CN103974329A - Improved wireless sensor network data compression scheme - Google Patents
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- CN103974329A CN103974329A CN201410206403.1A CN201410206403A CN103974329A CN 103974329 A CN103974329 A CN 103974329A CN 201410206403 A CN201410206403 A CN 201410206403A CN 103974329 A CN103974329 A CN 103974329A
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Abstract
The invention discloses an improved wireless sensor network data compression scheme. According to the scheme, at a node, first, a data distinguishing mechanism is used for judging whether a data compression mode based on wavelet transform is used or not; in an entire network system, due to the fact that collected data have large relevancy, wavelet lifting transform with the three steps of splitting, predicting and updating is conducted on the received data, the operand of data processing is effectively reduced, and the compression efficiency of the data is well improved. The compression algorithm can be applied to any sensor network based on a distribution type, and the compression efficiency is improved to a large extent.
Description
Technical field
The invention belongs to wireless sensor network data compression technique area, relate to a kind of wireless sensor network data compression scheme of improvement.
Background technology
MEMS (micro electro mechanical system) and wireless communication technology fast development, directly promoting sensor network cost degradation, high-intelligentization, wireless networked direction and constantly changing, and enriched greatly education resource and the learning ways of various wireless sensor network theories.Meanwhile, the opportunities and challenges that this transformation brings have also attracted domestic and international numerous scholars' concern, have facilitated wireless sensor network node energy to reduce research discussion and the practical application of the proposition of new ideas and the compress mode based on node image data.
At present, the research of wireless sensor network learning behavior is mostly concentrated on to the correlation theory research of low-power consumption study, such as topological control, Routing Protocol, low-power consumption MAC agreement, data compression mode etc., the node under wireless sensor network academic environment and network composition and data processing and transmitting procedure are studied, thereby effectively utilized theoretical foundation is provided for saving energy of wireless sensor network;
The structure of Wireless Sensor Network Platform, i.e. how research on the basis of correlation theory, used wireless communication technology that sensor node is carried out to networking integration, thereby convenient to the real-time collection of data message and transmission, and certain feedback mechanism is provided conventionally; Transducer was combined with intersecting of frontier, i.e. how research combines sensor node with the prior art of field of wireless communications networks, thereby provides technical support for the structure of Wireless Sensor Network Platform system.
As can be seen here, wireless sensor network research emphasis also turns to energy of wireless sensor network consumption problem from data information acquisition gradually.Especially radio sensing network becomes more and more cost degradation, high-intelligentization, wireless networked, presents and the diverse characteristic of conventional wireless self-organization network.Wireless sensor network is goed deep into research all sidedly, be conducive to the exploitation of sensing network perception data; Be conducive to the research in environmental monitoring and disaster countermeasure field; Be conducive to improve the reasonable utilization of limited resources; Be conducive to the ability in the human cognitive world.
Current for the research of wireless sensor network node data compression or in elementary, theoretical, complementary research, in the compression efficiency of data and in reduction, also exist certain weak point, veritably the compression based on Distributed Wireless Sensor Networks node data is carried out to systematic research little.
In existing research, the data compression mode that Chinese scholars is taked can be divided into the compression method of two kinds of method: a. based on Data Transmission Feature haply.B. the compression method based on correlation between node image data.The weak point of the first compression method is: although calculate simply, do not utilize fully the correlation of sensing data self, compression ratio is low; Second method belongs to distributed source coding, weak point is: the data-handling capacity of sensor node is had to certain requirement, and the disposal ability separately of the sensor node based on present stage is low, finite energy causes the technical research to distributed source coding to limit to some extent.
And owing to being subject to a little less than node computing capability and the little restriction of memory space, does not existing research provide gratifying research conclusion to some problems of wireless sensor node data compression, how undistorted transmission data? how to utilize fully the correlation of sensing data self? how maximized saving nodal information energy loss problem etc.
