A node localization method for wireless sensor networks based on ring-shaped salp algorithm

Abstract

The present invention discloses a wireless sensor network node positioning method based on a ring salp algorithm, the method comprising the following steps: selecting a number of beacon nodes for each unknown node; optimizing the original salp algorithm to obtain an optimized salp algorithm; converting the node positioning problem into a minimum value solving problem of a nonlinear equation group, combining the selected number of beacon nodes, using the optimized salp algorithm to calculate the coordinates of the unknown node, and completing the positioning of the unknown node. The positioning method proposed by the present invention has high positioning accuracy and good convergence performance, and is suitable for monitoring scenes with open areas, low cost investment, and no maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless sensor positioning, and more specifically to a wireless sensor network node positioning method based on a ring salp algorithm. Background Art

[0002] As an emerging technology, wireless sensor networks have greatly expanded the functions of modern networks and improved human ability to understand the world. A wireless sensor network is a self-organizing network system formed by many inexpensive sensor nodes with information collection functions, which are used to sense and collect various information and transmit the collected information to the base station for further processing. One of the key basic technologies of wireless sensor networks is to obtain the location information of nodes. This is mainly reflected in two aspects: First, without the location information of the node, the perception data is meaningless, which requires the combination of perception data and location information. Second, the location information of the node is the basis of many key technologies in wireless sensor networks. Therefore, node positioning technology determines the application prospects of wireless sensor networks to a certain extent. How to quickly and accurately determine the current location of sensor nodes is a research focus of scholars at home and abroad.

[0003] Existing wireless sensor network node positioning technologies can be divided into two categories according to whether the actual distance between nodes is measured during the positioning process: positioning algorithms based on ranging and positioning algorithms without ranging. Positioning algorithms based on ranging technology measure the point-to-point distance information between nodes and use triangulation, triangulation or maximum likelihood estimation to calculate the node position. The advantage of positioning algorithms based on ranging is high positioning accuracy, but the disadvantage is that the power consumption and cost of ranging hardware are relatively increased. Positioning algorithms without ranging do not need to accurately measure the actual distance between nodes, but only need to obtain the connectivity information between nodes to calculate the coordinates of unknown nodes. The advantage of positioning algorithms without ranging is that the power consumption and cost of ranging hardware are low, and the disadvantage is that the positioning error increases accordingly.

[0004] DV-Hop is a positioning algorithm based on the vector routing protocol that does not require ranging. The positioning process of this algorithm is mainly divided into three steps: 1) Obtain the minimum number of hops from the unknown node to the beacon node; 2) Estimate the average distance per hop between the unknown node and the beacon node; 3) According to the distance between the unknown node and three or more beacon nodes, use the three-sided positioning method or the maximum likelihood estimation method to calculate the coordinates of the unknown node. The DV-Hop algorithm is a typical case based on the non-ranging positioning algorithm. Although the algorithm is simple to implement, has low communication overhead and low power consumption, it can only be used for rough positioning applications due to the low positioning accuracy of the algorithm itself. When faced with anisotropic networks formed in complex environments, the positioning accuracy of the DV-Hop algorithm will be significantly reduced, making it difficult to meet the needs of actual application scenarios.

[0005] In the actual positioning process of the classic DV-Hop algorithm, there are interferences from external factors and deficiencies in the algorithm itself, which lead to errors in the positioning algorithm. The positioning errors of this algorithm mainly come from the following two aspects:

[0006] 1. The impact of network holes. When wireless sensor networks are applied to complex environments such as those with obstacles, failure of some nodes, and human damage, network holes may occur. The existence of network holes will cause the communication paths between nodes to be circuitous. Using the polyline distance of the hop segment to replace the Euclidean distance between nodes will cause the algorithm to have a large positioning error.

[0007] 2. Calculation method for solving equations. When solving the position coordinates of unknown nodes, the classic trilateration method or the maximum likelihood estimation method with high positioning accuracy is usually used. If the trilateration method is used, when the positions of the three selected beacon nodes are on a straight line, positioning cannot be performed. If the maximum likelihood estimation method is used, the calculation formula itself contains the error of the estimated distance and the beacon node error, making the calculated coordinates of the unknown node inaccurate.

