CN116033380A - Data collection method of wireless sensor network under non-communication condition - Google Patents
Data collection method of wireless sensor network under non-communication condition Download PDFInfo
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Abstract
The invention provides a data collection method of a wireless sensor network under the condition of no communication, which comprises the following steps: performing multi-resolution compression storage on data of a single network node based on integer wavelet transformation, and constructing a time dimension of a multi-resolution data set; performing multi-resolution hierarchical storage on data of a plurality of network nodes based on integer wavelet transformation, and constructing a space dimension of a multi-resolution data set; the wireless sensor network is accessed for multi-resolution data collection based on opportunistic network communication technology using the mobile terminal. The invention can solve the communication problem of the wireless sensor network under the condition of no communication, and can realize high-efficiency data collection by reducing data redundancy, saving node transmission energy consumption and multi-resolution hierarchical storage of data, thereby better meeting the data collection and transmission requirements of the wireless sensor network in a large scale and for a long time.
Description
Technical Field
The invention relates to the technical field of sensor data processing, in particular to a data collection method of a wireless sensor network under the condition of no communication.
Background
With the development of wireless communication technologies such as WIFI, bluetooth, zigbee and the like and the maturing and growing of embedded systems and distributed computing systems, the prospect of everything interconnection, smart cities and smart communities becomes possible, so that the Internet of things becomes a focus of attention at home and abroad. The wireless sensor network is an important component of the Internet of things, is composed of a large number of wireless sensor nodes, can collect data such as temperature, humidity and photos from the physical world, and can upload the data to a destination node in a one-hop or multi-hop mode through wireless transmission. The wireless sensor network has wide development and application prospects in the fields of environment monitoring, military monitoring, traffic monitoring, structural health monitoring and the like.
The acquisition and transmission of sensing data are basic tasks of a wireless sensor network, and efficient data collection is always a key problem of concern in academia and industry. Currently, the problem of data collection of wireless sensor networks has been widely studied, but the following problems still remain:
(1) How to balance the node energy to extend the network lifetime. Because the node sensor has limited battery power and is easy to form an 'energy cavity', the balanced use of node energy and the maximization of the service life of a network are important targets for data collection;
(2) How to utilize the calculation function of the node to reduce the energy consumption of node transmission. The data transmission energy consumption of the sensor node is far greater than the self-calculation energy consumption, for example: the energy required by the node to transmit 1 bit of data 100m distance is approximately equal to the energy consumed to perform 3000 calculations. Therefore, the data amount transmitted should be reduced by adopting a mode of 'calculating and transmitting simultaneously' as much as possible;
(3) How to collect effective data for the distributed sensor network which is separated from each other. Due to geographical environmental factors, sensor hardware failures, and sensor energy exhaustion, sensor data collection areas are isolated from each other, and traditional communication techniques require interconnections between nodes that cannot be implemented.
Disclosure of Invention
The invention aims to provide a data collection method of a wireless sensor network under the condition of no communication, which is used for solving the problems that the wireless sensor network is divided into a plurality of sub-networks due to environmental interference, sensor failure and the like, and the data collection cannot be implemented through the traditional communication technology, and the service life of the network is short.
The invention provides a data collection method of a wireless sensor network under the condition of no communication, which comprises the following steps:
step one, carrying out multi-resolution compression storage on data of a single network node based on integer wavelet transformation, and constructing a time dimension of a multi-resolution data set;
step two, carrying out multi-resolution hierarchical storage on data of a plurality of network nodes based on integer wavelet transformation, and constructing a space dimension of a multi-resolution data set;
and thirdly, accessing the wireless sensor network to collect multi-resolution data by using the mobile terminal based on the opportunistic network communication technology.
