Improved three-dimensional dv-hop positioning method based on continuous hop count and raccoon predation mechanism

Abstract

The application discloses an improved three-dimensional DV-Hop positioning method based on continuous hop count and raccoon predation mechanism and belongs to the field of wireless communication. The method firstly converts hop count from discrete value to continuous value, thereby reducing accumulated error in distance estimation stage. Then, the method introduces similar path weighting calculation of the best average hop distance of unknown nodes, effectively reducing the communication frequency between nodes and network energy consumption. Finally, the method combines raccoon predation mechanism and elite strategy, enhances the global search ability and convergence precision of the original raccoon population, converts the three-dimensional positioning problem into a minimum optimization problem, and further reduces positioning error and calculation cost. The raccoon predation mechanism is adopted, a fitness function is constructed through a mapping equation and a positioning error function, the method breaks through the application limitation of the existing node positioning method under three-dimensional wireless sensor network space deployment, and has wide engineering value.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication, specifically relating to an improved three-dimensional DV-Hop localization method based on the number of consecutive hops and the raccoon predation mechanism. Background Technology

[0002] Wireless sensor networks (WSNs) are multi-hop self-organizing networks composed of multiple sensor nodes. Within their operational domain, they serve as crucial infrastructure for collecting, processing, and transmitting information. Due to their cost-effectiveness and compact form factor, WSNs have gained significant attention in information network technology. Their versatility and wide range of advantages have led to their widespread application across various fields. Sensor node localization is a fundamental function of WSNs. In practical applications, it is not always possible to equip each node with a GPS module for precise positioning. Determining the location of sensor nodes is crucial for providing comprehensive feedback to users, which aids in target tracking and monitoring.

[0003] Depending on whether additional equipment is required, wireless sensor network node localization algorithms are generally divided into ranging algorithms and ranging-free algorithms. Ranging-based localization algorithms estimate the distance and angle between sensor nodes by measuring signal properties. These methods can provide high localization accuracy, but require additional hardware, increasing system complexity and cost. For example, in indoor environments or complex urban environments, the layout of buildings and other obstacles can severely affect signal propagation, leading to multipath effects or signal attenuation, making it difficult for ranging algorithms to achieve accurate distance measurements. Therefore, in this context, most researchers prefer ranging-free localization algorithms compared to high-precision localization methods that require additional hardware. These algorithms do not require specialized ranging equipment, calculating distances based on network topology and node connectivity information, reducing system cost and energy consumption, shortening deployment time, and offering greater adaptability and ease of use. The distance vector hop localization algorithm DV-Hop has attracted considerable attention from researchers due to its stability, simplicity, feasibility, and lower hardware requirements compared to other ranging-free localization algorithms.

[0004] In wireless sensor networks, most localization algorithms are designed for two-dimensional space. This is mainly due to the simplicity, low computational requirements, and suitability for most basic applications in 2D scenarios. While 2D localization algorithms are effective enough for many applications, they cannot provide vertical positional information, a fact that becomes apparent in complex 3D environments such as multi-story buildings or mountainous terrain. 2D localization algorithms may also fail if the positional relationships between objects are not confined to the horizontal plane in undulating environments. Therefore, 3D localization algorithms have greater practical significance and application potential. However, 3D localization algorithms also have some drawbacks. First, 3D algorithms are inherently more complex because they require processing more dimensional data, thus increasing computational complexity. Second, the stability of 3D localization algorithms is lower than that of 2D methods. Most current 3D localization algorithms are based on network topology and density; to achieve this, each node must be able to communicate effectively with each other. These algorithms perform poorly when nodes are unevenly distributed or communication is congested. While the DV-Hop algorithm simplifies deployment and reduces costs, it relies on accurate hop count estimation and average hop distance calculation, which are difficult to guarantee in real-world situations.

