Distributed opportunistic array radar wireless sensor network positioning method and system

Abstract

The invention discloses a distributed opportunistic array radar wireless sensor network positioning method and a distributed opportunistic array radar wireless sensor network positioning system, which relate to the technical field of wireless sensor network positioning, and are characterized in that hop counts among nodes are optimized by using multiple dynamic communication radii, then, a weighting coefficient is introduced to calculate an average hop distance of the nodes, and then, the average hop distance of the nodes is calculated; tent chaotic mapping is introduced into a raccoon optimization algorithm to initialize a population, the population diversity is improved, a fitness function is improved in the iterative optimization process, errors caused by increase of the number of anchor nodes are reduced, and finally the improved raccoon optimization algorithm is used for replacing a least square method to conduct optimal node positioning.

Description

Technical Field

[0001] The invention relates to the technical field of wireless sensor network positioning, and in particular to a distributed opportunity array radar wireless sensor network positioning method and system. Background Art

[0002] Wireless Sensor Networks (WSN) is a distributed sensor network that transmits information wirelessly, with the advantages of flexibility and convenience. With the continuous increase of wireless sensor devices, it has been widely used in environmental monitoring, smart grid, industrial control, intelligent transportation, medical systems and other fields, bringing great changes to people's production and life. Faced with the current construction of smart cities in China, the complex urban environment has posed a huge challenge to the research of WSN. In most cases, we not only need to collect sensor node data, but also need to obtain the specific location of the node so that we can detect and track the target and formulate corresponding strategies. Therefore, node positioning technology has become a research hotspot for scholars at this stage.

[0003] The positioning algorithm is mainly divided into ranging positioning and non-ranging positioning algorithms according to whether the distance between nodes is measured. Since ranging positioning requires information such as the distance and angle between nodes to determine the position of unknown points, the error of ranging positioning is small, but the cost requirement is high in practical applications and it is not easy to implement. Among them, the received signal strength indicator (RSSI) ranging, which belongs to ranging positioning, has relatively high accuracy, but the hardware cost is high and the energy loss is large. Summary of the invention

[0004] In order to solve the deficiencies mentioned in the above background technology, the purpose of the present invention is to provide a distributed opportunity array radar wireless sensor network positioning method and system.

[0005] In a first aspect, the purpose of the present invention can be achieved by the following technical solution: a distributed opportunity array radar wireless sensor network positioning method, the method comprising the following steps: Initialize the wireless sensors in the opportunity array radar, randomly distribute the wireless sensor nodes, set the communication radius of the wireless sensor nodes, and obtain the minimum hop number between the wireless sensor nodes based on the communication radius of the wireless sensor nodes and using the multi-communication radius method; The actual distance between the anchor nodes is obtained, and the average hop distance of the anchor nodes is calculated based on the actual distance between the anchor nodes and the minimum number of hops between the wireless sensor nodes, and the average hop distance of the anchor node closest to the unknown node is set as the average hop distance of the unknown node; The estimated distance between the unknown node and the anchor node is calculated using the average hop distance of the unknown node and the minimum number of hops between the unknown node and the anchor node. The estimated distance between the unknown node and the anchor node is combined with the improved raccoon optimization algorithm to locate the unknown node and obtain the position of the unknown node.

[0006] In combination with the first aspect, in some implementations of the first aspect, the method further includes: the calculation process of obtaining the minimum number of hops between wireless sensor nodes using the multi-communication radius method: Based on the distance vector routing theory, the anchor node broadcasts its own information in the network, including its own label, location coordinates and hop count. The initial value of the hop count is 0. When the node receives and records multiple anchor node information, it takes the smallest hop count as the minimum hop count between the unknown node and the anchor node, adds the hop count to 1, and forwards it to the network. The minimum number of hops is solved by using multiple communication radii. The communication radius is dynamically set and divided into four communication radius parts of different sizes. The minimum number of hops is determined according to the distance between nodes; as shown in expression (1): (1) in, , d are the number of hops and distance between the anchor node and the adjacent node, respectively. The communication radius is 1 / 4R, 2 / 4R, 3 / 4R, and R respectively. When the anchor node communicates with the adjacent node, the number of hops increases in units of 1 / 4R corresponding to the communication radius. The node continues to forward the data packet with a communication radius of 1 / 4R, 2 / 4R, 3 / 4R, and R until all sensor nodes in the network receive the data packet. If the sensor node receives a data packet from the same anchor node, it compares and saves the hop count information with a smaller hop value.

