CN116456356A - Reliability evaluation method for large-scale wireless sensor network based on reliable information coverage - Google Patents
Reliability evaluation method for large-scale wireless sensor network based on reliable information coverage Download PDFInfo
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Abstract
The invention discloses a reliability evaluation method of a large-scale wireless sensor network based on reliable information coverage, which comprises the following steps: (1) According to the target area monitoring coverage requirement, a network model is established; (2) Establishing a network coverage model through the trusted information coverage model, and calculating coverage rate; (3) According to the MTBF analysis, the operational status of a node is divided into three types: relay, off, on, and calculate the probability of each state occurrence; (4) Enumerating the node states, finding out node state combinations which ensure the normal operation of the network, and evaluating the reliability of the network. The method has obvious advantages when evaluating the reliability of the large-scale network, the spatial correlation of the monitoring reconstruction points of the coverage target area is comprehensively mined from the information coordination angle, and when calculating the network connectivity, the possible combination of parts is abandoned according to a certain rule, so that the operation speed of the algorithm is improved, the calculation of the connectivity under the large-scale nodes is supported, and the evaluation result of the reliability under the same distribution network is improved.
Description
Technical Field
The invention belongs to the technical field of the Internet of things, and particularly relates to a reliability evaluation method of a large-scale wireless sensor network based on information coverage.
Background
In recent years, wireless Sensor Networks (WSNs) have become a widely used technology, serving a variety of application scenarios, they play a key role in detecting events and measuring physical and environmental phenomena of interest, which means that the wireless sensor networks will have a serious impact if they fail to detect the occurrence of events or phenomena in a target area (RoI), and therefore, WSNs must operate normally within their expected mission time, which puts stringent reliability requirements on WSNs, and these problems also need to be solved in the design and deployment stages.
When the sensor network node of the internet of things is deployed, in order to extract and process the data of each target point and transmit the data to a sink node (sink node), a large number of nodes are deployed in a proper manner to ensure the normal operation of the sensor network and the reliability of acquiring the data. Under such conditions, how to evaluate the reliability of deployment of a sensor network node is of great significance to research.
The difficulty in reliability assessment of wireless sensor networks is mainly manifested in three aspects. Firstly, the network coverage model is used for covering and describing and defining differences of different actual scenes, and the selection of the model directly influences the applicability of the repair method and the actual application scenes; secondly, for a randomly deployed network, unlike a deterministic deployment wireless sensor network, the network topology may be extremely complex due to uncertainty of node deployment locations, which is a great challenge for evaluating the universality of the method; thirdly, a great deal of nodes are distributed in a large amount, so that great difficulty is brought to calculation, and particularly in a network with aggregation nodes, a great deal of time is spent in calculating the communication states of a great deal of nodes, so that the algorithm operation efficiency is low.
Disclosure of Invention
Aiming at the defects or improvement demands of the prior art, the invention provides a reliability evaluation method of a large-scale wireless sensor network based on reliable information coverage, and aims to evaluate the reliability of the Internet of things network rapidly and efficiently. In this context, we propose a reliability algorithm (CICRA) based on confidence information coverage to solve the reliability problem of evaluating WSNs with a large number of randomly deployed nodes. When nodes are deployed randomly, a large number of redundant and useless nodes may appear. Thus, in this algorithm, the calculation is first performed according to the overall deployment in the network, then nodes that do not contribute to the coverage and nodes that cannot be connected to the sink node are culled, and the reliability of the WSN is considered in the remaining nodes. According to the invention, the node state is subdivided into three types according to the damage condition of each component of the sensor node: relay, off and on. To compute connectivity of a WSN with a large number of nodes in an efficient time, we propose a Grid Cluster Connectivity Algorithm (GCCA). The algorithm clusters the nodes according to the communication radius of the nodes, and discards part of communication paths according to a certain rule so as to replace the calculation complexity is greatly reduced. For the coverage rate of the CIC model to the target point, the relevant range of the model is utilized to grid divide the target area, and the coverage rate is independently calculated based on grids.
