A WSN node localization method based on similar paths and quantum raccoon mechanism

Abstract

The present invention discloses a WSN node positioning method based on similar paths and a quantum raccoon mechanism. The method first establishes a distance estimation model based on similar paths; then establishes a node positioning model based on hop distance correction to initiate positioning; initializes a quantum raccoon swarm and sets relevant parameters; defines and calculates the fitness value of each quantum raccoon's quantum position, and determines the optimal quantum position of the quantum raccoon swarm; the quantum raccoons execute hunting and attack modes, using quantum rotation angles to evolve the quantum positions of the quantum raccoons in these hunting and attack modes; uses a selection mechanism to select the quantum position of the next generation of quantum raccoons and the optimal quantum position of the quantum raccoon swarm; determines the termination of the quantum raccoon mechanism evolution to achieve target node positioning; and outputs the positioning results of all target nodes. This method overcomes the application limitations of traditional positioning methods when the network topology is anisotropic, improves its applicability in anisotropic networks, and can be applied to actual static wireless sensor networks.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to a WSN node positioning method based on similar paths and a quantum raccoon mechanism. Background Art

[0002] Wireless sensor networks (WSNs), an emerging field in wireless communication technology, enable the integration of computing, communication, and sensing functions into a single device. These networks are gradually becoming integrated and scalable. WSNs are multi-hop, self-organizing networks composed of a large number of micro-sensor nodes equipped with sensing, computing, and communication capabilities. They have a wide range of applications in environmental monitoring, industrial transportation, and indoor surveillance. While wireless sensor networks perceive environmental changes, obtaining the specific location coordinates of the event is crucial. Without the location coordinates, the monitored information is meaningless. Therefore, node positioning technology is a key technology in WSNs. By using a small number of wireless sensors equipped with Global Positioning System receivers to accurately locate all wireless sensors within a monitoring area, WSNs are the foundation for wireless sensor networks to provide secure and reliable communication and control services at a low cost.

[0003] Wireless sensor network node positioning methods are divided into ranging positioning and non-ranging positioning according to whether additional hardware measurement equipment is required. The ranging positioning method mainly includes the following steps: first, using the hardware equipment provided by the sensor to measure the angle or distance, secondly, using the node positioning calculation method to determine the coordinates of the node to be located, and finally obtaining the final coordinates through optimization and correction. The ranging positioning method has high positioning accuracy, but is limited by hardware volume, cost and network energy consumption. The ranging positioning methods mainly include signal arrival time positioning TOA, signal arrival time difference positioning TDOA and signal arrival angle positioning AOA. Non-range-based positioning is to calculate the coordinates of the node by estimating the distance between the node to be located and the known nodes based on technologies such as inter-node connectivity and routing information exchange. It does not require additional measurement hardware equipment, can reduce equipment costs and network energy consumption, and has a fast response, but the positioning accuracy is low. Non-range-based positioning methods mainly include approximate triangle interior point positioning APIT, distance vector hopping positioning DV-Hop and centroid positioning. DV-Hop positioning is currently the most commonly used non-range-based positioning technology. It can achieve accurate node positioning without additional hardware equipment, and provides a good model design for the research on improvement strategies related to wireless sensor network positioning methods.

