Node positioning method, positioning system and product used in complex communication environment

Abstract

The invention relates to a node positioning method, a positioning system and a product used in a complex communication environment. The method comprises the following steps: S1, calculating the distance ri, q between a qth unknown node and an ith anchor node, and traversing the distances between the qth unknown node and Q anchor nodes to obtain a distance set; and S2, calculating coordinates of the unknown node based on the distance set and a heuristic algorithm. Estimated coordinates obtained by a traditional positioning algorithm are optimized by introducing a three-dimensional positioning algorithm combining a uniform point set, a Levy flight strategy and a self-adaptive strategy, specifically, in the process of calculating the coordinates of unknown nodes, firstly, a population is initialized by generating the uniform point set, the global optimization capacity is expanded by using step length search in the Levy strategy, and then the global optimization capacity is improved; according to the method, the cross probability and the scaling factor are dynamically adjusted in the offspring generation process, and the three strategies are introduced, so that the method can still maintain high positioning precision under the conditions of dynamic state, many obstacles, interference on signal propagation and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless sensors, and in particular to a node positioning method, a positioning system and a product used in a complex communication environment. Background Art

[0002] With the rapid development of Internet technology, wireless sensor networks are gaining more and more attention. In wireless sensor networks, multiple sensors are interconnected through wireless communication technology instead of wired connections to work together to collect data. The data collected by sensors is sometimes useless without location data. Therefore, collecting this data from sensors and finding the exact location of the communication node carrying the sensor is called positioning.

[0003] There are two general node positioning solutions. The first is to let all communication nodes carry positioning modules, such as GPS modules. However, this solution has many disadvantages. First, due to the expansion of the laying area, equipping each node with a positioning module will greatly increase the hardware cost; second, the positioning module usually consumes a lot of power during operation, which will significantly shorten the service life of the entire communication network; finally, due to the complex physical conditions in environments such as ports, which are manifested in the diverse terrain structures and severe obstruction, including large metal structures such as container stacking, viaducts, and metal tower cranes, these factors may cause signal multipath effects, non-line-of-sight propagation, and severe signal attenuation when building wireless sensor networks, thereby interfering with the normal operation of the positioning module. Therefore, relying solely on the positioning module cannot guarantee accurate positioning. The other is to use the positioning algorithm to calculate the coordinates of other unknown nodes with the help of the known coordinates of some nodes. On the one hand, this solution does not require a large number of positioning modules, saving costs, and on the other hand, it greatly reduces the impact of obstacles in the scene on the positioning signal.

[0004] Currently, the methods of using positioning algorithms commonly used in sensor networks can be divided into two types: positioning based on ranging methods and positioning based on non-ranging methods.

[0005] Positioning based on ranging methods includes time difference of arrival (TDOA), time of arrival (TOA), channel state information (CSI) or received signal strength indicator (RSSI) or angle of arrival (AOA) to estimate the target coordinates.

[0006] Although the positioning based on the ranging method can provide relatively accurate distance information by directly measuring the physical distance between the target and the known reference point, thereby directly determining the spatial position of the target. Higher positioning accuracy can be obtained through the intersection of multiple ranging points. But it has two disadvantages: 1. The implementation of the positioning scheme related to the ranging method is easily affected by noise, multipath fading and environmental changes. If it is to be applied in environments such as ports, it is not suitable. 2. In order to accurately obtain physical information such as time and arrival angle, special transceivers are required, which actually increases the overall cost.

[0007] Positioning based on non-range measurement methods mainly includes technologies such as Approximate Perfect Triangle Point (APIT), Centroid Positioning and Distance Vector Hopping (DV-Hop). Non-range measurement positioning technology relies only on the connection information between nodes in the wireless sensor network to achieve positioning without the need for precise physical information measurement. Due to its low cost, non-range measurement positioning solutions are widely used in large-scale, complex terrain or environments where precise ranging is impossible, and have been proven to be an effective and feasible solution in large-scale network scenarios.

[0008] The commonly used algorithm in non-range positioning is the DV-Hop algorithm. The DV-Hop algorithm has certain advantages in actual deployment due to its low hardware requirements, simple implementation, and small amount of calculation. However, in the process of calculating the coordinates of unknown nodes, especially in the calculation of the minimum number of hops, the average hop distance, and the final solution of the unknown node position, the DV-Hop algorithm will be affected by environmental changes, resulting in errors. For example, when building a wireless sensor network for positioning in a port, due to its obstacles (such as container stacking, steel structures, etc.) and multipath effects, the number of hops in the positioning method will be inaccurate, thereby affecting the positioning accuracy. In addition, the network topology in the port area may change continuously, and the dynamic nature of the nodes also makes it difficult to meet the assumptions of the DV-Hop algorithm (such as the linear relationship between the number of hops and the distance), further exacerbating the positioning error.

