CN111294922B - Method and device for accurately positioning wireless sensor network nodes in grading and rapid mode - Google Patents
Method and device for accurately positioning wireless sensor network nodes in grading and rapid mode Download PDFInfo
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Abstract
The application discloses a graded and rapid accurate positioning method, a graded and rapid accurate positioning device and a graded and rapid accurate positioning device for a wireless sensor network node, aiming at the problems of low efficiency and low positioning accuracy of the traditional node positioning scheme, firstly, a curve distance analysis method is utilized to roughly position a node to be positioned to obtain rough relative coordinates so as to improve the node positioning efficiency; converting the relative coordinates into absolute coordinates by linear transformation; and finally, global and local search optimization processing is carried out on the nodes to be positioned by the self-adaptive Levy flying whale optimization method, so that the positioning precision is improved, and the purposes of improving the positioning efficiency and the positioning accuracy at the same time are achieved.
Description
Technical Field
The present application relates to the field of wireless monitoring data positioning technologies, and in particular, to a method, an apparatus, a device, and a readable storage medium for accurately positioning wireless sensor network nodes in a hierarchical and fast manner.
Background
A Wireless Sensor Network (WSN) is a Network formed by deploying a large number of Sensor nodes with sensing, computing and Wireless communication capabilities, and the Sensor nodes cooperate with each other to complete a specific task. In many cases, node location information needs to be known. Because the sensor nodes are often deployed randomly, the efficiency is too low because the position information of each node is acquired through manpower; and the cost is too high by arranging a positioning module at each node to acquire the node position information. Therefore, it is necessary to acquire node location information of the wireless sensor network by an appropriate positioning method.
At present, node positioning methods mainly comprise two types of distance correlation and distance independence. The multidimensional scaling MDS-MAP method can be operated under two conditions, and has the characteristics that the needed anchor nodes are fewer, the positioning accuracy is higher than that of a common method, but the shortest path between the nodes is used as the true distance to calculate the node position, and the positioning error is larger. Firstly, an Extended Kalman Filter (EKF) and relative azimuth angle and node movement displacement information are adopted to carry out modeling filtering to refine the nonlinear relation between positioning coordinates of MDS-MAP, but in actual test, most coordinate values do not conform to normal distribution and easily generate sensor accumulated errors, and the improvement effect on later positioning accuracy is not ideal; secondly, iterative dimensionality reduction refinement is carried out on the coordinates estimated by multidimensional scaling by using a Curved Component Analysis (CCA), the calculation efficiency of the method is improved, but no optimization step is used, the Euclidean distance is used for replacing the real distance, and the improvement of the positioning precision is not obvious; and thirdly, solving the global Optimization problem by using a Whale Optimization method (WOA), wherein the Whale Optimization method has the advantages of simple adjustment parameters, high operation speed and weak convergence speed and local optimal jumping-out capability.
Therefore, the positioning accuracy of the conventional node positioning scheme is poor, the positioning efficiency is low, and how to improve the positioning accuracy and the positioning efficiency is a problem to be solved by technical personnel in the field.
Disclosure of Invention
The application aims to provide a method, a device, equipment and a readable storage medium for accurately positioning wireless sensor network nodes in a hierarchical and rapid mode, and the method, the device, the equipment and the readable storage medium are used for solving the problems that a traditional node positioning scheme is poor in positioning accuracy and low in positioning efficiency. The specific scheme is as follows:
in a first aspect, the present application provides a method for accurately positioning wireless sensor network nodes in a hierarchical and fast manner, including:
acquiring a distance matrix between nodes of a wireless sensor network;
carrying out nonlinear mapping on the distance matrix between the nodes by using a curve distance analysis method to obtain the relative coordinates of the nodes, wherein the nodes comprise anchor nodes and nodes to be positioned;
converting the relative coordinates of the node to be positioned into absolute coordinates according to the conversion relation between the relative coordinates of the anchor node and the absolute coordinates of the anchor node to obtain an absolute coordinate estimation value of the node to be positioned;
and determining the accurate value of the absolute coordinate of the node to be positioned by utilizing a self-adaptive Levy flying whale optimization method according to the estimated value of the absolute coordinate.