Optionally, the specific process of performing multi-resolution compression storage on the data of the single network node in the step one is as follows:
s1.1, preprocessing data, namely, normalizing a data mode, clearing abnormal data, correcting error data and clearing repeated data of original signal data acquired by a sensor through data clearing, and realizing integer transformation of the data by utilizing unit conversion and displacement means;
s1.2, performing data integer wavelet transformation, namely performing integer wavelet transformation on the preprocessed data based on second-generation wavelet transformation so as to perform lossless or lossy compression transformation on the sensor data;
s1.3, carrying out wavelet coefficient quantization by adopting a standard quantization mode, wherein the specific process is as follows: for a group of data, set where the maximum number is
The minimum number is +.>
The quantization bit number is +.>
Quantization step size +.>
；
S1.4, encoding the quantized wavelet coefficients by using a deflate algorithm.
Optionally, the specific process of performing integer wavelet transform decomposition on the preprocessed data based on the second-generation wavelet transform in S1.2 is as follows:
(one) the original sequence signals acquired by the sensor
Division into even sequences>
And odd number sequence->
The method comprises the following steps:
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
All signal sequences representing raw signal data acquired by the sensor,
，
is a natural integer;
(II) using the first difference between the true value and the estimated value of the odd sequence
To represent details of the signal, namely:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
For the estimation operator +.>
Representing a downward rounding;
(III) introducing an update operator
For->
Updating to obtain the first scale factor +.>
The method comprises the following steps:
；
(IV) first difference between the true value and the estimated value based on the odd-numbered sequence
And a first scale factor->
Repeating the steps (one) to (three) to obtain a second scale factor +.>
And a second difference->
The method comprises the steps of carrying out a first treatment on the surface of the Through->
After the sub-transform decomposition, the original signal +.>
The conversion is +.>
Wherein->
Representing the low frequency part of the sensor signal data
Representing the high frequency portion of the sensor signal data.
Optionally, the specific process of performing integer wavelet transform reconstruction and reduction on the preprocessed data in the second generation wavelet transform in S1.2 is as follows:
obtained by conversion decomposition
And->
Restoring even sequence +.>
The method comprises the following steps:
；
(II) use of
And->
Restoring odd sequence->
The method comprises the following steps:
；
(III) merging
And->
Restoring the original data set->
The method comprises the following steps:
；
(IV) for the following
Sub-transform decomposition signal->
Through->
After the secondary reconstruction operation, the original sequence signal can be restored>
。
Optionally, in the second step, the specific step of performing multi-resolution hierarchical storage on the data of the plurality of nodes of the network based on integer wavelet transformation is as follows:
s2.1, dividing a sensor needing to collect data into a plurality of mutually communicated sub-network blocks, arranging a plurality of sensors with mutually communicated signals in each sub-network block, and selecting one sensor from each sub-network block as a sub-convergence center node;
s2.2, dividing a plurality of sub-network blocks in a certain time interval into a plurality of layers, transmitting data to a last-level collecting central node by a bottom-layer collecting central node, and the like, so as to form a multi-layer tree-type sub-network structure;
s2.3, spatial dimension multi-resolution hierarchical storage of network data.
Optionally, the selecting process of the specific subnetwork convergence central node in S2.1 includes:
(1) calculating nodes of each sensor based on residual energy, storage space and relationship metric weight of a plurality of sensors contained in a sub-network block
Values, namely:
Wherein->
Denoted as +.>
Individual nodes
Currently owned battery energy, +.>
Denoted as +.>
Personal node->
Current remaining memory, +.>
Denoted as +.>
Personal node->
Relation metric weight with other nodes, +.>
Respectively expressed as adjustment parameters and +.>
；
(2) According to the calculated nodes
Selecting the convergence center node with the largest value as the convergence center node of the sub-network; and if maximum value juxtaposition occurs, randomly selecting one of the maximum value juxtaposition as a convergence center node.