[0005] A review of existing literature on localization methods revealed that Mani et al. proposed a three-dimensional DV-Hop algorithm based on improved adaptive differential evolution in their paper "Three-dimensional DV-Hop based on improved adaptive differential evolution." This method controls offspring generation behavior and utilizes improved adaptive differential evolution to optimize coordinate estimation. While enhancing the algorithm's search capability, differential evolution algorithms are sensitive to control parameters such as mutation factors and crossover probabilities. In scenarios with complex node distributions and varying beacon node ratios in three-dimensional wireless sensor networks, the robustness of the parameter adaptation mechanism is insufficient, easily leading to slow convergence or premature entrapment in local optima. In their paper "A Localization Algorithm for 3D Wireless Sensor Networks Based on Multi-Strategy Optimization" published in *Microelectronics & Computer* (2024, 41(08): 81-90), Peng Duo et al. used Sine chaotic mapping to process the initial population of the tuna swarm algorithm. By introducing a nonlinear convergence factor and a dynamic adaptive weight mechanism, they improved the position iteration process of the tuna swarm, overcoming the problem that the algorithm is prone to getting trapped in local optima. Finally, by adopting a multi-strategy collaborative optimization tuna swarm algorithm, they realized the calculation of the position of unknown nodes in 3D space, solving the problem of matrix inversion. However, when solving high-dimensional optimization problems, there may still be a problem that the population diversity decreases too quickly with the iteration process. At the same time, the algorithm does not consider the impact of hop count optimization on the localization performance, which will increase the localization error of network nodes due to large communication overhead and inaccurate hop count estimation. This invention provides an improved 3D DV-Hop localization algorithm and improves the localization accuracy of the algorithm in 3D scenes. Summary of the Invention

[0006] To address the shortcomings of traditional methods in terms of scalability and accuracy, this invention proposes an improved 3D DV-Hop localization method based on continuous hop count and raccoon predation mechanisms. First, theoretical analysis demonstrates a correlation between the volume of the intersection region of communication ranges between neighboring nodes and the number of nodes sharing a single hop. The number of nodes sharing a single hop is used to convert the hop count from a discrete value to a continuous value, thereby improving node localization accuracy. Second, the average hop distance of unknown nodes is weighted to improve the distance estimation accuracy. Finally, the raccoon predation mechanism and elite cooperative strategy are utilized to calculate the location, mimicking the behavior of raccoons hunting iguanas and escaping predators to more effectively search for node locations, further reducing localization errors.

[0007] This invention provides an improved three-dimensional DV-Hop localization method based on the number of consecutive jumps and the raccoon predation mechanism, comprising the following steps:

[0008] Step 1: Establish a 3D-WSNs model based on continuous hop count estimation;

[0009] Step 2: Calculate the estimated three-dimensional distance between the unknown node and the beacon node based on the optimal average hop distance of the unknown node;

[0010] Step 3: Establish a node localization model based on the localization error function of the unknown node, and begin localization;

[0011] Step 4: Calculate the location of unknown nodes using the raccoon predation mechanism and elite cooperation strategy; calculate the fitness value of all quantum raccoon individuals using the mapping equation and the positioning error function, and determine the optimal quantum position of the population using the elite cooperation strategy; quantum raccoon individuals use quantum rotation angle to evolve quantum position during the exploration phase, execute the escape strategy to evolve quantum position during the development phase, and finally use a greedy strategy to update the optimal quantum position of the population.

[0012] Step 5: If all unknown node labels have been located, the positioning process terminates and the positioning result is output; otherwise, the unknown node labels are updated and the process returns to Step 4 to continue locating unknown nodes.

[0013] Further, step 1 specifically includes:

[0014] Step 1.1: Given a random distribution of sensor nodes, estimate the... An unknown node With the beacon nodes Volume of the intersecting region ;

[0015]

[0016] in, , , The number of unknown nodes. This refers to the number of beacon nodes. Unknown node The number of single-hop nodes, beacon node The number of single-hop nodes, for and The number of single-hop nodes shared between them; The communication radius of the sensor node;

[0017] Step 1.2: Correct the volume of the intersection region The boundary;

[0018]

[0019] Step 1.3: According to the first An unknown node With the beacon nodes Volume of the intersecting region , estimate the first An unknown node With the beacon nodes Distance between ;

[0020]

[0021] Step 1.4: Place the first An unknown node With the beacon nodes Converting discrete jump counts to continuous values ;

[0022]

[0023] Furthermore, step 2 specifically involves:

[0024] Step 2.1: Calculate the first... neighboring beacon nodes average jump distance and the Similar beacon nodes average jump distance The first neighboring beacon nodes In order to be with the first An unknown node The beacon node with the smallest hop count, the first Similar beacon nodes In order to be with the first An unknown node The beacon node with the highest path similarity is determined based on the highest path similarity.

[0025]

[0026] in, For the first The neighboring beacon node or the first Several similar beacon nodes; Add labels to the beacon nodes; For the first The beacon node and the first The number of hops between beacon node labels. , It is an infinite value;

[0027] Step 2.3: The An unknown node To the beacon nodes Optimal average jump distance for:

[0028]

[0029] Step 2.4: An unknown node To the beacon nodes 3D estimated distance for:

[0030] .

[0031] Furthermore, the first An unknown node Positioning error function for:

[0032]

[0033] in, For the first An unknown node The coordinate vector; For the first beacon nodes The coordinate vector.

