CN116390112A - Wireless sensor network coverage reliability assessment method based on trusted information coverage - Google Patents
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Abstract
The invention discloses a wireless sensor network coverage reliability evaluation method based on trusted information coverage, which comprises the following steps: deploying a wireless sensor network according to a monitoring scene; comprehensively considering polymorphism of nodes, connectivity among nodes, network coverage rate, root mean square error and defining reliable information coverage reliability; evaluating state transitions of the sensor nodes based on the three-state node model; constructing a coverage table structure to uniformly describe the coverage reliability of each node in the network; evaluating coverage reliability of each sub-area by setting a virtual reconstruction node and using a coverage table of the virtual reconstruction node; and calculating the overall coverage rate and coverage reliability lower bound of the network by adopting a table combination mode. The invention defines the coverage of the sensor from the angle of information coordination, fully utilizes the spatial correlation of the detection variable and improves the coverage area of the network. And uniformly describing the coverage reliability of each node in the network by using the coverage table, and efficiently calculating the lower limit of the coverage reliability of the whole network by using a coverage table combination mode.
Description
Technical Field
The invention belongs to the technical field of wireless sensor networks, and particularly relates to a wireless sensor network coverage reliability assessment method based on trusted information coverage.
Background
Wireless Sensor Networks (WSNs) have been widely deployed in various practical applications as a powerful information acquisition technology. Overlay reliability problems quantify the ability of WSNs to provide functionality to meet specific perceived overlay requirements and successfully transmit perceived data to sink nodes. The higher coverage reliability can ensure information acquisition and transmission of the WSN, thereby improving quality of service (QoS). However, due to the characteristics of the wireless sensor network and the diversity and specificity of application environments, the operation of the wireless sensor network can be influenced by adverse factors such as the number of nodes, random faults of the nodes, interception of communication links, attack and the like, so that the local or even the whole network is invalid, the coverage capability of the network can not meet the application requirements, and therefore, the assessment of the coverage reliability of the network before the network deployment is an important task.
The difficulty in coverage reliability assessment of wireless sensor networks is mainly manifested in three aspects. Firstly, the coverage reliability is closely related to the coverage model describing the coverage capability of the sensor, and most of the previous researches on the coverage reliability are based on a disc coverage model, but the disc model is too idealized and simplified and does not meet the deployment requirement of practical application; secondly, consider all 3 comprehensively for a WSN consisting of k tri-state sensors k Possible network topology states for accurate computationReliability this is a #p-hard problem, and many network topologies are unlikely to have an accurate reliability solution; thirdly, most existing methods only consider coverage reliability or connection reliability independently, and the comprehensiveness of reliability evaluation is reduced.
Disclosure of Invention
Aiming at the defects or improvement demands of the prior art, the invention provides a wireless sensor network coverage reliability evaluation method based on reliable information coverage, which aims to comprehensively and effectively evaluate the coverage reliability of a wireless sensor network and assist engineering technicians to design a more reliable wireless sensor network. According to the reliability assessment method, a reliable information coverage model is adopted to define the coverage range of the sensor from the angles of prediction and information reconstruction, the spatial correlation of the monitored physical parameters and the cooperation between adjacent nodes are fully utilized, the coverage reliability of all sub-areas is uniformly described by using virtual reconstruction nodes, the connection reliability degree between the nodes is described by using a connection strength matrix, the coverage reliability of a network is efficiently assessed by constructing a virtual network and combining the coverage table combination operation, the assessment efficiency and the application range are improved, and therefore the reliability assessment problem of the wireless sensor network is solved, and the reliability assessment method has good guidance reference value.