Preferably, the performing nonlinear mapping on the distance matrix between the nodes by using a curve distance analysis method to obtain the relative coordinates of the nodes includes:
carrying out nonlinear mapping on the distance matrix between the nodes by using a curve distance analysis method, and optimizing a relative coordinate matrix obtained by mapping according to a cost function until an optimal relative coordinate matrix is obtained and used as a relative coordinate of the nodes;
wherein, the optimizing the relative coordinate matrix obtained by mapping comprises: and adjusting the relative coordinate of the second node on the basis of the fixed and unchanged relative coordinate of the first node until the cost value determined according to the cost function is minimum.
Preferably, the determining an absolute coordinate accurate value of the node to be positioned by using an adaptive levy flying whale optimization method according to the absolute coordinate estimated value includes:
taking the absolute coordinate estimation value as an initial prey, and randomly generating a whale population;
updating the positions of whales in the whale population according to a prey surrounding formula, a foaming net attack formula and a prey searching formula respectively, and updating the prey according to a fitness function until the maximum iteration times are reached to obtain an absolute coordinate accurate value of the node to be positioned; wherein the envelope step size in the prey envelope formula and the prey search formula is an envelope step size improved according to adaptive lave flight.
Preferably, the surrounding step size is:
A′·|C·XL(T)-X(T)|；
wherein, XL(T) is the location of the prey at time T, x (T) is the location of the whale at time T, C is a random number, a 'is 2 a'. Levy (λ) -a ', λ is a preset threshold, a' is a preset parameter of the rate of descent with increasing time.
Preferably, the preset threshold is 1.5.
Preferably, the preset parameters are as follows:
wherein T ismaxIs the maximum number of iterations.
Preferably, the fitness function is:
where k is the number of anchor nodes, djDistance, x, from node to be positioned to jth anchor nodeb、ybIs the absolute coordinate value of the node to be positioned,
is the absolute coordinate accurate value of anchor node j.
In a second aspect, the present application provides a hierarchical fast accurate positioning device for a wireless sensor network node, including:
a distance matrix acquisition module: the method comprises the steps of obtaining an inter-node distance matrix of the wireless sensor network;
a mapping module: the node distance analysis method is used for carrying out nonlinear mapping on the distance matrix between the nodes to obtain the relative coordinates of the nodes, wherein the nodes comprise anchor nodes and nodes to be positioned;
an estimation module: the relative coordinates of the node to be positioned are converted into absolute coordinates according to the conversion relation between the relative coordinates of the anchor node and the absolute coordinates of the anchor node, and an absolute coordinate estimation value of the node to be positioned is obtained;
an optimization module: and the method is used for determining the absolute coordinate accurate value of the node to be positioned by utilizing an adaptive Levy flying whale optimization method according to the absolute coordinate estimated value.
In a third aspect, the present application provides a hierarchical fast accurate positioning device for a wireless sensor network node, including:
a memory: for storing a computer program;
a processor: the computer program is executed to implement the steps of the hierarchical fast accurate positioning method for the wireless sensor network node.
In a fourth aspect, the present application provides a readable storage medium, which is characterized in that the readable storage medium stores thereon a computer program, and the computer program is used for implementing the steps of the hierarchical fast method for accurately positioning a wireless sensor network node as described above when being executed by a processor.
The application provides a hierarchical and rapid accurate positioning method for a wireless sensor network node, which comprises the following steps: acquiring a distance matrix between nodes of a wireless sensor network; carrying out nonlinear mapping on a distance matrix between nodes by using a curve distance analysis method to obtain relative coordinates of the nodes, wherein the nodes comprise anchor nodes and nodes to be positioned; converting the relative coordinate of the node to be positioned into an absolute coordinate according to the conversion relation between the relative coordinate of the anchor node and the absolute coordinate of the anchor node to obtain an absolute coordinate estimation value of the node to be positioned; and determining the accurate value of the absolute coordinate of the node to be positioned by utilizing an adaptive Levy flying whale optimization method according to the estimated value of the absolute coordinate.
Therefore, aiming at the problems of low efficiency and low positioning precision of the traditional node positioning scheme, the method firstly carries out rough positioning on the node to be positioned by utilizing a curve distance analysis method to obtain rough relative coordinates so as to improve the node positioning efficiency; converting the relative coordinates into absolute coordinates by linear transformation; and finally, global and local search optimization processing is carried out on the nodes to be positioned by the self-adaptive Levy flying whale optimization method, so that the positioning precision is improved, and the purposes of improving the positioning efficiency and the positioning accuracy at the same time are finally realized.