In order to achieve the above object, according to one aspect of the present invention, there is provided a reliability evaluation method for a large-scale wireless sensor network based on trusted information coverage, comprising the steps of:
(1) According to the target area monitoring coverage requirement, a network model is established;
(1.1) setting a variation and an estimated root mean square error threshold according to the spatial correlation of a detection target, and carrying out regional sub-grid division on a target coverage area according to the variation;
(1.2) according to the distribution of the sensor nodes, recording the number i of the sensor nodes, and according to the divided covering sub-grids, using the center point of the sub-grid as a reconstruction point, wherein the p is expressed;
(2) Establishing a network coverage model through the trusted information coverage model, and calculating coverage rate;
(3) According to the damage condition of each component of the sensor node, the running state of the node is divided into three types: the probability of each state occurrence is calculated by relay, off and on;
(4) Solving a relay set RC, an off set OC and a relay-off set RAO which can ensure network coverage and node communication aiming at different state combinations of the nodes;
(5) Storing the data in the form of tensors according to the state set meeting the requirements;
(6) The reliability R of the network is calculated from the data.
In one embodiment of the present invention, the step (2) specifically includes the following sub-steps:
(2.1) in the trusted information coverage model, for spatial points that are not sampled, calculating an estimated value of the environmental variable of the reconstruction point by adopting a common kriging interpolation function, namely calculating the estimated value of the environmental variable by adopting a weighted average of measured values of the sensor nodes τi in the reconstruction neighborhood Z (p); interpolation weight coefficient lambda of sensor node in neighborhood i Satisfy the following requirementsn is the sensor node tau in the reconstruction neighborhood Z (p) i Is the number of (3);
(2.2) calculating the root mean square error phi (p) of the reconstruction point p by combining the common kriging interpolation function, wherein the calculation expression is as follows:wherein->And μ (p) passing steps (2.1.1) and (2.1.2)Solving;
(2.3) if Φ (p) > ε, according to the definition of the trusted information coverage model 0 I.e. the root mean square error is greater than the set coverage threshold, the sub-grid is covered, otherwise, the sub-grid is not covered; recording the covered reconstruction point number j', the root mean square error being smaller than the threshold epsilon 0 The vulnerability reconstruction point number of (2) is j';
(2.4) calculating the coverage of the target area.
In one embodiment of the present invention, the coverage rate in the step (2.4) is calculated as follows:
wherein, NCT is the number of covered reconstruction points, and NT is the total number of reconstruction points.
In one embodiment of the invention, the step (2.1) comprises the following sub-steps:
(2.1.1) interpolating the weight coefficients to obtain a set of optimal solutions by the minimum kriging variance; introducing Lagrangian multiplier mu (p) 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 (tau) i ，τ j ) And gamma (tau) i P) is calculated by a variation function;
(2.1.2) calculating γ (τ) in step (2.1.1) i ，τ j ) And gamma (tau) i P); the Gaussian variation function is selected as the variation function of the environment variable and used for describing the sensor node tau i Spatial correlation between the acquired data; gaussian variation function the formula of (2) is:
wherein d τp Is the sensor node tau i And the euclidean distance of the reconstruction point p,is the sensor node tau i And τ j Euclidean distance of C 0 And C is a constant.
In one embodiment of the present invention, the step (3) specifically includes the following sub-steps:
(3.1) four components to a sensor node: the sensing unit, the communication unit, the processing unit and the power supply are used for analyzing, and the damage probability of each component is calculated: lambda (lambda) s ，λ c ，λ p And lambda (lambda) b The calculation formula is as follows:
wherein MTBF is the mean time to failure of the component;
(3.2) calculating the probability of the node being in each state according to the damage probability of each component, wherein the calculation formula is as follows:
P on ＝(1-λ s )·(1-λ c )·(1-λ p )·(1-λ b )
P relay ＝λ s ·(1-λ c )·(1-λ p )·(1-λ b )
P off ＝λ c ·(1-λ p )·(1-λ b )+λ p ·(1-λ b )+λ b
in one embodiment of the present invention, the step (4) includes the following sub-steps:
(4.1) according to the division of grids, independently calculating node state combinations which can still meet the requirements of coverage and communication when part of nodes in each grid are in a relay state and part of nodes are in an on state;
(4.2) according to the division of grids, independently calculating node state combinations which can still meet the requirements of coverage and communication when part of nodes in each grid are in an off state and part of nodes are in an on state;
(4.3) combining the calculated results of (4.1) and (4.2), and calculating the node combination which simultaneously has three states of relay, off and on in the network and still can meet the requirements of coverage and communication.
