CN110749346B - Urban environment monitoring-oriented mobile wireless sensor network data perception calibration method - Google Patents

Urban environment monitoring-oriented mobile wireless sensor network data perception calibration method Download PDF

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CN110749346B
CN110749346B CN201910864347.3A CN201910864347A CN110749346B CN 110749346 B CN110749346 B CN 110749346B CN 201910864347 A CN201910864347 A CN 201910864347A CN 110749346 B CN110749346 B CN 110749346B
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CN110749346A (en
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孙力娟
苏阳青
沈澍
蒋涵铭
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Nanjing University of Posts and Telecommunications
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    • G—PHYSICS
    • G01—MEASURING; TESTING
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
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    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
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Abstract

A calibration method for data perception of a mobile wireless sensor network facing urban environment monitoring mainly aims at calibration of sensors in the mobile wireless sensor network. A certain number of fixed sensors are deployed at specified locations in a mobile wireless sensor network and are divided into two different interactions: between the mobile sensor and the stationary sensor, and between the mobile sensor and the mobile sensor. Firstly, selecting four virtual mobile sensing node positions in respective areas by a fixed sensor, and carrying out cooperative calibration with the four virtual mobile sensing node positions; and then the calibrated mobile sensor is calibrated with the surrounding uncalibrated mobile sensor, so that the accuracy of data acquisition of the sensor is improved as a whole.

Description

Urban environment monitoring-oriented mobile wireless sensor network data perception calibration method
Technical Field
The invention belongs to the technical field of mobile wireless sensor networks, and particularly relates to a calibration method for data perception of a mobile wireless sensor network for urban environment monitoring.
Background
The mobile wireless sensor network is composed of large-scale mobile sensor nodes in an ad hoc or multi-hop mode, and the sensors are usually cheap, low in power consumption and miniature. The sensor nodes sense, acquire, process and transmit data and transmit the data in a multi-hop mode until the data is sent to the owner of the network. Sensor calibration is a fundamental problem for wireless sensor networks. If the sensor is not properly calibrated, the sensor may generate erroneous data, which may affect the accuracy of the data.
Since a mobile wireless sensor network is composed of a large number of mobile sensor nodes, it is very difficult to calibrate sensors individually in this case, and thus a systematic sensor calibration scheme is required.
In order to improve the data accuracy, the invention further performs distributed calibration on the mobile sensor network, namely performing cooperative calibration among sensors. A small number of fixed high-precision sensors are deployed in a mobile wireless sensor network, and the scheme is that the fixed high-precision sensors firstly select four virtual mobile sensing node positions in respective areas and perform cooperative calibration with the four virtual mobile sensing node positions; and then the calibrated mobile sensor is calibrated with the surrounding uncalibrated mobile sensor, so that the accuracy of data acquisition of the sensor is improved as a whole.
Disclosure of Invention
The invention aims to design a scheme for improving the data acquisition precision of a sensor, which is applied to a mobile wireless sensor network. The scheme solves the problem that the sensor has larger error due to drift and the like. The method improves the data precision, saves the energy consumption of the sensor nodes and effectively prolongs the network service life while ensuring that the cost is not too high.
A calibration method for urban environment monitoring-oriented mobile wireless sensor network data perception calibrates through cooperation among sensors, namely, a part of mobile sensors are calibrated firstly, and then uncalibrated sensors are calibrated by using calibrated sensors, and the method comprises the following steps:
step 1: dividing a monitored area, setting nodes, and carrying a sensor to sense surrounding environment data;
step 2: selecting a partial mobile sensor node; searching all mobile sensors with the fixed sensor distance of k by using a breadth-first algorithm to obtain a fixed sensor measurement value and a mobile sensor measurement value, and calibrating the mobile sensors and the fixed sensors when the difference value between the fixed sensor measurement value and the mobile sensor measurement value is greater than a certain threshold value;
and step 3: calibration between a stationary sensor and a mobile sensor; the readings of the fixed sensor and the mobile sensor are weighted and processed to obtain a calibration value for calibration;
and 4, step 4: calibration between mobile sensors
Step 4-1: converting the moving track of the mobile reference node into a undirected network model graph;
step 4-2: calibration between mobile sensors.