[0007] In combination with the first aspect, in some implementations of the first aspect, the method further includes: the average hop distance of the anchor node The calculation process: (2) Among them, Q is the total number of anchor nodes, , Point anchor nodes , The coordinates of for , The minimum hop value between

[0008] In combination with the first aspect, in some implementations of the first aspect, the method further includes: estimating the distance d between the unknown node and the anchor node ji The calculation process is as follows: (3) Among them, d ji is the estimated distance between the unknown node and the anchor node, the unknown node j The first hop distance is taken as the own hop distance. , Indicates unknown node j To the anchor node i The minimum number of hops.

[0009] In combination with the first aspect, in some implementations of the first aspect, the method further includes: the improved raccoon optimization algorithm determines the position update of the target in the predation stage and the escape stage according to the relationship between the number of individuals i and the population size N, and uses the formula involved and the optimized fitness function, and ends the optimization when the current iteration number t is greater than the maximum iteration number T, otherwise the iteration number is increased by one, and finally the optimal solution is obtained to determine the unknown node position; The process is as follows: Initialize the population size N and the maximum number of iterations T; Calculate the fitness value of each individual and determine the optimal position to be the location of the prey ; In the first stage of predation, half of the population on the tree is targeted to update individual positions; Update the positions of half of the population under the tree; Make a greedy choice; The second stage is the escape process, updating the individual position; Make a greedy choice; Initialize the population using the Tent map; For fitness function Optimize When the current number of iterations t is greater than the maximum number of iterations T, the optimization process ends. Otherwise, the number of iterations increases by one. Finally, the optimal solution is obtained and the position of the unknown node is determined.

[0010] In combination with the first aspect, in some implementations of the first aspect, the method further includes: in the improved raccoon optimization algorithm, the position of the individual in the search space is randomly initialized using formula (4): (4) in, is an individual in the search space, , are the lower and upper bounds of the decision variable, respectively. is a random real number between the interval [0,1]; During the predation phase, the optimal position in the population is the position of the prey In the theoretical assumption, half of the raccoons are under the tree and the other half are hunting together on the tree. Formula (5) is used to estimate the position of the raccoons on the tree: (5) in, is the population size; is the current iteration number; after the prey lands, the raccoon's position is constantly updated as the prey moves, and the position is estimated using formulas (6) and (7): (6) (7) in, is the population size, is the current iteration number, is the fitness function, belongs to {1,2}; Indicates the updated landing position of the prey; Indicates the j-th dimension variable value of the i-th individual in the current iteration; The latest location of raccoons; If the new position of each raccoon optimizes the value of the objective function, then the position is determined. Otherwise, the raccoon individual maintains its original position, which is considered a greedy choice, as shown in formula (8): (8) Escape phase: When escaping from the predator, the raccoon puts itself in a safe position and mathematically models the position update of the raccoon. The formula is as follows: (9) (10) (11) Where r represents a random number in [0,1], t is the current iteration number, and T is the maximum iteration number. , are the upper and lower bounds of the j-th dimension variable update, as in the predation stage, is the latest location of the raccoon; when the new location optimizes the objective function value, the location at this time is determined, otherwise the location remains unchanged, the same as formula (8).

[0011] In combination with the first aspect, in some implementations of the first aspect, the method further includes: an optimization improvement process of the improved raccoon optimization algorithm is as follows: Use multiple communication radius to obtain the minimum hop count information in the algorithm; Introduce weighted coefficients. The weighted coefficients of known nodes and unknown nodes are inversely proportional to the distance between the two types of nodes. Use the weighted coefficients to introduce the hop count relationship between multiple nodes in the network and optimize the average hop distance of network nodes. Tent chaotic mapping is introduced into the Raccoon optimization algorithm to initialize the population, and the fitness function is improved during the iterative optimization process. The improved Raccoon optimization algorithm is used to replace the least squares method in the Raccoon optimization DV-Hop algorithm to estimate the positioning coordinates of unknown nodes.

[0012] In a second aspect, in order to achieve the above-mentioned object, the present invention discloses a distributed opportunity array radar wireless sensor network positioning system, comprising: The hop number acquisition module is used to initialize the wireless sensors in the opportunity array radar, randomly distribute the wireless sensor nodes, set the communication radius of the wireless sensor nodes, and obtain the minimum hop number between the wireless sensor nodes based on the communication radius of the wireless sensor nodes and using the multi-communication radius method; The hop distance acquisition module is used to obtain the actual distance between the anchor nodes, calculate the average hop distance of the anchor nodes based on the actual distance between the anchor nodes and the minimum number of hops between the wireless sensor nodes, and set the average hop distance of the anchor node closest to the unknown node as the average hop distance of the unknown node; The position positioning module is used to calculate the estimated distance between the unknown node and the anchor node using the average hop distance of the unknown node and the minimum number of hops between the unknown node and the anchor node. The estimated distance between the unknown node and the anchor node is combined with the improved raccoon optimization algorithm to locate the unknown node and obtain the position of the unknown node.