Optionally, the specific process of the spatial dimension multi-resolution hierarchical storage of the network data in S2.3 is as follows:
1) Dividing multiple nodes of the same hierarchy in the network into even nodes in sequence
And odd nodes->
Wherein->
Raw data representing no wavelet transform, +.>
Represents the->
Are connected by (1)>
，
Taking natural integers;
2) Even number of nodes
Transmitting its data to adjacent odd nodes +.>
And calculates the high frequency coefficient ++after one transform in integer wavelet coefficient>
And low-frequency coefficients after one transformation +.>
Wherein->
Representing data after one wavelet transform;
3) Repeating the step 2) to obtain a primary wavelet transformation high-frequency coefficient
And first order wavelet transform low frequency coefficient +.>
；
4) The correlation node converts the primary wavelet into low-frequency coefficient
Transmitting to the upper layer convergence node to form new data sequence, repeating the steps 2) and 3) to obtain the secondary wavelet transformation high frequency coefficient
And second order wavelet transform low frequency coefficient +.>
；
5) The central node completes the first
The level wavelet transform gets +.>
High frequency coefficient of level wavelet transformation->
And->
Low frequency coefficient of level wavelet transformation +.>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Taking natural integer.
Optionally, the method for multi-resolution data collection based on the opportunistic network communication technology by using the mobile terminal to access the wireless sensor network in the third step includes:
data collection of the sensor network is implemented by means of the mobile node as a ferrying node or transmission relay;
the mobile node collects data from each convergence center node by using the storage-carrying-forwarding mode of the opportunistic network, and carries and transmits the data to the data center at smaller path cost and time cost so as to solve the problem that the data collection task of the sensor network cannot be carried out under the complex geographic condition;
based on a multi-resolution hierarchical storage mechanism, nodes in the wireless sensor network are divided into different layers, and data can be stored in a distributed hierarchical mode according to the size of resolution, namely high-frequency data is stored on a layer close to a central node, and low-frequency data is stored on a layer far away from the central node.
Compared with the prior art, the invention has the following beneficial effects:
the invention can solve the communication problem of the wireless sensor network under the condition of no communication, and can realize high-efficiency data collection by reducing data redundancy, saving node transmission energy consumption and multi-resolution hierarchical storage of data, thereby better meeting the data collection and transmission requirements of the wireless sensor network in a large scale and for a long time.
In addition to the objects, features and advantages described above, the present invention has other objects, features and advantages. The present invention will be described in further detail with reference to the drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
fig. 1 is a schematic overall flow chart of a data collection method of a wireless sensor network under a non-connected condition in the embodiment;
fig. 2 (a) -2 (D) are schematic diagrams respectively showing shaping wavelet transformation diagrams of experimental data formation for raw data of different time periods and different temperatures by applying a data collection method of a wireless sensor network under the condition of no communication in the embodiment;
fig. 3 is a schematic diagram of a process of multi-resolution data collection by applying a data collection method of a wireless sensor network under the condition of no connectivity in the present embodiment.
Detailed Description
The following are specific embodiments of the present invention and the technical solutions of the present invention will be further described with reference to the accompanying drawings, but the present invention is not limited to these embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
This embodiment:
referring to fig. 1, the data collection method of the wireless sensor network under the condition of no communication provided by the invention comprises the following steps:
step one, carrying out multi-resolution compression storage on data of a single network node based on integer wavelet transformation, and constructing a time dimension of a multi-resolution data set;
step two, carrying out multi-resolution hierarchical storage on data of a plurality of network nodes based on integer wavelet transformation, and constructing a space dimension of a multi-resolution data set;
and thirdly, accessing the wireless sensor network to collect multi-resolution data by using the mobile terminal based on the opportunistic network communication technology.
Optionally, the specific process of performing multi-resolution compression storage on the data of the single network node in the step one is as follows:
s1.1, preprocessing data, namely, normalizing a data mode, clearing abnormal data, correcting error data and clearing repeated data of original signal data acquired by a sensor through data clearing, and realizing integer transformation of the data by means of unit conversion, displacement and the like;
s1.2, performing data integer wavelet transformation, namely performing integer wavelet transformation on the preprocessed data based on second-generation wavelet transformation so as to perform lossless or lossy compression transformation on the sensor data;
s1.3, carrying out wavelet coefficient quantization by adopting a standard quantization mode, wherein the specific process is as follows: for a group of data, set where the maximum number is
Most, at bestDecimal is +.>
The quantization bit number is +.>
Quantization step size +.>
The method comprises the steps of carrying out a first treatment on the surface of the From this, the quantization bit number +.>
The larger the quantization step size +.>
The smaller the quantization accuracy is, the higher the wavelet coefficient compression ratio is;
s1.4, encoding the quantized wavelet coefficients by using a deflate algorithm.
