Wireless sensor network coverage optimization method and system for three-dimensional real terrain surface

Abstract

The invention discloses a wireless sensor network coverage optimization method and system for a three-dimensional real terrain surface, and relates to the technical field of sensor network optimization. With the maximum plane coverage rate as a first constraint condition, optimizing the plane coordinates of the sensor nodes by adopting a tangent search quadratic interpolation optimization algorithm, and determining a plane deployment strategy of the sensor nodes in the monitoring area; determining initial space coordinates of the sensor nodes; calculating a rotation angle of a sensor node by taking a grid point matched with the sensor as a center; determining the space coordinates of the sensor nodes according to the initial space coordinates and the rotation angles of the sensor nodes, and optimizing the space coordinates of the sensor nodes by adopting a tangent search quadratic interpolation optimization algorithm by taking the maximum comprehensive coverage rate as a second constraint condition, so as to determine a space deployment strategy of the sensor nodes; and correspondingly deploying the sensors on the monitoring area through the spatial deployment strategy of the sensor nodes. According to the method, the coverage rate of the wireless sensor network with the three-dimensional real surface can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sensor network optimization, and particularly to a method and system for optimizing the coverage of a wireless sensor network on the surface of a three-dimensional real terrain. Background Art

[0002] Wireless Sensor Networks (WSNs) can realize the real-time monitoring and collection of terrain environment parameters by virtue of their characteristics such as being distributed, self-organizing, low-power, and easy to deploy, providing data support for the early warning and prevention of natural disasters. In the field of natural disaster early warning and prevention, such as volcanic detection, landslide detection, and disaster monitoring, accurate collection and in-depth analysis of environmental data can effectively prevent and reduce disaster losses. In recent years, the coverage problem on the surface of real terrain has become a research topic that has received extensive attention.

[0003] Sensor nodes can sense various physical and chemical parameters of the surrounding environment and transmit the data wirelessly to the user side for further processing and analysis. However, due to the large number of sensor nodes and random deployment, there is node redundancy in some areas, resulting in waste of resources, while in other areas, the nodes cannot cover, forming coverage holes. Therefore, studying the coverage optimization strategy of WSNs nodes under real terrain is of great significance for improving the network coverage quality and ensuring efficient data collection. At present, most coverage optimization studies focus on two-dimensional regions, usually assuming that the sensing area is an ideal plane. The coverage strategy in a two-dimensional environment cannot be directly applied to a complex three-dimensional terrain environment. In addition, the two-dimensional sensor node model has obvious deficiencies in sensing complex terrain and cannot meet the monitoring requirements of a three-dimensional environment.

[0004] Therefore, there is an urgent need for a method that can solve the problem of low coverage rate of wireless sensor networks on the three-dimensional real surface. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide a method and system for optimizing the coverage of a wireless sensor network on the surface of a three-dimensional real terrain, which can improve the coverage rate of the wireless sensor network on the three-dimensional real surface.

[0006] The present invention adopts the following technical solutions:

[0007] The present invention provides a method for optimizing the coverage of a wireless sensor network on the surface of a three-dimensional real terrain, including:

[0008] Taking the maximum planar coverage rate as the first constraint condition, the tangent search quadratic interpolation optimization algorithm is used to optimize the planar coordinates of the sensor nodes, and the planar deployment strategy of the sensor nodes in the monitoring area is determined; the tangent search quadratic interpolation optimization algorithm is an algorithm in which the population update method includes an exploration method, a development method, and a tangent search method integrated;

[0009] Match the positions of the sensor nodes obtained from the planar deployment strategy of the sensor nodes with the grid point positions in the monitoring area, and fix the initial height of the sensor nodes to determine the initial spatial coordinates of the sensor nodes;

[0010] Taking the grid points matched with the sensor nodes as the center, calculate the rotation angle of the sensor nodes;

[0011] According to the initial spatial coordinates and rotation angle of the sensor nodes, determine the spatial coordinates of the sensor nodes, and taking the maximum comprehensive coverage rate as the second constraint condition, use the tangent search quadratic interpolation optimization algorithm to optimize the spatial coordinates of the sensor nodes, and determine the spatial deployment strategy of the sensor nodes in the monitoring area; the comprehensive coverage rate is obtained by considering the distance perception and angle perception of the sensor nodes;

[0012] Deploy the sensors on the monitoring area corresponding to the spatial deployment strategy of the sensor nodes.