To achieve the above object, according to one aspect of the present invention, there is provided a wireless sensor network coverage reliability evaluation method based on trusted information coverage, including the steps of:
(1) Monitoring a coverage scene according to a target area, and establishing a network model;
(1.1) determining the number, location, perceived radius, communication radius, and sensor perceived module operational probability P of deploying sensor nodes sens Probability of operation P of sensor communication module com The running process based on the three-state sensor node model has the following states: cs represents a perfect operation state, c represents a relay state of only communication, f represents a complete failure state, and the working probabilities of different states are as follows:
P cs (v)＝P com (v)×P sense (v)
P c (v)＝P com (v)×(1-P sense (v))
P f (v)＝1-P cs (v)-P c (v)＝1-P com (v)
(1.2) modeling a wireless sensor network WSN as an undirected graph W= { v sinj }∪V，E}；
(1.3) setting a proper variation CR and a root mean square error RMSE according to the spatial correlation of the monitored variables, and setting the target area delta=delta according to the variation l ×Δ w Divided into several grid areas g= { G 1 ，g 2 ，g 3 ，...，g n The center of each grid area is a reconstruction point;
(2) Constructing a coverage table for each sensor node, denoted C, based on the polymorphisms of the sensor node V ；
(3) Deploying virtual reconstruction nodes VRN in each grid area to construct a virtual network; defining wireless sensor network coverage reliability based on trusted information coverage;
(4) Calculating a coverage table of the VRN based on the trusted information coverage model;
(5) According to the communication state among all VRNs, calculating a reliable coverage table convergence path;
(6) According to the coverage table converging path, combining the coverage tables of the VRN one by one until all the coverage table information is combined to the VRN sink The coverage table of the node;
in one embodiment of the present invention, the coverage table construction method of the step (2) is as follows:
for any multi-state sensor node V e V in the network, its state set is expressed as
a sense (v u ) Representing the sensing area of the sensor node v in the u state, the area value possibly sensed by the sensor node v is:
a sense (v U )＝{a 1 ，a 2 ，a 3 ，...，a n }
coverage table C of sensor node v v The structure of (2) is expressed as follows:
C v [a i ]＝∑Prob(U v ：a sense (v U )＝a i )(a i ＞0)
C v [0]＝P c (v)＝1-∑C v [a i ]-P f (v)
in one embodiment of the present invention, the virtual reconstruction node in the step (3) refers to a virtual sensor node set at a reconstruction point position of each grid area. Virtual reconstruction node is similar to common sensor, has multiple working states and has a coverage table C describing node coverage capability VRN The working state is determined by all nodes in the grid together, and the coverage table describes the coverage reliability of the grid area where the coverage table is positioned.
In one embodiment of the present invention, the virtual network in the step (3) refers to a virtual wireless sensor network configured by VRN nodes, which is denoted as wrn= ({ VRN) sink } U VRB, VM), where VRN sink Representing virtual sensor nodes of a grid where sink nodes are located, and VM represents a set of communication links between VRNs.
In one embodiment of the present invention, the coverage reliability (CACREL) definition based on the trusted information coverage model in the step (3) means that for a deployment in S square WSN, A in an area req Is the coverage threshold for perceived service requirements, representing the percentage of minimum coverage area, then CACREL means that there is a subset of cs state nodes
The probability that the CIC-oriented data stream generated by V' itself and meeting the requirements of the perceived service can reach the sink node.
In one embodiment of the present invention, in the step (3), according to the step (2), it is known whether the entire wireless sensor network can meet the requirements of the sensing service, and the calculation formula of CACREL of the network after converting the ordinary WSN into the virtual WSN is:
S min ＝S square ×A req
in one embodiment of the present invention, the step (4) specifically includes the following sub-steps:
(4.1) calculating the coverage condition of the reliable information in different working states in each grid area;
(4.2) calculating the probability of occurrence of an operating state meeting the requirements of the trusted information coverage; for any operating state
The probability that the network is operating in this state is calculated by:
wherein V is g Is a set of sensor nodes located within grid g,
is cs state sensor set, +.>
Is a c-state sensor set, +.>
Is the f-state sensor set.
(4.3) constructing overlay Table C of VRN VRN The method comprises the steps of carrying out a first treatment on the surface of the A of each VRN sense (VRN) comprises two quantitative values, as follows:
wherein S is g Representing the area of the grid, the area value of the grid is typically CR x CR m 2 However, if the field is not exactly divided by CR, the area of some of the grids will be smaller than CR x CR m 2 。
Overlay table C of VRN VRN Containing 2 entries as follows:
C VRN [0]＝1-C VRN [S g ]-P f (VRN)
in one embodiment of the present invention, the step (4.1) specifically includes the following sub-steps:
(4.1.1) if the grid g includes y three-state sensor nodes, the set of possible working states in this grid area is:
(4.1.2) enumerating all possible working states, under each working state, using the sensor node in cs state to perform collaborative information reconstruction on the perceived data of the reconstruction point, and calculating Root Mean Square Error (RMSE).