[0013] In another aspect of the present invention, in order to achieve the above-mentioned purpose, a terminal device is disclosed, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, a distributed opportunity array radar wireless sensor network positioning method as described above is adopted.

[0014] In another aspect of the present invention, in order to achieve the above-mentioned purpose, a computer-readable storage medium is disclosed, in which a computer program is stored. When the computer program is loaded and executed by a processor, a distributed opportunity array radar wireless sensor network positioning method as described above is adopted.

[0015] Beneficial effects of the present invention: Based on the DV-Hop algorithm, the present invention introduces a multi-communication radius method to reduce the error caused by the large difference in the actual distance of nodes with the same hop number, so that the hop number between nodes is closer to the true value. Based on the raccoon optimization algorithm, the present invention introduces the Tent chaotic map to initialize the population, improve the population diversity, improve the fitness function in the iterative optimization process, and reduce the error caused by the increase in the number of anchor nodes; the present invention converts the calculation of the position of the unknown node in the third stage of DVHop into a problem of solving function optimization, and uses the improved raccoon optimization algorithm instead of the least squares method, which effectively improves the accuracy of node positioning and can solve the problem of poor positioning results in complex environments in practical applications. The present invention can adapt to wireless sensor networks in practical applications and effectively improve the positioning accuracy of nodes. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative work. Figure 1 It is a schematic flow chart of the method of the present invention; Figure 2 is an anchor node communication radius diagram in an embodiment of the present invention; Figure 3 is a flow chart of the CAODV-Hop algorithm in an embodiment of the present invention; Figure 4 is a graph of average positioning errors of five algorithms under different communication radii in an embodiment of the present invention; Figure 5 is a graph of average positioning errors of five algorithms under different anchor node ratios in the embodiment of the present invention; Figure 6 is a graph of average positioning errors of five algorithms under different total number of nodes in the embodiment of the present invention; Figure 7 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0017] 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.

[0018] Embodiment 1: The following is an introduction to the relevant terms involved in the embodiments of the present application: Raccoon Optimization Algorithm: The Raccoon Optimization Algorithm (COA) is an optimization algorithm based on the hunting behavior of raccoons, proposed in 2022. The algorithm simulates the behavior of raccoons hunting iguanas to find the best solution, and has the characteristics of strong optimization ability and fast convergence speed. The core of the algorithm includes two main stages: initializing the population and hunting and attacking strategies (exploration stage).

[0019] like Figure 1 As shown, a distributed opportunity array radar wireless sensor network positioning method, the method includes the following steps: S101: Initialize the wireless sensors in the opportunity array radar, randomly distribute the wireless sensor nodes, set the communication radius of the wireless sensor nodes, and obtain the minimum number of hops between the wireless sensor nodes based on the communication radius of the wireless sensor nodes and using a multi-communication radius method; The calculation process of obtaining the minimum number of hops between wireless sensor nodes using the multi-communication radius method: Based on the distance vector routing theory, the anchor node broadcasts its own information in the network, including its own label, location coordinates and hop count. The initial value of the hop count is 0. When the node receives and records multiple anchor node information, it takes the smallest hop count as the minimum hop count between the unknown node and the anchor node, adds the hop count to 1, and forwards it to the network. The minimum number of hops is solved by using multiple communication radii. The communication radius is dynamically set and divided into four communication radius parts of different sizes. The minimum number of hops is determined according to the distance between nodes; as shown in expression (1): (1) in, , d are the number of hops and distance between the anchor node and the adjacent node, respectively. The communication radius is 1 / 4R, 2 / 4R, 3 / 4R, and R respectively. When the anchor node communicates with the adjacent node, the number of hops increases in units of 1 / 4R corresponding to the communication radius. The node continues to forward the data packet with a communication radius of 1 / 4R, 2 / 4R, 3 / 4R, and R until all sensor nodes in the network receive the data packet. If the sensor node receives a data packet from the same anchor node, it compares and saves the hop count information with a smaller hop value.

[0020] S102: Acquire the actual distance between the anchor nodes, calculate the average hop distance of the anchor nodes based on the actual distance between the anchor nodes and the minimum number of hops between the wireless sensor nodes, and set the average hop distance of the anchor node closest to the unknown node as the average hop distance of the unknown node; Average hop distance of anchor nodes The calculation process: (2) Among them, Q is the total number of anchor nodes, , Point anchor nodes , The coordinates of for , The minimum hop value between j It is an unknown node.

[0021] S103: The estimated distance between the unknown node and the anchor node is calculated using the average hop distance of the unknown node and the minimum number of hops between the unknown node and the anchor node. The estimated distance between the unknown node and the anchor node is combined with the improved raccoon optimization algorithm to locate the unknown node and obtain the position of the unknown node.