[0004] Through searching the existing technical literature, it was found that Li Xinchun et al. used particle swarm optimization to calculate the coordinates of the positioning node in the "Research on DV-Hop Positioning Algorithm Based on Particle Swarm Optimization" published in "Measurement and Control Technology" (2017, 36(01):84-87+91), which reduced the error generated in the final calculation. Li Guangfei et al. used quantum tunneling effect to penetrate the energy barrier from the local optimum to the global optimum in the "Wireless Sensor Network Node Positioning Based on Quantum Annealing Algorithm" published in "Journal of Yunnan University" (2019, 41(S1):27-32), which effectively improved the calculation speed. Chai et al. proposed an improved DV-Hop positioning method based on the parallel whale optimization algorithm in "Aparallel WOA with two communication strategies applied in DV-Hop localization method". This method includes two inter-group information exchange strategies, which greatly enhances the global search capability and population diversity of the original whale optimization algorithm WOA, and can be well used to optimize the positioning of wireless sensor network nodes. In "DV-Hop-based range-free localization algorithm for wireless sensor networks using runner-root optimization," Kanwar et al. proposed an improved DV-Hop localization method based on the invasive weed algorithm. This method introduces a correction factor to modify the hop count of anchor nodes. By calculating the hop count of all anchor nodes to the target node, the communication between the target node and anchor nodes is reduced. To demonstrate the applicability of this localization method in anisotropic networks, they also considered a radio irregularity model to overcome the existing shortcomings of traditional DV-Hop. This localization method can minimize positioning error and computation time. A literature search reveals that existing non-ranging node localization methods for wireless sensor networks face technical limitations when the network topology is anisotropic, making it difficult for the network state to meet the desired ideal state. This leads to poor node localization accuracy and robustness. A simple, efficient, and adaptable node localization method is lacking. Summary of the Invention

[0005] The purpose of the present invention is to provide a WSN node positioning method based on similar paths and quantum raccoon mechanism. In the distance estimation stage, the target node determines its similar anchor node through the maximum similar path degree, and synchronously or asynchronously utilizes the average hop distance information of neighboring anchor nodes and similar anchor nodes; in the position calculation stage, the quantum raccoon mechanism is used to calculate the position, which is inspired by the hunting and attack mode of raccoons and combines the quantum rotation angle to evolve the quantum raccoon quantum state, which not only expands the application limitations but also achieves precise positioning.

[0006] The purpose of the present invention is achieved through the following technical solutions:

[0007] A WSN node positioning method based on similar paths and quantum raccoon mechanism, the specific steps are as follows:

[0008] Step 1: Establish a distance estimation model based on similar paths;

[0009] Step 2: Establish a node positioning model based on hop distance correction and start positioning;

[0010] Step 3: Initialize the quantum raccoon group and set relevant parameters;

[0011] Step 4: Define and calculate the fitness value of each quantum raccoon quantum position, and determine the optimal quantum position of the quantum raccoon group;

[0012] Step 5: The quantum raccoon executes the hunting and attack modes, and uses the quantum rotation angle to evolve the quantum position of the quantum raccoon in the hunting and attack modes;

[0013] Step 6: Use the selection mechanism to select the quantum position of the next generation of quantum raccoons and the optimal quantum position of the quantum raccoon group;

[0014] Step 7: The quantum raccoon mechanism evolution terminates and determines the location of the target node.

[0015] Step 8: Positioning termination judgment, output the positioning results of all target nodes.

[0016] Furthermore, the step 1 of establishing a distance estimation model based on similar paths is specifically as follows:

[0017] In a wireless sensor network, the number of nodes is set to N, the beacon ratio is η, and the number of anchor nodes is The target number of nodes is in, round(·) is the nearest integer function, the anchor node position coordinate set Represents the position coordinate vector of the i′th anchor node, x i′ and y i′ Represent the horizontal and vertical coordinates of the position of the i′th anchor node, i′ is the anchor node number, Anchor node pair minimum hop count matrix Among them, i′,j′ is the minimum hop count between the i′th and j′th anchor node pair, ξ is infinite, when o i′,j′ = 0, it means the two anchor nodes coincide; when o i′,j′ =ξ, it means that the two anchor nodes cannot communicate, j′ is the anchor node number, The average hop distance of the i′th anchor node The text description is the sum of the distances between other anchor nodes and this anchor node divided by the sum of the minimum hops of the anchor node pair, ∑ is the accumulation symbol, σ is the accumulation index,