Further, in the step 1, each bus stop board in a city serves as a fixed node, and a high-precision sensor is carried to sense information as reference; because the distribution range of the mobile nodes in the mobile sensor network is wide, firstly, a Voronoi diagram corresponding to the mobile nodes is made according to the position distribution of the fixed nodes, and the geographic positions of the sensors in the same Voronoi diagram are set to be the same.
Further, in step 2, in order to reduce the task load of the fixed sensor, a breadth first algorithm (BFS) is used to search all vertices Mi (mobile nodes) with a distance k from the vertex S (i.e. the fixed sensor node); in the algorithm, k is not appropriate, otherwise, the value of k exceeds the range of each area; according to a specific experiment, a more appropriate k value can be selected, and the following conditions are met:
Figure 169722DEST_PATH_IMAGE002
representing the value measured by the motion sensor,
Figure 128450DEST_PATH_IMAGE004
representing the value of the stationary sensor, the mobile sensor being calibrated with the stationary sensor when the difference between the mobile sensor and the stationary sensor is greater than a certain threshold value, i.e.:
Figure 338415DEST_PATH_IMAGE006
。
further, in step 3, it is assumed that errors of the selected mobile sensor and the selected fixed sensor belong to the same distribution, and readings of the fixed sensor are as follows:
Figure 153925DEST_PATH_IMAGE008
(ii) a The mobile sensor reads as
Figure 972976DEST_PATH_IMAGE010
(ii) a Fixed sensor and mobile sensorThe weighted sum of (a) is:
Figure 453505DEST_PATH_IMAGE012
（1）
wherein, w2jIs a motion sensor R2The error weight of (2); the variance of the above formula is solved and the variance is derived,
Figure 166246DEST_PATH_IMAGE014
（2）
obtained according to the formula (1)
Figure 90340DEST_PATH_IMAGE016
As the final calibration values for both sensors.
Further, in the step 4-1, G = (S ═ M, E) represents a undirected network model, where S represents a set of fixed sensors, M represents a set of mobile sensors, and E represents a calibration relationship between two sensors
Figure 193425DEST_PATH_IMAGE018
Or
Figure 916792DEST_PATH_IMAGE020
First, the sensor trajectory is represented as a table with the lateral direction representing the area
Figure 484040DEST_PATH_IMAGE022
(ii) a Longitudinal representative fixation sensor
Figure 251139DEST_PATH_IMAGE024
(ii) a The data in the table are
Figure 169416DEST_PATH_IMAGE026
Wherein
Figure 460589DEST_PATH_IMAGE028
Reference numerals are used to denote the movement sensors,
Figure 882343DEST_PATH_IMAGE030
the labels of the areas are represented, and the table is converted into the following matrix:
Figure 85923DEST_PATH_IMAGE032
and finally, converting the matrix X into a network model diagram.
Further, in step 4-2, the mobile sensor measurements are in the set { m }uThe calibrated data are all
Figure 429179DEST_PATH_IMAGE034
Wherein d isuRepresents drift of the mobile sensor;
in the monitoring area, the drift of each mobile sensor is related to other mobile sensors, and the drift value of a certain mobile sensor is set as
Figure 553737DEST_PATH_IMAGE036
In which the sensor is moved
Figure 95576DEST_PATH_IMAGE038
And
Figure 470057DEST_PATH_IMAGE040
all belong to k regions, and when the region has n mobile sensors, n equations similar to the above equation can form a Laplace matrix
Figure 97348DEST_PATH_IMAGE042
Wherein L represents a calibration matrix;
Figure 464744DEST_PATH_IMAGE044
expressing the drift value of the mobile sensor;
Figure 861090DEST_PATH_IMAGE046
representing a measured difference between the two motion sensors;
adding a constraint:
Figure 140893DEST_PATH_IMAGE048
i.e. the mean value of the drift of all the mobile sensors is almost zero, provided that there are a sufficient number of mobile sensors, the constraint being converted into a matrix
Figure 678316DEST_PATH_IMAGE050
Where M is an all 1 matrix;
the drift of the mobile sensor is thus calculated as
Figure 662452DEST_PATH_IMAGE052
(ii) a Then the obtained
Figure 178884DEST_PATH_IMAGE044
Substituting into formula
Figure 160747DEST_PATH_IMAGE034
And obtaining the calibrated data.