Optionally, the specific process of performing integer wavelet transform decomposition on the preprocessed data based on the second generation wavelet transform in S1.2 is as follows:
(one) the original sequence signals acquired by the sensor
Division into even sequences>
And odd number sequence->
The method comprises the following steps:
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
All signal sequences representing raw signal data acquired by the sensor,
，
is a natural integer;
(II) using the first difference between the true value and the estimated value (estimated by the even sequence) of the odd sequence
To represent details of the signal, namely:
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
For the estimation operator +.>
Representing a downward rounding;
(III) introducing an update operator
For->
Updating to obtain the first scale factor +.>
The method comprises the following steps:
；
(IV) first difference between the true value and the estimated value based on the odd-numbered sequence
And a first scale factor->
Repeating the steps (one) to (three) to obtain a second scale factor +.>
And a second difference->
The method comprises the steps of carrying out a first treatment on the surface of the Through->
After the sub-transform decomposition, the original signal +.>
The conversion is +.>
Wherein->
Representing the low frequency part of the sensor signal data
Representing the high frequency portion of the sensor signal data.
Optionally, the specific process of performing integer wavelet transform reconstruction and restoration on the preprocessed data in the second generation wavelet transform in S1.2 is as follows:
obtained by conversion decomposition
And->
Restoring even sequence +.>
The method comprises the following steps:
；
(II) use of
And->
Restoring odd sequence->
The method comprises the following steps:
；
(III) merging
And->
Restoring the original data set->
The method comprises the following steps:
；
(IV) for the following
Sub-transform decomposition signal->
Through->
After the secondary reconstruction operation, the original sequence signal can be restored>
。
Optionally, in the practical application process of integer wavelet transformation, an appropriate wavelet filter is selected according to the application situation and task requirements so as to perform a compromise between the compression effect and the calculation speed. Specifically, in the common integer wavelet filter, the Haar wavelet is the simplest, but the redundancy removing capability is weaker, and the data compression performance is limited; the 9/7 wavelet is based on floating point operation to perform lossy compression, and has better performance on image compression, but larger calculated amount; the 5/3 wavelet has only integer addition and shift operation, can not only perform lossless compression, but also realize lossy compression, and has the advantages of small calculated amount, good compression performance and the like. Therefore, the invention selects 5/3 wavelet to perform lossless or lossy compression transformation on the data collected by the wireless sensor network nodes. In the implementation of the 5/3 wavelet transform, a symmetric period continuation is used for the boundary data, wherein the specific calculation formula of the estimated value in the step (two) is shown in the formula (1):
introducing an update operator in the step (III)
For->
The specific calculation formula for updating is shown as formula (2):
wherein,,
level 1 shift decomposition +.>
Detail value->
Level 1 shift decomposition +.>
Approximation of->
Taking natural integer.
Alternatively, in addition to the above method, the quantization of the wavelet coefficients may be performed by the following method:
based on the characteristic that the coefficient has higher bit number and higher importance or lower bit number and lower importance, the unimportant low coefficient is discarded
Bit (+)>
Taking natural integers), i.e. quantized coefficients +.>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Indicate->
Coefficient of->
Represented as a sign function. This approach is generally better than scalar quantization and the values can be adjusted appropriately according to the compression quality requirements.
Optionally, in the second step, the specific step of performing multi-resolution hierarchical storage on the data of the plurality of nodes of the network based on integer wavelet transformation is as follows:
s2.1, selecting a convergence center node of a sub-network
Dividing a sensor needing to collect data into a plurality of mutually communicated sub-network blocks (a plurality of sensors with mutually communicated signals are arranged in each sub-network block), and selecting one sensor from each sub-network block as a sub-convergence center node for transmitting original signal data collected by the plurality of sensors contained in the sub-network block to the sub-convergence center node; the sub-convergence center nodes are communicated with the upper-level convergence center node so that the sub-convergence center nodes can uniformly transmit the original signal data acquired by the sensors contained in the sub-network block to the upper-level convergence center node arranged in the upper-level network block; thereby realizing the multilevel transmission of the sensor original signal data.