In addition, the technical effect of the hierarchical and fast accurate positioning device, the equipment and the readable storage medium for the wireless sensor network node provided by the application corresponds to the technical effect of the method, and the details are not repeated here.
Drawings
For a clearer explanation of the embodiments or technical solutions of the prior art of the present application, the drawings needed for the description of the embodiments or prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart illustrating a first implementation of a method for accurately positioning a wireless sensor network node in a hierarchical and fast manner according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram i illustrating a node distance value in an inter-node distance matrix provided in the present application;
fig. 3 is a schematic diagram of a node distance value in the inter-node distance matrix provided in the present application;
fig. 4 is a flowchart illustrating an implementation of a second method for accurately positioning a wireless sensor network node in a hierarchical and fast manner according to the present application;
fig. 5 is a detailed flowchart of a second embodiment of a method for accurately positioning a wireless sensor network node in a hierarchical and fast manner according to the present application;
FIG. 6 is a schematic diagram of a comparison of positioning errors provided herein;
FIG. 7 is a schematic diagram illustrating time consumption comparison at different network scales provided by the present application;
FIG. 8 is a schematic diagram illustrating a comparison of errors in the number of different anchor nodes provided by the present application;
FIG. 9 is a schematic diagram illustrating a comparison of positioning errors at different communication radii as provided herein;
FIG. 10 is a schematic diagram illustrating a comparison of positioning errors for different total node numbers provided by the present application;
fig. 11 is a functional block diagram of an embodiment of a hierarchical fast accurate positioning apparatus for a wireless sensor network node according to the present application;
fig. 12 is a schematic structural diagram of an embodiment of a hierarchical fast accurate positioning device for a wireless sensor network node according to the present application.
Detailed Description
In order that those skilled in the art will better understand the disclosure, the following detailed description will be given with reference to the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Aiming at the problems of low positioning efficiency and low positioning accuracy of an MDS-MAP method, the core of the application is to provide a wireless sensor network node accurate positioning method, device, equipment and readable storage medium which are graded and rapid, and the purpose of improving the node positioning efficiency and the positioning accuracy is achieved.
Referring to fig. 1, a first embodiment of a method for accurately positioning a wireless sensor network node in a hierarchical and fast manner provided by the present application is described below, where the first embodiment includes:
s101, obtaining a distance matrix between nodes of a wireless sensor network;
s102, carrying out nonlinear mapping on the distance matrix between the nodes by using a curve distance analysis method to obtain relative coordinates of the nodes, wherein the nodes comprise anchor nodes and nodes to be positioned;
s103, converting the relative coordinates of the node to be positioned into absolute coordinates according to the conversion relation between the relative coordinates of the anchor node and the absolute coordinates of the anchor node to obtain an absolute coordinate estimation value of the node to be positioned;
and S104, determining the absolute coordinate accurate value of the node to be positioned by utilizing a self-adaptive Levy flying whale optimization method according to the absolute coordinate estimated value.
Firstly, generating an inter-node distance matrix, and when the nodes are in a communication range, as shown in fig. 2, obtaining the distance between the nodes through RSSI ranging; otherwise, the distance between nodes is obtained using the shortest path method, as shown in fig. 3.
Similar to the MDS-MAP method, the Curve Distance Analysis (CDA) also performs positioning solution on the multi-hop subgraph to generate the relative coordinates of the subgraph. In the solving process, the output result is optimized step by step in a multi-round iteration mode. The CDA is developed on the basis of CCA, and is an ad hoc feature mapping neural network, and the positioning mathematical model of the network is as follows: giving a distance matrix D of n nodes, finding out coordinates of all nodes, and satisfying the following formula:
wherein d isijIs the measurable or known distance between node i and node j; p is a radical ofijThe distance between the node i and the node j is obtained according to the node positioning result.
As a preferred embodiment, the analysis process using the curve distance analysis method includes: carrying out nonlinear mapping on the distance matrix between the nodes by using a curve distance analysis method, and optimizing a relative coordinate matrix obtained by mapping according to a cost function until an optimal relative coordinate matrix is obtained and used as a relative coordinate of the nodes; wherein, the optimizing the relative coordinate matrix obtained by mapping comprises: and adjusting the relative coordinate of the second node on the basis of the fixed and unchanged relative coordinate of the first node until the cost value determined according to the cost function is minimum.