[0018] All anchor nodes calculate their own average hop distance and broadcast their own average hop distance and hop count information. The target node retains the average hop distance and hop count information of all reachable anchor nodes. The reachable anchor nodes are divided into two types: neighbor anchor nodes and similar anchor nodes. The neighbor anchor node is the anchor node with the minimum hop count to the target node. The neighbor anchor node takes its average hop distance as the average hop distance of the target node. i″ is the target node number, Similar anchor nodes are anchor nodes with the largest path similarity to the target node. Among them, A is the shortest path set from the target node to the anchor node, B is the shortest path set from other anchor nodes to the anchor node, and A∩B represents the same path set passed by the shortest path sets A and B. Represents the number of elements contained in the path set, then the path with the greatest similarity is the maximum similarity path, and the value obtained by dividing the distance of this path by the number of hops is taken as the average hop distance of this path, and the similar anchor node takes it as the similar hop distance of the target node.

[0019] The relative estimated distance from the target node to the anchor node in, Indicates the minimum number of hops between the target node and the anchor node, obtained according to the distance vector routing mechanism; Indicates the optimal hop distance from the target node to the anchor node, according to the threshold Make the following selections: Similarity coefficient μ i″,i′ is the maximum path similarity, the threshold When the similarity coefficient μ i″,i′ Greater than threshold When the optimal jump distance is the similarity jump distance, when the similarity coefficient μ i″,i′ Less than or equal to the threshold When the optimal jump distance is the average hop distance. Further, the step 2 is specifically as follows:

[0020] The target node's positioning coordinate set represents the positioning coordinate vector of the i″th target node, and Respectively represent the horizontal and vertical coordinates of the i″th target node positioning position, The node communication radius is γ, and the positioning error function of the i-th target node is set as The standard positioning error function of the i″th target node Initialize the target node to be located i″ to 1 and locate them one by one.

[0021] Furthermore, the step three is specifically as follows:

[0022] Assume that the size of the quantum raccoon group is h, the maximum number of iterations is G, the iteration number is g, g = 1, 2, ..., G, and at the g-th iteration, the quantum position of the i-th quantum raccoon in the q-dimensional search space is In the first generation, g = 1, and the quantum position of the quantum raccoon in each dimension of the search space is initialized to a uniform random number in the interval [0, 1].

[0023] Furthermore, the step 4 is specifically as follows:

[0024] At the g-th iteration, each dimension of all quantum raccoon quantum positions is mapped to the corresponding solution space to obtain the mapping state of the quantum position The mapping equation is defined as i=1,2,…,h,j=1,2,…,q,where and are the upper and lower limits of the j-th dimension of the quantum raccoon, is the j-dimensional quantum position of the i-th quantum raccoon, and the mapping state of the i-th quantum raccoon quantum position is substituted into the positioning error function of the i″th target node to obtain the corresponding fitness value The superscript T represents transposition; the fitness values ​​of all quantum raccoon quantum positions are arranged from small to large, and the quantum position with the smallest fitness value is recorded as the optimal quantum position of the quantum raccoon group. ρ is the quantum raccoon tag with the smallest fitness in the group.

[0025] Furthermore, the step five is specifically as follows:

[0026] Define the update equation for the j-dimensional quantum position of the i-th quantum raccoon in hunting and attack modes Hunting mode definition Definition in attack mode in, is the j-dimensional simulated quantum rotation angle of the i-th quantum raccoon, e1 is the forcing coefficient, γ1 is a random number uniformly distributed between [0,1], is the optimal j-th quantum position of the quantum raccoon group, e2 is the attack coefficient, γ2 is a random number uniformly distributed between [-1,1], i = 1, 2, ..., h, j = 1, 2, ..., q; the newly generated quantum position after the i-th quantum raccoon executes the hunting and attack mode

[0027] Furthermore, the step six is ​​specifically as follows:

[0028] Calculate the fitness values ​​of all quantum raccoons in the initial and newly generated quantum positions, apply the greedy strategy, and select the quantum positions from the set Select h quantum positions with smaller fitness values ​​as the quantum positions of the next generation of quantum raccoons i=1,2,…,h; Arrange the fitness values ​​of the quantum positions of the g+1 generation quantum raccoons from small to large, and update the optimal quantum position of the quantum raccoon group to