[0013] Optionally, taking the maximum planar coverage rate as the first constraint condition, using the tangent search quadratic interpolation optimization algorithm to optimize the planar coordinates of the sensor nodes, and determining the planar deployment strategy of the sensor nodes in the monitoring area, including:

[0014] Initialize the population size of the search agents; the population positions in the population size represent the planar coordinates of the sensor nodes;

[0015] According to the population positions, calculate the planar coverage rate of the search agent population, and take the planar coverage rate as the fitness value;

[0016] Compare the fitness values of the search agent population, and take the search agent individual with the maximum fitness value as the historical optimal position;

[0017] Use the adaptive switching probability to determine the population update method and update the population positions of the search agents;

[0018] According to the updated population positions of the search agents, recalculate the historical optimal position until the number of iterations reaches the maximum number of iterations, then output the historical optimal position and the planar deployment strategy of the sensor nodes corresponding to the historical optimal position.

[0019] Optionally, using the adaptive switching probability to determine the population update method and update the population positions of the search agents, including:

[0020] Obtain a random number;

[0021] If the random number is less than or equal to the adaptive switching probability, update the population position of the search agent in the exploration mode;

[0022] If the random number is greater than the adaptive switching probability model, determine whether the random number is greater than or equal to a specific value;

[0023] If the random number is greater than or equal to the specific value, update the population position of the search agent in the exploitation mode;

[0024] If the random number is less than the specific value, update the population position of the search agent in the tangent search mode.

[0025] Optionally, before calculating the planar deployment strategy of the sensor nodes in the monitoring area, the method further includes:

[0026] Determine the number of sensor nodes to be deployed in the monitoring area according to the sensing radius of the sensor nodes and the size of the monitoring area.

[0027] Optionally, calculating the rotation angle of the sensor node with the grid point matched with the sensor as the center, includes:

[0028] For the grid point matched with any sensor node, take the matched grid point as the center, and obtain data in the up, down, left, and right four directions according to the data length of the preset size, to obtain the grid area data of the preset size;

[0029] Obtain the x coordinate values of the first column and the last column, and the y coordinate values of the first row and the last row from the grid area data;

[0030] According to the z coordinate values of all the data in the first column, the first row, the middle column, the middle row, the last column, and the last row in the grid area data, calculate the z coordinate means of the first column, the first row, the middle column, the middle row, the last column, and the last row respectively;

[0031] Determine the rotation angle of the corresponding sensor node according to the x coordinate values of the first column and the last column, the y coordinate values of the first row and the last row, and the z coordinate means of the first column, the first row, the middle column, the middle row, the last column, and the last row.

[0032] Optionally, determining the spatial coordinates of the sensor node according to the initial spatial coordinates and the rotation angle of the sensor node, includes:

[0033] Determine the coordinates of the center of the bottom surface of the cone covered by the sensor node in the monitoring area according to the initial spatial coordinates and the rotation angle of the sensor node;

[0034] Determine the sensing direction of the sensor node according to the coordinates of the center of the bottom surface of the cone and the initial spatial coordinates.