[0008] In order to solve the problem that most non-range positioning algorithms have large positioning errors due to anisotropic networks formed in complex environments, a swarm intelligence bionic algorithm is used to quickly find the optimal solution to the optimization problem to effectively reduce the node positioning error. The Salp Algorithm is a new swarm intelligence optimization algorithm proposed by simulating the special chain structure and aggregation behavior of the salp group in the ocean. The algorithm has the characteristics of simple model, high optimization accuracy, and fast convergence speed. It can be used as an optimization algorithm to solve the optimization problem of node coordinate calculation in the DV-Hop algorithm. However, the original Salp Algorithm has the disadvantage of being easy to fall into local optimality, and the algorithm performance needs to be further improved. Summary of the invention

[0009] In order to solve the problem of low positioning accuracy of most of the above non-ranging positioning methods when solving anisotropic networks with coverage holes, the present invention provides a wireless sensor network node positioning method based on a ring salp algorithm.

[0010] In order to achieve the above-mentioned purpose of the present invention, the technical scheme adopted is as follows:

[0011] A wireless sensor network node positioning method based on a ring salp algorithm, the method comprising the following steps:

[0012] Select several beacon nodes for each unknown node;

[0013] The original salp algorithm is optimized to obtain an optimized salp algorithm;

[0014] The node positioning problem is transformed into a minimum value solving problem of a nonlinear equation group. Combined with a number of selected beacon nodes, the optimized Salp Alpine algorithm is used to calculate the coordinates of unknown nodes to complete the positioning of unknown nodes.

[0015] Preferably, three beacon nodes are selected as reliable beacon nodes for each unknown node according to two key criteria, and if three reliable beacon nodes are successfully selected, the coordinates of the unknown node are calculated using distance estimates provided by the selected three reliable beacon nodes.

[0016] Further, if it is determined that three reliable beacon nodes cannot be successfully selected, the estimated distances from all beacon nodes to the unknown node are used to calculate the coordinates of the unknown node.

[0017] Furthermore, the two key criteria include the following:

[0018] 1) The minimum number of hops from the selected reliable beacon node to the unknown node is less than 5 hops;

[0019] 2) The unknown node is located inside the triangle formed by three reliable beacon nodes;

[0020] The beacon node combination that meets the two key criteria and the beacon node combination with the smallest triangle area are selected as the optimal combination, that is, the three reliable beacon nodes corresponding to the unknown nodes.

[0021] Preferably, the original salp algorithm is optimized by using a ring topology structure. In the optimized salp algorithm, the follower updates its own position according to the positions of its 2k neighbor salps around it; the position update of the follower is shown in formula (4):

[0022]

[0023] Where TF represents the learning rate, TF∈[1,2]; and express The two neighboring salps of , and Nei1≠Nei2≠j; here the followers The two salps in front and the two salps in the back are considered as its neighbors. Two salps are randomly selected from its neighbors and recorded as and

[0024] Furthermore, the optimized salp algorithm is used to calculate the coordinates of unknown nodes, and the specific steps are as follows:

[0025] S301: Randomly initialize the number of salps N, the individual position of salps x i, i = 1, 2, 3, ..., N, the maximum number of iterations T, and calculate the upper and lower limits of the network node search space;

[0026] S302: Determine a beacon node selected as the unknown node;

[0027] S303: Calculate the fitness value of each salp individual according to the objective function of the positioning problem, and use the position of the salp individual with the largest fitness value in the salp population as the current position of the food source;

[0028] S304: Update the position of the leader;

[0029] S305: Update the position of the follower according to formula (4);

[0030] S306: Determine the salp individuals that do not exceed the upper limit or lower limit of the network node search space;

[0031] S307: Calculate the fitness value of each salp individual according to the objective function of the positioning problem, and save the position of the salp individual with the smallest fitness value in the current salp population as the latest position of the food source;

[0032] S308: Determine whether the maximum number of iterations T is reached, if not, return to step S303; if reached, execute step S309);

[0033] S309: Output the latest location of the food source as the coordinates of the unknown node.