In one embodiment of the present invention, the step (4.1) specifically includes the following sub-steps:
(4.1.1) statistics of the set S of sensor nodes within the ith grid i And the number of nodes N s ；
(4.1.2) from S i Random pick [1, N ] s -1]The state of each node is set as a relay, the states of the other nodes are set as on, and the coverage condition of the node with the on state on to the reconstruction point is calculated by utilizing the information coverage model;
(4.1.3) adding the node state to the state set RC if the reconstruction point is still covered, otherwise skipping;
(4.1.4) repeating: repeating steps (4.1.2) and (4.1.3) until all node combinations have been traversed.
In one embodiment of the present invention, the step (4.2) specifically includes the following sub-steps:
(4.2.1) statistics of the set S of sensor nodes within the ith grid i And the number of nodes N s ；
(4.2.2) from S i Random pick [1, N ] s -1]The states of the nodes are set to be off, and the states of the other nodes are set to be on;
(4.2.3) toDividing the mesh of the target area for the side length, wherein Rc is a communication radius, naming the mesh as a communication division mesh GC, calculating the overlapping part of the mesh i and the GC, taking out the part of the mesh from the GC, and removing the mesh which does not contain nodes, wherein the mesh is named M;
(4.2.4) removing (4.2.2) the selected node from M, continuing with step (4.2.5) if at least one node can still exist in each grid in M is satisfied, otherwise jumping to step (4.2.7);
(4.2.5) calculating the coverage condition of the node with the on state to the reconstruction point by using the trusted information coverage model;
(4.2.6) adding the node state to the state set OC if the reconstruction point is still covered, otherwise skipping;
(4.2.7) repeat: repeating steps (4.2.2) to (4.2.6) until all node combinations have been traversed.
In one embodiment of the present invention, the step (5) specifically includes the following sub-steps:
(5.1) designing a third-order tensor, wherein three dimensions of the tensor respectively represent a reconstruction point, a sensor node and an overlay mode, and the overlay mode represents the situation that the reconstruction point is cooperatively overlaid by the nodes and is expressed by the following formula:
wherein C is a sparse coefficient forming a core tensor transform domain, T represents a reconstruction point, N T S represents the sensor node, N s The number of the sensor nodes is the number, A is the coverage mode;
(5.2) calculating nodes in relay, off and on states in each combination of the node state sets according to the sets RC, OC and RAO, calculating a cooperative coverage mode of the nodes, and storing data into corresponding tensors.
In one embodiment of the present invention, the step (6) specifically includes the following sub-steps:
(6.1) taking out all node combinations participating in the k node collaborative coverage in the reconstruction point i area from the tensor X: x (i, k), the statistical combination number is L, and the occurrence probability is calculated by the following formula:
(6.2) calculating the probability of occurrence of all node combinations in all coverage modes in the reconstruction point i area:
(6.3) calculating the probability of occurrence of all node combinations in all the coverage modes in all the reconstruction point areas, and calculating the final reliability:
where RF is the excitation value, it may be set as a monotonically increasing function with coverage.
In general, compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:
(1) The improved network reliability of the same distribution is obviously improved, the spatial correlation of the monitoring reconstruction points of the coverage target area is comprehensively excavated from the angle of information coordination, the coverage error is estimated by using root mean square error, the coverage prediction is completed, and the coverage rate is improved so as to ensure the improvement of the reliability;
(2) The method has high operation speed, and the GCCA algorithm is used, so that the calculation of the node connectivity problem in the wireless sensor network is greatly accelerated on the premise of discarding part of paths;
(3) The method supports network reliability assessment under the distribution of a large number of nodes, and solves the problem that the reliability assessment algorithm is low in operation efficiency when the number of the nodes is too large by utilizing the information coverage model to carry out grid division on the area and utilizing the GCCA algorithm to calculate node connectivity;
(4) 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 evaluating the network reliability of different terrains, regions and different data monitoring targets.