Whales looking for prey, whose location corresponds to the global optimal solution to the problem, are regarded as location coordinate optimization, and whose predation behavior involves two phases, an upward spiral and a double cycle. In the whale optimization method, three mathematical models are provided, namely prey surrounding, foaming net attacking and prey searching. However, although the bubble net foraging is beneficial to improving the population diversity and the global search capability of the whale optimization method, the whale optimization method is easy to fall into local optimization along with the increase of the iteration times. Therefore, as a preferred implementation manner, the embodiment adopts a self-adaptive step length levy flight optimization whale positioning method, and the method can adaptively adjust the step length through search results in different stages, so as to expand the search range, accelerate the convergence speed, improve the convergence accuracy, jump out the local optimum and reduce the positioning error.
Specifically, the optimizing process using the adaptive levy flying whale optimizing method comprises the following steps: taking the absolute coordinate estimation value as an initial prey, and randomly generating a whale population; updating the positions of whales in the whale population according to a prey surrounding formula, a foaming net attack formula and a prey searching formula respectively, and updating the prey according to a fitness function until the maximum iteration times are reached to obtain an absolute coordinate accurate value of the node to be positioned; wherein the envelope step size in the prey envelope formula and the prey search formula is an envelope step size improved according to adaptive lave flight.
In the embodiment, aiming at the problems of low efficiency and low positioning accuracy of the traditional node positioning scheme, a curve distance analysis method is firstly utilized to roughly position a node to be positioned to obtain rough relative coordinates so as to improve the node positioning efficiency; converting the relative coordinates into absolute coordinates by linear transformation; and finally, global and local search optimization processing is carried out on the nodes to be positioned by the self-adaptive Levy flying whale optimization method, so that the positioning precision is improved, and the purposes of improving the positioning efficiency and the positioning accuracy at the same time are finally realized.
The second embodiment of the method for accurately positioning a wireless sensor network node in a hierarchical and fast manner provided by the present application is described in detail below, and the second embodiment is implemented based on the first embodiment and is expanded to a certain extent based on the first embodiment.
Referring to fig. 4, the second embodiment specifically includes:
s401, nonlinear mapping is carried out on the distance matrix between the nodes by using a curve distance analysis method, and a relative coordinate matrix obtained by mapping is optimized according to a cost function until an optimal relative coordinate matrix is obtained and is used as a relative coordinate of the nodes;
the essence of the curve distance analysis method is to maintain the topology of the nonlinear mapping process, i.e. for any node, adjust the coordinates of the output node, so that the distance calculated according to the output result is matched with the corresponding distance in the distance matrix between nodes. Therefore, a weighting factor is introduced to obtain a cost function of the curve distance analysis method:
in the formula, deltaijRepresenting the curve distance between the node i and the node j in the input space; dijThe Euclidean distance between a node i and a node j in the subspace after the target dimension reduction is carried out; f (d)ijτ (t)) is a bounded weight function that decreases over time, maintaining a local topology; τ (t) is a function that varies over time, where c is the total number of calculation rounds, also called training duration, τ (c) is 0.01, τ (0) is 0max{ded1,ded2,...,dedn}×3，dediIs the standard deviation of the ith column of the distance matrix D.
In the solving process, the output result is optimized step by step in a multi-round iteration mode, and in the optimizing process of each round, the updating rule of the cost function is as follows: setting the coordinate of the node i to be unchanged, updating the coordinates of other nodes j, and obtaining the following coordinates by a random gradient descent method:
where α (t) is a decreasing function of t, and decreases with turns as shown below, where α (0) is 0.5. The selection of the update rule in this way avoids n (n-1) computations per round, so the computational complexity of the algorithm per round is O (n).