[0029] Furthermore, the step seven is specifically as follows:

[0030] If the evolution of the quantum raccoon mechanism has not reached the maximum number of iterations G, then set g = g + 1 and return to step 5 to continue evolving the quantum position of the quantum raccoon. If the maximum number of iterations G is reached, the evolution is terminated and the mapping state of the optimal quantum position of the quantum raccoon group is used as the positioning position coordinate vector of the i′th target node to achieve the positioning of the target node.

[0031] Furthermore, the step eight is specifically as follows:

[0032] Determine whether all target nodes are located, that is, whether If not, set i″=i″+1 and return to step 3 to continue positioning the next target node; if satisfied, all target nodes are positioned, the positioning results are output, and positioning is completed.

[0033] The beneficial effects of the present invention are:

[0034] Compared with existing technologies, the proposed WSN node localization method based on similar paths and the quantum raccoon mechanism overcomes the limitations of traditional localization methods in anisotropic network topologies, improving its applicability in anisotropic networks and enabling its application in real-world static wireless sensor networks. To improve its ability to handle network fluctuations, the target node adopts a hop-distance correction strategy, leveraging the average hop distance information of neighboring anchor nodes and similar anchor nodes to estimate the distance, resulting in more accurate distance estimates from the target node to the anchor node. To enhance the accuracy of position calculations, a quantum raccoon mechanism is designed and implemented, demonstrating its strong optimization capabilities. Simulation experiments demonstrate that this node localization method demonstrates robustness and accuracy when the network undergoes changes in factors such as the number of nodes, monitoring area, communication radius, and beacon ratio. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1This is a schematic diagram of the WSN node positioning method based on similar paths and quantum raccoon mechanism proposed in the present invention.

[0036] Figure 2 This is the relationship curve between the standard positioning error and the sensor network parameters-node number.

[0037] Figure 3 It is the relationship curve between standard positioning error and sensor network parameters-monitoring area.

[0038] Figure 4 It is the relationship curve between standard positioning error and sensor network parameter-node communication radius.

[0039] Figure 5 It is the curve showing the relationship between the standard positioning error and the sensor network parameter-beacon ratio. DETAILED DESCRIPTION

[0040] The present invention will be further described below with reference to the accompanying drawings.

[0041] according to Figure 1 The present invention provides a WSN node positioning method based on similar paths and quantum raccoon mechanism, and the specific steps are as follows:

[0042] Step 1: Establish a distance estimation model based on similar paths.

[0043] In the early stage of operation of the wireless sensor network, the anchor node initiates the transmission of the positioning message to the wireless sensor network according to the positioning demand instruction issued by the sink node, including the distinguishing header, anchor node number, anchor node coordinates, path point number and the minimum hop number with an initial value of 1. This positioning message is broadcast in the network and lasts for a certain period of time. The positioning message can only be received and forwarded normally within the communication range of the node. The hop number is increased by 1 during forwarding. For the positioning message of the same anchor node, the receiving node only retains the message with the minimum hop number and destroys other messages. After the network positioning message broadcast is completed, all nodes record the minimum hop number between all communication reachable anchor nodes in the memory. The above mechanism is called distance vector routing mechanism.