This method, taking discriminating data mechanism and Lifting Wavelet theory as basic, has built Simulation Platform of Wireless Sensor Network, the new feature of the new and old data time correlation of application data determine mechanism A+E node; Utilize Wavelet Lifting Transform to compress node data, disclosed the new feature of wireless sensor data Space Time correlation, for solving, current wireless sensor network nodes data compression bottleneck problem is significant.
Summary of the invention
The object of the present invention is to provide a kind of wireless sensor network data compression scheme of improvement, solved the inefficient problem of compression ratio in existing wireless network data compression transmission.
The technical solution adopted in the present invention is to carry out according to following steps:
Step 1: initialization, sensor network is divided into multiple bunches, and can direct communication between supposition bunch interior nodes, a node of each bunch of election is as a bunch head, bunch head collect bunch in the data that monitor of each member node, and data message is sent to base station;
Step 2: be divided into grid by all bunches, each grid is chosen a node and is built into a ring, node contiguous on ring belongs to the adjacent virtual grid in space, and the node on ring receives data from neighbor node, data is sent to leader cluster node after contrast processing with the data of self.
Step 3: the transfer of data that virtual grid is gathered, to the node on ring, is started by eye, and eye determines by initial setting up, and then node on the ring is carried out the data compression of Wavelet Lifting Transform successively, the simultaneously stored data of new node more.
Further, in described step 2, node on ring receives data from neighbor node, contrasting processing procedure with the data of self is: if its difference meets certain threshold value, the data of this node collection just do not participate in Wavelet Lifting Transform again so, and only there is routing function, if the data that this node present position collects do not change or change in smaller situation, node will only reportedly be passed node as a number, if the data variation of node collection exceedes certain threshold value, represent that network internal need to carry out data processing and transfer of data, now node transfers data to the node on the ring of same grid.
The invention has the beneficial effects as follows to adopt to build ring joint point packed data, adopted structure loop network, carry out Data Comparison and select suitable node and compress, compression efficiency is high.
Brief description of the drawings
Fig. 1 selects machine-processed schematic block diagram for the present invention is based on wireless sensor data;
Fig. 2 is the data compression scheme schematic flow sheet that the present invention is based on wavelet arithmetic;
Fig. 3 is the virtual net trrellis diagram that the present invention is based on ring-type;
Fig. 4 is WLT of the present invention and the average energy consumption comparison of Huffman;
Fig. 5 is the contrast of totally consuming energy of WLT of the present invention and Huffman algorithm;
Fig. 6 is algorithm and the average energy consumption comparison of Huffman algorithm proposing in the present invention;
Fig. 7 is algorithm and the contrast of Huffman algorithm overall energy consumption proposing in the present invention;
Fig. 8 is algorithm and the contrast of Huffman compression algorithm rate proposing in the present invention.
Embodiment
Below in conjunction with the drawings and specific embodiments, the present invention is described in detail.
The present invention is according to the Space Time correlation between wireless sensor network image data, design a kind of virtual network (Ring Topology based on Virtual Grid based on ring model, be called for short VGRT), taking bunch as unit, by bunch in working node in each virtual network form a ring, tentatively solve the problem of sensing data spatial coherence.And then by carrying out threshold decision before and after node image data, see whether data large variation occurs, carry out data selection scheme, then transferring data to node on the ring, the present invention adopts WLT mode, reduces the needed energy of transmission data by node data being carried out to Wavelet Lifting Transform.
Technic relization scheme is totally divided into term explanation, wireless sensor data determine mechanism and 3 parts of data compression scheme based on wavelet arithmetic:
1. term explanation:
DWT: wavelet transform,
SN:Sensor Node, sensor node,
Sink: gateway node,
Cluster Node: leader cluster node,
△ I:Delta information, amount of information threshold value,
WSN:Wireless sensor networks wireless sensor network,
Mobile ad-hoc network: wireless self-organization network,
Odd samples: very sampling,
Even samples: even sampling,
VGRT:Ring topology based on virtual grid, the virtual grid of ring-type,
LWT:Lazy Wavelet Transformation, laziness wavelet transformation,
Data compressing: data compression,
Split stage: the division stage,
Date selecting: data selection,
Prediction stage: forecast period,
Update stage: the more new stage,
WLT:Wavelet lifting transform, wavelet arithmetic.