[0034] Furthermore, step 4 specifically includes:

[0035] Step 4.1: Set initialization parameters for the quantum raccoon population; During the nth iteration, the 1st A quantum raccoon in Quantum position in 3D space Calculate the fitness value of all quantum raccoon individuals. The top three individuals with the highest fitness values ​​in the quantum raccoon population are designated as elite individuals. Calculate the quantum position after elite cooperation. If the fitness value of the quantum position resulting from elite cooperation is better than the fitness value of the worst individual's quantum position, then update the quantum position of the worst individual in the quantum raccoon population. After updating the worst individual, calculate the fitness value of all quantum raccoon individuals. The quantum position of the quantum raccoon individual with the lowest fitness value in the population is designated as the optimal quantum position. , The optimal quantum raccoon label for the population;

[0036] Step 4.2: Quantum Raccoon Exploration Phase; Using the optimal quantum position of the raccoon population in Step 4.1 as the iguana position, the quantum raccoon climbs the tree to execute a hunting strategy against the iguana, and executes an attack strategy against iguanas that fall from the tree, using the quantum rotation angle to evolve the quantum position. ; Calculate the fitness values ​​of the quantum positions of all individual quantum raccoons in the initial and exploration phases, from Select the top fitness values ​​from smallest to largest The quantum position as the quantum position of the quantum raccoon population in the development phase , ;

[0037] Step 4.3: Quantum Raccoon Development Phase; Quantum Raccoon Executes Predator Escape Strategy, Evolving the Quantum Raccoon's Quantum Position ;

[0038] Step 4.4: Calculate the fitness values ​​of all individual quantum raccoons at their quantum positions during the exploration and development phases, from... Select the top fitness values ​​from smallest to largest The quantum position of the quantum raccoon population has been updated to the next-generation exploration phase. , The optimal quantum position of the population is updated using the quantum position with the lowest fitness value. ;

[0039] Step 4.5: If the maximum number of iterations is reached Output the mapping state of the optimal quantum position of the population as the coordinates of the unknown node; if the maximum number of iterations has not been reached... Then let Then return to step 4.2 to continue evolving the quantum position.

[0040] Further, in step 4.1, the fitness value of each individual in the quantum raccoon population is:

[0041]

[0042] in, For the first The mapping state of each quantum raccoon quantum position in each dimension of the solution space. , , , and The positions of the quantum raccoon are respectively The upper and lower bounds of a dimension.

[0043] Furthermore, in step 4.1, the elite collaborative quantum position... Calculated by weighting the positions of the top three elite individuals;

[0044]

[0045] in, , and The fitness value represents the position of the top three elite individuals. , and ; , , , This represents the sum of individual fitness values.

[0046] Furthermore, in step 4.2, when the quantum raccoon executes its hunting strategy against the iguana, the quantum raccoon's first... The update equation for the quantum position is:

[0047] ,

[0048] in, for The random step size factor between; for Random factors between; The first quantum raccoon of the optimal population 3D quantum position;

[0049] When the quantum raccoon executes its attack strategy against the iguana, the quantum raccoon's first... The update equation for the quantum position is:

[0050] ,

[0051]

[0052] in, The iguana's first day after landing Quantum Positioning for Innovation; for Random numbers between; for Random integer factors between; New quantum position after the iguana lands The mapping state; and The quantum positions are respectively The upper and lower bounds of a dimension, ;

[0053] Among them, the first The first quantum raccoon Quantum position The limiting equations under hunting and attack strategies are:

[0054] ,

[0055] Further, in step 4.2, the first The first quantum raccoon Quantum position Under hunting and attack strategies, the quantum rotation formula is updated as follows:

[0056]

[0057]

[0058] in, For the first The generation The first quantum raccoon 3D simulated quantum rotation angle, and Quantum position and The mapping state, , , For hunting coefficient, This represents the attack coefficient. for A random adjustment factor that is uniformly distributed among them; for Random perturbation factors that are uniformly distributed among them.

[0059] Furthermore, in step 4.3, the quantum raccoon executes a predator escape strategy, and the raccoon population generates random, safe quantum locations near its current location as follows:

[0060]

[0061]

[0062] in, for Random numbers between; and The first The upper and lower bounds of how the dimension variable is updated with the number of iterations. , and The quantum positions are respectively The upper and lower bounds of a dimension, , , .

[0063] The present invention also provides a computer device / equipment / system, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the improved three-dimensional DV-Hop localization method based on the number of consecutive jumps and raccoon predation mechanism described above.