[0022] The estimated distance d between the unknown node and the anchor node ji The calculation process is as follows: (3) Among them, the unknown node j The first hop distance is taken as the own hop distance. , Indicates unknown node j To the anchor node i The minimum number of hops.

[0023] The improved raccoon optimization algorithm determines the position update of the target in the predation stage and the escape stage according to the relationship between the number of individuals i and the population size N, and uses the formula involved and the optimized fitness function. The optimization is terminated when the current iteration number t is greater than the maximum iteration number T, otherwise the iteration number is increased by one, and finally the optimal solution is obtained to determine the unknown node position; The process is as follows: Initialize the population size N and the maximum number of iterations T; Calculate the fitness value of each individual and determine the optimal position to be the location of the prey ; In the first stage of predation, half of the population on the tree is targeted to update individual positions; Update the positions of half of the population under the tree; Make a greedy choice; The second stage is the escape process, updating the individual position; Make a greedy choice; Initialize the population using the Tent map; For fitness function Optimize When the current number of iterations t is greater than the maximum number of iterations T, the optimization process ends. Otherwise, the number of iterations increases by one. Finally, the optimal solution is obtained and the position of the unknown node is determined.

[0024] Tent chaotic mapping is introduced into the Raccoon optimization algorithm to initialize the population and improve the population diversity. The fitness function is improved during the iterative optimization process to reduce the error caused by the increase in the number of anchor nodes. The improved Raccoon optimization algorithm is used to replace the least squares method in the traditional DV-Hop algorithm to estimate the positioning coordinates of unknown nodes.

[0025] The COA algorithm has obvious advantages and strong competitiveness by balancing the use of global search and local search. It is applied in the fields of image classification and parameter optimization and has strong effectiveness.

[0026] (1) Their behavior in attacking and hunting for food. (2) Their behavior in escaping from predators.

[0027] Algorithm initialization process. In the COA algorithm, the position of the individual in the search space determines the value of the decision variable and represents the candidate solution to the problem. The position of the individual in the search space is randomly initialized using formula (4).

[0028] (4) in, is an individual in the search space, , are the lower and upper bounds of the decision variable, respectively. is a random real number between the interval [0,1].

[0029] Theory of mathematical model of the algorithm. The process of updating individual positions in the COA algorithm is based on modeling two natural behaviors of raccoons. These behaviors include the strategies of coatis when hunting and the strategies of coatis when escaping from predators, so they need to be updated in two different stages.

[0030] During the predation phase, the optimal position in the population is the position of the prey ( ), in the theoretical assumption, half of the raccoons are under the tree and the other half are hunting together on the tree. The position of the raccoons on the tree can be estimated using formula (5).

[0031] (5) After the prey lands, the raccoon's position is constantly updated as the prey moves, and the position can be estimated using formulas (6) and (7).

[0032] (6) (7) in, is the population size, is the current iteration number, is the fitness function, belongs to {1,2}; Indicates the updated landing position of the prey; Indicates the j-th dimension variable value of the i-th individual in the current iteration; Latest location of raccoons If the new position of each raccoon optimizes the value of the objective function, then the position is determined; otherwise, the raccoon individual maintains its original position, which is considered a greedy choice, as shown in formula (8).

[0033] (8) in, The latest location of raccoons; is the fitness function; Escape phase. When escaping from a predator, the raccoon puts itself in a safe position and mathematically models its position update, as shown below.

[0034] (9) (10) (11) Among them, r represents a random number in [0,1], is the population size, t is the current number of iterations, T is the maximum number of iterations, , are the upper and lower bounds in the update of the j-th dimension variable; is the latest location of the raccoon. As in the predation stage, when the new location can optimize the objective function value, the current location is determined, otherwise the location remains unchanged, as in formula (8).

[0035] In summary, the calculation process of the COA algorithm is as follows.

[0036] Initialize the population size N and the maximum number of iterations T, calculate the fitness value of each individual, and determine ; In the first stage of the predation process, for half of the population on the tree, the individual positions are updated using formula (5); Use formulas (6) and (7) to update the positions of half of the population under the tree, and use formula (8) for greedy selection; In the second stage of the escape process, the individual positions are updated using formulas (9) and (10), and formula (8) is also used for greedy selection.

[0037] The first stage of the DV-Hop algorithm is optimized, and multiple communication radii are used to solve the minimum number of hops. The communication radius is dynamically set and divided into four communication radius parts of different sizes. The minimum number of hops is determined according to the distance between nodes, as shown in Expression (11).