The present invention carries out according to following steps:
Step 1: initialization, due to reasons such as the randomnesss (as aircraft is thrown in immediately) of network design, in order effectively to carry out data processing, sensor network is divided into multiple bunches (cluster), and can direct communication between supposition bunch interior nodes, a node of each bunch of election is as a bunch head (cluster head), bunch head collect bunch in the data that monitor of each member node, and data message is sent to base station.Bunch and bunch between can form super bunch.Due to reasons such as the randomnesss of sensor network disposition,, therefore there is redundancy in the member node skewness in making bunch, and these redundant nodes have been brought extra energy consumption because intercepting, receive and transmit data to network.
Step 2: be divided into grid by all bunches, each grid is chosen a node and is built into a ring, node contiguous on ring belongs to the adjacent virtual grid in space, and the node on ring receives data from neighbor node, data is sent to leader cluster node after contrast processing with the data of self.Particular content is as follows: be divided into little region by all bunches, each zonule is exactly a little virtual grid, two adjacent virtual grids can be communicated by letter mutually arbitrarily, suppose that the nodes of simultaneously working in each virtual grid only has one here, and other nodes are in resting state.
Step 3: in the time of working node fault, do not need to build new ring like this.So, so just can ensure the normal work of network, reach the object that extends network lifetime,
As shown in Figure 3, in figure, grid A is adjacent with virtual grid B for the grid that the present invention builds, and in A, node can be communicated by letter with arbitrary node in B arbitrarily, and vice versa.On ring, adjacent node belongs to the adjacent network in space, node on ring is accepted data from neighbor node, after processing with its data, be sent to next neighbor node, only have the node of " suitable " just to transmit data to the leader cluster node encircling, on ring, carry out Wavelet Lifting Transform, the node of " suitable " is exactly the node of storing low frequency wavelet coefficient and being greater than the high frequency wavelet coefficient of a certain threshold value.In network initial condition, select a node as eye node, Wavelet Lifting Transform starts to carry out at eye node.The change of round subsequently, on ring, each node becomes eye node successively.In the time encircling upper certain node due to energy consumption or other reasons inefficacy, can allow the awake dormancy node in same grid replace, without rebuilding a new ring, save network energy consumption, and can ensure the metastable operation of network.
Be illustrated in figure 1 wireless sensor data of the present invention and select mechanism.Fig. 2 is the data compression scheme schematic flow sheet of wavelet arithmetic of the present invention.Because distributed data compress mode needs to carry out information interaction between node, nodal distance greatly needs more energy loss to transmit interactive information.So the present invention sets up the virtual network of a ring-type, carry out Wavelet Lifting Transform by the node on ring, the mode that the node updates on ring is introduced by epimere is carried out.Ring does not comprise all nodes, and node on the ring is carried out information to the virtual grid node at place separately, then on ring, carries out data compression.Encircling main effect is to reduce the required energy loss of information interaction between each node of Wavelet Lifting Transform.
The transfer of data that virtual grid is gathered, to the node encircling, is started by eye, and eye is determined by initial setting up.Then node on the ring is carried out the data compression of Wavelet Lifting Transform successively.