[0064] The present invention also provides a computer-readable storage medium having a computer program / instruction thereon stored thereon, which, when executed by a processor, implements the steps of the improved three-dimensional DV-Hop localization method based on consecutive jump count and raccoon predation mechanism described above.

[0065] The present invention also provides a computer program product, including a computer program / instruction that, when executed by a processor, implements the steps of the improved three-dimensional DV-Hop localization method based on consecutive jump count and raccoon predation mechanism described above.

[0066] The beneficial effects of this invention are as follows:

[0067] The improved 3D DV-Hop localization method proposed in this invention, based on continuous hop count and raccoon predation mechanism, solves the problem of matrix inversion in the DV-Hop localization algorithm. By converting the hop count from discrete to continuous values, the cumulative error in the distance estimation stage is reduced. The optimal average hop distance for unknown nodes is calculated using similar path weighting, effectively reducing the number of communication attempts between nodes and network energy consumption. Finally, by combining the raccoon predation mechanism and an elite strategy, the global search capability and convergence accuracy of the original raccoon population are enhanced, transforming the 3D localization problem into a minimum optimization problem, further reducing localization error and computational overhead. Simulation experiments show that, when sensor network parameters change, this node localization method exhibits better localization accuracy than other benchmark localization methods, overcoming the application limitations of existing node localization methods in the spatial deployment of 3D wireless sensor networks and possessing broad engineering value. Attached Figure Description

[0068] Figure 1 This is a flowchart of the improved three-dimensional DV-Hop localization method based on the number of consecutive jumps and the raccoon predation mechanism proposed in this invention;

[0069] Figure 2 This is a bar chart showing the standard positioning error as a function of beacon scale.

[0070] Figure 3 This is a bar chart showing the variation of standard positioning error with node communication radius;

[0071] Figure 4 This is a bar chart showing the relationship between standard positioning error and the size of the monitoring area;

[0072] Figure 5 This is a bar chart showing the change in standard positioning error and the number of nodes. Detailed Implementation

[0073] The present invention will be further described below with reference to the accompanying drawings. The embodiments are only used to explain the present invention and are not intended to limit the scope of protection of the present invention.

[0074] In this invention, a shared single-hop node is defined as a node in 3D-WSNs that can be directly communicated with; the average hop distance is defined as the average hop distance, which is calculated by the ratio of the cumulative sum of Euclidean distances from a beacon node to all other beacon nodes in the network to the cumulative sum of hop counts.

[0075] This invention discloses an improved three-dimensional DV-Hop localization method based on the number of consecutive jumps and the raccoon predation mechanism, such as... Figure 1 As shown, the main steps include:

[0076] Step 1: Establish a 3D-WSNs model based on continuous hop count estimation.

[0077] In a 3D wireless sensor network scenario, the number of nodes is set. beacon ratio ,in, This refers to the number of beacon nodes. The number of unknown nodes. The set of location coordinates of the beacon nodes. The set of location coordinates of unknown nodes Assuming each sensor node has a spherical communication range, the node communication radius is used as... express.

[0078] In the initial stage of 3D-WSN operation, beacon nodes transmit data packets to the wireless sensor network according to positioning requirements. These data packets are broadcast across the network and are only received correctly within the node's communication range. Assume... and These are unknown nodes and beacon nodes in 3D-WSNs, respectively. and beacon nodes Volume of the intersection region between Composed of their communication range, unknown nodes and beacon nodes The relative distance between them is used This indicates that, to improve the positioning accuracy of 3D-WSNs, unknown nodes... To beacon node Converting discrete jump counts to continuous values ,in, , Once the beacon node receives the updated hop count, it propagates the information until each node reaches the required minimum hop count, and then propagates it to other nodes.

[0079] Theorem 1: Based on relative distance and the volume of the intersection region The relationship between them can determine the volume of the intersection region. The range of values ​​for the unknown node is then... and beacon nodes between volumes , Compared to derivative From this we can know It is a decreasing function, as The increase in the volume of the intersection region Gradually decrease, therefore when The range is hour, The range is By analogy with Theorem 1, the number of shared single-hop nodes can also be used to estimate the cross-section volume range, specifically the volume of the cross-section region in an ideal scenario where nodes are evenly distributed. When sensor nodes are randomly distributed, there is a correlation between the intersection region between unknown nodes and beacon nodes and the number of nodes sharing a single hop. Therefore, the volume of the intersection region can be estimated. ,in, Unknown node The number of single-hop nodes, beacon node The number of single-hop nodes, for and The number of single-hop nodes shared between them. This is derived from the derivation. ,but It may exceed this range when the volume of the intersection region is... When the volume exceeds the range shown above, Take the boundary value of the volume range By Replace with Calculate unknown nodes and beacon nodes relative distance between This leads to the continuous jump values. .