The invention has the beneficial effects that: firstly, selecting a small number of mobile sensors, and then calibrating the small number of mobile sensors by using fixed sensors so as to improve the numerical accuracy of the small number of mobile sensors; and then the uncalibrated mobile sensor is calibrated by using the calibrated mobile sensor, so that the sensor can be calibrated, and the accuracy of the measurement data of the sensor can be improved on the whole.
Drawings
Fig. 1 is a topology structure diagram of a mobile sensor network in an embodiment of the present invention.
Fig. 2 is a diagram illustrating a mobile node according to an embodiment of the present invention.
FIG. 3 is a diagram of a fixed set of sensor nodes in an embodiment of the present invention
Figure 762629DEST_PATH_IMAGE054
Voronoi diagram of (a).
FIG. 4 is a flowchart illustrating a data calibration procedure according to an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
A calibration method for urban environment monitoring-oriented mobile wireless sensor network data perception calibrates through cooperation among sensors, namely, a part of mobile sensors are calibrated firstly, and then uncalibrated sensors are calibrated by using calibrated sensors, and the method comprises the following steps:
step 1: and dividing the monitored area, setting nodes and carrying sensors to sense the surrounding environment data.
In the step 1, because the distances between the bus stop boards are approximately equal, each bus stop board in a city serves as a fixed node, and a high-precision sensor is carried to sense information as reference; because the distribution range of the mobile nodes in the mobile sensor network is wide, firstly, a Voronoi diagram corresponding to the mobile nodes is made according to the position distribution of the fixed nodes, and the geographic positions of the sensors in the same Voronoi diagram are set to be the same. FIG. 3 is an example of a Voronoi diagram, where the area is 100 x 100, which corresponds to a set of control points (coordinates of the geographical position of the fixed node)
Figure 550457DEST_PATH_IMAGE054
The area is divided into 6 Voronoi cells.
Step 2: selecting a partial mobile sensor node; and searching all the mobile sensors with the fixed sensor distance of k by using a breadth-first algorithm to obtain a fixed sensor measurement value and a mobile sensor measurement value, and calibrating the mobile sensors and the fixed sensors when the difference value between the fixed sensor measurement value and the mobile sensor measurement value is greater than a certain threshold value.
In step 2, in order to reduce the task load of the fixed sensor, searching all vertexes Mi (mobile nodes) with the distance k between the vertexes S (namely the fixed sensor nodes) by using a breadth-first algorithm (BFS); in the algorithm, k is not appropriate, otherwise, the value of k exceeds the range of each area; according to a specific experiment, a more appropriate k value can be selected, and the following conditions are met:
Figure 842767DEST_PATH_IMAGE002
representing the value measured by the motion sensor,
Figure 729951DEST_PATH_IMAGE004
representing the value of the stationary sensor, the mobile sensor being calibrated with the stationary sensor when the difference between the mobile sensor and the stationary sensor is greater than a certain threshold value, i.e.:
Figure 553551DEST_PATH_IMAGE006
. As in fig. 2, the fixed sensor S derives 4 mobile nodes using a breadth first algorithm.
And step 3: calibration between a stationary sensor and a mobile sensor; calibration values are derived by weighting and processing the readings of the fixed and moving sensors to perform calibration.
In step 3, it is assumed that the errors of the selected mobile sensor and the fixed sensor belong to the same distribution, and the reading of the fixed sensor is as follows:
Figure 570835DEST_PATH_IMAGE008
(ii) a The mobile sensor reads as
Figure 796280DEST_PATH_IMAGE010
(ii) a The weighted sum of the stationary and moving sensors is:
Figure 854366DEST_PATH_IMAGE012
（1）
wherein, w2jIs a motion sensor R2The error weight of (2); the variance of the above formula is solved and the variance is derived,
Figure 430841DEST_PATH_IMAGE014
（2）
obtained according to the formula (1)
Figure 481842DEST_PATH_IMAGE056
As the final calibration values for both sensors.
And 4, step 4: calibration between mobile sensors.
Step 4-1: and converting the moving track of the mobile reference node into a undirected network model graph.