[0009] In summary, when the traditional DV-Hop algorithm is used for node positioning in a complex communication environment, the obstacles and terrain in the environment will cause the node distribution to be irregular, which will cause large errors in the steps of the traditional algorithm. Therefore, it is necessary to improve the steps in the traditional DV-Hop algorithm to adapt to the node positioning requirements in complex communication environments. Summary of the invention

[0010] Based on this, it is necessary to provide a node positioning method, positioning system and product for complex communication environments to address the problem of low positioning accuracy of existing communication module nodes carrying sensors using the DV-Hop positioning algorithm in complex environments.

[0011] In a first aspect, the present invention proposes a node positioning method for a complex communication environment, which is used to position O communication nodes in a wireless sensor network; the O communication nodes include Q anchor nodes with known position coordinates and I unknown nodes to be positioned; O=Q+I. The node positioning method for a complex communication environment includes the following steps:

[0012] S1. Calculate the distance r between the qth unknown node and the i-th anchor node i,q , i∈[1,Q],q∈[1,I] traverses the distance between the qth unknown node and the Q anchor nodes to obtain the distance set {r 1,q ,r 2,q ,r 3,q ……r Q,q};

[0013] S2, based on {r 1,q ,r 2,q ,r 3,q ……r Q,q} and heuristic algorithms to calculate the coordinates of unknown nodes.

[0014] Wherein, S1 comprises the following steps:

[0015] S11. Get the average hop size of each anchor node i,q , the minimum number of hops between the qth unknown node and the i-th anchor node i,q ;

[0016] S12, according to the density of nodes around the i-th anchor node and hop i,q , for hopsize′ i,q Weighted to get the corresponding modified jump distance hopsize i,q ;

[0017] S13, for Q hopsize 1,q ~hopsize Q,q After summing up, we get the average hop size of the unknown node. q ;

[0018] HopSize q and hop i,q Multiply to get the distance r between the qth unknown node and its i-th anchor node i,q ;

[0019] Wherein, S2 includes the following steps:

[0020] S21. Establish the qth population Z q And initialize; among them, the qth population Z qRepresents the qth unknown node; the qth population Z q Including J experimental individuals S 1 ~S J , S j (1≤j≤J) is S 1 ~S J The jth individual in ;

[0021] S22, based on Z q For S 1 ~S J Iterate and obtain J optimized experimental vectors u' after completing the highest T rounds of iterations T,1 ~u' T,J , u′ t,j (1≤j≤J) represents the jth experimental vector generated in the tth iteration;

[0022] Among them, the mutation processing and crossover processing in the t-th iteration use an adaptive strategy to achieve adaptive adjustment of the scaling factor Sf and the crossover probability Cp; t∈[1,T].

[0023] Among them, the scaling factor Sf satisfies:

[0024]

[0025] The crossover probability Cp satisfies:

[0026]

[0027] In the formula, Sf (min) is the minimum value of Sf, Sf (max) is the maximum value of Sf, Cp (min) is the minimum value of Cp, Cp (max) is the maximum value of Cp, and vi represents the variation factor.

[0028] S23. Calculate u' T,1 ~u' T,J The fitness f′ T,1 ~f′ T,J .

[0029] Compare f′ T,1 ~f′ T,J The size of , the experimental vector corresponding to its minimum value is the optimal solution; the coordinates of the optimal solution are used as the coordinates of the unknown nodes.

[0030] In a second aspect, the present invention further proposes a node three-dimensional positioning system for a complex communication environment, which uses the node positioning method for a complex communication environment in the first aspect. The node three-dimensional positioning system for a complex communication environment includes a weighting module, a distance calculation module, a generation module, a variation and crossover module, an iteration module and a selection module.

[0031] The weighted module is used to obtain the average hop size of the i-th anchor node i,q , and perform hop weighting on the average hop distance to obtain the weighted average hop distance hopsize of the unknown node q ;

[0032] Distance calculation module, which is used to calculate the distance according to hopsize q and hop i,q Calculate the distance r between the qth unknown node and the i-th anchor node i,q ;

[0033] Generation module, which is used to establish the qth population Z q ;

[0034] A mutation and crossover module is used to sequentially perform mutation processing and crossover processing on individuals in the population to obtain processed individuals; it is also used to use an adaptive strategy in the mutation processing and crossover processing process to achieve adaptive adjustment of the scaling factor Sf and the crossover probability Cp;

[0035] Iteration module, which is used for R-based i,q Calculate the fitness and select individuals to be retained according to the fitness. The retained individuals are iteratively calculated until the maximum number of iterations is reached to obtain J optimized experimental vectors u' T,1 ~u' T,J ;

[0036] Select the module used to calculate u' T,1 ~u' T,J The fitness f′ T,1 ~f′ T,J , and compare f′ T,1 ~f′ T,J The size of , the experimental vector corresponding to its minimum value is the optimal solution, and the coordinates of the optimal solution are used as the coordinates of the i-th unknown node.

[0037] In a third aspect, the present invention further proposes a software program product, which includes program instructions. When the software program product is run on an electronic device, the electronic device executes the steps of the node positioning method in a complex communication environment in the first aspect.