In the actual input process, the distance matrix D is used as the initial data set xiAnd an internal distance matrix element dijIs input. According to the curve distance analysis method, after a distance matrix D between nodes is given, the solution of the estimated value of the point to be positioned is completed through two steps:
(1) the first two columns of the input data matrix D are averaged, and the average value is subjected to Gaussian noise addition by the standard deviation of the column to be used as an output vector yiAn initial estimate of (a);
(2) in each round of calculation, a new coordinate value of node i (j ≠ i) is selected, as follows:
curve distance analysis method and MDS-MAP methodSimilarly, the positioning solution is carried out on the r-jump sub-graph, and relative coordinates of the sub-graph are generated. The difference is that MDS-MAP adopts centralized calculation, and the time complexity for obtaining the shortest path distance between nodes is O (n)2) The MDS-MAP method has a time complexity of O (n)4). In the process of solving the curve distance analysis method, because the position of one point i in each round is set to be constant, O (l) is provided2) The time complexity (l is the average neighbor number of the node i), and the computation complexity of the global relative coordinate of the curve distance analysis method is O (l)2n)。
S402, converting the relative coordinate of the node to be positioned into an absolute coordinate according to a conversion relation between the relative coordinate of the anchor node and the absolute coordinate of the anchor node to obtain an absolute coordinate estimation value of the node to be positioned;
specifically, the curve distance analysis method maps the three-dimensional network to a two-dimensional plane for relative positioning, and converts the relative coordinates into absolute coordinates by linear transformation. Is provided with
Is the relative coordinates of the anchor node(s),
the absolute coordinates of the anchor nodes are j ═ 1, 2., and k is the number of the anchor nodes;
is the relative coordinates of the node to be located,
for the absolute coordinates of the node to be located, i ═ k +1, k + 2.
The parameter vector x can be obtained by:
Bx＝g
x＝[b1 b2 g1 b3 b4 g2]T
wherein B is a matrix constructed by relative coordinates of anchor nodes; g is a vector of anchor node absolute coordinate constructs. Obtaining a parameter vector x by using a least square method, substituting the parameter vector x into a linear transformation formula, and obtaining absolute coordinates of the nodes
S403, randomly generating whale populations by taking the absolute coordinate estimation values as initial preys;
s404, respectively updating the positions of whales in the whale population according to a prey surrounding formula, a foaming net attack formula and a prey searching formula, and updating the prey according to a fitness function until the maximum iteration number is reached to obtain an absolute coordinate accurate value of the node to be positioned; and the surrounding step length in the prey surrounding formula and the prey searching formula is a surrounding step length improved according to the self-adaptive Levis flight.
In the whale optimization method, the whale population size is assumed to be N, and the predation space is w dimension; the position of the ith whale in the w-dimensional space is
The whale optimization method mainly comprises three processes of prey surrounding, foaming net attacking and prey searching, wherein the three processes are introduced respectively as follows:
the prey surrounds: assuming that prey in the current population is the optimal position, other whales in the population surround the optimal individual, and the prey surrounding formula adopted by the whale updating position is as follows:
X(T+1)＝XL(T)-A′|C·XL(T)-X(T)
wherein T is the current iteration number,
as prey position, X (T) is coordinate vector of whale at T moment, X (T +1) is target coordinate vector after T +1 iterations, and XL(T) is the best position vector so far, A' | C · XL(T) -X (T) is the surrounding step length, and A' and C are coefficients, respectively shown as follows:
A′＝2a′·Levy(λ)-a′
C＝2·rand
where rand is a random number between [0,1], Levy (λ) is a lavian random search path, which obeys a lavian probability distribution with parameter λ, as shown in the following formula:
Levy～u＝T-λ,1＜λ≤3
for calculation convenience, the Levy random number is generally generated by using the following formula, and in order to reduce the operation overhead of the whole algorithm, λ is 1.5:
in consideration of the problems that the coefficient A in the traditional whale optimization method is linearly converged, the method is easy to fall into local optimum and the positioning precision is not high, the algorithm has higher capability of jumping out of the local optimum by changing the coefficient A into the coefficient A ', a' slowly descends in the initial stage of iteration and rapidly descends in an exponential situation in the later stage, and the local search capability can be improved, and global search can be carried out by jumping out of the local optimum.
Wherein T is the current iteration number, TmaxIs the maximum number of iterations.
Foaming net attack: in the whale optimization method, two different mathematical models represent the foaming net attack behavior of whales, namely a shrinkage and wrapping model and a spiral updating model. The shrink wrapping model is implemented as the convergence factor decreases; the spiral updating model simulates spiral movement of whales to capture prey, and the foaming net attack formula is as follows:
X(T+1)＝XL(T)+|XL(T)-X(T)|·ehzcos(2πz)
wherein h is a limiting constant of the logarithmic spiral shape, and is 1 by default; z is a random number of [ -1,1 ].
Searching prey: whales can also randomly search for food according to the positions of whales, and the hunting search formula is as follows:
X(T+1)＝Xra(T)-A′·|C·Xra(T)-X(T)|
wherein, XraIs a position vector of random whales.