Drawings
FIG. 1 is a flowchart of a reliability evaluation method of a large-scale wireless sensor network based on trusted information coverage in an embodiment of the invention;
FIG. 2 is a diagram of the operational state of a sensor node versus the state of a component in an embodiment of the present invention;
FIG. 3 is a schematic diagram of the inclusion relationship of sets RC, OC and RAO;
FIG. 4 is a schematic diagram of tensor modeling in an embodiment of the invention;
FIG. 5 (a) is a graph showing the effect of the present example in comparison with the conventional method using the disc overlay model at the RMSE threshold value of 0.8-1.0;
FIG. 5 (b) is a graph of the conventional two-mode and three-mode effects of the present example algorithm with a disk overlay model when the RMSE threshold is 1.0;
FIG. 5 (c) is a graph of the RMSE threshold of 0.9, the present example algorithm compares to the effect of the conventional two-mode using the disk overlay model and the conventional three-mode using the disk overlay model;
FIG. 5 (d) is a graph of the conventional two-mode and three-mode effects of the present example algorithm with a disk overlay model when the RMSE threshold is 0.8;
FIG. 5 (e) is a comparison of the present example algorithm with two modes using a trusted information overlay model for a node number of 200;
FIG. 5 (f) is a comparison of the present example algorithm with two modes using a trusted information overlay model for a node number of 250;
FIG. 5 (g) is a comparison of the present example algorithm with two modes using the trusted information coverage model at a node number of 300.
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, root Mean Squared Error): 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.
Information overlay (Confident Information Coverage): 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 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 (a) x ，a y ) To point B (B) x ，b y ) The Euclidean distance of (2)
Shortest coordinate distance: each time the sensor node moves, it is moved 1 unit distance from the up, down, left and right directions, point a (a x ，a y ) To point B (B) x ，b y ) The shortest coordinate distance of (a) is |a x -b x |+|a y -b y |。
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.
The solution to the difficulties existing in the prior art is:
aiming at the difficulty, most of the existing methods adopt a disc model to define the coverage range of the sensor node, and the model is too ideal and simple. The adoption of the information coverage model can carry out information collaborative reconstruction from the dimension of the airspace, and the coverage degree of the reconstruction points is predicted by utilizing the correlation of the spatial points. The reliable information coverage model fully utilizes the spatial correlation and can be well applied to practice. Aiming at the second difficulty, most of the existing methods aim at determining the deployment or random deployment of a small number of nodes, the large-scale random deployment wireless sensor network is not considered, and the methods are difficult to apply to other large-scale networks due to the problems of universality and expansibility. The trusted information model can divide the target area into independent grid independent calculation nodes, so that the calculated amount is reduced, and meanwhile, the method has good expansibility. Aiming at the third difficulty, the existing communication reliability evaluation method generally needs to analyze all paths connected to the sink node in the network, and obtains the communication reliability of the network through the reliability of the comprehensive path. However, these methods have the problems of excessive calculation amount and excessive calculation complexity, and it is difficult to calculate the result in an effective time when facing a large network. The GCCA algorithm can reduce the calculation complexity by discarding part of the communication links according to a certain rule, can calculate the communication reliability of a large-scale network faster, and improves the algorithm efficiency.
As shown in fig. 1, the reliability evaluation method of the large-scale wireless sensor network based on the trusted information coverage comprises the following steps:
(1) And building a network model according to the target area monitoring coverage requirement.
And (1.1) setting a variation and an estimated root mean square error threshold according to the spatial correlation of the detection target, and performing regional sub-grid division on the target coverage region according to the variation.
(1.2) recording the sensor node number i according to the sensor node distribution, and reconstructing a point by using a sub-grid center point according to the divided covering sub-grid, wherein the p is expressed as p.