[0034] Furthermore, in step S302, the salps are sorted in ascending order according to the fitness values ​​to obtain a salp chain; the first N / 2 salps in the sorted salp chain are regarded as leaders, and the remaining N / 2 salps are regarded as followers.

[0035] Furthermore, the objective function of the positioning problem is shown in formula (5):

[0036]

[0037]

[0038] Where M represents the number of beacon nodes. represents the estimated position of the i-th unknown node, (x k ,y k ) represents the location of the kth beacon node, represents the estimated distance from the i-th unknown node to the k-th beacon node; h ik represents the number of hops from the i-th unknown node to the k-th beacon node; γ ik Represents the weight coefficient.

[0039] A computer system comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a wireless sensor network node positioning method based on a ring salp algorithm are implemented.

[0040] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a wireless sensor network node positioning method based on a ring salp algorithm.

[0041] The beneficial effects of the present invention are as follows:

[0042] The present invention proposes a wireless sensor network node positioning method based on a ring salp algorithm. First, a beacon node is selected for each unknown node to provide accurate distance estimation information; then, a ring topology structure is used to optimize the original salp algorithm to improve the local mining ability and convergence speed of the algorithm; finally, the node positioning problem is converted into a minimum value solution problem of a nonlinear equation group, and the improved salp algorithm is used to calculate the coordinates of the unknown nodes. The positioning method proposed by the present invention has high positioning accuracy and good convergence performance, and is suitable for scenarios where the monitoring area is open, the cost investment is low, and there is no maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of a wireless sensor network node positioning method based on a ring salp algorithm in Example 1.

[0044] Figure 2 It is the ring topology structure of the salp algorithm described in Example 1.

[0045] Figure 3 It is a uniform network topology.

[0046] Figure 4 It is an O-type network topology.

[0047] Figure 5 is the node positioning error under uniform network topology (the number of beacon nodes is 20 and the node communication radius is 20m).

[0048] Figure 6 is the node positioning success rate under uniform network topology (the number of beacon nodes is 20 and the node communication radius is 20m).

[0049] Figure 7 is the node positioning error in O network (the number of beacon nodes is 20 and the node communication radius is 20m).

[0050] Figure 8is the node positioning success rate in O network (the number of beacon nodes is 20 and the node communication radius is 20m). DETAILED DESCRIPTION

[0051] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] Example 1

[0053] like Figure 1 As shown, a wireless sensor network node positioning method based on a ring salp algorithm, the method comprising the following steps:

[0054] Select several beacon nodes for each unknown node;

[0055] The original salp algorithm is optimized by using a ring topology structure to obtain an optimized salp algorithm.

[0056] The node positioning problem is converted into a minimum value solution problem of a nonlinear equation group, and combined with the selection of a number of beacon nodes, the optimized Salp Alpine algorithm is used to calculate the coordinates of unknown nodes to complete the positioning of unknown nodes.

[0057] In a specific embodiment, in order to reduce the impact of circuitous paths caused by coverage holes in the network, this embodiment proposes a reliable beacon node selection strategy based on triangle constraints. First, three beacon nodes are selected as reliable beacon nodes for each unknown node based on two key criteria. If it is determined that three reliable beacon nodes are successfully selected, the distance estimates provided by the selected three reliable beacon nodes are used to calculate the coordinates of the unknown node. If it is determined that three reliable beacon nodes cannot be successfully selected, the estimated distance from all beacon nodes to the unknown node is used to calculate the coordinates of the unknown node. Therefore, this embodiment can also directly select all beacon nodes to calculate the coordinates of unknown nodes, or other methods can be used to select corresponding beacon nodes according to needs.

