CN110798848A - Wireless sensor data fusion method and device, readable storage medium and terminal - Google Patents
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Abstract
A wireless sensor data fusion method and device, a readable storage medium and a terminal are provided, the method comprises the following steps: acquiring raw data of each wireless sensor node; fusing the acquired original data of each wireless sensor node according to a preset optimal weight weighting algorithm model; each wireless sensor node in the optimal weight weighting algorithm model has a corresponding optimal weight distribution coefficient, and the optimal weight distribution coefficient corresponding to each wireless sensor node is related to the importance degree of the data of the wireless sensor node. The invention can improve the accuracy and efficiency of data measurement and save resources when measuring the data of the wireless sensor.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a wireless sensor data fusion method and device, a readable storage medium and a terminal.
Background
At present, a Wireless Sensor Network (WSN) is a Wireless Network linked by Wireless communication, and has the characteristics of no center, multiple hops, self-organization, and the like. Sometimes, we also refer to wireless sensor networks as infrastructure-less networks, ad hoc networks, multi-hop networks. In each application of the wireless sensor network, the most widely used function and the most basic function are data acquisition. The wireless sensor routes various sensed data through multiple hops. The wireless sensor nodes transmit sensed data to the gateway nodes in a multi-hop routing and wireless transmission mode, and then the wireless sensors further process the gateway nodes and further transmit the data to the gateway nodes for subsequent processing. In the wireless sensor network, the accuracy, real-time performance, node energy consumption and the final data post-processing step are all influenced by the data sensing and collecting capability of the wireless sensor network. In addition, when the wireless sensor needs to sense a large amount of data, it directly causes a sharp increase in power consumption. The direct consequence of the above effect is that part of the wireless sensors will consume a lot of power due to excessive sensing data and long operation time. Meanwhile, environmental factors also influence the working state of the wireless sensor. For example, a shift in environmental factors can lead to premature node failure, causing changes in the network architecture. The most serious result is that the life cycle of the whole network is greatly shortened, and the time for sensing data is greatly reduced.
In order to solve the disadvantages of large energy consumption, large occupied communication bandwidth and low data collection capability of a wireless sensor during data collection, the most effective method is to deploy a large number of wireless sensor nodes, so that sensing areas of the sensor nodes must be overlapped with each other, but the above operation results in data stacking to a certain extent.
Therefore, how to improve the accuracy of data measurement of the wireless sensor and improve the efficiency of data transmission and save energy consumption is an urgent problem to be solved.
Disclosure of Invention
The invention aims to improve the accuracy and efficiency of data measurement and save resources when measuring the data of the wireless sensor.
The technical scheme adopted by the invention is as follows:
a wireless sensor data fusion method, comprising:
acquiring raw data of each wireless sensor node;
fusing the acquired original data of each wireless sensor node according to a preset optimal weight weighting algorithm model; each wireless sensor node in the optimal weight weighting algorithm model has a corresponding optimal weight distribution coefficient, and the optimal weight distribution coefficient corresponding to each wireless sensor node is related to the importance degree of the data of the wireless sensor node.
The optimal weight weighting algorithm model is as follows:
wherein the content of the first and second substances,
representing a data fusion value, TjRaw data, W, representing the jth wireless sensor nodejAnd the optimal weight distribution coefficient corresponding to the jth wireless sensor node is represented, and n represents the number of the wireless sensor nodes.
And the optimal weight distribution coefficient corresponding to the wireless sensor is determined by the mean square error of the wireless sensor node data.
According to the wireless sensor data fusion method, the optimal weight distribution coefficient corresponding to each wireless sensor is calculated by adopting the following formula:
wherein, WiRepresents the optimal weight distribution coefficient corresponding to the ith wireless sensor node,
represents the mean square error of data corresponding to the ith wireless sensor node,
and the sum of the mean square deviations of the data from the first wireless sensor to the corresponding node of the nth wireless sensor is represented.
A wireless sensor data fusion apparatus, comprising:
the acquisition unit is suitable for acquiring raw data of each wireless sensor node;
the fusion unit is suitable for fusing the acquired original data of each wireless sensor node according to a preset optimal weight weighting algorithm model; each wireless sensor node in the optimal weight weighting algorithm model has a corresponding optimal weight distribution coefficient, and the optimal weight distribution coefficient corresponding to each wireless sensor node is related to the importance degree of the data of the wireless sensor node.
The optimal weight weighting algorithm model adopted by the fusion unit is as follows:
wherein the content of the first and second substances,
representing a data fusion value, TjRaw data, W, representing the jth wireless sensor nodejAnd the optimal weight distribution coefficient corresponding to the jth wireless sensor node is represented, and n represents the number of the wireless sensor nodes.