(4.1.3) in the trusted information coverage model, for reconstruction Point x i Calculating a reconstruction point x by adopting a common kriging interpolation function i The estimated value of the environment variable, i.e. using the reconstruction neighborhood Z (x i ) Calculating an environmental variable estimate from a weighted average of measurements of sensor nodes in a cs state; interpolation weight coefficient omega of sensor node in neighborhood i Satisfy the following requirements
To reconstruct the neighborhood Z (x i ) Sensor node v in i Is the number of (3);
(4.1.4) calculating the root mean square error phi (x) of the reconstruction point x by combining the common kriging interpolation functionThe calculation expression is:
wherein->
And μ (x) is solved by;
(4.1.5) interpolation weight coefficient lambda i Obtaining a group of optimal solutions through the minimum kriging variance; introducing Lagrangian multiplier mu (x) to generate a linear kriging system composed of n+1 equation sets with n+1 unknowns, and solving to obtain an interpolation weight coefficient lambda i ；
Wherein, gamma (v) i ，v j ) And gamma (v) i X) is calculated by a variation function;
(4.1.6) calculation of gamma (v) in step (4.1.5) i ，v j ) And gamma (v) i X); the Gaussian variation function is selected as the variation function of the environment variable and used for describing the sensor node v i Spatial correlation between the acquired data; the formula of the Gaussian variation function is:
wherein,,
is a sensor node v i And the Euclidean distance of the reconstruction point x, +.>
Is a sensor node v i And v j Euclidean distance of (2)，C 0 And C 1 Are all constant;
(4.1.7) according to the definition of the trusted information coverage model, if
That is, the time average root mean square error is larger than the set coverage threshold, the grid area meets the requirement of information coverage in the working state, otherwise, the grid area is not covered, and the invalid working state is ignored for continuous enumeration;
in one embodiment of the present invention, the step (5) specifically includes the following sub-steps:
(5.1) computing a VM; for the network w= { { v sink U.V, E } where communication links E between nodes ij E is defined as:
given two adjacent grids g a And g b ，VM ab K represents the link connection g with k a And g b Is included in the VRN. VM (virtual machine) ab Calculated by the following formula:
(5.2) VM reflects the reliability of the communication connection between VRNs, VM ab The larger the value of (2), the VRN a And VRN b The stronger the connection between them. In order to make each C VRN More reliably converge to sink node, we set the connection weight matrix h=1/VM to represent the selection priority of links in VM. The smaller h, the more likely the path is selected for coverage table aggregation.
(5.3) generating a minimum spanning tree by using dijkstra algorithm based on the connection weight matrix h, wherein the tree path is a reliable coverage table convergence path RPath;
in one embodiment of the present invention, the step (6) specifically includes the following sub-steps:
(6.1) A combination calculation of the coverage table; for two wireless sensor networks W 1 、W 2 Two networks only share the same sink node, and the other nodes are not intersected, so that the network W 1 And W is equal to 2 The coverage table of the combined network is as follows:
wherein C is 1 Is W 1 Covering table C of (2) 2 Is W 2 Covering table C of (2) 1 ×C 2 A combination operation representing an overlay table; if cover the table C i Having n i Stripe overlay record, then cm]At most have n 1 ×n 2 A bar overlay record;
(6.2) calculation
Outputting a reliability evaluation value CACREL of the network; performing overlay table combination operation with parent nodes one by one from leaf nodes according to the reliable overlay table convergence path RPath calculated in the step (5.3); removing all the leaf nodes after all the leaf nodes execute the combination operation; the combining-removing operation is continuously performed until the coverage tables of all nodes are combined, and only VRNs remain in the network sink A node; obtaining a final reliability evaluation value CACREL of the whole network according to a CACREL calculation formula;
in general, compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:
(1) The coverage reliability is high, the spatial correlation of the coverage target area monitoring reconstruction points is comprehensively excavated from the angle of information coordination, the coverage error is estimated by utilizing root mean square error, the coverage prediction is completed, the coverage rate is improved, and the coverage reliability is improved;
(2) The evaluation speed is high, the coverage capacity of each node is uniformly described by using the coverage table, the problem of complex calculation caused by the enumeration of the network node in the whole state is avoided through the partition calculation reliability, and the evaluation time is saved;
(3) The invention comprehensively considers the influence of factors such as multi-state nodes, link reliability, network coverage area, coverage quality and the like on the network coverage reliability. The network coverage reliability can be reflected more comprehensively;
(4) The method has strong universality, calculates the network coverage reliability by combining the virtual network with the coverage table combination, and is suitable for high-density large-range networks. In addition, the Internet of things related to the invention is a universal network and is suitable for various application scenes. The information coverage model adopted in the invention can be used for coverage of different terrains, regions and different data monitoring targets.