[0058] In a specific embodiment, the two key criteria include the following:

[0059] 1) The minimum number of hops from the selected reliable beacon node to the unknown node is less than 5 hops;

[0060] 2) The unknown node is located inside the triangle formed by three reliable beacon nodes;

[0061] Since there may be multiple beacon node combinations that can simultaneously meet the two judgment criteria, as a supplement, the beacon node combination that meets the two key criteria is selected from the beacon node combinations that simultaneously meet the two key criteria, and the beacon node combination with the smallest triangle area is selected as the optimal combination, that is, the three reliable beacon nodes corresponding to the unknown nodes.

[0062] In a specific embodiment, the specific implementation steps of selecting three reliable beacon nodes for the unknown node a are as follows:

[0063] 1) Each beacon node in the wireless sensor network broadcasts its data packet message by flooding, and calculates the minimum number of hops between all nodes and each beacon node;

[0064] 2) Calculate the average hop distance of each beacon node;

[0065] 3) Each unknown node calculates the distance between each unknown node and the beacon node based on the minimum number of hops obtained in step 1) and the average hop distance obtained in step 2);

[0066] 4) Select all beacon nodes whose minimum hop count to the unknown node a is less than 5 from all beacon nodes, and record them as set A;

[0067] 5) Select three beacon nodes at random from set A (with a total of combinations), determine whether the unknown node a is located inside the triangle formed by the three beacon nodes; if the unknown node a is located inside the triangle formed by the beacon node combination, retain the group of beacon node combinations; otherwise, discard the group of beacon node combinations;

[0068] 6) Calculate the area of ​​the triangle formed by each group of retained beacon node combinations, where the group of beacon node combinations with the smallest area is the optimal three reliable beacon nodes corresponding to the unknown node a.

[0069] In a specific embodiment, when the traditional salp algorithm searches for the optimal solution in the search space, the entire salp population maintains a chain structure for searching; the salp at the head of the chain is called the leader, and the remaining salps are called followers. The leader guides the entire salp population to move toward the target area, and the followers follow the previous salp one by one to search.

[0070] The leader's position update formula is shown in formula (1):

[0071]

[0072] In formula (1), is the position of the j-th salp in the i-dimensional space, ub i lb i are the upper and lower limits corresponding to the position of the i-th dimension respectively; F i represents the location of the food source in the i-th dimension; coefficients r2 and r3 represent random numbers in the range [0,1]. r1 represents the control parameter, which is responsible for balancing the global exploration ability and local exploitation ability of the algorithm. The calculation of r1 is shown in formula (2):

[0073]

[0074] In formula (2), t represents the current number of iterations; T represents the maximum number of iterations.

[0075] The traditional follower position update formula is shown in formula (3):

[0076]

[0077] In order to improve the optimization accuracy and convergence speed of the traditional salp algorithm, this embodiment adopts a ring topology structure to optimize the performance of the salp algorithm. The ring structure of the optimized salp algorithm is as follows: Figure 2 As shown. In the optimized salp algorithm, the leader's position update method remains unchanged, but the follower updates its own position based on the positions of its 2k (k = 2) neighbor salps. After adopting the ring topology, the follower's position update is shown in formula (4):

[0078]

[0079] Where TF represents the learning rate, TF∈[1,2]; and express The two neighbor salps of , and Nei1≠Nei2≠j. Here the follower The two salps in front and the two salps in the back are considered as its neighbors. Two salps are randomly selected from its neighbors and recorded as and

[0080] In a specific embodiment, due to the interference and influence of obstacles, noise, etc. in the monitoring environment, various ranging technologies have the problem of low accuracy of the measured distance value. In order to improve the positioning accuracy of the maximum likelihood estimation method in an environment with large ranging errors, and considering the energy consumption and communication capabilities of the node itself, this embodiment transforms the node positioning problem into a minimum value solution problem of a nonlinear equation group, and uses the improved Salp Alveolar Algorithm based on the ring topology structure to estimate the node position. The objective function of the positioning problem is shown in formula (5):

[0081]

[0082]

[0083] Where M represents the number of beacon nodes. represents the estimated position of the i-th unknown node, (x k ,y k ) represents the location of the kth beacon node, Represents the estimated distance from the i-th unknown node to the k-th beacon node. ik Represents the number of hops from the i-th unknown node to the k-th beacon node. γ ik Represents the weight coefficient. When the number of hops from beacon node k to unknown node i is larger, γ ik The smaller the value of; when the number of hops from beacon node k to unknown node i is smaller, γ ik The larger the value of .