In the wireless sensor data fusion device, the optimal weight distribution coefficient corresponding to the wireless sensor is determined by the mean square error of the wireless sensor node data.
In the wireless sensor data fusion method, the fusion unit calculates the optimal weight distribution coefficient corresponding to each wireless sensor by adopting the following formula:
wherein, WiRepresents the optimal weight distribution coefficient corresponding to the ith wireless sensor node,
represents the mean square error of data corresponding to the ith wireless sensor node,
and the sum of the mean square deviations of the data from the first wireless sensor to the corresponding node of the nth wireless sensor is represented.
A computer readable storage medium having stored thereon computer instructions which, when executed, perform the steps of the wireless sensor data fusion method.
A terminal comprising a memory and a processor, the memory having stored thereon computer instructions capable of being executed on the processor, the processor executing the steps of the wireless sensor data fusion method when executing the computer instructions.
According to the data fusion method, the original data of each wireless sensor node is obtained, and the obtained original data of each wireless sensor node is fused according to a preset optimal weight weighting algorithm model, wherein each wireless sensor node in the optimal weight weighting algorithm model has a corresponding optimal weight distribution coefficient, and the optimal weight distribution coefficient corresponding to each wireless sensor node is related to the importance degree of the data of the wireless sensor node, so that the data redundancy is reduced, and the accuracy of data measurement is improved.
The wireless sensor data fusion device of the invention utilizes an acquisition unit to acquire the original data of each wireless sensor node; the acquired original data of each wireless sensor node is fused according to a preset optimal weight weighting algorithm model by using a fusion unit, so that the data redundancy is reduced, and the accuracy of data measurement is improved.
The computer readable storage medium executes the wireless sensor data fusion method, so that the data redundancy is reduced, and the accuracy of data measurement is improved.
The terminal executes the wireless sensor data fusion method during operation, reduces data redundancy and improves data measurement accuracy.
Drawings
FIG. 1 is a flow chart of a method of sensor data fusion in accordance with the present invention;
FIG. 2 is a schematic diagram of the comparison of the optimal weight (DOWA) algorithm with the central weighted (Centralized) algorithm and the Minimum Spanning Tree (MST) algorithm in the embodiment of the present invention;
FIG. 3 is a schematic diagram of a comparison between data fusion costs of an optimal weight algorithm and a central weight-set algorithm and a minimum spanning tree algorithm, respectively, according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a comparison between the optimal weight algorithm and the central weight-set algorithm and the minimum spanning tree algorithm according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a wireless sensor data fusion device in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application. The directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship between the components, the movement, etc. in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indication is changed accordingly.
As described in the background art, the data fusion method of the wireless sensor node in the prior art has the problems of high data redundancy and inaccurate measurement.
According to the technical scheme, the original data of each wireless sensor node is obtained, the obtained original data of each wireless sensor node is fused according to a preset optimal weight weighting algorithm model, each wireless sensor node in the optimal weight weighting algorithm model has a corresponding optimal weight distribution coefficient, and the optimal weight distribution coefficient corresponding to each wireless sensor node is related to the importance degree of the data of the wireless sensor node, so that the data redundancy is reduced, and the accuracy of data measurement is improved.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
Fig. 1 is a schematic flowchart of a wireless sensor data fusion method according to an embodiment of the present invention. Referring to fig. 1, a method for fusing data of a wireless sensor specifically includes the following steps:
step S101: raw data is acquired for each wireless sensor node.
In an implementation, sensing areas between the wireless sensor nodes overlap with each other, so that data stacking occurs to a corresponding degree between raw data collected by the wireless sensors.
Step S102: and fusing the acquired raw data of each wireless sensor node according to a preset optimal weight weighting algorithm model.
In specific implementation, each wireless sensor node in the optimal weight weighting algorithm model has a corresponding optimal weight distribution coefficient, and the optimal weight distribution coefficient corresponding to each wireless sensor node is related to the importance degree of the data of the wireless sensor node. The higher the accuracy is, the data error of the wireless sensor node is relatively small, and the optimal weight distribution coefficient is large; the lower the accuracy, the larger the data error of the wireless sensor node is, and the smaller the corresponding optimal weight distribution coefficient is.
In one embodiment of the invention, the optimal weight distribution of the data of each wireless sensor node is realized by using a minimum variance method. The most weight-distributed basic algorithm formula, that is, the optimal weight weighting model, is:
and:
wherein the content of the first and second substances,
representing a data fusion value, WjRepresents the optimal weight distribution coefficient, T, of the jth wireless sensor nodejThe data acquisition method comprises the steps of representing raw data acquired by the jth wireless sensor node, and representing the number of the wireless sensor nodes by n.