[0080] Step 2: Estimate the optimal average hop distance of the unknown node to obtain the estimated three-dimensional distance between the unknown node and the beacon node.

[0081] All beacon nodes calculate their own average hop distance and broadcast their average hop distance and path information. Unknown nodes retain the average hop distance and path information of all reachable beacon nodes. Average hop distance of each beacon node ,in, Add labels to the beacon nodes. For the first The beacon node and the first The number of hops between beacon node labels (cumulative symbols). , It is an infinite value.

[0082] Will be with unknown nodes The communication reachable beacon node with the smallest hop count is denoted as the neighboring node. Will be with unknown nodes The beacon node with the highest path similarity is denoted as a similar node. , , Path similarity ,in, Unknown node To beacon node The set of shortest paths For other beacon nodes to beacon nodes The set of shortest paths as a means and The same set of paths, The number of nodes contained in the path set; obtained by the first... The location of the first beacon node and the first Calculate the average hop distance for each beacon node location. and .

[0083] Unknown node Optimal average jump distance Unknown node To beacon node 3D estimated distance ,in, These are weighting coefficients. , Adjacent nodes The average jump distance, Similar nodes The average jump distance, , .

[0084] Step 3: Establish a node localization model and begin localization.

[0085] Setting the first Location error function of unknown nodes ,in, For the first The coordinate vectors of the unknown nodes, For the first The coordinate vectors of each beacon node. , Label the unknown node to be located. Initialize to 1, then begin positioning.

[0086] Step 4: Set initial parameters for the quantum raccoon population, calculate the fitness value of all individual quantum raccoons using the mapping equation and the positioning error function, and determine the optimal quantum position of the population using an elite cooperation strategy.

[0087] The size of the quantum raccoon population is set as follows: The maximum number of iterations is The iteration number is labeled as , , At that time, each dimension of the quantum raccoon's quantum position is initialized to... Uniformly distributed random numbers within the interval. During the nth iteration, the 1st A quantum raccoon in Quantum position in 3D space The first quantum position of its quantum position dimension , No. The mapping state of each quantum raccoon quantum position in each dimension of the solution space , No. The mapping equation for 3D quantum position is defined as follows: , , ,in, and The positions of the quantum raccoon are respectively The upper and lower bounds of a dimension.

[0088] Will Substitute the first The fitness function is obtained from the localization error function of the unknown nodes. , where superscript This indicates transpose. Calculate the fitness value of all quantum raccoon individuals. The top three individuals in the quantum raccoon population with the highest fitness values ​​are designated as elite individuals, and their quantum positions are denoted as follows: , and The fitness values ​​of elite individuals are denoted as follows: , and Calculate the weight coefficients of the three elite individuals: , , ,in, Quantum positions are generated through elite collaboration after weighting. .

[0089] If the fitness value of the elite cooperative quantum position is better than the fitness value of the worst individual's quantum position in the population, then the quantum position of the worst individual in the quantum raccoon population is updated. After updating the quantum raccoon population, the fitness values ​​of all quantum raccoon individuals are calculated, and the quantum position with the highest fitness value in the population is recorded as the optimal quantum position. ,in, The optimal quantum raccoon label for the population.

[0090] Step 5: Exploration Phase: The quantum raccoon executes its hunting and attack strategy against the iguana and uses quantum rotation angles to evolve quantum positions.

[0091] The optimal location for any individual raccoon in the current population is where the iguana is. Half of the raccoons will climb the tree to hunt the iguana, while the other half will wait for the iguana to fall to the ground before attacking. The raccoons in the tree... The update equation for quantum position , After the iguana landed on the ground, the other half of the raccoon... The update equation for quantum position , , ,in, This represents the current iteration number. for The random step size factor between for The random factor between these factors is used to randomly generate new quantum positions within the solution space. for A random integer factor between these values ​​is used to adjust the direction between individual raccoons and the optimal individual in the population. The first quantum raccoon of the optimal population Quantum position, The iguana's first day after landing Quantum Positioning (QP) New quantum position after the iguana lands The mapping state, and The quantum positions are respectively The upper and lower bounds of a dimension, Considering the first The first quantum raccoon Quantum position It exceeds the range [0,1], therefore a boundary constraint is imposed, and the constraint equation is: , .