[0038] (11) in, , d are the number of hops and the distance between the anchor node and the adjacent node, respectively. The communication radius is 1 / 4R, 2 / 4R, 3 / 4R, and R, respectively. When the anchor node communicates with the adjacent node, the number of hops increases gradually in units of 1 / 4R corresponding to the communication radius.

[0039] From the above analysis of the error in the second stage of the DV-Hop algorithm, due to the singleness of the anchor node selection, different anchor nodes will have different effects on unknown nodes. The average hop distance optimization is achieved by using the minimum number of hops between multiple unknown nodes and anchor nodes for weighted operations. Specifically, the average hop distance between several anchor nodes and unknown nodes is weighted to enhance the relationship between the unknown node and the entire communication network, better reflecting the overall status of the system. According to the different influences of anchor nodes at different locations on unknown nodes, weight coefficients are introduced. Since the distance from the anchor node to the unknown node is inversely proportional to the influence it produces, the weight coefficient can be obtained The expression is as follows.

[0040] (12) in, is the weight coefficient, the number of anchor nodes is n, is the minimum number of hops; Use the weight coefficient to calculate the average hop distance of the weighted unknown node The expression is as follows: (13) in, is the average hop distance of unknown nodes after weighting, is the original average hop distance of the anchor node. The estimated distance d between the anchor node and the unknown node can be obtained by expression (13): ij The expression is as follows: (14) Among them, d ij is the estimated distance between the anchor node and the unknown node; is the average hop distance of unknown nodes after weighting; is the estimated distance from unknown node j to anchor node i; Tent mapping can effectively generate relatively uniform initial values. Therefore, in this algorithm, Tent mapping is used to initialize the population. The Tent mapping function The expression is as follows: (15) in is a random number between [0.1], is the control coefficient, when =0.5, the Tent mapping sequence is evenly distributed.

[0041] Using Tent Mapping Function Substituting the random number in formula (4) The following expression can be obtained: (16) in, is an individual in the search space, , are the lower and upper boundaries of the decision variables, respectively. After substitution, the controllability of random distribution is improved, the range of mapping sequence is enlarged, and the population initialization is uniform.

[0042] The improved COA optimization algorithm is used to replace the least square method in the third stage of the DV-Hop algorithm for positioning. The matrix operation problem is transformed into the problem of solving the optimal value.

[0043] The COA algorithm is used for calculation, and the fitness function of the COA algorithm is set as follows: Assume that there are n anchor nodes around the unknown node j in the known network, and their coordinates are , , ……, , because there is an error in estimating the distance, so when solving the distance between the unknown node j and the anchor node i , ,……, Auxiliary error , ……, as follows: (17) The fitness function (18) in the COA algorithm is obtained by solving the equation set. The COA algorithm is used to find the optimal solution, which solves the problem that there may be no solution in the least squares method.

[0044] (18) in, is the fitness function, , are the coordinates of unknown node j and anchor node i respectively; is the estimated distance between j and i; Considering the global communication network, on the one hand, the number of hops from the unknown node to the anchor node is proportional to the number of nodes between the two, resulting in will also increase, resulting in errors in the solution process; On the other hand, considering that the more anchor nodes there are, the more error accumulation there will be, so the fitness function After optimization, we get expression (19): (19) Among them, the hop count of anchor node i and unknown node j is introduced , and the total number n of anchor nodes in the communication network; , are the coordinates of unknown node j and anchor node i respectively; is the estimated distance between j and i; Specifically, the present invention is further described below by way of embodiments: like Figure 2 The communication radius of the anchor node shown in the figure indicates that during the anchor node broadcasting process, an anchor node is determined, and within the circular area formed by the communication radius R, the number of hops between the unknown node and the anchor node is 1 hop. This is completely inconsistent with the actual situation and there is a big difference.

[0045] The CAODV-Hop algorithm process is as follows Figure 3 As shown, the steps are as follows.

[0046] Initialize network nodes and deploy all nodes in the set communication area.

[0047] Solve for the minimum number of hops. Use the multi-channel radius method mentioned above to calculate the minimum number of hops. Average hop distance optimization. The distance between weighted nodes is estimated by equations (12), (13), and (14): Positioning optimization. The improved COA optimization algorithm is used to locate unknown nodes, mainly based on the relationship between the number of individuals i and the population size N, and the involved formulas and optimized fitness functions are used to determine the position update of the target in the predation stage and the escape stage. The optimization is terminated when the current iteration number t is greater than the maximum iteration number T, otherwise the iteration number is increased by one. Finally, the optimal solution is obtained to determine the position of the unknown node.