As described above, the curve distance analysis method maps the three-dimensional network to the two-dimensional plane, the fitness function of the adaptive levy flight optimization whale positioning method is shown as follows, and the optimization goal is to minimize the fitness function:
wherein k is the number of anchor nodes around the node to be positioned, djIs the distance from the node to be positioned to the jth anchor node.
The present embodiment aims to perform local and global optimization search by using an adaptive levy flight optimization whale method on the absolute coordinates after linear transformation to improve the positioning accuracy, and the whole optimization search process is shown in fig. 5.
The embodiment provides a hierarchical and rapid accurate positioning method for a wireless sensor network node, which includes roughly positioning a node to be positioned by using a curve distance analysis method to obtain rough relative coordinates, so as to improve the node positioning efficiency; converting the relative coordinates into absolute coordinates by linear transformation; and finally, optimizing node positioning by adopting a self-adaptive Levy flight optimization whale method, searching for an optimal solution through global search, and the method has the advantages of simple parameter adjustment, high operation speed, high positioning precision and the like, and finally achieves the purpose of simultaneously improving positioning efficiency and positioning accuracy.
In order to verify the effectiveness of An adaptive Levy whale wireless positioning method (AWL-MC) based on a mapping curve, Matlab2019a is used for respectively carrying out simulation contrast analysis on An MDS-MAP method, An MDS-EKF method and An AWL-MC method, and a simulation network topological structure is generated randomly for a region node.
Selecting normalized average positioning error eηThe evaluation index was represented by the following formula:
wherein n is the total number of nodes, (x)η,yη) Is the true coordinate of the η node, (x'η,y′η) Is the estimated coordinates of the η node.
FIG. 6 is a comparison graph of the positioning errors of the three methods, AWL-MC, MDS-MAP, and MDS-EKF. The area of the area is 200m multiplied by 200m, the communication radius is 50m, the number of unknown nodes is 100, the number of anchor nodes is 20, and the simulation experiment is repeated for 50 times. As can be seen from FIG. 6, the positioning error value of the MDS-MAP method is 0.9-2.2 m, the positioning error value of the MDS-EKF method is 0.5-1.6 m, the positioning error value of the AWL-MC method is 0.2-0.7 m, the average positioning error of the AWL-MC method is 0.46m less than the average positioning error of the MDS-MAP method of 1.37m by 0.91m, which is 33.58% of the MDS-MAP method; the mean positioning error is reduced by 0.63m compared with 1.09m of the MDS-EKF method, which is 42.20 percent of that of the MDS-EKF method.
Fig. 7 compares the time required for the three methods to run at different total node counts and the simulation experiment was repeated 50 times. As can be seen from FIG. 7, the time consumed by the three positioning methods tends to be almost the same when the number of nodes changes, but the AWL-MC method is superior to the other two methods in different node numbers, and the running time is obviously the minimum (0.516 s on average), which is 34.00% and 47.41% of the MDS-EKF method and MDS-MAP method respectively.
Fig. 8 shows the comparison of positioning errors of 3 methods, in which 200 nodes are randomly arranged in a simulation area, and the number of anchor nodes is 5, 10, 15, 20, 25, 30, 35, and 40, respectively, when the communication radius is 50 m. The general trend of the positioning errors of the three methods in the graph 8 is reduced along with the increase of the number of the anchor nodes, the positioning errors of the three methods tend to be stable basically when the number of the anchor nodes is larger than or equal to 25, but the positioning error of the AWL-MC method is obviously the smallest and 0.24m, and is superior to the positioning errors of the other two methods under different numbers of the anchor nodes, and the positioning errors of the AWL-MC method are 32.90% and 43.10% of those of the MDS-EKF method and the MDS-MAP method respectively.
200 nodes are randomly arranged in a simulation area, the number of anchor nodes is 30, the communication radius is changed between 10 m and 50m, and the positioning error ratio of the three methods is shown in FIG. 9. The general trend of the positioning errors of the three methods is reduced along with the increase of the communication radius, because the connectivity of the WSN is increased along with the increase of the communication radius. When the communication radius is 10-35 m, errors of the three methods are large, and due to the fact that the network communication rate is poor, the number of available anchor nodes of unknown nodes is small. The positioning error of the AWL-MC method in FIG. 9 is lower than that of the other two methods under different communication radiuses, and the average positioning error is 0.62m, which is 50.10% and 39.78% of MDS-EKF and MDS-MAP respectively.