[0038] The beneficial effects of the present invention are:

[0039] 1. The present invention optimizes the estimated coordinates obtained by the traditional positioning algorithm by introducing a three-dimensional positioning algorithm that combines a uniform point set, a Levy flight strategy, and an adaptive strategy. Specifically, in the process of calculating the coordinates of unknown nodes, the population is first initialized by generating a uniform point set, and the Levy strategy uses a step search to expand the global optimization capability, and the crossover probability and the scaling factor are dynamically adjusted in the offspring generation process. By introducing these three strategies, the present invention can maintain a high positioning accuracy under dynamic conditions, many obstacles, and interference with signal propagation.

[0040] 2. The present invention uses a solution for correcting the number of node hops using multiple communication radii. It utilizes a multi-hop broadcast method. Compared with the traditional DV-Hop algorithm, in which different nodes within the communication radius use the same minimum number of hops to estimate the distance between them and the anchor node, the multi-hop broadcast method can obtain a more accurate minimum number of hops. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor.

[0042] Figure 1 is a flow chart of a node positioning method for a complex communication environment in an embodiment;

[0043] Figure 2 A schematic diagram of a hop count model in an embodiment;

[0044] Figure 3 A schematic diagram of the visual distribution of good point sets in the embodiment;

[0045] Figure 4 The experimental results of LE under different communication ranges are shown in Figure 2.

[0046] Figure 5 The experimental results of LE under different numbers of anchor nodes are shown in Figure 2.

[0047] Figure 6 The experimental results of LE under different total numbers of communication nodes. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "or / and" used herein includes any and all combinations of one or more of the related listed items.

[0050] This embodiment is described by taking the entire wireless sensor system as an example. The wireless sensor system includes O communication nodes carrying sensors. The O communication nodes include Q anchor nodes with known position coordinates and I unknown nodes to be located. Wherein, O=Q+I.

[0051] Please refer to Figure 1 This embodiment provides a node positioning method for a complex communication environment, which mainly includes two stages: the first stage: S1, calculate the distance r between the qth unknown node and the i-th anchor node i,q , i∈[1,Q],q∈[1,I], traverse the distance between the qth unknown node and the Q anchor nodes to obtain the distance set {r 1,q ,r 2,q ,r 3,q ……r Q,q};

[0052] The second stage: S2, based on {r 1,q ,r 2,q ,r 3,q ……r Q,q} and heuristic algorithm to calculate the coordinates of unknown nodes;

[0053] The following is a detailed introduction to the two stages S1 and S2:

[0054] 1. Phase 1

[0055] S11. Get the average hop size of each anchor node i,q , the minimum number of hops between the qth unknown node and the i-th anchor node is hop i,q ;

[0056] On the one hand, when estimating the minimum hop count, the traditional calculation method has limitations in the calculation process. When unknown nodes are located in the communication radius of the anchor node, their hop counts will be recorded as 1, which will cause significant errors in positioning. Especially when different unknown nodes within the communication radius use the same minimum hop count to estimate the distance between them and the anchor node, it is necessary to propose a solution to correct the node hop count in multiple communication radii to address the situation where the hop count calculation error leads to low accuracy.

[0057] The scheme of multi-communication radius correction of node hop count utilizes multi-radius broadcasting method (or multi-hop broadcasting method), which is as follows: the i-th anchor node broadcasts within the R / M distance for the first time, and the subsequent broadcast distance increases by R / M distance each time until the broadcast distance reaches the communication radius R; M is a predefined value; i∈[1,Q].

[0058] If the qth unknown node is at the mth R / M distance of the ith anchor node, then hop i,q is mR / M, and broadcasts mR / M outward; m∈[1,M], and m is a positive integer.

[0059] If the qth unknown node is outside the R of the ith anchor node, then hop i,q =hop min +1; among them, hop min It is the minimum hop value broadcasted by other I-1 unknown nodes.

[0060] For details, please refer to Figure 2 .by Figure 2The hop count model in is used as an example to illustrate: the anchor node with known location coordinate information is surrounded by five other unknown nodes within its communication radius R (for ease of description, the anchor node is represented as A below, and the five unknown nodes are represented as B, C, D, E, and G in order of distance from A, and G is outside the communication radius of A). First, A initiates a broadcast with a radius of R / 4. At this stage, only B is within the range of receiving the broadcast, and node B records the hop value as 1 / 4. Then in the second round of broadcast, A increases the radius to R / 2, and both B and C receive the broadcast. At this time, B discards the second broadcast and forwards the smallest hop value 1 / 4 to other unknown nodes, while C counts the hop value as 1 / 2 and forwards its hop value to other unknown nodes. In the third round of broadcast, A increases the broadcast range to 3R / 4. At this time, B and C discard the broadcast and forward their previously recorded tune values ​​to other unknown nodes. D records the hop value as 3R / 4 and forwards its hop value to other unknown nodes. In the next round of broadcast, A increases the broadcast range to R, B, C, and D discard the broadcast, and E receives the broadcast information and records the hop count as 1, and forwards its hop count value to other unknown nodes. Finally, G does not receive the broadcast from A, but receives the broadcast information from B, C, D, and E, and selects the smallest hop count value. The smallest hop count value comes from 1 / 4 of B, and then it is added by 1, so that the minimum hop count value from G to A is recorded as 5 / 4. Through this multi-communication radius correction node hop count scheme, the hop count of each unknown node to the anchor node is obtained. i .