[0084] In a specific embodiment, the optimized salp algorithm is used to calculate the coordinates of unknown nodes, and the specific steps are as follows:

[0085] S301: Randomly initialize the number of salps N, the individual position of salps x i , i = 1, 2, 3, ..., N, the maximum number of iterations T, and calculate the upper and lower limits of the network node search space;

[0086] S302: Determine the beacon node selected by the unknown node; in this embodiment, three reliable beacon nodes are selected by judging whether the unknown node has passed the reliable beacon node selection strategy. If three reliable beacon nodes are successfully selected, only the estimated distances from the three reliable beacon nodes to the unknown node are used for calculating the node coordinates; otherwise, the estimated distances from all beacon nodes to the unknown node are selected for calculating the node coordinates;

[0087] S303: According to the objective function of the positioning problem, the fitness value of each salp individual is calculated, and the position of the salp individual with the largest fitness value in the salp population is used as the current position of the food source; in step S302, the salps are sorted in ascending order according to the fitness values ​​to obtain a salp chain; the first N / 2 salps individuals in the sorted salp chain are regarded as leaders, and the remaining N / 2 salps individuals are regarded as followers.

[0088] S304: Update the position of the leader according to formula (1);

[0089] S305: Update the position of the follower according to formula (4);

[0090] S306: Determine the salp individuals that do not exceed the upper limit or lower limit of the network node search space; perform out-of-bounds processing on the salp individuals that are determined to exceed the upper limit or lower limit of the network node search space;

[0091] S307: Calculate the fitness value of each salp individual according to the objective function of the positioning problem, and save the position of the salp individual with the smallest fitness value in the current salp population as the latest position of the food source;

[0092] S308: Determine whether the maximum number of iterations T is reached, if not, return to step S303; if reached, execute step S309;

[0093] S309: Output the latest location of the food source as the coordinates of the unknown node.

[0094] In view of the problem that most non-ranging positioning methods have large positioning errors due to anisotropic networks formed in complex environments, this embodiment proposes a wireless sensor network node positioning method based on a ring salp algorithm. In order to reduce the impact of coverage holes that are prevalent in anisotropic networks, this method adopts a reliable beacon node selection strategy based on triangle constraints to select three beacon nodes that can provide accurate distance estimates for each unknown node for positioning, which can effectively eliminate the impact of large distance estimation errors caused by beacon nodes with circuitous paths. At the same time, in order to improve the positioning accuracy of the maximum likelihood estimation method when there are large errors in the distance estimation between nodes, this embodiment converts the node positioning problem into a minimum value solution problem of a nonlinear set of equations, and uses an improved salp algorithm based on a ring topology structure to calculate the node position. The positioning method proposed in this embodiment has high positioning accuracy and good convergence performance, and is suitable for scenarios where the monitoring area is open, the cost investment is low, and there is no maintenance.

[0095] In order to verify the effectiveness of the node localization method (RSLA) proposed in this embodiment, RSLA is compared with three non-range positioning methods, including DV-Hop, RANN and RAPS. At the same time, in order to analyze the positioning performance of the four positioning methods under network topologies of different shapes, two types of network topologies are designed in the experiment, namely uniform network and O-type network. The two types of network topologies are as follows: Figure 3 , Figure 4 The computer configuration of the experimental environment is as follows: CPU is CoreI7-8700, memory is 16G, operating system is Windows10, and programming language is MATLAB 2018a. The experimental parameters used in this experiment are shown in Table 1:

[0096] Table 1 Network simulation parameter list

[0097]

[0098] In the experimental simulation analysis, in order to verify the effectiveness of the positioning method proposed in this embodiment, the node positioning error and the node positioning success rate are used as evaluation indicators to evaluate the performance of the positioning algorithm.