Introduce comparison threshold value analytic approach by the new feature A+E on sensing data temporal correlation.Build the data selection mechanism based on node unit, to gradually reduce the energy consumption of node transmission data, the power consumption of saving whole wireless sensor network.On the WSN basis based on ring-type virtual grid, in order to save the ability of node transmission data institute loss, the node in each grid need to carry out preliminary processing to data, and this patent proposes a kind of selection scheme based on node image data.Due to shortcomings such as transducer image data amount are large, and node self power supply energy, storage capacity and computing capability are limited.We need to be in WSN grid further process data when each node image data, remove the amount of information of redundancy in image data by data selection scheme.The present invention carries out a comparison to the data that gathered and original data: if its difference meets certain threshold value (setting of this threshold value is determined according to got environment), the data of this node collection just do not participate in Wavelet Lifting Transform again so, and only there is routing function, if the data that this node present position collects do not change or change in smaller situation, node will only reportedly be passed node as a number.If the data variation of node collection exceedes certain threshold value, represent that network internal need to carry out data processing and transfer of data, now node transfers data to the node on the ring of same grid, further carries out Wavelet Lifting Transform in node on the ring, the more stored data of new node of while.
The data selection code process of node is as follows:
The data compression method that the present invention is based on existing wavelet arithmetic carries out data compression to node.Adopt the data compression scheme based on wavelet arithmetic, according to the basis that is characterized as of existing theory, wireless sensor network data, in conjunction with distributed source coding theory, the experimental design of sensor technology and wireless communication technology, the modeling tool (as Omnet++) of uses advanced and Experimental Network framework instrument (as UML C++) etc., in conjunction with distributed data compressed encoding theory, comparatively comprehensively specification and the method for the wireless sensor network node data compression of quantification are built.On this basis, unified, extendible wireless sensor network model system have been built.To realizing the low-power consumption of wireless sensor network, regularly gather all kinds of detection data, improve the reasonable utilization of limited resources.Each node on the ring starts to carry out Wavelet Lifting Transform by the data of each virtual ring interior nodes from eye.The data compression changing based on small echo belongs to information source coding, for distributed sensor network, between transducer image data, exists: a temporal correlation.B space (between multiattribute) correlation.And wavelet transformation can effectively be removed the statistical redundancy between these data, improve compression efficiency, and Lossless Compression mode based on 5/3 small echo can be effectively, packed data accurately.But these traditional small echos variation compression algorithms do not fully take into account the finiteness of wireless sensor node internal memory and energy.And the data compression algorithm based on Lifting Wavelet proposing in this application can be realized Wavelet Transformation Algorithm faster, improve the compression efficiency of node and effectively save memory space, effectively reduce the transmission of redundant data.
If the length of signal is N, in the time that N is even number, the low frequency signal after conversion and the length of high-frequency signal are N/2; In the time that N is odd number, the length of the low frequency signal after conversion is (N+1)/2, and high-frequency signal is (N-1)/2.Therefore the conclusion drawing: no matter the length of signal how, all, all, its medium and low frequency is to round up to the length of high-frequency signal to the low frequency signal length after wavelet transformation, and high frequency is to round downwards.Consider low frequency signal and high-frequency signal that signal obtains after one-level wavelet transformation.So in general, wavelet transformation boosting algorithm is made up of three steps: Split (division), Predict (prediction), Update (renewal).
Its concrete steps are:
(a) Split (division)
The object of division be by given data set resolve into be mutually related two little subsets and, and the correlation of two subsets is stronger, splitting effect better.The decomposition method that division adopts inertia (Lazy) wavelet transformation, carries out interval sampling according to the odd even sequence number of data to data, and even number set is; Odd number set is.
(b) Predict (prediction)
After first step division, data centralization has left a lot of redundancies, and the object of prediction is to eliminate the redundancy that the first step stays, and provides compacter data representation.
For a signal that local correlations is stronger, its odd even subset is height correlation.Therefore, know wherein any one, just can utilize it in rational accuracy rating, to predict another one.Conventionally predict strange subset by even subset.The fallout predictor of general conduct, predicated error.
(c) Update (renewal)
The object of upgrading is to ensure a certain global nature, and a key property of low frequency signal is: it should have identical mean value with original signal, namely
S and j are irrelevant, so just can ensure that conversion coefficient is the mean value of original signal.Upgrade operation and can ensure this character establishment.
Above three step operations are equivalent to signal to carry out one-level wavelet transformation, are low frequency and high frequency by signal decomposition.More than operation can in-situ accomplishes, is that even number position low frequency rewrites without increasing internal memory again, and odd positions rewrites by details.