[0061] S12, according to the density of nodes around the i-th anchor node and hop i,q , for hopsize′ i,q Weighted to get the corresponding modified jump distance hopsize i,q ;

[0062] On the other hand, in the traditional calculation method, the average hop distance of the anchor node closest to the unknown node is used as the average hop distance of the unknown node. However, since each communication node is randomly distributed in the environment and the network topology is irregular, a large number of communication paths are composed of multi-hop paths, which makes the calculated distance and the actual distance have a large error. In order to reduce the error caused by the irregular topology, the hop weighting strategy is adopted in this embodiment.

[0063] Specifically, we first obtain the average hop size′ between the i-th anchor nodes. i,q , and then according to the node density and hop near the i-th anchor node i,q , for hopsize′ i,q Weighted to get the corresponding modified jump distance hopsize i,q ;

[0064] hopsize i,q =w i,q ×hopsize′ i,q

[0065]

[0066] In the formula, w i,q represents the weight coefficient, d i It represents the node density near the i-th anchor node, and the higher the density, the greater the weight. α represents the weight adjustment parameter, which represents the influence of the hop count on the weight. As can be seen from the above formula, when the unknown node is closer to the anchor node, the minimum hop count is i,q smaller, which means that the weight w i,q will be larger, and when calculating the average hop distance of unknown nodes, the corrected hopsize from nearby anchor nodes i,q The accuracy is improved by weighting the average hop distance of each anchor node communicating with the unknown node.

[0067] S13, for Q hopsize 1,q ~hopsize Q,q After summing up, we get the average hop size of the qth unknown node. q , the summation formula is:

[0068]

[0069] According to the obtained hopsize q and hop i,q Calculate the distance r between the qth unknown node and the i-th anchor node i , and its calculation formula is:

[0070] r i,q =hop i,q ×hopsize q

[0071] Then traverse the distances between the qth unknown node and the remaining anchor nodes to obtain the distance set {r 1,q ,r 2,q ,r 3,q ……r Q,q}; So far, the distance set {r 1,q ,r 2,q ,r 3,q ……r Q,q}has been obtained.

[0072] 2. Second Stage

[0073] The second stage uses an improved heuristic algorithm and is based on the {r 1,q ,r 2,q ,r 3,q ……r Q,q}Calculate the coordinates of the unknown nodes. The second stage includes the following steps:

[0074] S21. Establish the qth population Z q And initialize; among them, the qth population Z q Represents the qth unknown node; the qth population Z q Including J experimental individuals S 1 ~S J , S j (1≤j≤J) is S 1 ~S J The jth individual in .

[0075] In the heuristic algorithm, the selection of the population has an important influence on the performance and convergence speed of the algorithm. In this embodiment, in order to improve the accuracy of unknown node positioning and the search efficiency, a uniform point set is used to initialize the entire population. In general, a set of unknown node coordinates obtained by the least squares method is used to initialize the first experimental individual of the population, and the remaining experimental individuals in the entire population are generated by a uniform point set. The uniform point set initialization method can ensure that the population is evenly distributed in the search space, avoid excessive concentration of the population in certain areas, and improve the comprehensiveness of the search and the global exploration ability. In this way, the algorithm can be optimized in a uniformly distributed manner from the beginning, which helps to improve the search efficiency of subsequent iterations.

[0076] Specifically, assuming that V D is the search space of a D-dimensional cube. If the deviation of a point set If the standard is met, it is considered to be a good point set P n (k) Figure 3 shown.

[0077] deviation The formula that meets the criteria is:

[0078]

[0079] Good point set P n The distribution formula of (k) is:

[0080]

[0081] In the formula, C(r,ε)n -1+ε is a constant that depends only on r and ε. r belongs to V D , is a parameter related to dimension. In this embodiment, r is defined as Where p is the smallest prime number that satisfies (p-3) / 2≥n. ε is a positive number used to control the error bound. n represents P n (k) The number of points in r. D represents the scaling factor of the Dth dimension, and k represents P n (k) is the point index. n (k) Then the points can be mapped to the search space to form a population.

[0082] S22, based on Z q For S 1 ~S J Iterate and obtain J optimized experimental vectors u' after completing the highest T rounds of iterations T,1 ~u' T,J , u′ t,j (1≤j≤J) represents the jth experimental vector generated in the tth round of iteration, where the mutation processing and crossover processing in the tth round of iteration use an adaptive strategy to achieve adaptive adjustment of the scaling factor Sf and the crossover probability Cp, t∈[1,T].