And (3) arranging 25 anchor nodes in the simulation area, wherein the communication radius is 45m, and the node numbers are respectively 50, 100, 150, 200, 250, 300, 350 and 400, and comparing the positioning errors by the three methods, as shown in FIG. 10. The general trend of the positioning errors of the three methods is reduced along with the increase of the total node number, the AWL-MC method is obviously superior to the other two methods, the average positioning error is 0.15m, and the average positioning error is 44.40 percent and 21.17 percent of MDS-EKF and MDS-MAP respectively.
Therefore, according to the mapping curve-based self-adaptive Levy whale wireless positioning method, a curve distance analysis method is adopted to roughly position nodes relatively, so that the node calculation efficiency is improved; converting the relative coordinates into absolute coordinates by linear transformation; and finally, introducing an adaptive Levy flying whale optimization method to search for optimizing the positioning coordinates so as to avoid generating a local optimal solution and improve the positioning accuracy. Simulation results show that from the aspects of running time, the number of anchor nodes, communication radius and total number of nodes, the positioning error difference of the embodiment of the application is respectively 42.20% and 33.58% of a multidimensional scaling extended Kalman filtering (MDS-EKF) method and an MDS-MAP method, and the positioning error difference has obvious advantages in positioning accuracy and calculation efficiency.
The following introduces a device for accurately positioning a wireless sensor network node in a hierarchical and fast manner provided in an embodiment of the present application, and a device for accurately positioning a wireless sensor network node in a hierarchical and fast manner described below and a method for accurately positioning a wireless sensor network node in a hierarchical and fast manner described above may be referred to in correspondence.
As shown in fig. 11, the apparatus includes:
distance matrix acquisition module 1101: the method comprises the steps of obtaining an inter-node distance matrix of the wireless sensor network;
the mapping module 1102: the node distance analysis method is used for carrying out nonlinear mapping on the distance matrix between the nodes to obtain the relative coordinates of the nodes, wherein the nodes comprise anchor nodes and nodes to be positioned;
the estimation module 1103: the relative coordinates of the node to be positioned are converted into absolute coordinates according to the conversion relation between the relative coordinates of the anchor node and the absolute coordinates of the anchor node, and an absolute coordinate estimation value of the node to be positioned is obtained;
the optimization module 1104: and the method is used for determining the absolute coordinate accurate value of the node to be positioned by utilizing an adaptive Levy flying whale optimization method according to the absolute coordinate estimated value.
The device for accurately positioning a wireless sensor network node in a hierarchical manner is used to implement the aforementioned method for accurately positioning a wireless sensor network node in a hierarchical manner, and therefore, specific embodiments of the device may be found in the foregoing embodiments of the method for accurately positioning a wireless sensor network node in a hierarchical manner, for example, the distance matrix obtaining module 1101, the mapping module 1102, the estimating module 1103, and the optimizing module 1104 are respectively used to implement steps S101, S102, S103, and S104 in the method for accurately positioning a wireless sensor network node in a hierarchical manner. Therefore, specific embodiments thereof may be referred to in the description of the corresponding respective partial embodiments, and will not be described herein.
In addition, since the device for accurately positioning a wireless sensor network node at a high speed in a hierarchical manner of this embodiment is used for implementing the method for accurately positioning a wireless sensor network node at a high speed in a hierarchical manner, the function of the device corresponds to that of the method, and details are not repeated here.
In addition, the present application further provides a hierarchical fast accurate positioning device for a wireless sensor network node, as shown in fig. 12, including:
the memory 100: for storing a computer program;
the processor 200: for executing the computer program to implement the steps of the hierarchical fast wireless sensor network node accurate positioning method as described above.