(2) And establishing a network coverage model through the trusted information coverage model, and calculating coverage rate. The method specifically comprises the following substeps:
(2.1) in the trusted information coverage model, for spatial points that are not sampled, a common kriging interpolation function is used to calculate the reconstructionEstimation of point environment variables, i.e. using sensor nodes τ in the reconstruction neighborhood Z (p) i Calculating an environmental variable estimate from a weighted average of the measurements of (a); interpolation weight coefficient lambda of sensor node in neighborhood i Satisfy the following requirementsFor reconstructing sensor nodes tau in the neighborhood Z (p) i Is a number of (3). Wherein lambda is i The calculation of (2) comprises the following substeps:
(2.1.1) interpolation weight coefficient lambda i A set of optimal solutions can be obtained by the minimum kriging variance. Introducing Lagrangian multiplier mu (p) 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 (tau) i ，τ j ) And gamma (tau) i P) is calculated by a variogram.
(2.1.2) calculating γ (τ) in step (2.1.1) i ，τ j ) And gamma (tau) i P). The Gaussian variation function is selected as the variation function of the environment variable and used for describing the sensor node tau i Spatial correlation between the acquired data. The general formula for the gaussian variation function is:
wherein d τp Is the sensor node tau i And the euclidean distance of the reconstruction point p,is the sensor node tau i And τ j Euclidean distance of C 0 And C is a constant, when C 0 When=0 and c=1, the standard gaussian function is used.
(2.2) calculating the root mean square error phi (p) of the reconstruction point p by combining the common kriging interpolation function, wherein the calculation expression is as follows:wherein->And μ (p) is solved by steps (2.1.1) and (2.1.2).
(2.3) if Φ (p) > ε, according to the definition of the trusted information coverage model 0 I.e. the root mean square error is greater than the set coverage threshold, then the sub-grid is covered, otherwise not covered. Recording the covered reconstruction point number j', the root mean square error being smaller than the threshold epsilon 0 The vulnerability reconstruction point number of (c) is j ".
(2.4) calculating the coverage of the target area, wherein the calculation formula is as follows:
wherein, NCT is the number of covered reconstruction points, and NT is the total number of reconstruction points.
(3) According to the damage condition of each component of the sensor node, the running state of the node is divided into three types: the relay, off, on calculates the probability of each state occurrence, and specifically includes the following sub-steps:
(3.1) four components to a sensor node: the sensing unit, the communication unit, the processing unit and the power supply are used for analyzing, and the damage probability of each component is calculated: lambda (lambda) s ，λ c ，λ p And lambda (lambda) b The calculation formula is as follows:
where MTBF is the mean time between failure of a component.
(3.2) as shown in FIG. 2, when the sensing unit of the node is damaged and other units are normal, the node is in a relay state; when any one of the communication unit, the data processing unit and the power supply of the node is damaged, the node is in an off state; when all units of the node work normally, the node is in an on state. According to the damage probability of each component, the probability that the node is in each state is calculated according to the following calculation formula:
P on ＝(1-λ s )·(1-λ c )·(1-λ p )·(1-λ b )
P relay ＝λ s ·(1-λ c )·(1-λ p )·(1-λ b )
P off ＝λ c ·(1-λ p )·(1-λ b )+λ p ·(1-λ b )+λ b
(4) Solving a relay set RC, an off set OC and a relay-off set RAO which can ensure network coverage and node communication aiming at different state combinations of nodes, wherein the method comprises the following substeps:
(4.1) independently calculating node state combinations which still can meet the requirements of coverage and communication when part of nodes in each grid are in a relay state and part of nodes are in an on state according to the division of grids, wherein the node state combinations specifically comprise the following sub-steps:
(4.1.1) statistics of the set S of sensor nodes within the ith grid i And the number of nodes N s 。
(4.1.2) from S i Random pick [1, N ] s -1]And each node sets the states of the nodes as relay, sets the states of the other nodes as on, and calculates the coverage condition of the node with the on state on to the reconstruction point by using the information coverage model.
(4.1.3) adding the node state to the state set RC if the reconstruction point is still covered, otherwise skipping.
(4.1.4) repetition: repeating steps (4.1.2) and (4.1.3) until all node combinations have been traversed.