[0083] Among them, the scaling factor Sf controls the process of mutation processing, and the crossover probability Cp controls the process of crossover processing. These two factors control the generation of offspring. The better these values ​​are, the better the offspring generated. In the traditional differential evolution algorithm, Sf and Cp are selected from the range [0,1] and are fixed values. The present invention changes the values ​​of Sf and Cp within a given range instead of selecting a fixed value. In this embodiment, an adaptive strategy is added to the mutation processing and crossover processing process so that the scaling factor Sf and the crossover probability Cp are changed within the range, which must meet the following conditions:

[0084] The scaling factor Sf satisfies:

[0085]

[0086] The crossover probability Cp satisfies:

[0087]

[0088] Where t represents the number of current iterations in the heuristic algorithm, Sf (min) is the minimum value of Sf, Sf (max) is the maximum value of Sf, Cp (min) is the minimum value of Cp, Cp (max)is the maximum value of Cp, and vi represents the variation factor. The calculation formulas for the above scaling factor Sf and crossover probability Cp take the range of variables in the form of maximum and minimum values. The scaling factor Sf and the crossover probability Cp regulate the production of offspring, and the higher their quality, the better the offspring will be. The motivation behind this adjustment is that when the population tends to aggregate, a higher mutation probability and a lower crossover probability are required, because the offspring need to be stronger to survive in densely populated areas. Conversely, if the population is more dispersed, a lower mutation probability and a higher crossover probability are required, resulting in the diversity of offspring. The purpose of this dynamic adjustment is to optimize the evolutionary process under a given environment, and it will try to find the optimal solution from the aggregate space to the dispersed space.

[0089] The adaptive strategy is used to dynamically adjust the values ​​of Sf and Cp. This adjustment helps to enhance the performance of the algorithm and its ability to effectively explore the solution space. In the process of mutation processing, the Levy flight strategy is also introduced to enhance the global search capability. The calculation formula for the step length LF of the Levy flight strategy is:

[0090]

[0091]

[0092] In the formula, u~N(0,σ 2 ), v~N(0,σ 2 ). Γ is the gamma function, Γ(n) = (n-1)!, β is an adjustable factor.

[0093] The added adaptive strategy and Levy flight strategy are used to optimize the mutation processing and crossover processing. Specifically, taking the first round of iteration as an example, the process of mutation processing and crossover processing is as follows:

[0094] S221, S j After mutation processing, the donor vector v is obtained j Among them, the calculation formula for mutation processing is:

[0095] v j =X best +Sf(X best -S j )+Sf(X r1 -X r2 )+LF(X best -S j )

[0096] In the formula, v j is the donor vector, X best is the individual with the best fitness score from the population, X r1 and X r2are two random vectors selected from the current population after iteration.

[0097] S222, will v j , S j In the input cross processing, u′ is calculated by comparison t,j , where the calculation formula for cross processing is:

[0098]

[0099] In the formula, r takes a random integer value between 0 and 1. Represents the randomly selected variable position, and its range depends on how many values ​​there are in any single individual of the population. In the present invention, it is set to three values ​​and is used to represent the spatial three-dimensional coordinates of the unknown node.

[0100] The specific iteration process is described below. In T rounds of iteration, the method for the tth round of iteration includes the following steps:

[0101] Step 1: u 0,1 '~u 0,J 'Respectively S 1 ~S J , which means that population Z q The initial experimental individuals in are used as the initial input for the first iteration. Then, as the iteration begins, the obtained u′ t-1,1 ~u′ t-1,J As new individuals form a new population and use it as the input of the tth iteration.

[0102] The optimized experimental vector u′ obtained from the t-1th round of iteration t-1,j Perform mutation and crossover processing in sequence to obtain the experimental vector u′ after the tth round of processing t,j ; j∈[1,J]; t∈[1,T].

[0103] Step 2: Based on r i,q Calculate u′ separately t-1,j and u′ t,j The fitness f′ t-1,j and f′ t,j .

[0104] Compare f′ t-1,j and f′ t,j If f′ t-1,j ≤f′ t,j , then keep the current solution as u′ t-1,j Otherwise, update the current solution to u′ t,j .

[0105] Step 3: Traverse u′ t-1,j ~u′ t-1,J, J current solutions are obtained as the input of the tth round of iteration. The current solution can be regarded as the optimal solution in the tth round of iteration. The process of finding an optimal solution in a round of iteration is generally regarded as a selection.

[0106] S23. Calculate u' T,1 ~u' T,J The fitness f′ T,1 ~f′ T,J . Compare f′ T,1 ~f′ T,J The size of , the experimental vector corresponding to its minimum value is the global optimal solution, which means that one of the multiple solutions representing the i-th unknown node is selected as the global optimal solution. The coordinates of the global optimal solution are used as the coordinates of the i-th unknown node.