[0092] No. The first quantum raccoon Quantum position Using the quantum rotation formula under hunting and attack strategies Update the quantum state, defined when executing the hunting strategy. , , , Define when executing the attack strategy , ,in, For the first The generation The first quantum raccoon 3D simulated quantum rotation angle, and Quantum position and The mapping state, For hunting coefficient, This represents the attack coefficient. for A random adjustment factor, uniformly distributed among the components, is used to adjust the magnitude of the quantum rotation angle. for A random perturbation factor is uniformly distributed among the components to enhance the randomness of the local search. . No. A quantum raccoon generates new quantum positions during the exploration phase. Calculate the fitness values ​​of the quantum positions of all individual quantum raccoons in the initial and exploration phases, from... Select the top fitness values ​​from smallest to largest The quantum position as the quantum position of the quantum raccoon population in the development phase , .

[0093] Step Six: Development Phase: The quantum raccoon executes a strategy to escape predators and evolves the quantum position of the quantum raccoon.

[0094] When a predator attacks a raccoon, it will flee from its current location, generating a random, safe quantum location near the current location of the raccoon population. Considering quantum position It exceeds the range [0,1], therefore a boundary constraint is imposed, and the constraint equation is: ,in, for A random number between these values ​​is used to randomize the escape direction of an individual raccoon. and The first The upper and lower bounds of how the dimension variable is updated with the number of iterations. , and The quantum positions are respectively The upper and lower bounds of a dimension, , , .

[0095] Step 7: Use a greedy strategy to select the quantum position of the next generation of quantum raccoon population from the quantum positions of the exploration and development phases and update the optimal quantum position of the population.

[0096] Calculate the fitness values ​​of all individual quantum raccoons at their quantum positions during the exploration and development phases, from... Select the top fitness values ​​from smallest to largest The quantum position of the quantum raccoon population has been updated to the next-generation exploration phase. , And update the optimal quantum position of the population. .

[0097] Step 8: Determine whether the evolution of the raccoon predation mechanism has terminated, thus locating the unknown node.

[0098] If the raccoon predation mechanism evolves to its maximum number of iterations... If the target value is not reached, the algorithm evolution terminates, and the mapping state of the optimal quantum position of the population is used as the coordinates of the unknown node to locate it. If the target value is not reached... Then let Then return to step five to continue evolving the quantum position.

[0099] Step 9: Determine whether the location of all unknown nodes has been achieved, and output the location results.

[0100] If the node label is unknown satisfy If the conditions are met, the positioning process terminates and the positioning result is output; otherwise, the positioning is terminated. Then let Update the unknown node label and return to step four to continue locating the unknown node.

[0101] Example 1

[0102] Wireless sensor network nodes in the monitoring area The nodes are randomly and uniformly distributed in a three-dimensional space, and their positions do not move over time after deployment, thus forming a self-organizing network. All sensor nodes have the same computing and communication capabilities, and there are no obstacles in the monitoring area that could hinder wireless signal transmission. The sensors can self-adjust their wireless transmission power, and the wireless communication link has a symmetrical structure. The following four positioning scenarios are simulated by changing the network parameters. Scenario 1: Number of nodes... Node communication radius Monitoring area beacon ratio Increase from 0.1 to 0.5; Scenario 2, number of nodes beacon ratio Monitoring area Node communication radius from Raise to Scenario 3, Number of Nodes beacon ratio Node communication radius The monitoring area is from Raise to Scenario 4: Node communication radius beacon ratio Monitoring area Number of nodes Increase from 100 to 140. This is set in all the above positioning scenarios. Weighting coefficients .

[0103] Based on the above four scenarios, the improved 3D DV-Hop localization method proposed in this invention, based on the number of consecutive hops and the raccoon predation mechanism, is denoted as QCOA-DV-Hop; the comparative 3D DV-Hop algorithm based on improved adaptive differential evolution is denoted as IADE-DV-Hop, specifically referring to "Three-dimensional DV-Hop based on improved adaptive differential evolution algorithm" published by Mani et al.; the comparative 3D DV-Hop algorithm based on multi-strategy enhanced tuna swarm algorithm is denoted as ESTO-DV-Hop, specifically referring to "3D wireless sensor network localization algorithm based on multi-strategy optimization" published by Peng Duo et al. (Setting the group size...) Maximum number of iterations Hunting coefficient Attack coefficient Upper bound of dimensions Dimensional lower bound , , .from Figure 2 It can be seen that as the proportion of beacons increases, the standard positioning error of all algorithms shows a decreasing trend, indicating that the more beacon nodes there are, the more accurate the optimal hop distance information for unknown nodes, and the higher the positioning accuracy. Figure 3 It can be seen that a node communication radius that is too small will lead to poor network connectivity, while a radius that is too large will increase hop count error. QCOA-DV-Hop's positioning error fluctuation is much smaller than other benchmark positioning methods, making it more robust. From Figure 4 It can be seen that as the monitoring area expands, the positioning error of all algorithms continuously increases. This is because the more dispersed the node distribution, the greater the cumulative error from hop count propagation, significantly increasing the difficulty of positioning. Figure 5 It can be seen that too few nodes lead to a reduction in the number of hop paths between unknown nodes and beacon nodes, resulting in insufficient positioning reference information and higher errors. Conversely, excessively dense node distribution increases redundancy in hop count calculation, increasing hop count errors and reducing positioning accuracy. Since sensor nodes are randomly deployed, the impact of changes in the monitoring area size and node number on network connectivity is uncertain. Simulation results show that the improved 3D DV-Hop positioning method proposed in this invention, based on continuous hop count and the raccoon predation mechanism, exhibits good robustness and can be applied to the engineering deployment of wireless sensor networks in real-world 3D scenarios.