[0044] In a wireless sensor network, the number of nodes is set to N, the beacon ratio is η, and the number of anchor nodes is The target number of nodes is in, round(·) is the nearest integer function, the node communication radius is γ, and the anchor node position coordinate set is represents the position coordinate vector of the i′th anchor node, Anchor node pair minimum hop count matrix Among them, i′,j′is the minimum hop count between the i′th and j′th anchor node pair, ξ is infinite, when o i′,j′ = 0, it means the two anchor nodes coincide; when o i′,j′ =ξ, it means that the two anchor nodes cannot communicate. The average hop distance of the i′th anchor node The text description is the sum of the distances between other anchor nodes and this anchor node divided by the sum of the minimum hops between anchor nodes, σ is the cumulative index,

[0045] All anchor nodes calculate their own average hop distance and broadcast their own average hop distance and hop count information. The target node retains the average hop distance and hop count information of all reachable anchor nodes. The reachable anchor nodes are divided into two types: neighbor anchor nodes and similar anchor nodes. The neighbor anchor node is the anchor node with the minimum hop count to the target node. The neighbor anchor node takes its average hop distance as the average hop distance of the target node. Similar anchor nodes are anchor nodes with the largest path similarity to the target node. Among them, A is the shortest path set from the target node to the anchor node, B is the shortest path set from other anchor nodes to the anchor node, and A∩B represents the same path set passed by the shortest path sets A and B. Represents the number of elements contained in the path set, then the path with the greatest similarity is the maximum similarity path, and the value obtained by dividing the distance of this path by the number of hops is taken as the average hop distance of this path, and the similar anchor node takes it as the similar hop distance of the target node.

[0046] The relative estimated distance from the target node to the anchor node in, Indicates the minimum number of hops between the target node and the anchor node, obtained according to the distance vector routing mechanism; Indicates the optimal hop distance from the target node to the anchor node, according to the threshold Make the following selections: Similarity coefficient μ i″,i′ is the maximum path similarity, When the similarity coefficient μ i″,i′ Greater than threshold When the optimal jump distance is the similarity jump distance, when the similarity coefficient μ i″,i′ Less than or equal to the threshold When the optimal jump distance is the average hop distance.

[0047] Step 2: Establish a node positioning model based on hop distance correction and start positioning.

[0048] The target node's positioning coordinate set represents the positioning coordinate vector of the i″th target node, Set the positioning error function of the i″th target node The standard positioning error function of the i″th target node Initialize the target node to be located i″ to 1 and locate them one by one.

[0049] Step 3: Initialize the quantum raccoon swarm and set relevant parameters.

[0050] Assume that the size of the quantum raccoon group is h, the maximum number of iterations is G, the iteration number is g, g = 1, 2, ..., G, and at the g-th iteration, the quantum position of the i-th quantum raccoon in the q-dimensional search space is In the first generation, g = 1, and the quantum position of the quantum raccoon in each dimension of the search space is initialized to a uniform random number in the interval [0, 1].

[0051] Step 4: Define and calculate the fitness value of each quantum raccoon quantum position, and determine the optimal quantum position of the quantum raccoon group.

[0052] At the g-th iteration, each dimension of all quantum raccoon quantum positions is mapped to the corresponding solution space to obtain the mapping state of the quantum position The mapping equation is defined as i=1,2,...,h,j=1,2,...,q,where, and are the upper and lower limits of the j-th dimension of the quantum raccoon, respectively. Substituting the mapping state of the quantum position of the i-th quantum raccoon into the positioning error function of the i″th target node, the corresponding fitness value is obtained. The superscript T represents transposition. Arrange the fitness values ​​of all quantum raccoon quantum positions from small to large, and the quantum position with the smallest fitness value is recorded as the optimal quantum position of the quantum raccoon group.

[0053] Step 5: The quantum raccoon executes hunting and attacking modes, and uses the quantum rotation angle to evolve the quantum position of the quantum raccoon in hunting and attacking modes.

[0054] Define the update equation for the j-dimensional quantum position of the i-th quantum raccoon in hunting and attack modes Hunting mode definition Definition in attack mode in, is the j-dimensional simulated quantum rotation angle of the i-th quantum raccoon, e1 is the forcing coefficient, γ1 is a random number uniformly distributed between [0,1], e2 is the attack coefficient, γ2 is a random number uniformly distributed between [-1,1], i=1,2,...,h,j=1,2,...,q. After the i-th quantum raccoon executes the hunting and attack modes, its newly generated quantum position

[0055] Step 6: Use the selection mechanism to select the quantum position of the next generation of quantum raccoons and the optimal quantum position of the quantum raccoon group.