[0048] In order to accurately evaluate the performance of the COADV-Hop algorithm, the experiment mainly verifies the algorithm by controlling the number of anchor nodes, the total number of nodes, the communication radius and other variables. In similar communication networks, the performance is compared with DV-Hop, the DGSA algorithm based on the simulated annealing method, the SSIDV-Hop algorithm using the squirrel value search algorithm, the SSIDV-Hop algorithm using the improved differential evolution algorithm, and the HICPSO algorithm based on the particle swarm compression method. The test results are evaluated and analyzed to verify the effectiveness of the COADV-Hop algorithm.

[0049] The communication network and parameter settings of the COA algorithm in the experiment are shown in Tables 1 and 2.

[0050] Table 1 WSN parameters Table 2 COA algorithm parameters Experiment 1: Compare the average positioning errors of the algorithm of the present invention, DV-Hop, DGSA, SSIDV-Hop, SSIDV-Hop and HICPSO algorithms under different communication radii.

[0051] In the experiment, within the range of 100×100, the total number of nodes is set to 100, the number of anchor nodes is set to 10, and the control communication radius is increased from 20 m to 50 m in units of 5 m.

[0052] The results are as follows Figure 4 As shown in the figure, with the increase of communication radius, the average positioning error of the proposed algorithm and the comparison algorithm decreases. The communication radius gradually increases, which increases the number of anchor nodes within the communication range, and more data is required to determine the unknown nodes, thereby improving the positioning accuracy of the unknown nodes.

[0053] From the analysis of the data, it can be seen that the average positioning error of the COADV-Hop algorithm is reduced by 52.13%, 25.92%, 34.11% and 37.48% respectively compared with the DV-Hop, DGSA, SSIDV-Hop and DEIDV-Hop algorithms. The communication radius is dynamically designed in the proposed algorithm, so the positioning accuracy of unknown nodes is higher.

[0054] Experiment 2: Compare the average positioning errors of the algorithm of the present invention, DV-Hop, DGSA, SSIDV-Hop, SSIDV-Hop and HICPSO under different anchor node ratios.

[0055] In the experimental network, the total number of nodes is set to 100, the communication radius is 20 m, and the number of anchor nodes increases from 5 to 35 in units of 5.

[0056] The results are as follows Figure 5 As shown in the figure, the degree to which the average positioning error of different algorithms is affected by the proportion of anchor nodes is compared. The larger the proportion of anchor nodes, the lower the average positioning error of the five algorithms. According to the second stage of the algorithm, the increase of anchor nodes can reduce the error of the average hop distance and increase the positioning accuracy of the algorithm. From the analysis of the data, it can be seen that the average positioning error of the COADV-Hop algorithm is reduced by 55.96%, 20.52%, 27.73% and 43.08% respectively compared with the DV-Hop algorithm, DGSA algorithm, SSIDV-Hop algorithm and DEIDV-Hop algorithm.

[0057] Experiment 3: Compare the average positioning error of the algorithm of the present invention with that of DV-Hop, DGSA, SSIDV-Hop, SSIDV-Hop and HICPSO under different total number of nodes.

[0058] In the communication range of 100×100, the proportion of anchor nodes is 10%, the communication radius is 20 m, and the total number of control nodes increases from 100 to 400 in units of 50.

[0059] The results are as follows Figure 6 As shown in the figure, with the increase of the total number of nodes in the communication network, the average positioning errors of the five algorithms after processing show a decreasing trend. The reason is that the increase of the total number of nodes improves the connectivity of the network communication, reduces the distance between nodes, and obtains more data information to help locate unknown nodes.

[0060] From the analysis of the data, we can see that the average positioning error of the COADV-Hop algorithm is reduced by 58.39%, 16.76%, 22.4%, and 30.11% respectively compared with the DV-Hop algorithm, DGSA algorithm, SSIDV-Hop algorithm, and DEIDV-Hop algorithm. Therefore, the COADV-Hop algorithm can ensure a smaller average positioning error with the same total number of nodes.

[0061] The present invention analyzes the factors that cause errors in the traditional DV-Hop algorithm process and optimizes each stage of the algorithm. A distributed opportunity array radar wireless sensor network positioning method is proposed. In the first stage, multiple communication radii are used to determine the number of hops between nodes; in the second stage, weight factors are introduced to calculate the average hop distance; in the third stage, the Tent chaotic map is introduced into the raccoon optimization algorithm to initialize the population, improve the population diversity, improve the fitness function in the iterative optimization process, and reduce the error caused by the increase in the number of anchor nodes. Finally, the raccoon optimization algorithm is used to replace the traditional least squares method to estimate the position of unknown nodes. Comparative experimental simulation verifies the effectiveness of the proposed algorithm.