(4.2) independently calculating node state combinations which still can meet the requirements of coverage and communication when part of nodes in each grid are in an off state and part of nodes are in an on state according to the division of grids, wherein the node state combinations comprise the following substeps:
(4.2.1) statistics of the set S of sensor nodes within the ith grid i And the number of nodes N s 。
(4.2.2) from S i Random pick [1, N ] s -1]And each node sets the states of the nodes to off, and the states of the other nodes to on.
(4.2.3) toMeshing the target area for side length, where R c For the communication radius, this mesh is named as a communication division mesh GC, the overlapping part of the mesh i and GC is calculated, this part of mesh is taken out from the GC, and the mesh containing no node is removed, which is named as M.
(4.2.4) removing (4.2.2) the selected node from M, continuing with step (4.2.5) if at least one node still exists within each mesh in M is satisfied, otherwise jumping to step (4.2.7).
(4.2.5) calculating the coverage condition of the node with the on state to the reconstruction point by using the trusted information coverage model.
(4.2.6) adding the node state to the state set OC if the reconstruction point is still covered, otherwise skipping.
(4.2.7) repeat: repeating steps (4.2.2) to (4.2.6) until all node combinations have been traversed.
(4.3) using RAO to represent nodes with three states of relay, off and on in the network, and still meeting the node combination of coverage and communication requirements. The inclusion relationship between the three sets of RC, OC and RAO can be obtained according to the node function, as shown in fig. 3. According to this inclusion relationship, the results of the calculations of (4.1) and (4.2) are combined to calculate the elements in the set RAO.
(5) Storing the data in the form of tensors according to the set of state meeting the requirements, comprising the sub-steps of:
(5.1) designing a third-order tensor, wherein three dimensions of the tensor respectively represent reconstruction points, sensor nodes, coverage modes and the structure of the tensor is shown in fig. 4. Wherein the coverage mode represents the case where the reconstruction point is cooperatively covered by the node, and is expressed by the following formula:
wherein C is a sparse coefficient forming a core tensor transform domain, T represents a reconstruction point, N T S represents the sensor node, N s For the number of sensor nodes, a is the overlay mode.
(5.2) calculating nodes in relay, off and on states in each combination of the node state sets according to the sets RC, OC and RAO, calculating a cooperative coverage mode of the nodes, and storing data into corresponding tensors.
(6) Calculating the reliability R of the network according to the data, comprising the following steps:
(6.1) taking out all node combinations participating in the k node collaborative coverage in the reconstruction point i area from the tensor X: x (i, k), the statistical combination number is L, and the occurrence probability is calculated by the following formula:
(6.2) calculating the probability of occurrence of all node combinations in all coverage modes in the reconstruction point i area:
(6.3) calculating the probability of occurrence of all node combinations in all the coverage modes in all the reconstruction point areas, and calculating the final reliability:
where RF is the excitation value, it may be set as a monotonically increasing function with coverage.
FIG. 5 shows an example of the visual results of the reliability evaluation of the present invention, and FIG. 5 (a) shows the effect of the present example compared with the conventional method using the disc overlay model when the RMSE threshold is in the interval of 0.8-1.0; FIG. 5 (b) is a graph of the conventional two-mode and three-mode effects of the present example algorithm with a disk overlay model when the RMSE threshold is 1.0; FIG. 5 (c) is a graph of the RMSE threshold of 0.9, the present example algorithm compares to the effect of the conventional two-mode using the disk overlay model and the conventional three-mode using the disk overlay model; FIG. 5 (d) is a graph of the conventional two-mode and three-mode effects of the present example algorithm with a disk overlay model when the RMSE threshold is 0.8; FIG. 5 (e) is a comparison of the present example algorithm with two modes using a trusted information overlay model for a node number of 200; FIG. 5 (f) is a comparison of the present example algorithm with two modes using a trusted information overlay model for a node number of 250; FIG. 5 (g) is a comparison of the present example algorithm with two modes using the trusted information coverage model at a node number of 300. As can be seen from fig. 5, the method can obtain more accurate reliability evaluation results in the network with the same distribution.