In step 4-1, G = (S ═ M, E) represents a undirected network model, where S represents a set of stationary sensors, M represents a set of mobile sensors, and E represents a calibration relationship between two sensors
Figure 765056DEST_PATH_IMAGE018
Or
Figure 649835DEST_PATH_IMAGE020
First, the track of the motion sensor is expressed as a table, and the area is represented horizontally
Figure 323393DEST_PATH_IMAGE022
(ii) a Longitudinal representative fixation sensor
Figure 256714DEST_PATH_IMAGE024
(ii) a The data in the table are
Figure 456752DEST_PATH_IMAGE026
Wherein
Figure 872952DEST_PATH_IMAGE028
Reference numerals are used to denote the movement sensors,
Figure 361702DEST_PATH_IMAGE030
the labels of the areas are represented, and the table is converted into the following matrix:
Figure 895451DEST_PATH_IMAGE032
and finally, converting the matrix X into a network model diagram.
Step 4-2: calibration between mobile sensors.
In step 4-2, the mobile sensor measurements are in the set { m }uThe calibrated data are all
Figure 825361DEST_PATH_IMAGE034
Wherein d isuRepresenting drift of the moving sensor.
In the monitoring area, the drift of each mobile sensor is related to other mobile sensors, and the drift value of a certain mobile sensor is set as
Figure 989626DEST_PATH_IMAGE036
In which the sensor is moved
Figure 496831DEST_PATH_IMAGE038
And
Figure 958905DEST_PATH_IMAGE040
all belong to k regions, and when the region has n mobile sensors, n equations similar to the above equation can form a Laplace matrix
Figure 336797DEST_PATH_IMAGE042
Wherein L represents a calibration matrix;
Figure 203122DEST_PATH_IMAGE044
expressing the drift value of the mobile sensor;
Figure 604147DEST_PATH_IMAGE046
representing the measured difference between the two motion sensors.
Adding a constraint:
Figure 417382DEST_PATH_IMAGE048
i.e. the mean value of the drift of all the mobile sensors is almost zero, provided that there are a sufficient number of mobile sensors, the constraint being converted into a matrix
Figure 446518DEST_PATH_IMAGE050
Where M is an all 1 matrix.
The drift of the mobile sensor is thus calculated as
Figure 372493DEST_PATH_IMAGE052
(ii) a Then the obtained
Figure 119869DEST_PATH_IMAGE044
Substituting into formula
Figure 736795DEST_PATH_IMAGE034
And obtaining the calibrated data.
The above description is only a preferred embodiment of the present invention, and the scope of the present invention is not limited to the above embodiment, but equivalent modifications or changes made by those skilled in the art according to the present disclosure should be included in the scope of the present invention.

Claims (5)

1. A calibration method for data perception of a mobile wireless sensor network facing urban environment monitoring is characterized by comprising the following steps: the method carries out calibration through cooperation among the sensors, namely, a part of the mobile sensors are calibrated firstly, and then uncalibrated sensors are calibrated by using calibrated sensors, and the method comprises the following steps:
step 1: dividing a monitored area, setting nodes, and carrying a sensor to sense surrounding environment data;
step 2: selecting a partial mobile sensor node; searching all mobile sensors with the fixed sensor distance of k by using a breadth-first algorithm to obtain a fixed sensor measurement value and a mobile sensor measurement value, and calibrating the mobile sensors and the fixed sensors when the difference value between the fixed sensor measurement value and the mobile sensor measurement value is greater than a certain threshold value;
and step 3: calibration between a stationary sensor and a mobile sensor; the readings of the fixed sensor and the mobile sensor are weighted and processed to obtain a calibration value for calibration;
and 4, step 4: calibration between mobile sensors
Step 4-1: converting the moving track of the mobile reference node into a undirected network model graph;
step 4-2: calibration between mobile sensors;
in step 4-2, the mobile sensor measurements are in the set { m }uThe calibrated data are all
Figure 161877DEST_PATH_IMAGE002
Wherein d isuRepresents drift of the mobile sensor;
in the monitoring area, the drift of each mobile sensor is related to other mobile sensors, and the drift value of a certain mobile sensor is set as
Figure 344597DEST_PATH_IMAGE004
In which the sensor is moved
Figure 593175DEST_PATH_IMAGE006
And
Figure 191647DEST_PATH_IMAGE008
all belong to k regions, and when the region has n mobile sensors, n equations similar to the above equation can form a Laplace matrix
Figure 474861DEST_PATH_IMAGE010
Wherein L represents a calibration matrix;
Figure 562902DEST_PATH_IMAGE012
expressing the drift value of the mobile sensor;
Figure 33198DEST_PATH_IMAGE014
representing a measured difference between the two motion sensors;
adding a constraint:
Figure 497677DEST_PATH_IMAGE016
i.e. the mean value of the drift of all the mobile sensors is almost zero, provided that there are a sufficient number of mobile sensors, the constraint being converted into a matrix
Figure 635398DEST_PATH_IMAGE018
Where M is an all 1 matrix;
the drift of the mobile sensor is thus calculated as
Figure 426216DEST_PATH_IMAGE020
(ii) a Then the obtained
Figure 383808DEST_PATH_IMAGE012
Substituting into formula
Figure 386399DEST_PATH_IMAGE002
And obtaining the calibrated data.