[0062] Embodiment 2: In the second aspect, as Figure 7 As shown, in order to achieve the above-mentioned purpose, the present invention discloses a distributed opportunity array radar wireless sensor network positioning system, comprising: The hop number acquisition module 11 is used to initialize the wireless sensors in the opportunity array radar, randomly distribute the wireless sensor nodes, set the communication radius of the wireless sensor nodes, and acquire the minimum hop number between the wireless sensor nodes based on the communication radius of the wireless sensor nodes and using a multi-communication radius method; The hop distance acquisition module 12 is used to obtain the actual distance between the anchor nodes, calculate the average hop distance of the anchor nodes based on the actual distance between the anchor nodes and the minimum number of hops between the wireless sensor nodes, and set the average hop distance of the anchor node closest to the unknown node as the average hop distance of the unknown node; The position positioning module 13 is used to calculate the estimated distance between the unknown node and the anchor node using the average hop distance of the unknown node and the minimum number of hops between the unknown node and the anchor node. The estimated distance between the unknown node and the anchor node is combined with the improved raccoon optimization algorithm to locate the unknown node to obtain the position of the unknown node.

[0063] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is used to implement one or more instructions, specifically for loading and executing one or more instructions in a computer storage medium to implement the above method.

[0064] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium, on which a computer program is stored, and the computer program is executed by the processor to execute the above method. The storage medium can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0065] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0066] The above shows and describes the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, the present disclosure may have various changes and improvements, and these changes and improvements fall within the scope of the present disclosure to be protected.

Claims

1. A distributed opportunity array radar wireless sensor network positioning method, characterized in that: The method comprises the following steps: Initialize the wireless sensors in the opportunity array radar, randomly distribute the wireless sensor nodes, set the communication radius of the wireless sensor nodes, and obtain the minimum hop number between the wireless sensor nodes based on the communication radius of the wireless sensor nodes and using the multi-communication radius method; The actual distance between the anchor nodes is obtained, and the average hop distance of the anchor nodes is calculated based on the actual distance between the anchor nodes and the minimum number of hops between the wireless sensor nodes, and the average hop distance of the anchor node closest to the unknown node is set as the average hop distance of the unknown node; The estimated distance between the unknown node and the anchor node is calculated using the average hop distance of the unknown node and the minimum number of hops between the unknown node and the anchor node. The estimated distance between the unknown node and the anchor node is combined with the improved raccoon optimization algorithm to locate the unknown node and obtain the position of the unknown node.

2. A distributed opportunity array radar wireless sensor network positioning method according to claim 1, characterized in that: The calculation process of using the multi-communication radius method to obtain the minimum number of hops between wireless sensor nodes is as follows: Based on the distance vector routing theory, the anchor node broadcasts its own information in the network, including its own label, location coordinates and hop count. The initial value of the hop count is 0. When the node receives and records multiple anchor node information, it takes the smallest hop count as the minimum hop count between the unknown node and the anchor node, adds the hop count to 1, and forwards it to the network. The minimum number of hops is solved by using multiple communication radii. The communication radius is dynamically set and divided into four communication radius parts of different sizes. The minimum number of hops is determined according to the distance between nodes; as shown in expression (1): (1) in, , d are the number of hops and distance between the anchor node and the adjacent node, respectively, and the communication radius is 1 / 4R, 2 / 4R, 3 / 4R, and R respectively; When an anchor node communicates with an adjacent node, the number of hops increases in units of 1 / 4R corresponding to the communication radius; The node continues to forward the data packet with a communication radius of 1 / 4R, 2 / 4R, 3 / 4R, and R until all sensor nodes in the network receive the data packet. If the sensor node receives a data packet from the same anchor node, it compares and saves the hop count information with a smaller hop value.

3. The distributed opportunity array radar wireless sensor network positioning method according to claim 1 is characterized in that: The average hop distance of the anchor node The calculation process: (2) in, is the average hop distance of the anchor nodes, Q is the total number of anchor nodes, , Point anchor nodes , The coordinates of for , The minimum hop value between 4. The distributed opportunity array radar wireless sensor network positioning method according to claim 1 is characterized in that: The estimated distance d between the unknown node and the anchor node ji The calculation process is as follows: (3) Among them, d ji is the estimated distance between the unknown node and the anchor node, the unknown node j The first hop distance is taken as the own hop distance. , Indicates unknown node j To the anchor node i The minimum number of hops.