S2.2, constructing a hierarchical structure of sub-networks
Taking a plurality of sub-convergence center nodes in a certain time interval as an example, connecting the sub-convergence center nodes arranged in the sub-network blocks in the same time period with the higher-level convergence center nodes in the sub-network blocks in the adjacent time period so as to transmit the data of the sub-convergence center nodes to the higher-level convergence center nodes; and so on, thereby forming a multi-layer tree-type sub-network structure; the specific construction process of the sub-network layered structure is as follows:
for any local connected area network, the local connected area network is divided into the following areas by taking a convergence center node as the center
A sub-network area in which->
Expressed as the maximum radius of the subnetwork area, < >>
The size of the side length of the sub-network;
if the nodes in each sub-network can directly communicate with each other, then there are
Wherein->
Expressed as the maximum communication distance of the sub-network area node; the network node can calculate which sub-network it belongs to according to the position data and the hop count between the network node and the central node, and the sub-network hierarchical structure is constructed.
S2.3 space-dimensional multi-resolution hierarchical storage of network data
Regarding a single node (the data flow generated by the single node has stronger time correlation because of short interval, and the data generated by a plurality of nodes in the network has certain space correlation because of similar distance) as a component in a certain network data sequence signal, the cooperation of a plurality of nodes can be used for completing integer wavelet transformation together, so that the space correlation among the nodes is removed; the specific process is as follows:
1) Dividing multiple nodes of the same hierarchy in the network into even nodes in sequence
And odd nodes->
Wherein->
Raw data representing no wavelet transform, +.>
Represents the->
Are connected by (1)>
，
Taking natural integers;
2) Even number of nodes
Transmitting its data to adjacent odd nodes +.>
And calculates the high frequency coefficient ++after one transform in integer wavelet coefficient>
And low-frequency coefficients after one transformation +.>
Wherein->
Representing data after one wavelet transform;
3) Repeating the step 2) to obtain a primary wavelet transformation high-frequency coefficient
And first order wavelet transform low frequency coefficient +.>
；
4) The correlation node converts the primary wavelet into low-frequency coefficient
Transmitting to the upper layer convergence node to form new data sequence, repeating the steps 2) and 3) to obtain the secondary wavelet transformation high frequency coefficient
And second order wavelet transform low frequency coefficient +.>
；
5) The central node completes the first
The level wavelet transform gets +.>
Stage (+)>
Taking natural integer) wavelet transform high frequency coefficients
And->
Low frequency coefficient of level wavelet transformation +.>
The method comprises the steps of carrying out a first treatment on the surface of the The converted wavelet low-frequency coefficient is received by each level of convergence node, so that redundancy among node data is eliminated, and the data quantity, storage space and transmission energy consumption during transmission can be effectively reduced.
Optionally, the selecting process of the specific subnetwork convergence central node in S2.1 is as follows:
(1) calculating nodes of each sensor (node) from remaining energy, storage space and relationship metric weights of a plurality of sensors contained in a sub-network block
Values, namely:
Wherein->
Denoted as +.>
Personal node->
Currently owned battery energy, +.>
Denoted as +.>
Personal node->
Current remaining memory, +.>
Denoted as the first
Personal node->
Relation metric weight with other nodes, +.>
Respectively expressed as adjustment parameters and
；
(2) according to the calculated nodes
Selecting the convergence center node with the largest value as the convergence center node of the sub-network; and if maximum value juxtaposition occurs, randomly selecting one of the maximum value juxtaposition as a convergence center node.
