CN101932012A - 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 abnormal values 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 handled, the local data that aggregation node gathers all node collections obtains the Global Information of area-of-interest.In wireless sensor network, owing to be subjected to multiple factor to have the influence 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, handles and transmits, and have to a certain degree uncertainty, yet allow certain error to exist in some applications usually.Promptly under the prerequisite that guarantees application requirements, can reduce data quantity transmitted in network, thereby reduce the energy consumption of node in the network by reducing certain data precision.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 take place is always less relatively, do not having under the unusual situation about taking place, same sensing node is when the continuous acquisition data, front and back moment institute's image data continuously certainly exist very big 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 the node lot of energy, therefore how to eliminate the node perceived data effectively in the time, redundancy on the space has become the critical problem that data compression will solve in the radio sensing network.How at WSNs at the concrete application need aspect the environmental monitoring, design valid wireless sensor network data compression algorithm, be 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 some group node perception datas earlier, it is constantly different therefrom to extract same node, the different internodal correlations of synchronization, again in conjunction with distributed coding, in place, base station recovery nodes raw sensed data, only there is simple modulo operation at the node place, big quantity algorithm computing is transferred to the base station, so just can be under the situation that guarantees certain precision, reduce the energy consumption of node unit of transfer data to greatest extent, and also be feasible only in the practical application, so this kind algorithm has wider application prospect base station periodic replacement battery or directly long-term power supply.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 the node lot of energy, 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, promptly introduce redundant coefficient correlation, thereby made the base station amount of calculation excessive, and then had influence on the 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 must deeply be inquired into and solve so how to define the original data volume that will transmit effectively.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 each node initial data one by one according to relative coefficient, like this when calculating first node and last node data, certainly exist big time difference, so network size is big more, time difference is big more, this has just increased system's time delay, influenced the range of application of this algorithm, based on this 2 point, optimal order distributed clustering structure tree compression algorithm is introduced optimal order on the one hand and is estimated, thereby the initial data group numbers that the system of defining will transmit, should obtain effective relative coefficient, avoid the generation of redundant coefficient again, on the other hand network being carried out sub-clustering handles, bunch being the processed in units node data, so not only can improve the base station and handle each node data efficient, can also strengthen the base station and locate the ability that produces different value or abnormal nodes occurs rapidly, thereby effectively improve the comprehensively monitoring ability of whole wireless sensing network system monitored space.
Summary of the invention
The objective of the invention is at 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, the feasible overall target optimization that constitutes by data precision and the average 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 transmits monitor value separately, the every transmission of all nodes finishes 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 the data set 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 transmitted
m, and set up structure tree at base station place, wherein N represents the node transmission data set 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, bunch to be 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 in turn a bunch interior nodes to take on a bunch head, 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 transmits simultaneously;
Step (4). the base station is bunch being that unit is 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, mSo, in this subsequence from
Nearest sequential value is the final estimated value of node
Simultaneously
Become the known conditions of estimating the synchronization next node again, promptly 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 transmits, thereby has reduced the average energy consumption of node; Calculating is concentrated on time-domain and the spatial domain and waits to estimate on the more approaching serial nodal value of node, rejected and the outlying node of waiting to estimate that the node correlation is faint, thereby effectively reduce the dimension of data that algorithm is handled, improved algorithm arithmetic speed and node data and recovered precision; If the monitored space network size is bigger, after handle 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 system's time delay, improve the system monitoring real-time.Adopt the inventive method, under different network sizes, estimate on the overall target of data precision and the average energy consumption formation of node, to be better than direct distributed frame tree compression algorithm with the method for compressing sensor network data of distributed clustering based on optimal order.
Description of drawings
Fig. 1 is a 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 optimal order on the one hand and estimate, thereby the initial data group numbers that the system of defining will transmit, should obtain effective relative coefficient, avoid the generation of redundant coefficient again, on the other hand network being carried out sub-clustering handles, bunch being the processed in units node data, so not only can improve the base station and handle each node data efficient, can also strengthen the base station and locate the ability that produces different value or abnormal nodes occurs rapidly, thereby effectively improve the monitoring capacity of whole wireless sensing network system 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 two or more nodes perception simultaneously 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 correlated with the independent same distribution discrete series for 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 compact model, 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, restore each node primary monitoring value at last to each node transmission compressed code number of bits separately.
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 transmits N/3, the big I of N determines that according to concrete the application 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 a less sequence of values 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 the primary monitoring value of this i position compressed code correspondence, all nodal values of current time recover to finish, and prediction coefficient matrix and next figure place of encoding constantly 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, the tailor-made really further instruction of predictive coefficient and i value, structure tree at first is that the average with aforementioned N/3 group initial data is a starting point, with the Δ is to expand to two ends at interval, Δ size decision arithmetic accuracy, spreading range is determined by concrete application, to above-mentioned be that at interval sequence spreading carries out the sequence of parity division with the Δ, 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 decode structures tree as shown in Figure 2 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,, thereby recover this node monitor value by searching and the immediate sequential value of estimated value in the subsequence of location.
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 transmits monitor value separately, the every transmission of all nodes finishes 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 the data set 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 transmitted
m, and set up structure tree at base station place, wherein N represents the node transmission data set 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, bunch to be 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 in turn a bunch interior nodes to take on a bunch head, 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 a probability, and Δ is a 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 transmits;
Step (4). the base station is bunch being that unit is 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, mSo, in this subsequence from
Nearest sequential value is the final estimated value of node
, simultaneously,
Become the known conditions of estimating the synchronization next node again, promptly 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 the recovery and the reduction of monitoring nodes value coding, node is transmission of monitoring value in the predictive coefficient initialization procedure only, only need make simple modulo operation then and realize monitoring value coding and the less relatively coding of transmitted data amount, the power consumption of node average energy enough is greatly reduced, and then effectively improves sensor network comprehensively monitoring performance.Should be noted that method such as the mode difference that base station place utilizes optimal order and estimate (as be 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 this method comprises the steps:
Step (1). by the monitoring section node to base station transmits monitor value separately, the every transmission of all nodes finishes one group of monitor value, the base station adopts the united information criterion to carry out the optimal order judgement, does not pass the data set number above N/3 if the gained exponent number is not optimum and node, and 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 transmitted
m, and set up structure tree at base station place, wherein N represents the node transmission data set 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; Bunch to be 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 in turn a bunch interior nodes to take on a bunch head, 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 transmits simultaneously;
Step (4). the base station is bunch to be 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 the known conditions of estimating the synchronization next node again, promptly 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,, then jump to step (1) if the numerical value group number that node monitors reaches N by the base station calculating and sending; If the numerical value group number that node monitors does not reach N, then jump to step (2).
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| CN103957582A (en) * | 2014-05-17 | 2014-07-30 | 浙江大学宁波理工学院 | Wireless sensor network self-adaptation compression method |
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| CN106604211A (en) * | 2016-12-19 | 2017-04-26 | 南京邮电大学 | Hierarchical adaptive space-time compression method based on sensor network |
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