5. The distributed opportunity array radar wireless sensor network positioning method according to claim 1 is characterized in that: The improved raccoon optimization algorithm determines the position update of the target in the predation stage and the escape stage according to the relationship between the number of individuals i and the population size N, and uses the formula involved and the optimized fitness function. The optimization is terminated when the current iteration number t is greater than the maximum iteration number T, otherwise the iteration number is increased by one, and finally the optimal solution is obtained to determine the unknown node position; The process is as follows: Initialize the population size N and the maximum number of iterations T; Calculate the fitness value of each individual and determine the optimal position to be the location of the prey ; In the first stage of predation, half of the population on the tree is targeted to update individual positions; Update the positions of half of the population under the tree; Make a greedy choice; The second stage is the escape process, updating the individual position; Make a greedy choice; Initialize the population using the Tent map; For fitness function Optimize When the current number of iterations t is greater than the maximum number of iterations T, the optimization process ends. Otherwise, the number of iterations increases by one. Finally, the optimal solution is obtained and the position of the unknown node is determined.

6. A distributed opportunity array radar wireless sensor network positioning method according to claim 5, characterized in that: In the improved raccoon optimization algorithm, the position of the individual in the search space is randomly initialized using formula (4): (4) in, is an individual in the search space, , are the lower and upper bounds of the decision variable, respectively. is a random real number between the interval [0,1]; During the predation phase, the optimal position in the population is the position of the prey In the theoretical assumption, half of the raccoons are under the tree and the other half are hunting together on the tree. Formula (5) is used to estimate the position of the raccoons on the tree: (5) in, is the optimal position of prey in the population, Indicates the j-th dimension variable value of the i-th individual in the current iteration; The latest location of raccoons; is a random real number between the interval [0,1]; is the population size, is the current iteration number; After the prey lands, the raccoon's position is constantly updated as the prey moves, and the position is estimated using formulas (6) and (7): (6) (7) in, is the population size, is the current iteration number, is the fitness function, belongs to {1,2}; Indicates the updated landing position of the prey; Indicates the j-th dimension variable value of the i-th individual in the current iteration; The latest location of raccoons; If the new position of each raccoon optimizes the value of the objective function, then the position is determined. Otherwise, the raccoon individual maintains its original position, which is considered a greedy choice, as shown in formula (8): (8) in, The latest location of raccoons; is the fitness function; Escape phase: When escaping from the predator, the raccoon puts itself in a safe position and mathematically models the position update of the raccoon. The formula is as follows: (9) (10) (11) Where r represents a random number in [0,1], t is the current iteration number, and T is the maximum iteration number. , are the upper and lower bounds in the update of the j-th dimension variable; The latest location of raccoons; is the population size; as in the predation stage, when the new position optimizes the objective function value, the position at this time is determined, otherwise the position remains unchanged, the same as formula (8).

7. A distributed opportunity array radar wireless sensor network positioning method according to claim 6, characterized in that: The optimization and improvement process of the improved raccoon optimization algorithm is as follows: Use multiple communication radius to obtain the minimum hop count information in the algorithm; Introduce weighted coefficients. The weighted coefficients of known nodes and unknown nodes are inversely proportional to the distance between the two types of nodes. Use the weighted coefficients to introduce the hop count relationship between multiple nodes in the network and optimize the average hop distance of network nodes. Tent chaotic mapping is introduced into the Raccoon optimization algorithm to initialize the population, and the fitness function is improved during the iterative optimization process. The improved Raccoon optimization algorithm is used to replace the least squares method in the Raccoon optimization DV-Hop algorithm to estimate the positioning coordinates of unknown nodes.

8. A distributed opportunity array radar wireless sensor network positioning system, using a distributed opportunity array radar wireless sensor network positioning method according to any one of claims 1 to 7, characterized in that: include: The hop number acquisition module is used to initialize the wireless sensors in the opportunity array radar, randomly distribute the wireless sensor nodes, set the communication radius of the wireless sensor nodes, and obtain the minimum hop number between the wireless sensor nodes based on the communication radius of the wireless sensor nodes and using the multi-communication radius method; The hop distance acquisition module is used to obtain the actual distance between the anchor nodes, calculate the average hop distance of the anchor nodes based on the actual distance between the anchor nodes and the minimum number of hops between the wireless sensor nodes, and set the average hop distance of the anchor node closest to the unknown node as the average hop distance of the unknown node; The position positioning module is used to calculate the estimated distance between the unknown node and the anchor node using the average hop distance of the unknown node and the minimum number of hops between the unknown node and the anchor node. The estimated distance between the unknown node and the anchor node is combined with the improved raccoon optimization algorithm to locate the unknown node and obtain the position of the unknown node.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: The memory stores a computer program that can be run on the processor. When the processor loads and executes the computer program, a distributed opportunity array radar wireless sensor network positioning method according to any one of claims 1 to 7 is adopted.

10. A computer-readable storage medium having a computer program stored therein, characterized in that: When the computer program is loaded and executed by the processor, a distributed opportunity array radar wireless sensor network positioning method according to any one of claims 1 to 7 is adopted.

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