[0104] In particular, in some preferred embodiments of the present invention, a computer device is also provided, including a memory and a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the improved three-dimensional DV-Hop localization method based on the number of consecutive jumps and the raccoon predation mechanism described in any of the above embodiments.

[0105] In some other preferred embodiments of the present invention, a computer-readable storage medium is also provided, on which a computer program / instruction is stored, wherein when the computer program is executed by a processor, the steps of the improved three-dimensional DV-Hop localization method based on the number of consecutive jumps and the raccoon predation mechanism described in any of the above embodiments are implemented.

[0106] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above embodiments of the improved three-dimensional DV-Hop positioning method based on the number of consecutive jumps and the raccoon predation mechanism, which will not be repeated here.

[0107] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0108] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0109] The above description is only a preferred embodiment of the present invention. Given that those skilled in the art can make appropriate changes and modifications to the above embodiments, the present invention is not limited to the specific embodiments described above, and some modifications and changes to the present invention should also fall within the protection scope of the claims of the present invention.

Claims

1. An improved three-dimensional DV-Hop localization method based on the number of consecutive jumps and the raccoon predation mechanism, characterized in that, Includes the following steps: Step 1: Establish a 3D-WSNs model based on continuous hop count estimation; Step 2: Calculate the estimated three-dimensional distance between the unknown node and the beacon node based on the optimal average hop distance of the unknown node; Step 3: Establish a node localization model based on the localization error function of the unknown node, and begin localization; Step 4: Calculate the location of unknown nodes using the raccoon predation mechanism and elite cooperation strategy; calculate the fitness value of all quantum raccoon individuals using the mapping equation and the positioning error function, and determine the optimal quantum position of the population using the elite cooperation strategy; quantum raccoon individuals use quantum rotation angle to evolve quantum position during the exploration phase, execute the escape strategy to evolve quantum position during the development phase, and finally use a greedy strategy to update the optimal quantum position of the population. Step 5: If all unknown node labels have been located, the positioning process terminates and the positioning result is output; otherwise, the unknown node labels are updated and the process returns to Step 4 to continue locating unknown nodes.

2. The improved three-dimensional DV-Hop positioning method according to claim 1, characterized in that, Step 1 specifically involves: Step 1.1: Given a random distribution of sensor nodes, estimate the... An unknown node With the beacon nodes Volume of the intersecting region ; in, , , The number of unknown nodes. This refers to the number of beacon nodes. Unknown node The number of single-hop nodes, beacon node The number of single-hop nodes, for and The number of single-hop nodes shared between them; The communication radius of the sensor node; Step 1.2: Correct the volume of the intersection region The boundary; Step 1.3: According to the first An unknown node With the beacon nodes Volume of the intersecting region , estimate the first An unknown node With the beacon nodes Distance between ; Step 1.4: Place the first An unknown node With the beacon nodes Converting discrete jump counts to continuous values ; 3. The improved three-dimensional DV-Hop positioning method according to claim 1, characterized in that, Step 2 is as follows: Step 2.1: Calculate the first... neighboring beacon nodes average jump distance and the Similar beacon nodes average jump distance The first neighboring beacon nodes In order to be with the first An unknown node The beacon node with the smallest hop count, the first Similar beacon nodes In order to be with the first An unknown node The beacon node with the highest path similarity is determined based on the highest path similarity. in, For the first The neighboring beacon node or the first Several similar beacon nodes; Add labels to the beacon nodes; For the first The beacon node and the first The number of hops between beacon node labels. , It is an infinite value; Step 2.3: The An unknown node To the beacon nodes Optimal average jump distance for: Step 2.4: An unknown node To the beacon nodes 3D estimated distance for: 。 4. The three-dimensional DV-Hop positioning method based on the number of consecutive hops according to claim 1, characterized in that, The first An unknown node Positioning error function for: in, For the first An unknown node The coordinate vector; For the first beacon nodes The coordinate vector.