[0107] In S22 and S23, the fitness calculation methods are similar. t,j For example, the calculation method includes the following steps:

[0108] S231. Calculate u′ t,j The distance r′ between the qth anchor node q,j ;

[0109] r′ q,j With r i,q Subtract and get the difference;

[0110] S232, traverse Q anchor nodes, obtain Q corresponding differences, and sum and average the differences to obtain f′ t,j .

[0111] Among them, f′ t,j The calculation formula is:

[0112]

[0113] In the formula, represents the jth u′ in the tth iteration t,j The coordinates of Represents the coordinates of the qth anchor node.

[0114] The experimental results of the node positioning method for complex communication environment provided by the present invention and the existing positioning algorithm are compared:

[0115] The simulation results are used to evaluate and compare the performance of the communication node three-dimensional positioning method proposed in the present invention under different parameter settings. These parameters include the proportion of communication nodes with known positions, the communication range of communication nodes, and the total number of communication nodes. By simulating these parameters with different configurations, the performance of the present invention in different scenarios is analyzed.

[0116] The experiment was simulated on MATLAB. In the experiment, the communication nodes were randomly distributed in 100*100*100m 3 This random distribution is used to simulate real-world scenarios. Through this random distribution, the performance of the algorithm can be evaluated in a more representative and diverse environment. The parameter settings are shown in Table 1.

[0117] Table 1: Simulation parameter settings

[0118]

[0119] The experiment adopts the positioning error (LE) as the evaluation index of the node positioning method for complex communication environment proposed by the present invention and other methods. The positioning error is the sum of the total error divided by the number of unknown nodes and the communication range, which can be considered to be sufficient to evaluate the positioning accuracy of the algorithm and various factors, including the communication node density, the number of communication nodes with known locations, and the communication range. It is defined as follows:

[0120]

[0121] in and Represent the optimized estimated coordinates and actual coordinates of the i-th unknown node respectively. R represents the communication range of the communication node, O represents the total number of communication nodes in the area, Q represents the number of communication nodes with known locations (i.e., anchor nodes), and I represents the number of unknown nodes.

[0122] Please refer to Figure 4 , which shows the change of LE with respect to the communication range. During the experiment, the total number of communication nodes in the area is 100, the number of anchor nodes is 20, and the communication range varies from 20 to 45m. Figure 4 The data changes shown indicate that, as the communication range increases, the positioning errors of the four algorithms become smaller. This can be attributed to the fact that as the communication range increases, the communication nodes form more connections with each other. This ultimately proves that the 3D-DV-Hop combined with Levy and adaptive strategies mentioned in the present invention has a much smaller positioning error than 3D-DV-Hop, I3D-DVLAIN and 2DHYPGA DV-Hop in the prior art.

[0123] Please refer to Figure 5 , which shows the impact of changes in the number of anchor nodes on LE. Similarly, the total number of communication nodes in the area is 100, and the communication range is fixed at 20m, and the number of anchor nodes varies from 20 to 80. Figure 5It can be seen that it shows an obvious trend: as the number of anchor nodes increases, the positioning errors of the four algorithms decrease. The reason for this is that the more anchor nodes there are, the smaller the number of hops will be, so the distance between the unknown node and the anchor node will be estimated more accurately, which ultimately proves that the positioning error in the method proposed in the present invention is smaller than the positioning error of other algorithms.

[0124] Please refer to Figure 6 , which shows the impact of the change in the total number of communication nodes on LE. The number of anchor nodes is fixed at 20, the communication range is fixed at 20m, and the total number of communication nodes varies from 50 to 300. Figure 6 It can be seen that the positioning error decreases as the total number of communication nodes increases. This trend is because the path between the anchor node and the unknown node approaches a straight line as the total number of communication nodes increases, making the distance calculation of each hop more accurate, thereby reducing the positioning error. Finally, it is shown that under the same conditions, the positioning error of the method proposed in the present invention is smaller than that of other algorithms.

[0125] In summary, the experimental results show that the communication node positioning method for complex environments proposed in the present invention aims to improve the calculation method of unknown node coordinates in the DV-Hop algorithm in complex environments. A strict examination of mathematical errors shows that the 3D-DV-Hop in the present invention that combines Levy with adaptive strategies is compared with traditional 3D-DV-Hop, I3D-DVLAIN and 2DHYPGA DV-Hop. The communication node positioning method for complex environments proposed in the present invention exhibits much less error propagation, and this improvement emphasizes the effectiveness of the present invention in achieving higher positioning accuracy. The experimental results show that compared with other similar algorithms, the performance of the proposed algorithm is better and the positioning error is lower. Taking these results into account, it is obvious that the communication node positioning method for complex environments proposed in the present invention has been proven to be an effective positioning method, which exceeds the performance of the comparison method in terms of accuracy.

[0126] In some other embodiments, a node three-dimensional positioning system for complex communication environments is also proposed, which uses the node positioning method for complex communication environments as described above. The communication node three-dimensional positioning system includes a weighting module, a distance calculation module, a generation module, a variation and crossover module, an iteration module and a selection module.