Optionally, the method for multi-resolution data collection based on the opportunistic network communication technology by using the mobile terminal to access the wireless sensor network in the third step includes: data collection of the sensor network is implemented by means of a mobile node (such as a unmanned plane or other structure which can be used for data extraction) as a ferrying node or transmission relay; the mobile node uses the opportunistic network to collect data from each convergence center node in a storage-carrying-forwarding mode, and carries and transmits the data to a data center (such as an upper computer) at a lower path cost and a lower time cost so as to solve the problem that a sensor network data collection task cannot be carried out under a complex geographic condition; based on a multi-resolution hierarchical storage mechanism, nodes in the wireless sensor network are divided into different layers, and data can be stored in a distributed hierarchical mode according to the size of resolution, such as high-resolution data (namely high-precision data or high-frequency data) stored on a layer close to a central node, and low-resolution data (namely low-precision data or low-frequency data) stored on a layer far from the central node; in the data collection process, the mobile node can collect data with different resolutions according to actual task demands, and each sensor node is not required to provide high-precision data, so that the data transmission quantity can be reduced, and the data collection efficiency is improved.
As a further embodiment of the present invention, the process of collecting and processing sensor data by using the disclosed real sensor data as a data source and applying the data collecting method of the wireless sensor network under the condition of no connectivity specifically includes the following steps:
marine projects (the Tropical Atmosphere Ocean, TAO) with tropical atmosphere. This project deployed about 100 sensors from 1984 at different depths of 71 places (moorings) in the tropical pacific to collect the temperature of the sea in the relevant area;
(A) Integer wavelet transform
The tropical atmosphere ocean project data set disclosed on the network is taken as experimental data, temperature data collected by a sensor (T0N 125W) in 2011 at 12:00 noon every day are selected during experiments, and the reason for the selection is that the data generated by the sensor are relatively complete; the sensor data is multi-resolution compressed with an integer wavelet transform (5/3 wavelet filter).
Referring to fig. 2 (a) -2 (D), it can be seen that after the integer wavelet transform decomposition, the low-frequency coefficient retains the overall contour characteristics of the original data and can be used as an approximate approximation of the original data; the high-frequency coefficient contains the main characteristic detail information of the original data. It can be seen that the data collected by the sensor still has clear original data characteristics after 3-5 times of wavelet transformation. That is, in practical application, after 3-5 times of aggregation, the data still has a certain reference meaning, and the data volume is greatly reduced.
(B) Data traffic analysis
Referring to fig. 3, in the experiment, raw data of 96 sensors are taken from a tropical atmosphere marine project data set, three-level integer wavelet transform decomposition is performed, and the boundary of the three-level integer wavelet transform decomposition is subjected to continuation treatment. First-order wavelet coefficient after integer wavelet transformation
And->
Common->
Data; second order wavelet coefficients->
And->
Then there are 30 data in total; three-level wavelet coefficient->
And->
There are 19 data in total. During data transmission, the bottom node>
(i.e., first-order convergence node->
) The decomposed low frequency coefficient +.>
Transmitting the data to a secondary convergence node>
，
And break down the->
Transmitting to the third-level convergence node->
. At this time, the data in the node memories of each level are respectively:
(
And->
)、
(
And->
)、
(
And->
). When a user inquires data, the user firstly inquires the data from the three-level convergent node +.>
Is received data->
And data outline display is performed. If the data meets the user requirement, corresponding original data needs to be obtained, nodes are respectively added with>
、
、
Detail data->
、
、
And performing wavelet inverse transformation to obtain data +.>
、
、
. In this process, the amount of data received by the user is +.>
. Wherein the minimum is only 19 data +.>
(the user is not interested in the data, or the precision of the data can meet the user requirement) at most 19+19+30+52=120 (in order to obtain the original data, the data are received in turn +.>
、
、
And->
And the recovery is performed, the received data is 24 more data than the original data because the boundary processing is needed, and the data amount is only 60 in the average case (the probability of discarding or continuing when the data is queried is 50%). The wavelet transformed data traffic clearly has certain advantages over the conventional communication mode (as shown in table 1).