2. The urban environment monitoring-oriented mobile wireless sensor network data perception calibration method according to claim 1, characterized in that: in the step 1, each bus stop board in a city serves as a fixed node, and a high-precision sensor is carried to sense information as reference; because the distribution range of the mobile nodes in the mobile sensor network is wide, firstly, a Voronoi diagram corresponding to the mobile nodes is made according to the position distribution of the fixed nodes, and the geographic positions of the sensors in the same Voronoi diagram are set to be the same.
3. The urban environment monitoring-oriented mobile wireless sensor network data perception calibration method according to claim 1, characterized in that: in the step 2, in order to reduce the task load of the fixed sensor, a breadth-first algorithm BFS is used for searching and all mobile nodes Mi with the distance k of a vertex S, namely the fixed sensor node;
Figure 378626DEST_PATH_IMAGE022
representing the value measured by the motion sensor,
Figure 11732DEST_PATH_IMAGE024
representing a value of a stationary sensor, the difference between the mobile sensor and the stationary sensor being greater than a certain valueAt threshold, then the mobile sensor is calibrated with the fixed sensor, namely:
Figure 518937DEST_PATH_IMAGE026
。
4. the urban environment monitoring-oriented mobile wireless sensor network data perception calibration method according to claim 1, characterized in that: in the step 3, it is assumed that the errors of the selected mobile sensor and the fixed sensor belong to the same distribution, and the reading of the fixed sensor is as follows:
Figure 262902DEST_PATH_IMAGE028
(ii) a The mobile sensor reads as
Figure 109636DEST_PATH_IMAGE030
(ii) a The weighted sum of the stationary and moving sensors is:
Figure 710381DEST_PATH_IMAGE032
（1）
wherein, w2jIs a motion sensor R2The error weight of (2); the variance of the above formula is solved and the variance is derived,
Figure 908144DEST_PATH_IMAGE034
（2）
obtained according to the formula (1)
Figure 190221DEST_PATH_IMAGE036
As the final calibration values for both sensors.
5. The urban environment monitoring-oriented mobile wireless sensor network data perception calibration method according to claim 1, characterized in that: in the step 4-1, G = (S ═ M, E) represents an undirected network model, where S represents a set of stationary sensing unitsM denotes a set of motion sensors, E denotes the calibration relationship between the two sensors
Figure 953778DEST_PATH_IMAGE038
Or
Figure 692801DEST_PATH_IMAGE040
First, the track of the motion sensor is expressed as a table, and the area is represented horizontally
Figure 846702DEST_PATH_IMAGE042
，
Figure 198049DEST_PATH_IMAGE044
(ii) a Longitudinal representative fixation sensor
Figure 19375DEST_PATH_IMAGE046
，
Figure 227502DEST_PATH_IMAGE044
(ii) a The data in the table are
Figure 134278DEST_PATH_IMAGE048
Wherein
Figure 758157DEST_PATH_IMAGE050
Reference numerals are used to denote the movement sensors,
Figure 496306DEST_PATH_IMAGE052
the labels of the areas are represented, and the table is converted into the following matrix:
Figure 78597DEST_PATH_IMAGE054
and finally, converting the matrix X into a network model diagram.
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