Drawings
Fig. 1 is a flowchart of a coverage reliability evaluation method of a wireless sensor network based on trusted information coverage according to an embodiment of the present invention;
fig. 2 is a schematic diagram showing the topology of a normal network (left) and a virtual network (right) according to an embodiment of the present invention; wherein fig. 2 (a) is a general network and fig. 2 (b) is a virtual network;
FIG. 3 is a schematic diagram of trusted information reconstruction in a grid area according to an embodiment of the present invention;
fig. 4 is a schematic diagram (right) of weight coefficients of each side of a normal network (left) converted into a virtual network according to an embodiment of the present invention; wherein fig. 4 (a) is a general network and fig. 4 (b) is a virtual network;
fig. 5 is a combination operation of coverage tables provided in an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
The technical terms of the present invention are explained and explained below:
variant (Correlation Range, CR): a distance threshold that characterizes the spatial correlation of the environmental variable. For a particular environmental variable and spatial point, only the values of other spatial points within the range of the change are correlated to the current spatial point.
Root Mean Square Error (RMSE): to measure and evaluate the reconstruction and estimation quality of the values of the non-employed spatial environment variables, i.e. the error measure between the estimated values and the reference point values.
Trusted information coverage (confidenformationcoverage): in the target monitoring area, if the root mean square error of the reconstructed information on a spatial point in the area is smaller than or equal to a threshold value mu set by the actual application requirement, the spatial point is covered by the trusted information.
Euclidean distance: the absolute distance between two points or vectors in multidimensional space, i.e. the square root of the difference between the vectors, is measured. Point A (x i ,y i ) To point B (x) j ,y j ) The Euclidean distance of (2)
Interpolation of kriging: the kriging method is essentially a sliding weighted average method with optimal, linear, unbiased, etc. characteristics. The Kriging method (Kriging) is a regression algorithm that spatially models and predicts (interpolates) a random process or random field according to a covariance function. The kriging method can give an optimal linear unbiased estimate in a specific random process, e.g. an inherently stationary process, and is therefore also referred to in geostatistics as a spatially optimal unbiased estimator.
Adjacent nodes: the Euclidean distance from the sensor node v is within its communication range R c Other sensor nodes in the network are adjacent nodes of v.
Virtual reconstruction node: virtual nodes arranged at the positions of reconstruction points of each grid, the working state of the virtual nodes is determined by all nodes in the grid together, and the virtual nodes cover a table C VRN The coverage reliability of the area of the grid where it is located is described.
Virtual network: refers to a virtual wireless sensor network constructed by VRN nodesComplex, denoted as W VRN ＝({VRN sink }∪VRN，VM)。
Coverage table: a table constructed from the polymorphisms of the sensor nodes for describing the sensor coverage and operational reliability.
Coverage table combination: for a new network formed by combining two wireless sensor networks which only share the sink node, the coverage table can be calculated through the coverage table combination operation of the two sub-networks.
The solution to the difficulties existing in the prior art is:
aiming at the difficulty, in the research of the coverage reliability of the existing wireless sensor network, a disc coverage model is mostly adopted to describe the coverage capability of the sensor node, so that the coverage capability is too simple and ideal. Sensor coverage can be defined from both a prediction and information reconstruction perspective using a novel, trusted information coverage model. The reliable information coverage model fully utilizes the spatial correlation of the monitored physical parameters and the cooperative cooperation of adjacent nodes, can meet the actual application requirements, and can reduce the number of required sensors under the same condition. For the second difficulty, enumerating all network topology states of the wireless sensor network to calculate reliability is a #P-hard problem, and some topologies do not have an exact reliability evaluation value. The network is converted into a virtual network formed by virtual reconstruction nodes, the coverage reliability of a subarea is accurately described by using a coverage table of the virtual nodes, and then the lower bound of the coverage reliability of the whole network is calculated by the combination operation of the coverage table. Aiming at the third difficulty, most of the existing researches on the coverage reliability of the wireless sensor network only consider the coverage reliability or the connection reliability independently. And the coverage and the communication are comprehensively considered, the connection intensity matrix is adopted to describe the communication reliability between the virtual reconstruction nodes, and the reliability and the accuracy of the coverage table combination operation are improved.
