 | Bridge structure collecting method, device, computer equipment and storage medium |
| CN112672301B (en) * | 2020-12-21 | 2022-05-17 | 兰州工业学院 | Network data aggregation method for wireless sensor |
| CN112799898B (en) * | 2021-01-08 | 2023-03-24 | 北京科技大学 | Interconnection system fault node positioning method and system based on distributed fault detection |
| CN116388768B (en) * | 2023-06-06 | 2023-08-22 | 上海海栎创科技股份有限公司 | Compression method and system for signal data |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1981446A (en) * | 2004-03-23 | 2007-06-13 | 加利福尼亚大学董事会 | Apparatus and method for improving reliability of collected sensor data over a network |
| CN101394425A (en) * | 2008-11-06 | 2009-03-25 | 清华大学 | Method and system for adaptively cluster dividing |
| EP2120181A1 (en) * | 2008-05-15 | 2009-11-18 | Deutsche Telekom AG | A sensor node, a sensor network and a method for autonomous decision-making in sensor networks |
| CN101621855A (en) * | 2009-07-24 | 2010-01-06 | 北京航空航天大学 | Network data processing method, network node equipment and synthesis center equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1981446A (en) * | 2004-03-23 | 2007-06-13 | 加利福尼亚大学董事会 | Apparatus and method for improving reliability of collected sensor data over a network |
| EP2120181A1 (en) * | 2008-05-15 | 2009-11-18 | Deutsche Telekom AG | A sensor node, a sensor network and a method for autonomous decision-making in sensor networks |
| CN101394425A (en) * | 2008-11-06 | 2009-03-25 | 清华大学 | Method and system for adaptively cluster dividing |
| CN101621855A (en) * | 2009-07-24 | 2010-01-06 | 北京航空航天大学 | Network data processing method, network node equipment and synthesis center equipment |
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