The overall mean variance of the system is then:
wherein σ2Represents the overall mean variance of the system, T represents the true value of the raw data,
represents the mean value operation, Wi、WjRespectively representing the optimal weight distribution coefficients T of the ith and the j wireless sensor nodesjRepresenting the raw data collected by the jth wireless sensor node.
The constraint condition of the total mean variance of the system is formula (2), since the total mean variance σ is2Is a multivariate quadratic function of the optimal weight distribution coefficients, so the overall mean variance must have a minimum value. Thus, using Lagrangian's law yields:
wherein F (WW... W, lambda) represents the Lagrangian function after constraint condition is added to the objective function,the mean square error of the data of the ith wireless sensor node is shown, and lambda represents a Lagrange parameter.
Next, for W in the formula (4)jAnd lambda to obtain the minimum mean variance
Comprises the following steps:
therefore, the optimal weight distribution coefficient W of the jth wireless sensor node (j is more than or equal to 1 and less than or equal to n)jMean square error of data by wireless sensor node j
And (6) determining. However, mean square error
It is not known that monitoring data is obtained from a single wireless sensor node, and a corresponding formula is used for solving the variance. Since the value of the variance is affected by the measurement values and noise of the sensor nodes and the outside world, each wireless sensor node needs to be sampled individually.
In one embodiment of the invention, an optimal variance estimation is used to take the variance value and the arithmetic mean of the samples as an estimate of the measured variance. Wherein, define
The result of the sampling a times of the ith wireless sensor node is the arithmetic mean value of the ith wireless sensor node sampled N times
Comprises the following steps:
however, the data true value of each wireless sensor node is unknown, so let the unbiased estimation of the data true value of the ith wireless sensor node beThe mean square error of unbiased estimation data of the ith wireless sensor nodeComprises the following steps:
subsequently, the estimated values of the measurement method of the ith wireless sensor node sampled each time are averaged:
wherein the content of the first and second substances,
and representing the data variance estimation of the ith wireless sensor node at the a-th sampling time.
And representing the mean square error estimation of data at the a-th sampling of the ith wireless sensor node.
The left side of the formula (8) is the estimated value of the data variance of the ith wireless sensor node at the a-th sampling time, and the estimated value is obtained:
the optimal weight distribution coefficient of the ith wireless sensor node obtained by integrating the above is:
wherein, WiRepresents the optimal weight distribution coefficient corresponding to the ith wireless sensor node,
and the mean square error of the data corresponding to the ith wireless sensor node is represented.
And the sum of the mean square deviations of the data from the first wireless sensor to the corresponding node of the nth wireless sensor is represented.
In an actual cable data monitoring environment, each wireless sensor node and a monitored object are influenced by various interference factors, so that deviation from the actual situation is generated, in a temperature monitoring example, the temperature is increased by using a heating device, 10 wireless sensor nodes with temperature sensor modules are deployed, and a high-temperature device is connected to a terminal. When the wireless sensor node predicts that the temperature exceeds the preset temperature, the alarm device is started.
Wherein, a group of data is randomly extracted, the unit of temperature value is centigrade, which are: 46.78, 46.82, 46.63, 46.75, 46.93, 46.55, 46.80, 47.05, 47.01, 46.40 the variance of the nodes for each sensor, respectively, is: 0.34, 0.38, 0.43, 0.5, 0.73, 0.47, 0.66, 055, 0.58, 0.40. Then the optimal weight distribution coefficient of each wireless sensor node can be obtained according to equation (10), as shown in the following table:
TABLE 1
The node with large temperature measurement value error has a small optimal weight distribution value, and the node with small temperature measurement value error has a large optimal weight distribution value.
Then, the data fusion value can be calculated by the formula (1)Comprises the following steps:
in specific implementation, a corresponding optimal weight distribution value can be obtained according to the variance of each wireless sensor node, a high-precision node is distributed with a higher weight value, and the node calculates the corresponding optimal weight value corresponding to each monitoring time point along with the continuous increase of monitoring data, so that the attention degree of the node can be objectively reflected by the optimal weight value, the influence of a node with a larger measurement error on the overall result can be finally reduced, and the precision of the overall system can be improved.
After researching the traditional data fusion processing and analyzing algorithm, the inventor of the application learns that the data fusion is carried out by adopting the K-means algorithm or the distributed K-means algorithm, though the original data can be simply and intuitively processed in real time, the original data is too ideal, a lot of interference exists in the real data acquisition environment, the influence on the sink node is larger, and due to the mutual correlation among the data, one error data can cause the error of the whole sink node, and the accuracy of the data is seriously influenced.