Obtain the wavelet transformation of forward, be easy to just obtain reciprocal transformation, symbol is added and subtracted in just changing of need to making.So just can realize inverse wavelet transform.
Model of energy and simulation result:
We select a kind of wireless model to carry out network energy consumption analysis.Under this model, under distance B, transmit K bit data transmission energy consumption with receive energy consumption can by below formula.
Transmission energy consumption, receives energy consumption.Wherein,, represent the coefficient of energy dissipation of this transmission power amplifier.The energy consumption of wavelet compression data processing, is the operation cycle number of wavelet transformation each time at this N, and C is the data volume of changing in per operation cycle, is the voltage that node provides.
We carry out emulation to the data compression scheme proposing on OMNeT++ software.Random 100 of the nodes that produce, node average distance is 5 meters, and the energy value of each node definition is 8 units, and experimental data is taken from the temperature detection data set of Berkeley-Intel (Berkeley Intel) research laboratory.Temperature threshold is set to 3 °, and be 200 seconds running time.Recently verify with compression from energy consumption.Then simulated experiment is averaged as experimental result for 50 times.
With Huffman algorithm contrast, Wavelet Lifting Transform has lower operand, and computing is faster defeated, so low 0.6 unit of average energy loss-rate Huffman is as Fig. 4: overall energy consumption is as Fig. 5.
Carry out energy consumption comparison in conjunction with WLT and DSM algorithm and Huffman, because node temperature changes and do not exceed threshold value and only need a small amount of data i of transmission.So there is again comparatively large minimizing in energy consumption.As Fig. 6, the average energy consumption comparison diagram of algorithm.Fig. 7 is overall energy consumption comparison diagram, can find out that energy consumption of the present invention is obviously lower.
In order better to verify our experimental result, carried out the contrast of compression algorithm rate here.With respect to Huffman algorithm, specifically there is more high compression ratio in conjunction with WLT and DSM algorithm, Figure 8 shows that the algorithm and the contrast of Huffman compression algorithm rate that in the present invention, propose.Can find out that compression ratio of the present invention is higher than Huffman compression algorithm rate.
The present invention adopts a kind of data compression mode based on wavelet transformation, based on distributed sensor network, can apply this compression algorithm to arbitrarily, has improved largely compression efficiency, and then significantly reduces the energy loss in sensor network.In the network system based on wireless senser, the energy loss of data processing and transfer of data directly affects the operating efficiency of node, is restricting the life cycle of wireless sensor network simultaneously.
Claims (2)
1. a wireless sensor network data compression scheme for improvement, is characterized in that carrying out according to following steps:
Step 1: initialization, sensor network is divided into multiple bunches, and can direct communication between supposition bunch interior nodes, a node of each bunch of election is as a bunch head, bunch head collect bunch in the data that monitor of each member node, and data message is sent to base station;
Step 2: be divided into grid by all bunches, each grid is chosen a node and is built into a ring, node contiguous on ring belongs to the adjacent virtual grid in space, and the node on ring receives data from neighbor node, data is sent to leader cluster node after contrast processing with the data of self;
Step 3: the transfer of data that virtual grid is gathered, to the node on ring, is started by eye, and eye determines by initial setting up, and then node on the ring is carried out the data compression of Wavelet Lifting Transform successively, the simultaneously stored data of new node more.
2. according to the wireless sensor network data compression scheme of a kind of improvement described in claim 1, it is characterized in that: in described step 2, node on ring receives data from neighbor node, contrasting processing procedure with the data of self is: if its difference meets certain threshold value, the data of this node collection just do not participate in Wavelet Lifting Transform again so, and only there is routing function, if the data that this node present position collects do not change or change in smaller situation, node will only reportedly be passed node as a number, if the data variation of node collection exceedes certain threshold value, represent that network internal need to carry out data processing and transfer of data, now node transfers data to the node on the ring of same grid.
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Application publication date: 20140806 |