As shown in fig. 1, the coverage reliability evaluation method of the wireless sensor network based on the trusted information coverage of the invention comprises the following steps:
(1) As shown in fig. 2 (a), a network model is built according to the coverage scene monitored by the target area;
(1.1) determining the number, location, perceived radius, communication radius, and sensor perceived module operational probability P of deploying sensor nodes sense Probability of operation P of sensor communication module com The running process based on the three-state sensor node model has the following states: cs represents a perfect operation state, c represents a relay state of only communication, f represents a complete failure state, and the working probabilities of different states are as follows:
P cs (v)＝P com (v)×P sense (v)
P c (v)＝P com (v)×(1-P sense (v))
P f (v)＝1-P cs (v)-P c (v)＝1-P com (v)
(1.2) modeling a wireless sensor network WSN as an undirected graph W= { V sink }∪V，E}；
(1.3) setting a proper variation CR and a root mean square error RMSE according to the spatial correlation of the monitored variables, and setting the target area delta=delta according to the variation l ×Δ w Divided into several grid areas g= { G 1 ，g 2 ，g 3 ，...，g n The center of each grid area is a reconstruction point;
(2) For any multi-state sensor node V e V in the network, its state set is expressed as
a sense (v u ) Representing the sensing area of the sensor node v in the u state, the area value possibly sensed by the sensor node v is:
a sense (v U )＝{a 1 ，a 2 ，a 3 ...，a n }
coverage table C of sensor node v v The structure of (2) is expressed as follows:
C v [a i ]＝∑Prob(U v ：a sense (v U )＝a i ) (a i ＞0)
C v [0]＝P c (v)＝1-∑C v [a i ]-P f (v)
(3) As shown in fig. 2 (b), virtual reconstruction nodes VRN are deployed in each grid area, and a virtual network is constructed; defining wireless sensor network coverage reliability based on trusted information coverage;
the virtual reconstruction node is a virtual sensor node arranged at the reconstruction point position of each grid region. Virtual reconstruction node is similar to common sensor, has multiple working states and has a coverage table C describing node coverage capability VRN The working state is determined by all nodes in the grid together, and the coverage table describes the coverage reliability of the grid area where the coverage table is positioned.
The virtual network refers to a virtual wireless sensor network constructed by VRN nodes, denoted as W VRN ＝({VRN sink } U VRN, VM), where VRN sink Representing virtual sensor nodes of a grid where sink nodes are located, and VM represents a set of communication links between VRNs.
Coverage reliability CACREL definition based on trusted information coverage model means that for one deployment at S square WSN, A in an area req Is the coverage threshold for perceived service requirements, representing the percentage of minimum coverage area, then CACREL means that there is a subset of cs state nodes
The probability that the CIC-oriented data stream generated by V' itself and meeting the requirements of the perceived service can reach the sink node.
According to the step (2), whether the whole wireless sensor network can meet the requirement of sensing service can be obtained by calculating a covering table of sink nodes, and after the common WSN is converted into the virtual WSN according to the definition of the virtual reconstruction nodes and the virtual network, the CACREL calculation formula of the network is as follows:
S min ＝S square ×A req
(4) Calculating a coverage table of the VRN based on the trusted information coverage model;
(4.1) calculating the coverage condition of the reliable information in different working states in each grid area;
(4.1.1) if the grid g includes y three-state sensor nodes, the set of possible working states in this grid area is:
(4.1.2) enumerating all possible working states, using the sensor node in cs state to perform collaborative information reconstruction on the perceived data of the reconstruction point under each working state, and calculating root mean square error RMSE, as shown in fig. 3.