Therefore, in the technical scheme of the application, the Optimal Weight algorithm is adopted, and the change situation of the error rate of the Optimal Weight algorithm (Dynamic Optimal Weight Allocation, DOWA) along with the increase of the nodes is displayed by comparing the data of the Optimal Weight distribution algorithm and the data of the central Weight collection algorithm.
Due to the characteristics of large scale, self-organization, dynamics and reliability of the wireless sensor network, the optimal weight distribution algorithm has data loss in the iteration process, so that the accuracy of the clustering result is reduced. But in general the error rate of the optimal weight assignment algorithm is very low. As shown in table 2:
TABLE 2
In order to further study the optimal weight assignment algorithm, the data of the algorithm is further refined. It is assumed that the initial conditions of the optimal weight assignment algorithm and the central weight-set algorithm are the same, and they have the same number k of centroids. Clusters that can be divided after the algorithm is executed are basically the same, so the results of the two experiments are comparable. Comparing the experimental result of the optimal weight distribution algorithm with the experimental result of the central weight collection algorithm, the optimal weight distribution algorithm has an advantage in execution time with the increase of the number of network nodes, as shown in table 3:
TABLE 3
In the aspect of algorithm overhead, the data fusion process can have certain influence on the algorithm. Assuming that the effective communication radius of the node is 30 meters, the correlation radius range of the data is defined as 50 meters, the minimum spanning tree algorithm, the optimal weight distribution algorithm and the central weight collection algorithm are respectively adopted for data fusion, and the comparison data of the total energy consumption, the communication cost and the fusion cost of the three are respectively shown in fig. 2, fig. 3 and fig. 4.
As can be seen from fig. 2, fig. 3 and fig. 4, it is obvious that when the unit fusion cost is the lowest, the minimum spanning tree algorithm and the optimal weight distribution algorithm have a better data fusion effect, and both methods can eliminate redundant data at a lower fusion cost.
Each node needs to participate in data fusion in the execution process of the minimum spanning tree algorithm and the central weight-collecting algorithm, so that the communication energy consumption is approximately a constant. Each node in the algorithm execution process participates in data fusion, and the data fusion cost is increased rapidly. And because the data fusion cost is high, some nodes are prohibited from participating in the data fusion processing in the optimal weight distribution algorithm. Therefore, the most difference between the optimal weight assignment algorithm and the other two algorithms is that the energy consumption of data fusion is reduced as the fusion cost is increased.
In summary, the efficiency and the precision of data fusion processing can be effectively improved by controlling the variance of the node and the optimal weight distribution value, and the efficiency of processing redundant data can be improved.
The wireless sensor data fusion method in the embodiment of the present invention is described in detail above, and apparatuses corresponding to the method are described below.
Fig. 5 shows a schematic structural diagram of a wireless sensor data fusion apparatus in an embodiment of the present invention. Referring to fig. 5, a wireless sensor data fusion apparatus may include an acquisition unit and a fusion unit, wherein:
the acquisition unit is suitable for acquiring raw data of each wireless sensor node;
the fusion unit is suitable for fusing the acquired original data of each wireless sensor node according to a preset optimal weight weighting algorithm model; each wireless sensor node in the optimal weight weighting algorithm model has a corresponding optimal weight distribution coefficient, and the optimal weight distribution coefficient corresponding to each wireless sensor node is related to the importance degree of the data of the wireless sensor node. In a specific implementation, the optimal weight distribution coefficient corresponding to the wireless sensor is determined by the mean square error of the data of the wireless sensor node.
In specific implementation, the optimal weight weighting algorithm model adopted by the fusion unit is as follows:
wherein the content of the first and second substances,
representing a data fusion value, TjRepresents the raw data, W, collected by the jth wireless sensor nodejRepresents the optimal weight distribution coefficient corresponding to the jth wireless sensor node, and n represents the number of the wireless sensor nodesAnd (4) counting.
And the optimal weight distribution coefficient corresponding to the wireless sensor is determined by the mean square error of the wireless sensor node data.
In the embodiment of the present invention, the fusion unit calculates the optimal weight distribution coefficient corresponding to each wireless sensor by using the following formula:
wherein, WiRepresents the optimal weight distribution coefficient corresponding to the ith wireless sensor node,
and the mean square error of the data corresponding to the ith wireless sensor node is represented.
And the sum of the mean square deviations of the data from the first wireless sensor to the corresponding node of the nth wireless sensor is represented.