[0099] The normalized root mean square error (NRMSE) of the positioning error is defined as follows:

[0100]

[0101] Among them, N u represents the number of unknown nodes, represents the estimated position of the unknown node, (x i ,y i ) represents the true position of the unknown node.

[0102] The node positioning success rate (SR) is defined as follows:

[0103] SR = number of nodes with positioning error less than the threshold / total number of nodes * 100% (8)

[0104] like Figure 5 The normalized average node positioning error of four non-range positioning methods in a uniform network is shown. Figure 5 It can be seen that with the increase of node density, the positioning errors of the four positioning methods decrease significantly. The RSLA method proposed in this embodiment has the smallest node positioning error among the four positioning methods. When the node density reaches 0.024, the average positioning error of the RSLA method is less than 8m, while under the same conditions, the average positioning error of the DV-Hop method is 10m, the average positioning error of the RANN method is 12m, and the average positioning error of the RAPS method is as high as 17m. The simulation results show that the positioning method proposed in this embodiment has a higher positioning accuracy under a uniform network.

[0105] like Figure 6 The positioning success rates of the four positioning methods under uniform networks are shown. Figure 6 It can be seen that the RSLA algorithm proposed in this embodiment has the highest positioning success rate among the four non-ranging positioning methods. When the network node density reaches 0.012, the positioning success rate of the RSLA algorithm is 92%. Under the same conditions, the positioning success rate of the DV-Hop method is 47%, the positioning success rate of the RANN method is 68%, and the positioning success rate of the RAPS method is 50%. The simulation results show that the RSLA method proposed in this embodiment can achieve better positioning performance when the node density is low.

[0106] like Figure 7 The normalized average node positioning error of four non-distance positioning methods in O-type network is shown. O-type network is a common complex network type in practical scenarios, such as lake environment monitoring. O-type network has a large hole in the center of the monitoring area, and the multi-hop communication path between nodes is circuitous, which leads to large errors in the distance estimation between nodes, thereby reducing the positioning accuracy of the algorithm. Figure 7It can be seen that the RSLA method proposed in this embodiment still shows the best positioning performance in the O-type network topology environment. When the node density is greater than 0.012, the average positioning error of this method is less than 10m, while the positioning errors of the other three positioning methods are all greater than 10m. The simulation results show that the positioning method proposed in this embodiment can effectively eliminate the influence of anisotropic factors such as coverage holes, uneven node distribution, and irregular node communication range, and still has high positioning accuracy in a complex network environment.

[0107] like Figure 8 The positioning success rates of four non-ranging positioning methods under O-type network are shown. Figure 8 It can be seen that the RSLA method has a higher positioning success rate under different node densities; when the node density is greater than 0.012, the positioning success rate of this method is higher than 90%. In contrast, the positioning success rates of the other three positioning methods are all less than 60%. The simulation results show that the RSLA method proposed in this embodiment still has a high positioning success rate when solving complex network scenarios, and can meet the needs of most practical applications.

[0108] To sum up, the non-ranging positioning method based on the annular salp algorithm proposed in this embodiment can show high positioning accuracy and node positioning success rate, whether in a uniform network scenario or in an anisotropic complex network environment, and is suitable for scenarios with open monitoring areas, low cost investment, and no maintenance.

[0109] Example 2

[0110] A computer system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of implementing a wireless sensor network node positioning method based on a ring salp algorithm are as follows:

[0111] Select several beacon nodes for each unknown node;

[0112] The original salp algorithm is optimized by using a ring topology structure to obtain an optimized salp algorithm.

[0113] The node positioning problem is converted into a minimum value solution problem of a nonlinear equation group, and combined with the selection of a number of beacon nodes, the optimized Salp Alpine algorithm is used to calculate the coordinates of unknown nodes to complete the positioning of unknown nodes.

[0114] Example 3

[0115] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of implementing a wireless sensor network node positioning method based on a ring salp algorithm are as follows:

[0116] Select several beacon nodes for each unknown node;

[0117] The original salp algorithm is optimized by using a ring topology structure to obtain an optimized salp algorithm.