(4.1.3) in the trusted information coverage model, for reconstruction Point x i Calculating a reconstruction point x by adopting a common kriging interpolation function i The estimated value of the environment variable, i.e. using the reconstruction neighborhood Z (x i ) Calculating an environmental variable estimate from a weighted average of measurements of sensor nodes in a cs state; interpolation weight coefficient omega of sensor node in neighborhood i Satisfy the following requirements
To reconstruct the neighborhood Z (x i ) Sensor node s in i Is the number of (3);
(4.1.4) calculating the root mean square error phi (x) of the reconstruction point x by combining the common kriging interpolation function, wherein the calculation expression is as follows:
wherein->
And μ (x) is solved by;
(4.1.5) interpolation weight coefficient lambda i Obtaining a group of optimal solutions through the minimum kriging variance; introducing Lagrangian multiplier mu (x) to generate a linear kriging system composed of n+1 equation sets with n+1 unknowns, and solving to obtain an interpolation weight coefficient lambda i ；
Wherein, gamma (v) i ，v j ) And gamma (v) i X) is calculated by a variation function;
(4.1.6) calculation of gamma (v) in step (4.1.5) i ，v j ) And gamma (v) i X); the Gaussian variation function is selected as the variation function of the environment variable and used for describing the sensor node v i Spatial correlation between the acquired data; the formula of the Gaussian variation function is:
wherein,,
is a sensor node v i And the Euclidean distance of the reconstruction point x, +.>
Is a sensor node v i And v j Euclidean distance of C 0 And C 1 Are all constant;
(4.1.7) according to the definition of the trusted information coverage model, if
I.e., the time-averaged root mean square error is greater than the set coverage threshold, the grid region satisfies the information coverage requirement in this operating state,otherwise, the device is not covered, and the invalid working state is ignored for continuing enumeration;
(4.2) calculating the probability of occurrence of an operating state meeting the requirements of the trusted information coverage; for any operating state
The probability that the network is operating in this state is calculated by:
wherein V is g Is a set of sensor nodes located within grid g,
is cs state sensor set, +.>
Is a c-state sensor set, +.>
Is the f-state sensor set.
(4.3) constructing overlay Table C of VRN VRN The method comprises the steps of carrying out a first treatment on the surface of the A of each VRN sense (VRN) comprises two quantitative values, as follows:
wherein S is g Representing the area of the grid, the area value of the grid is typically CR x CR m 2 However, if the field is not exactly divided by CR, the area of some of the grids will be smaller than CR x CR m 2 。
Overlay table C of VRN VRN Containing 2 entries as follows:
C VRN [0]＝1-C VRN [S g ]-P f (VRN)
(5) According to the communication state among all VRNs, calculating a reliable coverage table convergence path;
(5.1) computing a VM; for the network w= { { v sink U.V, E } where communication links E between nodes ij E is defined as:
given two adjacent grids g a And g b ，VM ab K represents the link connection g with k a And g b Is included in the VRN. VM (virtual machine) ab Calculated by the following formula:
(5.2) VM reflects the reliability of the communication connection between VRNs, VM ab The larger the value of (2), the VRN a And VRN b The stronger the connection between them. In order to make each C VRN More reliably converge to sink node, we set the connection weight matrix h=1/VM to represent the selection priority of links in VM. The smaller h, the more likely the path is to be selected for coverage table aggregation, and fig. 4 (b) shows the virtual network structure and weight matrix values corresponding to the network of fig. 4 (a).
(5.3) generating a minimum spanning tree by using dijkstra algorithm based on the connection weight matrix h, wherein the tree path is a reliable coverage table convergence path RPath;
(6) According to the coverage table converging path, combining the coverage tables of the VRN one by one until all the coverage table information is combined to the VRN sink The coverage table of the node;
(6.1) a combined calculation of the coverage table; for two wireless sensor networks W 1 、W 2 Two networks only share the same sink node, and the other nodes are not intersected, so that the network W 1 And W is equal to 2 Combined netThe coverage table of the network is:
wherein C is 1 Is W 1 Covering table C of (2) 2 Is W 2 Covering table C of (2) 1 ×C 2 A combination operation representing an overlay table; if cover the table C i Having n i Stripe overlay record, then cm]At most have n 1 ×n 2 A bar overlay record;
(6.2) calculation
Outputting a reliability evaluation value CACREL of the network; according to the reliable coverage table convergence path RPath calculated in the step (5.3), as shown in fig. 5, from the leaf node, performing coverage table combination operation with the parent node one by one according to the step (6.1); removing all the leaf nodes after all the leaf nodes execute the combination operation; the combining-removing operation is continuously performed until the coverage tables of all nodes are combined, and only VRNs remain in the network sink A node; obtaining a final reliability evaluation value CACREL of the whole network according to a CACREL calculation formula;
it will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. The wireless sensor network coverage reliability evaluation method based on the trusted information coverage is characterized by comprising the following steps of:
(1) Monitoring a coverage scene according to a target area, and establishing a network model;
(1.1) determining the number, location, perceived radius, communication radius, and sensor perceived module operational probability P of deploying sensor nodes sense Probability of operation P of sensor communication module com Based on three statesThe running process of the sensor node model has the following states: cs represents a perfect operation state, c represents a relay state of only communication, f represents a complete failure state, and the working probabilities of different states are as follows:
P cs (υ)＝P com (υ)×P sense (υ)
P c (v)＝P com (v)×(1-P sense (v))
P f (v)＝1-P cs (v)-P c (v)＝1-P com (v)
(1.2) modeling a wireless sensor network WSN as an undirected graph W= { v sink }∪V，E}；
(1.3) setting a proper variation CR and a root mean square error RMSE according to the spatial correlation of the monitored variables, and setting the target area delta=delta according to the variation l ×Δ w Divided into several grid areas g= { G 1 ，g 2 ，g 3 ，...，g n The center of each grid area is a reconstruction point;
(2) Constructing a coverage table for each sensor node, denoted C, based on the polymorphisms of the sensor node V ；
(3) Deploying virtual reconstruction nodes VRN in each grid area to construct a virtual network; defining wireless sensor network coverage reliability based on trusted information coverage;
(4) Calculating a coverage table of the VRN based on the trusted information coverage model;
(5) According to the communication state among all VRNs, calculating a reliable coverage table convergence path;
(6) According to the coverage table converging path, combining the coverage tables of the VRN one by one until all the coverage table information is combined to the VRN sink The overlay table of the node.