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 reliability evaluation method of the large-scale wireless sensor network based on the trusted information coverage is characterized by comprising the following steps of:
(1) According to the target area monitoring coverage requirement, a network model is established;
(1.1) setting a variation and an estimated root mean square error threshold according to the spatial correlation of a detection target, and carrying out regional sub-grid division on a target coverage area according to the variation;
(1.2) according to the distribution of the sensor nodes, recording the number i of the sensor nodes, and according to the divided covering sub-grids, using the center point of the sub-grid as a reconstruction point, wherein the p is expressed;
(2) Establishing a network coverage model through the trusted information coverage model, and calculating coverage rate;
(3) According to the damage condition of each component of the sensor node, the running state of the node is divided into three types: the probability of each state occurrence is calculated by relay, off and on;
(4) Solving a relay set RC, an off set OC and a relay-off set RAO which can ensure network coverage and node communication aiming at different state combinations of the nodes;
(5) Storing the data in the form of tensors according to the state set meeting the requirements;
(6) The reliability R of the network is calculated from the data.
2. The reliability evaluation method of a large-scale wireless sensor network based on reliable information coverage as claimed in claim 1, wherein the step (2) specifically comprises the following sub-steps:
(2.1) for non-sampled spatial points in the trusted information coverage model, computing estimates of reconstructed point environment variables using a common kriging interpolation function, i.e., using sensor nodes τ in the reconstruction neighborhood Z (p) i Calculating an environmental variable estimate from a weighted average of the measurements of (a); interpolation weight coefficient lambda of sensor node in neighborhood i Satisfy the following requirementsn is the sensor node tau in the reconstruction neighborhood Z (p) i Is the number of (3);
(2.2) calculating the root mean square error phi (p) of the reconstruction point p by combining the common kriging interpolation function, wherein the calculation expression is as follows:wherein gamma (tau) i P) is the point τ i And the spatial correlation of the point p, μ (p) being the Lagrangian multiplier;
(2.3) if Φ (p) > ε, according to the definition of the trusted information coverage model 0 I.e. the root mean square error is greater than the set coverage threshold, the sub-grid is covered, otherwise, the sub-grid is not covered; recording the covered reconstruction point number j', the root mean square error being smaller than the threshold epsilon 0 Vulnerability weight of (2)The construction point number is j';
(2.4) calculating the coverage of the target area.
3. The reliability evaluation method of a large-scale wireless sensor network based on reliable information coverage as claimed in claim 2, wherein the coverage in the step (2.4) is calculated as follows:
wherein, NCT is the number of covered reconstruction points, and NT is the total number of reconstruction points.
4. A method for evaluating reliability of a large-scale wireless sensor network based on trusted information coverage as claimed in claim 2 or 3, wherein said step (2.1) comprises the sub-steps of:
(2.1.1) interpolation weight coefficient lambda i Obtaining a group of optimal solutions through the minimum kriging variance; introducing Lagrangian multiplier mu (p) 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 (tau) i ，τ j ) And gamma (tau) i P) is calculated by a variation function;
(2.1.2) calculating γ (τ) in step (2.1.1) i ，τ j ) And gamma (tau) i P); the Gaussian variation function is selected as the variation function of the environment variable and used for describing the sensor node tau i Spatial correlation between the acquired data; the formula of the Gaussian variation function is:
wherein d τp Is the sensor node tau i And the euclidean distance of the reconstruction point p,is the sensor node tau i And τ j Euclidean distance of C 0 And C is a constant.
5. The reliability evaluation method of a large-scale wireless sensor network based on trusted information coverage as claimed in claim 1 or 2, wherein said step (3) specifically comprises the following sub-steps:
(3.1) four components to a sensor node: the sensing unit, the communication unit, the processing unit and the power supply are used for analyzing, and the damage probability of each component is calculated: lambda (lambda) s ，λ c ，λ p And lambda (lambda) b The calculation formula is as follows:
wherein MTBF is the mean time to failure of the component;
(3.2) calculating the probability of the node being in each state according to the damage probability of each component, wherein the calculation formula is as follows:
P on ＝(1-λ s )·(1-λ c )·(1-λ p )·(1-λ b )
P relay ＝λ s ·(1-λ c )·(1-λ p )·(1-λ b )
P off ＝λ c ·(1-λ p )·(1-λ b )+λ p ·(1-λ b )+λ b
6. the reliability evaluation method of a large-scale wireless sensor network based on trusted information coverage as claimed in claim 1 or 2, wherein said step (4) specifically comprises the following sub-steps:
(4.1) according to the division of grids, independently calculating node state combinations which can still meet the requirements of coverage and communication when part of nodes in each grid are in a relay state and part of nodes are in an on state;
(4.2) according to the division of grids, independently calculating node state combinations which can still meet the requirements of coverage and communication when part of nodes in each grid are in an off state and part of nodes are in an on state;
(4.3) combining the calculated results of (4.1) and (4.2), and calculating the node combination which simultaneously has three states of relay, off and on in the network and still can meet the requirements of coverage and communication.