Finally, the present application provides a readable storage medium having stored thereon a computer program for implementing the steps of a hierarchical fast method for accurately positioning a wireless sensor network node as described above when the computer program is executed by a processor.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above detailed descriptions of the solutions provided in the present application, and the specific examples applied herein are set forth to explain the principles and implementations of the present application, and the above descriptions of the examples are only used to help understand the method and its core ideas of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (7)
1. A hierarchical and rapid accurate positioning method for wireless sensor network nodes is characterized by comprising the following steps:
acquiring a distance matrix between nodes of a wireless sensor network;
carrying out nonlinear mapping on the distance matrix between the nodes by using a curve distance analysis method to obtain the relative coordinates of the nodes, wherein the nodes comprise anchor nodes and nodes to be positioned;
converting the relative coordinates of the node to be positioned into absolute coordinates according to the conversion relation between the relative coordinates of the anchor node and the absolute coordinates of the anchor node to obtain an absolute coordinate estimation value of the node to be positioned;
determining the accurate value of the absolute coordinate of the node to be positioned by utilizing an adaptive Levy flying whale optimization method according to the estimated value of the absolute coordinate, wherein the method comprises the following steps:
taking the absolute coordinate estimation value as an initial prey, and randomly generating a whale population;
updating the positions of whales in the whale population according to a prey surrounding formula, a foaming net attack formula and a prey searching formula respectively, and updating the prey according to a fitness function until the maximum iteration times are reached to obtain an absolute coordinate accurate value of the node to be positioned; wherein, the surrounding step length in the prey surrounding formula and the prey searching formula is a surrounding step length improved according to the self-adaptive Levis flight, and the surrounding step length is as follows:
A′·|C·XL(T)-X(T)|；
wherein, XL(T) is the location of the prey at time T, x (T) is the location of the whale at time T, C is a random number, a 'is 2 a'. Levy (λ) -a ', λ is a preset threshold, a' is a preset parameter of the rate of descent with increasing time; the preset parameters are
Wherein T ismaxIs the maximum number of iterations.
2. The method of claim 1, wherein said non-linearly mapping said inter-node distance matrix using a curvilinear distance analysis method to obtain relative coordinates of nodes comprises:
carrying out nonlinear mapping on the distance matrix between the nodes by using a curve distance analysis method, and optimizing a relative coordinate matrix obtained by mapping according to a cost function until an optimal relative coordinate matrix is obtained and used as a relative coordinate of the nodes;
wherein, the optimizing the relative coordinate matrix obtained by mapping comprises: and adjusting the relative coordinate of the second node on the basis of the fixed and unchanged relative coordinate of the first node until the cost value determined according to the cost function is minimum.
3. The method of claim 1, wherein the predetermined threshold is 1.5.
4. The method of claim 1, wherein the fitness function is:
where k is the number of anchor nodes, djDistance, x, from node to be positioned to jth anchor nodeb、ybIs the absolute coordinate value of the node to be positioned,
is the absolute coordinate precision value of the anchor node j, xbThe ybFor updating to a prey position after the current iteration when the fitness function takes a minimum.
5. The utility model provides a hierarchical quick accurate positioner of wireless sensor network node which characterized in that includes:
a distance matrix acquisition module: the method comprises the steps of obtaining an inter-node distance matrix of the wireless sensor network;
a mapping module: the node distance analysis method is used for carrying out nonlinear mapping on the distance matrix between the nodes to obtain the relative coordinates of the nodes, wherein the nodes comprise anchor nodes and nodes to be positioned;
an estimation module: the relative coordinates of the node to be positioned are converted into absolute coordinates according to the conversion relation between the relative coordinates of the anchor node and the absolute coordinates of the anchor node, and an absolute coordinate estimation value of the node to be positioned is obtained;
an optimization module: the method for determining the accurate value of the absolute coordinate of the node to be positioned by utilizing the self-adaptive Levy flying whale optimization method according to the estimated value of the absolute coordinate comprises the following steps:
taking the absolute coordinate estimation value as an initial prey, and randomly generating a whale population;
updating the positions of whales in the whale population according to a prey surrounding formula, a foaming net attack formula and a prey searching formula respectively, and updating the prey according to a fitness function until the maximum iteration times are reached to obtain an absolute coordinate accurate value of the node to be positioned; wherein, the surrounding step length in the prey surrounding formula and the prey searching formula is a surrounding step length improved according to the self-adaptive Levis flight, and the surrounding step length is as follows:
A′·|C·XL(T)-X(T)|；
wherein, XL(T) is the location of the prey at time T, x (T) is the location of the whale at time T, C is a random number, a 'is 2 a'. Levy (λ) -a ', λ is a preset threshold, a' is a preset parameter of the rate of descent with increasing time; the preset parameters are
Wherein T ismaxIs the maximum number of iterations.
6. The utility model provides a hierarchical quick accurate positioning device of wireless sensor network node which characterized in that includes:
a memory: for storing a computer program;
a processor: the computer program is executed to implement the steps of a hierarchical fast wireless sensor network node accurate positioning method according to any one of claims 1 to 4.
7. A readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, is configured to implement the steps of the method for hierarchical fast accurate positioning of wireless sensor network nodes according to any one of claims 1 to 4.
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