TABLE 1 traffic of multiresolution data
(C) Service life of wireless sensor network
And (3) performing simulation experiments by using MATLAB R2019a to analyze the death condition of the network node so as to evaluate the service life of the wireless sensor network. The experimental parameter settings are shown in table 2.
Table 2 simulation experiment parameter settings
Traditional algorithm for comparing with the election algorithm of the convergent node provided by the invention: LEACH: randomly selecting sensor nodes meeting discrimination conditions from the environment monitoring area in a round-robin mode to serve as final cluster head nodes; fuzzy: executing a clustering process by using a K-means++ algorithm, and dynamically selecting cluster head nodes by using a fuzzy logic method; k-means++: and clustering and cluster head election are carried out on the sensor nodes in the area by using a clustering method.
The experimental results are shown in table 3, and compared with the traditional algorithm, the algorithm provided by the invention reduces the transmission quantity of data in the network, thereby reducing a large amount of energy consumed by communication, greatly pushing the death rounds of all nodes, and prolonging the overall service life of the wireless sensor network.
TABLE 3 number of rounds of node death occurrences
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. The data collection method of the wireless sensor network under the condition of no communication is characterized by comprising the following steps:
step one, carrying out multi-resolution compression storage on data of a single network node based on integer wavelet transformation, and constructing a time dimension of a multi-resolution data set;
step two, carrying out multi-resolution hierarchical storage on data of a plurality of network nodes based on integer wavelet transformation, and constructing a space dimension of a multi-resolution data set;
and thirdly, accessing the wireless sensor network to collect multi-resolution data by using the mobile terminal based on the opportunistic network communication technology.
2. The method for collecting data in a wireless sensor network without connectivity according to claim 1, wherein the specific process of performing multi-resolution compression storage on data of a single network node in the first step is as follows:
s1.1, preprocessing data, namely, normalizing a data mode, clearing abnormal data, correcting error data and clearing repeated data of original signal data acquired by a sensor through data clearing, and realizing integer transformation of the data by utilizing unit conversion and displacement means;
s1.2, performing data integer wavelet transformation, namely performing integer wavelet transformation on the preprocessed data based on second-generation wavelet transformation so as to perform lossless or lossy compression transformation on the sensor data;
s1.3, carrying out wavelet coefficient quantization by adopting a standard quantization mode, wherein the specific process is as follows: for a group of data, set where the maximum number is
The minimum number is +.>
The quantization bit number is +.>
Quantization step size +.>
；
S1.4, encoding the quantized wavelet coefficients by using a deflate algorithm.
3. The method for collecting data of a wireless sensor network under the condition of no communication according to claim 2, wherein the specific process of performing integer wavelet change decomposition on the preprocessed data based on the second generation wavelet transform in S1.2 is as follows:
(one) the original sequence signals acquired by the sensor
Division into even sequences>
And odd number sequence->
The method comprises the following steps:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
All signal sequences representing raw signal data acquired by the sensor,
，
is a natural integer;
(II) using the first difference between the true value and the estimated value of the odd sequence
To represent details of the signal, namely:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
For the estimation operator +.>
Representing a downward rounding;
(III) introducing an update operator
For->
Updating to obtain the first scale factor +.>
The method comprises the following steps:
；
(IV) first difference between the true value and the estimated value based on the odd-numbered sequence
And a first scale factor->
Repeating the steps (one) to (three) to obtain a second scale factor +.>
And a second difference->
The method comprises the steps of carrying out a first treatment on the surface of the Through->
After the sub-transform decomposition, the original signal
The conversion is +.>
Wherein->
Representing the low frequency part of the sensor signal data
Representing the high frequency portion of the sensor signal data.