The embodiment of the invention also provides a computer readable storage medium, wherein computer instructions are stored on the computer readable storage medium, and the computer instructions execute the steps of the wireless sensor data fusion method when running. For the wireless sensor data fusion method, please refer to the description in the previous section, and the description is omitted.
The embodiment of the invention also provides a terminal, which comprises a memory and a processor, wherein the memory is stored with computer instructions capable of being operated on the processor, and the processor executes the steps of the wireless sensor data fusion method when operating the computer instructions. For the wireless sensor data fusion method, please refer to the description in the previous section, and the description is omitted.
By adopting the scheme in the embodiment of the invention, the original data of each wireless sensor node is obtained, and the obtained original data of each wireless sensor node is fused according to the preset optimal weight weighting algorithm model, wherein each wireless sensor node in the optimal weight weighting algorithm model has a corresponding optimal weight distribution coefficient, and the optimal weight distribution coefficient corresponding to each wireless sensor node is related to the importance degree of the data of the wireless sensor node, so that the data redundancy is reduced and the accuracy of data measurement is improved.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the foregoing description only for the purpose of illustrating the principles of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the invention as defined by the appended claims, specification, and equivalents thereof.
Claims (10)
1. A method for wireless sensor data fusion, comprising:
acquiring raw data of each wireless sensor node;
fusing the acquired original data of each wireless sensor node according to a preset optimal weight weighting algorithm model; each wireless sensor node in the optimal weight weighting algorithm model has a corresponding optimal weight distribution coefficient, and the optimal weight distribution coefficient corresponding to each wireless sensor node is related to the importance degree of the data of the wireless sensor node.
2. The wireless sensor data fusion method of claim 1, wherein the optimal weight weighting algorithm model is:
wherein the content of the first and second substances,representing a data fusion value, TjRaw data, W, representing the jth wireless sensor nodejAnd the optimal weight distribution coefficient corresponding to the jth wireless sensor node is represented, and n represents the number of the wireless sensor nodes.
3. The method of claim 2, wherein the optimal weight distribution coefficient corresponding to the wireless sensor is determined by the mean square error of the wireless sensor node data.
4. The method for fusing the data of the wireless sensors according to claim 3, wherein the optimal weight distribution coefficient corresponding to each wireless sensor is calculated by adopting the following formula:
wherein, WiRepresents the optimal weight distribution coefficient corresponding to the ith wireless sensor node,
represents the mean square error of data corresponding to the ith wireless sensor node,
and the sum of the mean square deviations of the data from the first wireless sensor to the corresponding node of the nth wireless sensor is represented.
5. A wireless sensor data fusion apparatus, comprising:
the acquisition unit is suitable for acquiring raw data of each wireless sensor node;
the fusion unit is suitable for fusing the acquired original data of each wireless sensor node according to a preset optimal weight weighting algorithm model; each wireless sensor node in the optimal weight weighting algorithm model has a corresponding optimal weight distribution coefficient, and the optimal weight distribution coefficient corresponding to each wireless sensor node is related to the importance degree of the data of the wireless sensor node.
6. The wireless sensor data fusion device of claim 5, wherein the optimal weight weighting algorithm model adopted by the fusion unit is as follows:
wherein the content of the first and second substances,
representing a data fusion value, TjRaw data, W, representing the jth wireless sensor nodejAnd the optimal weight distribution coefficient corresponding to the jth wireless sensor node is represented, and n represents the number of the wireless sensor nodes.
7. The device for fusing data of wireless sensors according to claim 6, wherein the optimal weight distribution coefficient corresponding to the wireless sensor is determined by the mean square error of the data of the wireless sensor nodes.
8. The data fusion method of the wireless sensor, as claimed in claim 3, wherein the fusion unit calculates the optimal weight distribution coefficient corresponding to each wireless sensor by using the following formula:
wherein, WiRepresents the optimal weight distribution coefficient corresponding to the ith wireless sensor node,
represents the mean square error of data corresponding to the ith wireless sensor node,
represents fromAnd the sum of the mean square deviations of the data of the nodes corresponding to the first wireless sensor to the nth wireless sensor.
9. A computer readable storage medium having stored thereon computer instructions, wherein the computer instructions when executed perform the steps of the wireless sensor data fusion method of any one of claims 1 to 4.
10. A terminal comprising a memory and a processor, the memory having stored thereon computer instructions capable of being executed on the processor, the processor when executing the computer instructions performing the steps of the wireless sensor data fusion method of any one of claims 1 to 4.
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| CN113341704A (en) * | 2021-05-28 | 2021-09-03 | 北京理工大学 | Composite cycle energy conversion system |
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