[0056] Calculate the fitness values ​​of all quantum raccoons in the initial and newly generated quantum positions, apply the greedy strategy, and select the quantum positions from the set Select h quantum positions with smaller fitness values ​​as the quantum positions of the next generation of quantum raccoons i=1,2,…,h. Arrange the fitness values ​​of the quantum positions of the g+1 generation quantum raccoons from small to large, and update the optimal quantum position of the quantum raccoon group to

[0057] Step 7: The quantum raccoon mechanism evolution terminates and determines the location of the target node.

[0058] If the evolution of the quantum raccoon mechanism has not reached the maximum number of iterations G, then set g = g + 1 and return to step 5 to continue evolving the quantum position of the quantum raccoon. If the maximum number of iterations G is reached, the evolution is terminated and the mapping state of the optimal quantum position of the quantum raccoon group is used as the positioning position coordinate vector of the i′th target node to achieve the positioning of the target node.

[0059] Step 8: Positioning termination judgment, output the positioning results of all target nodes.

[0060] Determine whether all target nodes are located, that is, whether If not, set i″=i″+1 and return to step 3 to continue positioning the next target node; if satisfied, all target nodes are positioned, the positioning results are output, and positioning is completed.

[0061] The wireless sensor network of the present invention is deployed in a two-dimensional plane with a monitoring area of ​​100m x 100m. All sensor nodes have the same initial conditions in terms of computing, communication, and structure. The network parameters (number of nodes, monitoring area, node communication radius, and beacon ratio) are varied to simulate different positioning scenarios. The impact of these parameters on node positioning is analyzed, specifically the following four positioning scenarios. Scenario 1: Node communication radius γ = 30m, beacon ratio η = 0.2, monitoring area 100m × 100m, and the number of nodes N increased from 80 to 120; Scenario 2: Node number N = 150, beacon ratio η = 0.2, node communication radius γ = 30m, and monitoring area increased from 100m × 100m to 140m × 140m; Scenario 3: Node number N = 120, beacon ratio η = 0.2, monitoring area 100m × 100m, and node communication radius γ increased from 30m to 50m; Scenario 4: Node number N = 150, node communication radius γ = 30m, monitoring area 100m × 100m, and beacon ratio η increased from 0.1 to 0.5. In the above positioning scenarios, ξ = 1000 is set, and the threshold

[0062] exist Figure 2-Figure 5 In this paper, the WSN node localization method based on similar paths and quantum raccoon mechanism proposed in this invention is denoted as QCOA-DV-Hop; the improved DV-Hop localization method based on invasive weed algorithm is denoted as IWO-DV-Hop, specifically referring to "DV-Hop-based range-free localization algorithm for wireless sensor network using runner-root optimization" published by Kanwar et al.; the improved DV-Hop localization method based on parallel whale optimization algorithm is denoted as EWO-DV-Hop, specifically referring to "A parallel WOA with two communication strategies applied in DV-Hop localization method" published by Chai et al. In IWO-DV-Hop and EWO-DV-Hop, the group size and maximum number of iterations are set to 20 and 100 respectively. In QCOA-DV-Hop, the quantum raccoon group size h = 20, the maximum number of iterations G = 100, the forcing coefficient e1 = -0.0001, the attack coefficient e2 = 0.1, and the dimension upper limit are set to 0. Dimension lower limit j=1,2,…,q,q=2. Figure 2 and Figure 3It can be seen that changes in the number of nodes and monitoring areas will have a certain impact on network connectivity, but since sensor nodes are randomly deployed, this impact is uncertain; Figure 4 It can be seen that when the node communication radius is low, the network connectivity is poor and accurate positioning cannot be achieved. As the node communication radius continues to expand, the network connectivity and positioning accuracy are improved. Figure 5 As the beacon ratio increases, the average hop distance information of similar anchor nodes becomes more accurate, and positioning accuracy improves accordingly. Simulation results show that the proposed WSN node localization method based on similar paths and the quantum raccoon mechanism outperforms the benchmark positioning technology in positioning accuracy and robustness, has more reliable processing capabilities for network changes, and can be applied to practical wireless sensor network engineering.