4. The method for collecting data in the case of no communication by using the wireless sensor network according to claim 3, wherein the specific process of performing integer wavelet transform reconstruction and restoration on the preprocessed data in the second generation wavelet transform in S1.2 is as follows:
obtained by conversion decomposition
And->
Restoring even sequence +.>
The method comprises the following steps:
；/>
(II) use of
And->
Restoring odd sequence->
The method comprises the following steps:
；
(III) merging
And->
Restoring the original data set->
The method comprises the following steps:
；
(IV) for the following
Sub-transform decomposition signal->
Through->
After the secondary reconstruction operation, the original sequence signal can be restored>
。
5. The method for collecting data of a wireless sensor network under the condition of no connectivity according to claim 1, wherein the specific steps of performing multi-resolution hierarchical storage on data of a plurality of nodes of the network based on integer wavelet transform in the second step are as follows:
s2.1, dividing a sensor needing to collect data into a plurality of mutually communicated sub-network blocks, arranging a plurality of sensors with mutually communicated signals in each sub-network block, and selecting one sensor from each sub-network block as a sub-convergence center node;
s2.2, dividing a plurality of sub-network blocks in a certain time interval into a plurality of layers, transmitting data to a last-level collecting central node by a bottom-layer collecting central node, and the like, so as to form a multi-layer tree-type sub-network structure;
s2.3, spatial dimension multi-resolution hierarchical storage of network data.
6. The method for collecting data in the case of no connectivity of a wireless sensor network according to claim 5, wherein the selecting process of the specific subnetwork convergence center node in S2.1 is as follows:
(1) calculating nodes of each sensor based on residual energy, storage space and relationship metric weight of a plurality of sensors contained in a sub-network block
Values, namely:
Wherein->
Denoted as +.>
Personal node->
Currently owned battery energy, +.>
Denoted as +.>
Personal node->
Current remaining memory, +.>
Denoted as +.>
Individual nodes are->
Is a relationship measure weight of->
Respectively expressed as adjustment parameters and +.>
；
(2) According to the calculated nodes
Selecting the convergence center node with the largest value as the convergence center node of the sub-network; and if maximum value juxtaposition occurs, randomly selecting one of the maximum value juxtaposition as a convergence center node.
7. The method for collecting data in the case of no connectivity of the wireless sensor network according to claim 5, wherein the specific process of spatial dimension multi-resolution hierarchical storage of network data in S2.3 is as follows:
1) Dividing multiple nodes of the same hierarchy in the network into even nodes in sequence
And odd nodes->
Wherein, the method comprises the steps of, wherein,
raw data representing no wavelet transform, +.>
Represents the->
Are connected by (1)>
，
Taking natural integers;
2) Even number of nodes
Transmitting its data to adjacent odd nodes +.>
And calculates the high frequency coefficient ++after one transform in integer wavelet coefficient>
And low-frequency coefficients after one transformation +.>
Wherein->
Representing data after one wavelet transform;
3) Repeating the step 2) to obtain a primary wavelet transformation high-frequency coefficient
And first order wavelet transform low frequency coefficient +.>
；
4) The correlation node converts the primary wavelet into low-frequency coefficient
Transmitting to the upper layer convergence node to form new data sequence, repeating the steps 2) and 3) to obtain the secondary wavelet transformation high frequency coefficient +_>
And second order wavelet transform low frequency coefficient +.>
；
5) The central node completes the first
The level wavelet transform gets +.>
High frequency coefficient of level wavelet transformation->
And->
Low frequency coefficient of level wavelet transformation +.>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Taking natural integer.
8. The method for collecting data of a wireless sensor network in a non-connected state according to any one of claims 1 to 7, wherein the method for collecting data in multiple resolutions by using a mobile terminal to access the wireless sensor network based on opportunistic network communication technology in the third step is as follows:
data collection of the sensor network is implemented by means of the mobile node as a ferrying node or transmission relay;
the mobile node collects data from each convergence center node by using the storage-carrying-forwarding mode of the opportunistic network, and carries and transmits the data to the data center at smaller path cost and time cost so as to solve the problem that the data collection task of the sensor network cannot be carried out under the complex geographic condition;
based on a multi-resolution hierarchical storage mechanism, nodes in the wireless sensor network are divided into different layers, and data can be stored in a distributed hierarchical mode according to the size of resolution, namely high-frequency data is stored on a layer close to a central node, and low-frequency data is stored on a layer far away from the central node.
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