2. The method for evaluating coverage reliability of a wireless sensor network based on trusted information coverage as claimed in claim 1, wherein the coverage table construction method of the step (2) is as follows:
for any multi-state sensor node V e V in the network, its state set is expressed as
a sense (v u ) Representing the sensing area of the sensor node v in the u state, the area value possibly sensed by the sensor node v is:
a sense (v U )＝{a 1 ，a 2 ，a 3 ，...，a n }
coverage table C of sensor node v v The structure of (2) is expressed as follows:
C v [a i ]＝∑Prob(U v ：a sense (v U )＝a i ) (a i ＞0)
C v [0]＝P c (v)＝1-∑C v [a i ]-P f (v)。
3. the method for evaluating coverage reliability of a wireless sensor network based on trusted information coverage according to claim 1 or 2, wherein the virtual reconstruction node in the step (3) means: a virtual sensor node is arranged at the position of the reconstruction point of each grid area; virtual reconstruction node is similar to common sensor, has multiple working states and has a coverage table C describing node coverage capability VRN The working state is determined by all nodes in the grid together, and the coverage table describes the coverage reliability of the grid area where the coverage table is positioned.
4. The method for evaluating coverage reliability of wireless sensor network based on trusted information coverage according to claim 1 or 2, wherein the virtual network in step (3) is a virtual wireless sensor network constructed by VRN nodes, denoted as W VRN ＝({VRN sink } U VRN, VM), where VRN sink Representing virtual sensor nodes of a grid where sink nodes are located, and VM represents a set of communication links between VRNs.
5. The method for evaluating coverage reliability of a wireless sensor network based on trusted information coverage according to claim 1 or 2, wherein the definition of coverage reliability CACREL based on the trusted information coverage model means that for a deployment in S square WSN, A in an area req Is the coverage threshold for perceived service requirements, representing the percentage of minimum coverage area, then CACREL means that there is a subset of cs state nodes
The probability that the CIC-oriented data stream generated by V' itself and meeting the requirements of the perceived service can reach the sink node.
6. The method for evaluating coverage reliability of a wireless sensor network based on trusted information coverage according to claim 1 or 2, wherein in the step (3), whether the entire wireless sensor network can meet the requirements of the perceived service or not can be obtained by calculating a coverage table of sink nodes according to the step (2), and the calculation formula of CACREL of the network after converting a normal WSN into a virtual WSN according to claim 4 and claim 5 is as follows:
S min ＝S square ×A req
7. the method for evaluating coverage reliability of a wireless sensor network based on trusted information coverage according to claim 1 or 2, wherein the step (4) specifically comprises the steps of:
(4.1) calculating the coverage condition of the reliable information in different working states in each grid area;
(4.2) calculating the probability of occurrence of an operating state meeting the requirements of the trusted information coverage; for any operating state
The probability that the network is operating in this state is calculated by:
wherein V is g Is a set of sensor nodes located within grid g,
is cs state sensor set, +.>
Is a c-state sensor set, +.>
Is the f state sensor set;
(4.3) constructing overlay Table C of VRN VRN The method comprises the steps of carrying out a first treatment on the surface of the A of each VRN sense (VRN) comprises two quantitative values, as follows:
wherein S is g Representing the area of the grid, the area value of the grid is typically cr=crm 2 However, if the field is not exactly divided by CR, the area of some of the grids will be smaller than CR x CR m 2 ；
Overlay table C of VRN VRN Containing 2 entries as follows:
8. the method for evaluating coverage reliability of a wireless sensor network based on trusted information coverage according to claim 7, wherein the step (4.1) specifically comprises the following sub-steps:
(4.1.1) if the grid g includes y three-state sensor nodes, the set of possible working states in this grid area is:
(4.1.2) enumerating all possible working states, under each working state, using the sensor node in the cs state to carry out collaborative information reconstruction on the perceived data of the reconstruction point, and calculating Root Mean Square Error (RMSE);