[0127] The weighted module is used to obtain the average hop size of the i-th anchor node i,q , and perform hop weighting on the average hop distance to obtain the weighted average hop distance hopsize of the qth unknown node q ;

[0128] Distance calculation module, which is used to calculate the distance according to hopsize qand hop i,q Calculate the distance r between the qth unknown node and the i-th anchor node i,q ;

[0129] Generation module, which is used to establish the qth population Z q ;

[0130] A mutation and crossover module is used to sequentially perform mutation processing and crossover processing on individuals in the population to obtain processed individuals; it is also used to use an adaptive strategy in the mutation processing and crossover processing process to achieve adaptive adjustment of the scaling factor Sf and the crossover probability Cp;

[0131] Iteration module, which is used for R-based i,q Calculate the fitness and select individuals to be retained according to the fitness. The retained individuals are iteratively calculated until the maximum number of iterations is reached to obtain J optimized experimental vectors u' T,1 ~u' T,J ;

[0132] Select the module used to calculate u' T,1 ~u' T,J The fitness f′ T,1 ~f′ T,J , and compare f′ T,1 ~f′ T,J The size of , the experimental vector corresponding to its minimum value is the optimal solution, and the coordinates of the optimal solution are used as the coordinates of the i-th unknown node.

[0133] In some other embodiments, an electronic device is also provided. The electronic device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps of the communication node positioning method in a complex environment in the above embodiment are implemented.

[0134] Some other embodiments also provide a computer-readable storage medium, which stores a computer program, which implements the steps of the communication node positioning method in a complex environment in the above embodiments when the computer program is executed by a processor.

[0135] Some other embodiments also provide a software program product, which includes program instructions, and when the software program product is run on an electronic device, the electronic device executes the steps of the communication node positioning method in a complex environment in the above embodiment.

[0136] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0137] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A node positioning method for a complex communication environment, which is used to locate O communication nodes in a wireless sensor network; the O communication nodes include Q anchor nodes with known position coordinates and I unknown nodes to be located; O = Q + I, characterized in that, The communication node positioning method for complex environment includes the following steps: S1. Calculate the distance r between the qth unknown node and the i-th anchor node i,q , i∈[1,Q],q∈[1,I], traverse the distance between the qth unknown node and the Q anchor nodes to obtain the distance set {r 1,q ,r 2,q ,r 3,q ……r Q,q }; S2, based on {r 1,q ,r 2,q ,r 3,q ……r Q,q } and heuristic algorithm to calculate the coordinates of unknown nodes; Wherein, S1 comprises the following steps: S11. Get the average hop size of each anchor node i ' ,q , the minimum number of hops between the qth unknown node and the i-th anchor node i,q ; S12, according to the density of nodes around the i-th anchor node and hop i,q , for hopsize i ' ,q Weighted to get the corresponding modified jump distance hopsize i,q ; S13, for Q hopsize 1,q ~hopsize Q,q After summing up, we get the average hop size of the qth unknown node. q ; HopSize q and hop i,q Multiply to get the distance r between the qth unknown node and its i-th anchor node i,q ; Wherein, S2 includes the following steps: S21. Establish the qth population Z q And initialize; among them, the qth population Z q Represents the qth unknown node; the qth population Z q Including J experimental individuals S1~S J , S j (1≤j≤J) is S1~S J The jth individual in ; S22, based on Z i For S1~S J Iterate and obtain J optimized experimental vectors u' after completing the highest T rounds of iterations T,1 ~u' T,J ,u t ' ,j (1≤j≤J) represents the jth experimental vector generated in the tth iteration; Among them, the mutation processing and crossover processing in the tth round of iteration use an adaptive strategy to achieve adaptive adjustment of the scaling factor Sf and the crossover probability Cp; t∈[1,T]; Among them, the scaling factor Sf satisfies: The crossover probability Cp satisfies: In the formula, Sf (min) is the minimum value of Sf, Sf (max) is the maximum value of Sf, Cp (min) is the minimum value of Cp, Cp (max) is the maximum value of Cp, and vi represents the variation factor; S23. Calculate u' T,1 ~u' T,J The fitness f T ' ,1 ~f T ' ,J ; Compare T ' ,1 ~f T ' ,J The size of , the experimental vector corresponding to its minimum value is the optimal solution; the coordinates of the optimal solution are taken as the coordinates of the i-th unknown node.

2. The node positioning method for a complex communication environment according to claim 1, characterized in that: In S11, get hop i,q The method comprises the following steps: The i-th anchor node broadcasts within the R / M distance for the first time, and the subsequent broadcast distance increases by R / M distance each time until the broadcast distance reaches the communication radius R; M is a predefined value; i∈[1,Q]; If the qth unknown node is at m R / M distances from the ith anchor node, then hop i,q is mR / M, and broadcasts mR / M outward; m∈[1,M], and m is a positive integer; If the qth unknown node is outside the R of the ith anchor node, then hop i,q =hop min +1; among them, hop min It is the minimum hop value broadcasted by other I-1 unknown nodes.