[0118] The node positioning problem is converted into a minimum value solution problem of a nonlinear equation group, and combined with the selection of a number of beacon nodes, the optimized Salp Alpine algorithm is used to calculate the coordinates of unknown nodes to complete the positioning of unknown nodes.

[0119] Obviously, the above embodiments of the present invention are only examples for clearly explaining the present invention, and are not intended to limit the implementation methods of the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. A wireless sensor network node positioning method based on a ring salp algorithm, characterized in that: The method comprises the following steps: Select several beacon nodes for each unknown node: select three non-collinear beacon nodes as reliable beacon nodes for each unknown node according to the key criteria, and determine that three reliable beacon nodes are successfully selected, and then use the distance estimates provided by the selected three reliable beacon nodes to calculate the coordinates of the unknown node; The original salp algorithm is optimized to obtain the optimized salp algorithm. In the optimized salp algorithm, the follower updates its position according to the positions of its 2a neighbor salps. The position update of the follower is shown in formula (4): Where TF represents the learning rate, TF∈[1,2]; and express The two neighboring salps of , and Nei1≠Nei2≠j; here the followers The two salps in front and the two salps in the back are considered as its neighbors. Two salps are randomly selected from its neighbors and recorded as and F i Represents the location of the food source in the i-dimensional space; The node positioning problem is transformed into a minimum value solution problem of a nonlinear equation group. Combined with the selected beacon nodes, the optimized Salp Unica algorithm is used to calculate the coordinates of the unknown nodes to complete the positioning of the unknown nodes. The objective function of the positioning problem is shown in formula (5): Where M represents the number of beacon nodes. represents the estimated position of the i-th unknown node, (x k ,y k ) represents the location of the kth beacon node, represents the estimated distance from the i-th unknown node to the k-th beacon node; h ik represents the number of hops from the i-th unknown node to the k-th beacon node; γ ik Represents the weight coefficient.

2. The wireless sensor network node positioning method based on the ring salp algorithm according to claim 1 is characterized in that: If it is determined that three reliable beacon nodes cannot be successfully selected, the estimated distances from all beacon nodes to the unknown node are used to calculate the coordinates of the unknown node.

3. The wireless sensor network node positioning method based on the ring salp algorithm according to claim 1 is characterized in that: The key criteria described include the following: 1) The minimum number of hops from the selected reliable beacon node to the unknown node is less than 5 hops; 2) The unknown node is located inside the triangle formed by three reliable beacon nodes; The beacon node combination that meets the two key criteria and the beacon node combination with the smallest triangle area are selected as the optimal combination, that is, the three reliable beacon nodes corresponding to the unknown nodes.

4. The wireless sensor network node positioning method based on the ring salp algorithm according to claim 1 is characterized in that: The optimized salp algorithm is used to calculate the coordinates of unknown nodes. The specific steps are as follows: S301: Randomly initialize the number of salps N, the individual position of salps x i , i = 1, 2, 3, ..., N, the maximum number of iterations T, and calculate the upper and lower limits of the network node search space; S302: Determine a beacon node selected as the unknown node; S303: Calculate the fitness value of each salp individual according to the objective function of the positioning problem, and use the position of the salp individual with the largest fitness value in the salp population as the current position of the food source; S304: Update the position of the leader; S305: Update the position of the follower according to formula (4); S306: Determine the salp individuals that do not exceed the upper limit or lower limit of the network node search space; S307: Calculate the fitness value of each salp individual according to the objective function of the positioning problem, and save the position of the salp individual with the smallest fitness value in the current salp population as the latest position of the food source; S308: Determine whether the maximum number of iterations T is reached, if not, return to step S303; if reached, execute step S309; S309: Output the latest location of the food source as the coordinates of the unknown node.

5. The wireless sensor network node positioning method based on the ring salp algorithm according to claim 4 is characterized in that: In step S302, the salps are sorted in ascending order according to the fitness values ​​to obtain a salp chain; the first N / 2 salps in the sorted salp chain are regarded as leaders, and the remaining N / 2 salps are regarded as followers.

6. A computer system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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