5. The improved three-dimensional DV-Hop positioning method according to claim 1, characterized in that, Step 4 specifically involves: Step 4.1: Set initialization parameters for the quantum raccoon population; During the nth iteration, the 1st A quantum raccoon in Quantum position in 3D space Calculate the fitness value of all quantum raccoon individuals. The top three individuals with the highest fitness values ​​in the quantum raccoon population are designated as elite individuals. Calculate the quantum position after elite cooperation. If the fitness value of the quantum position resulting from elite cooperation is better than the fitness value of the worst individual's quantum position, then update the quantum position of the worst individual in the quantum raccoon population. After updating the worst individual, calculate the fitness value of all quantum raccoon individuals. The quantum position of the quantum raccoon individual with the lowest fitness value in the population is designated as the optimal quantum position. , The optimal quantum raccoon label for the population; Step 4.2: Quantum Raccoon Exploration Phase; Using the optimal quantum position of the raccoon population in Step 4.1 as the iguana position, the quantum raccoon climbs the tree to execute a hunting strategy against the iguana, and executes an attack strategy against iguanas that fall from the tree, using the quantum rotation angle to evolve the quantum position. ; Calculate the fitness values ​​of the quantum positions of all individual quantum raccoons in the initial and exploration phases, from Select the top fitness values ​​from smallest to largest The quantum position as the quantum position of the quantum raccoon population in the development phase , ; Step 4.3: Quantum Raccoon Development Phase; Quantum Raccoon Executes Predator Escape Strategy, Evolving the Quantum Raccoon's Quantum Position ; Step 4.4: Calculate the fitness values ​​of all individual quantum raccoons at their quantum positions during the exploration and development phases, from... Select the top fitness values ​​from smallest to largest The quantum position of the quantum raccoon population has been updated to the next-generation exploration phase. , The optimal quantum position of the population is updated using the quantum position with the lowest fitness value. ; Step 4.5: If the maximum number of iterations is reached Output the mapping state of the optimal quantum position of the population as the coordinates of the unknown node; if the maximum number of iterations has not been reached... Then let Then return to step 4.2 to continue evolving the quantum position.

6. The improved three-dimensional DV-Hop positioning method according to claim 5, characterized in that, In step 4.1, the fitness value of each individual in the quantum raccoon population is: in, For the first The mapping state of each quantum raccoon quantum position in each dimension of the solution space. , , , and The positions of the quantum raccoon are respectively The upper and lower bounds of a dimension.

7. The improved three-dimensional DV-Hop positioning method according to claim 5, characterized in that, In step 4.1, the elite collaborative quantum position Calculated by weighting the positions of the top three elite individuals; in, , and The fitness value represents the position of the top three elite individuals. , and ; , , , This represents the sum of individual fitness values.

8. The improved three-dimensional DV-Hop positioning method according to claim 5, characterized in that, In step 4.2, when the quantum raccoon executes its hunting strategy against the iguana, the quantum raccoon's first... The update equation for the quantum position is: ， in, for The random step size factor between; for Random factors between; The first quantum raccoon of the optimal population 3D quantum position; When the quantum raccoon executes its attack strategy against the iguana, the quantum raccoon's first... The update equation for the quantum position is: ， in, The iguana's first day after landing Quantum Positioning for Innovation; for Random numbers between; for Random integer factors between; New quantum position after the iguana lands The mapping state; and The quantum positions are respectively The upper and lower bounds of a dimension, ; Among them, the first The first quantum raccoon Quantum position The limiting equations under hunting and attack strategies are: ， 。 9. The improved three-dimensional DV-Hop positioning method according to claim 8, characterized in that, In step 4.2, the first The first quantum raccoon Quantum position Under hunting and attack strategies, the quantum rotation formula is updated as follows: in, For the first The generation The first quantum raccoon 3D simulated quantum rotation angle, and Quantum position and The mapping state, , , For hunting coefficient, This represents the attack coefficient. for A random adjustment factor that is uniformly distributed among them; for Random perturbation factors that are uniformly distributed among them.

10. The improved three-dimensional DV-Hop positioning method according to claim 5, characterized in that, In step 4.3, the quantum raccoon executes a predator escape strategy, and the raccoon population generates random, safe quantum locations near its current location: in, for Random numbers between; and The first The upper and lower bounds of how the dimension variable is updated with the number of iterations. , and The quantum positions are respectively The upper and lower bounds of a dimension, , , .

Images