7. The reliability evaluation method of a large-scale wireless sensor network based on trusted information coverage as claimed in claim 6, wherein said step (4.1) comprises the sub-steps of:
(4.1.1) statistics of the set S of sensor nodes within the ith grid i And the number of nodes N s ；
(4.1.2) from S i Random pick [1, N ] s -1]The state of each node is set as a relay, the states of the other nodes are set as on, and the coverage condition of the node with the on state on to the reconstruction point is calculated by utilizing the information coverage model;
(4.1.3) adding the node state to the state set RC if the reconstruction point is still covered, otherwise skipping;
(4.1.4) repeating steps (4.1.2) and (4.1.3) until all node combinations have been traversed.
8. The reliability evaluation method of a large-scale wireless sensor network based on trusted information coverage as claimed in claim 6, wherein said step (4.2) specifically comprises the following sub-steps:
(4.2.1) statistics of the set S of sensor nodes within the ith grid i And the number of nodes N s ；
(4.2.2) from S i Random pick [1, N ] s -1]Each node sets the state of each node to be off, and the states of other nodes are set to be on;
(4.2.3) toMeshing the target area for side length, where R c For the communication radius, the grid is named as a communication division grid GC, the overlapping part of the grid i and the GC is calculated, the part of the grid is taken out from the GC, and the grid which does not contain nodes is removed and named as M;
(4.2.4) removing (4.2.2) the selected node from M, continuing with step (4.2.5) if at least one node can still exist in each grid in M is satisfied, otherwise jumping to step (4.2.7);
(4.2.5) calculating the coverage condition of the node with the on state to the reconstruction point by using the trusted information coverage model;
(4.2.6) adding the node state to the state set OC if the reconstruction point is still covered, otherwise skipping;
(4.2.7) repeat: repeating steps (4.2.2) to (4.2.6) until all node combinations have been traversed.
9. The reliability evaluation method of a large-scale wireless sensor network based on trusted information coverage as claimed in claim 1 or 2, wherein said step (5) specifically comprises the following sub-steps:
(5.1) designing a third-order tensor, wherein three dimensions of the tensor respectively represent a reconstruction point, a sensor node and an overlay mode, and the overlay mode represents the situation that the reconstruction point is cooperatively overlaid by the nodes and is expressed by the following formula:
wherein C is a sparse coefficient forming a core tensor transform domain, T represents a reconstruction point, N T For the number of reconstruction points, S represents the sensor node,N s the number of the sensor nodes is the number, A is the coverage mode;
(5.2) calculating nodes in relay, off and on states in each combination of the node state sets according to the sets RC, OC and RAO, calculating a cooperative coverage mode of the nodes, and storing data into corresponding tensors.
10. The reliability evaluation method of a large-scale wireless sensor network based on trusted information coverage as claimed in claim 1 or 2, wherein said step (6) specifically comprises the following sub-steps:
(6.1) taking out all node combinations participating in the k node collaborative coverage in the reconstruction point i area from the tensor X: x (i, k), the statistical combination number is L, and the occurrence probability is calculated by the following formula:
(6.2) calculating the probability of occurrence of all node combinations in all coverage modes in the reconstruction point i area:
(6.3) calculating the probability of occurrence of all node combinations in all the coverage modes in all the reconstruction point areas, and calculating the final reliability:
where RF is the excitation value, it may be set as a monotonically increasing function with coverage.
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