[0063] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A WSN node positioning method based on similar paths and quantum raccoon mechanism, characterized by: The specific steps are as follows: Step 1: Establish a distance estimation model based on similar paths; Step 2: Establish a node positioning model based on hop distance correction and start positioning; Step 3: Initialize the quantum raccoon group and set relevant parameters; Step 4: Define and calculate the fitness value of each quantum raccoon quantum position, and determine the optimal quantum position of the quantum raccoon group; Step 5: The quantum raccoon executes the hunting and attack modes, and uses the quantum rotation angle to evolve the quantum position of the quantum raccoon in the hunting and attack modes; Step 6: Use the selection mechanism to select the quantum position of the next generation of quantum raccoons and the optimal quantum position of the quantum raccoon group; Step 7: The quantum raccoon mechanism evolution terminates and determines the location of the target node. Step 8: Positioning termination judgment, output the positioning results of all target nodes.

2. A WSN node positioning method based on similar paths and quantum raccoon mechanism according to claim 1, characterized in that: The step 1 of establishing a distance estimation model based on similar paths is specifically as follows: In a wireless sensor network, the number of nodes is set to N, the beacon ratio is η, and the number of anchor nodes is The target number of nodes is in, round(·) is the nearest integer function, the anchor node position coordinate set Represents the position coordinate vector of the i′th anchor node, x i′ and y i′ Respectively represent the horizontal and vertical coordinates of the position of the i′th anchor node, i′ is the anchor node number, Anchor node pair minimum hop count matrix Among them, i′,j′ is the minimum hop count between the i′th and j′th anchor node pair, ξ is infinite, when o i′,j′ = 0, it means the two anchor nodes coincide; when o i′,j′ =ξ, it means that the two anchor nodes cannot communicate, j′ is the anchor node number, The average hop distance of the i′th anchor node The text description is the sum of the distances between other anchor nodes and this anchor node divided by the sum of the minimum hops of the anchor node pair, ∑ is the accumulation symbol, σ is the accumulation index, All anchor nodes calculate their own average hop distance and broadcast their own average hop distance and hop count information. The target node retains the average hop distance and hop count information of all reachable anchor nodes. The reachable anchor nodes are divided into two types: neighbor anchor nodes and similar anchor nodes. The neighbor anchor node is the anchor node with the minimum hop count to the target node. The neighbor anchor node takes its average hop distance as the average hop distance of the target node. i″ is the target node number, Similar anchor nodes are anchor nodes with the largest path similarity to the target node. Among them, A is the shortest path set from the target node to the anchor node, B is the shortest path set from other anchor nodes to the anchor node, and A∩B represents the same path set passed by the shortest path sets A and B. Represents the number of elements contained in the path set, then the path with the greatest similarity is the maximum similarity path, and the value obtained by dividing the distance of this path by the number of hops is taken as the average hop distance of this path, and the similar anchor node takes it as the similar hop distance of the target node. The relative estimated distance from the target node to the anchor node in, Indicates the minimum number of hops between the target node and the anchor node, obtained according to the distance vector routing mechanism; Indicates the optimal hop distance from the target node to the anchor node, based on the threshold Make the following selections: Similarity coefficient μ i″,i′ is the maximum path similarity, the threshold When the similarity coefficient μ i″,i′ Greater than threshold When the optimal jump distance is the similarity jump distance, when the similarity coefficient μ i″,i′ Less than or equal to the threshold When the optimal jump distance is the average hop distance.