3. The node positioning method for complex communication environment according to claim 1, characterized in that: In S12, hopsize i,q The calculation formula is: hopsize i,q =w i,q ×hopsize i ' ,q hopsize q The calculation formula is: In the formula, w i,q represents the weight coefficient, d i represents the node density near the i-th anchor node, and α represents the weight adjustment parameter.

4. The node positioning method for complex communication environment according to claim 1, characterized in that: In S21, establish the qth population Z q The methods include: Establish the first experimental individual with known coordinates; Use the good point set P n (k) Initialize the distribution of the remaining J-1 experimental individuals; Among them, the deviation of the good point set Meet the standards; deviation The formula that meets the criteria is: Good point set P n The distribution formula of (k) is: P n (k)={({r1 (n) ,k},{r2 (n) ,k},{r3 (n) ,k},...,{r D (n) ,k}),1≤k≤n} In the formula, C(r,ε)n -1+ε is a constant, n represents P n The number of points in (k), r D represents the scaling factor of the Dth dimension, and k represents P n The point index in (k).

5. The node positioning method for complex communication environment according to claim 1, characterized in that: In S22, the Levy flight strategy is also added during the mutation processing to expand the global optimization capability during the mutation processing; Among them, the calculation formula of the step length LF of the Levy flight strategy is: In the formula, u~N(0,σ 2 ), v~N(0,σ 2 ), Γ is the gamma function, and β is an adjustable factor.

6. The node positioning method for complex communication environment according to claim 5, characterized in that: In S22, in the first iteration, S j After mutation processing, the donor vector v is obtained j ; v j As the input of the cross processing, u is calculated t ' ,j ; The calculation formula for mutation processing is: v j =X best +Sf(X best -S j )+Sf(X r1 -X r2 )+LF(X best -S j ) In the formula, v j is the donor vector, X best is the individual with the best fitness score from the population, X r1 and X r2 are two random vectors selected from the current population after iteration; The calculation formula for cross processing is: In the formula, r takes a random integer value between 0 and 1. represents a randomly selected variable location.

7. The node positioning method for complex communication environment according to claim 1, characterized in that: In S22, the method for the t-th round of iteration includes the following steps: Step 1: u 0,1 '~u 0,J ' are S1~S J ; The optimized experimental vector u obtained by t-1 rounds of iteration t ' -1,j Perform mutation and crossover processing in sequence to obtain the experimental vector u after the tth round of processing t ' ,j , j∈[1,J]; Step 2: Calculate u separately t ' -1,j and u t ' ,j The fitness f t ' -1,j and f t ' ,j ; Compare t ' -1,j and f t ' ,j The size of f t ' -1,j ≤f t ' ,j , then keep the current solution as u t ' -1,j Otherwise, update the current solution to u t ' ,j ; Step 3: Traverse u t ' -1,j ~u t ' -1,J , and obtain J current solutions as the input for t rounds of iterations.

8. The node positioning method for complex communication environment according to claim 7, characterized in that: Calculate f t ' ,j The method comprises the following steps: S231, calculate u t ' ,j The distance r from the qth anchor node q ' ,j ; r q ' ,j With r i,q Subtract and get the difference; S232, traverse Q anchor nodes, obtain Q corresponding differences, and sum and average the differences to obtain f t ' ,j .

9. A node three-dimensional positioning system for complex communication environments, characterized in that: It uses the node positioning method for a complex communication environment as described in any one of claims 1 to 8; the node three-dimensional positioning system for a complex communication environment comprises: Weighted module, which is used to obtain the average hop size of the i-th anchor node i ' ,q , and perform hop weighting on the average hop distance to obtain the weighted average hop distance hopsize of the qth unknown node q ; Distance calculation module, which is used to calculate the distance according to hopsize q and hop i,q Calculate the distance r between the qth unknown node and the i-th anchor node i,q ; Generation module, which is used to establish the qth population Z q ; A mutation and crossover module is used to sequentially perform mutation processing and crossover processing on individuals in the population to obtain processed individuals; it is also used to use an adaptive strategy in the mutation processing and crossover processing process to achieve adaptive adjustment of the scaling factor Sf and the crossover probability Cp; Iteration module, which is used for R-based i,q Calculate the fitness and select individuals to be retained according to the fitness. The retained individuals are iteratively calculated until the maximum number of iterations is reached to obtain J optimized experimental vectors u' T,1 ~u' T,J ; Select the module used to calculate u' T,1 ~u' T,J The fitness f T ' ,1 ~f T ' ,J , and compare f T ' ,1 ~f T ' ,J The size of , the experimental vector corresponding to its minimum value is the optimal solution, and the coordinates of the optimal solution are used as the coordinates of the unknown nodes.

10. A software program product, characterized in that The software program product includes program instructions, and when the software program product is run on an electronic device, the electronic device executes the steps of the node positioning method in a complex communication environment as claimed in any one of claims 1 to 8.

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