(4.1.3) in the trusted information coverage model, for reconstruction Point x i Calculating a reconstruction point x by adopting a common kriging interpolation function i The estimated value of the environment variable, i.e. using the reconstruction neighborhood Z (x i ) Calculating an environmental variable estimate from a weighted average of measurements of sensor nodes in a cs state; interpolation weight coefficient omega of sensor node in neighborhood i Satisfy the following requirements
|Z(x i ) I reconstruct neighborhood Z (x i ) Inside sensor node v i Is the number of (3);
(4.1.4) calculating the root mean square error phi (x) of the reconstruction point x by combining the common kriging interpolation function, wherein the calculation expression is as follows:
wherein->
And μ (x) is solved by;
(4.1.5) interpolation weight coefficient lambda i Obtaining a group of optimal solutions through the minimum kriging variance; introducing Lagrangian multiplier mu (x) to generate a linear kriging system composed of n+1 equation sets with n+1 unknowns, and solving to obtain an interpolation weight coefficient lambda i ；
Wherein, gamma (v) i ，v j ) And gamma (v) i X) is calculated by a variation function;
(4.1.6) calculation of gamma (v) in step (4.1.5) i ，v j ) And gamma (v) i X); the Gaussian variation function is selected as the variation function of the environment variable and used for describing the sensor node v i Spatial correlation between the acquired data; the formula of the Gaussian variation function is:
wherein,,
is a sensor node v i And the Euclidean distance of the reconstruction point x, +.>
Is a sensor node v i And v j Euclidean distance of C 0 And C 1 Are all constant;
(4.1.7) according to the definition of the trusted information coverage model, if
I.e. the time average root mean square error is larger than the set coverage threshold, the grid area meets the requirement of information coverage in the working state, otherwise, the grid area is not covered, and the invalid working state is ignored for continuous enumeration.
9. The method for evaluating coverage reliability of a wireless sensor network based on trusted information coverage according to claim 1 or 2, wherein the step (5) specifically comprises the steps of:
(5.1) computing a VM; for the network w= { { v sink U.V, E }, wherein the nodesCommunication link e between ij E is defined as:
given two adjacent grids g a And g b ，VM ab K represents the link connection g with k a And g b Is a VRN in (1); RM (RM) ab Calculated by the following formula:
(5.2) VM reflects the reliability of the communication connection between VRNs, VM ab The larger the value of (2), the VRN a And VRN b The stronger the connection between them; in order to make each C VRN More reliably converging to sink nodes, we set a connection weight matrix h=1/VM to represent the selection priority of links in the VM; the smaller h, the more likely the path is selected for coverage table aggregation;
(5.3) generating a minimum spanning tree based on the connection weight matrix h by using dijkstra algorithm, wherein the tree path is a reliable coverage table convergence path RPath.
10. The method for evaluating coverage reliability of a wireless sensor network based on trusted information coverage according to claim 1 or 2, wherein the step (6) specifically comprises the steps of:
(6.1) a combined calculation of the coverage table; for two wireless sensor networks W 1 、W 2 Two networks only share the same sink node, and the other nodes are not intersected, so that the network W 1 And W is equal to 2 The coverage table of the combined network is as follows:
wherein C is 1 Is W 1 Covering table C of (2) 2 Is W 2 Covering table C of (2) 1 ×C 2 A combination operation representing an overlay table; if cover the table C i Having n i Stripe overlay record, then cm]At most have n 1 ×n 2 A bar overlay record;
(6.2) calculation
Outputting a reliability evaluation value CACREL of the network; performing overlay table combination operation with parent nodes one by one from leaf nodes according to the reliable overlay table convergence path RPath calculated in the step (5.3); removing all the leaf nodes after all the leaf nodes execute the combination operation; the combining-removing operation is continuously performed until the coverage tables of all nodes are combined, and only VRNs remain in the network sink A node; and obtaining a final reliability evaluation value CACREL of the whole network according to the CACREL calculation formula.
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