3. The WSN node positioning method based on similar paths and quantum raccoon mechanism according to claim 1 is characterized in that: The step 2 is specifically as follows: The target node's positioning coordinate set Represents the positioning coordinate vector of the i″th target node, and Respectively represent the horizontal and vertical coordinates of the i″th target node positioning position, The node communication radius is γ, and the positioning error function of the i-th target node is set as The standard positioning error function of the i″th target node Initialize the target node to be located i″ to 1 and locate them one by one.

4. The WSN node positioning method based on similar paths and quantum raccoon mechanism according to claim 1 is characterized in that: The step three is specifically as follows: Assume that the size of the quantum raccoon group is h, the maximum number of iterations is G, the iteration number is g, g = 1, 2, ..., G, and at the g-th iteration, the quantum position of the i-th quantum raccoon in the q-dimensional search space is In the first generation, g = 1, and the quantum position of the quantum raccoon in each dimension of the search space is initialized to a uniform random number in the interval [0, 1].

5. The WSN node positioning method based on similar paths and quantum raccoon mechanism according to claim 1 is characterized in that: The step 4 is specifically as follows: At the g-th iteration, each dimension of all quantum raccoon quantum positions is mapped to the corresponding solution space to obtain the mapping state of the quantum position The mapping equation is defined as in, and are the upper and lower limits of the j-th dimension of the quantum raccoon, is the j-dimensional quantum position of the i-th quantum raccoon, and the mapping state of the i-th quantum raccoon quantum position is substituted into the positioning error function of the i″th target node to obtain the corresponding fitness value The superscript T represents transposition; the fitness values ​​of all quantum raccoon quantum positions are arranged from small to large, and the quantum position with the smallest fitness value is recorded as the optimal quantum position of the quantum raccoon group. ρ is the quantum raccoon tag with the smallest fitness in the group.

6. The WSN node positioning method based on similar paths and quantum raccoon mechanism according to claim 1 is characterized in that: The step five is specifically as follows: Define the update equation for the j-dimensional quantum position of the i-th quantum raccoon in hunting and attack modes Hunting mode definition Definition in attack mode in, is the j-dimensional simulated quantum rotation angle of the i-th quantum raccoon, e1 is the forcing coefficient, γ1 is a random number uniformly distributed between [0,1], is the optimal j-th quantum position of the quantum raccoon group, e2 is the attack coefficient, γ2 is a random number uniformly distributed between [-1,1], i = 1, 2, ..., h, j = 1, 2, ..., q; the newly generated quantum position after the i-th quantum raccoon executes the hunting and attack mode 7. The WSN node positioning method based on similar paths and quantum raccoon mechanism according to claim 1 is characterized by: The step six is ​​specifically as follows: Calculate the fitness values ​​of all quantum raccoons in the initial and newly generated quantum positions, apply the greedy strategy, and select the quantum positions from the set Select h quantum positions with smaller fitness values ​​as the quantum positions of the next generation of quantum raccoons i=1,2,...,h; Arrange the fitness values ​​of the quantum positions of the g+1 generation quantum raccoons from small to large, and update the optimal quantum position of the quantum raccoon group to 8. The WSN node positioning method based on similar paths and quantum raccoon mechanism according to claim 1 is characterized in that: The step seven is specifically as follows: If the evolution of the quantum raccoon mechanism has not reached the maximum number of iterations G, then set g = g + 1 and return to step 5 to continue evolving the quantum position of the quantum raccoon. If the maximum number of iterations G is reached, the evolution is terminated and the mapping state of the optimal quantum position of the quantum raccoon group is used as the positioning position coordinate vector of the i′th target node to achieve the positioning of the target node.

9. The WSN node positioning method based on similar paths and quantum raccoon mechanism according to claim 1, characterized in that: The step eight is specifically as follows: Determine whether all target nodes are located, that is, whether If not, set i″=i″+1 and return to step 3 to continue positioning the next target node; if satisfied, all target nodes are positioned, the positioning results are output, and positioning is completed.

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