[0035] Determine the spatial coordinates of the sensor node according to the sensing direction, scaling factor, and initial spatial coordinates. Optionally, the calculation formula for the spatial coordinates of the sensor node is:

[0036] New nc = Direction * τ + nc;

[0037] Direction = cc - nc;

[0038]

[0039] where New nc represents the spatial coordinates of the sensor node, Direction represents the sensing direction, τ represents the scaling factor, nc represents the initial spatial coordinates, cc represents the coordinates of the center of the bottom surface of the cone of the sensor node, cc x 、cc y and cc z respectively represent the coordinates of the center of the bottom surface of the cone on the x-axis, y-axis, and z-axis, nc x and nc y respectively represent the x-coordinate value and y-coordinate value in the initial spatial coordinates, Rs represents the sensing radius, θ represents half of the sensing range, degree results_α 、degree results_β represent the rotation angle of the sensor node in the horizontal direction and the rotation angle in the vertical direction.

[0040] Optionally, in the process of optimizing the spatial coordinates of the sensor node using the tangent search quadratic interpolation optimization algorithm, the algorithm optimizes the scaling factor τ.

[0041] Optionally, the calculation method of the comprehensive coverage rate includes:

[0042] For any grid point, calculate the distance perception of each sensor for the grid point according to the spatial coordinates of the sensor node and the coordinates of the grid point;

[0043] Calculate the angle perception of each sensor node for the grid point according to the horizontal deflection angle and vertical deflection angle between the sensor node and the grid point;

[0044] According to the distance perception and angle perception of each sensor node for the grid point, obtain the overall perception probability of each grid point, and determine the comprehensive coverage rate according to the overall perception probability of each grid point.

[0045] The present invention provides a wireless sensor network coverage optimization system for a three-dimensional real terrain surface, including:

[0046] A planar optimization algorithm module is used to optimize the planar coordinates of sensor nodes with the maximum planar coverage rate as the first constraint condition, and adopt the tangent search quadratic interpolation optimization algorithm to determine the planar deployment strategy of sensor nodes in the monitoring area; the tangent search quadratic interpolation optimization algorithm is an algorithm that integrates exploration mode, exploitation mode and tangent search mode in the population update method;

[0047] An angle optimization module is used to match the positions of sensor nodes obtained from the planar deployment strategy of the sensor nodes with the positions of grid points in the monitoring area, fix the initial height of the sensor nodes, and determine the initial spatial coordinates of the sensor nodes; taking the grid points matched with the sensor nodes as the center, calculate the rotation angle of the sensor nodes;

[0048] A height optimization module is used to determine the spatial coordinates of the sensor nodes according to the initial spatial coordinates and rotation angle of the sensor nodes, and adopt the tangent search quadratic interpolation optimization algorithm to optimize the spatial coordinates of the sensor nodes with the maximum comprehensive coverage rate as the second constraint condition, and determine the spatial deployment strategy of sensor nodes in the monitoring area; the comprehensive coverage rate is obtained by considering the distance perception and angle perception of sensor nodes;

[0049] A deployment module is used to deploy sensors on the monitoring area corresponding to the spatial deployment strategy of the sensor nodes.

[0050] The present invention provides a computer-readable storage medium, and the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned wireless sensor network coverage optimization method for a three-dimensional real terrain surface is realized.

[0051] The present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the above-mentioned wireless sensor network coverage optimization method for a three-dimensional real terrain surface is realized.

[0052] The above-mentioned at least one technical solution adopted by the present invention can achieve the following beneficial effects:

[0053] In the present invention, with the maximum planar coverage rate as the first constraint condition, this algorithm is used to optimize the planar coordinates of sensor nodes. This algorithm integrates the exploration method, the exploitation method, and the tangent search method. The exploration method enables the algorithm to search for potential high-quality solution spaces within a large range, without being confined to local areas; the exploitation method focuses on the discovered potential high-quality regions and conducts fine-grained searches to find better solutions; the tangent search method utilizes the characteristics of the tangent function and can search in a unique path within the solution space. The combination of these three methods enables the algorithm to efficiently explore different position combinations when searching for the planar coordinates of sensor nodes, thereby finding the node planar deployment strategy that maximizes the planar coverage rate. With the maximum comprehensive coverage rate as the second constraint condition, the tangent search quadratic interpolation optimization algorithm is used again to optimize the spatial coordinates of sensor nodes. The comprehensive coverage rate takes into account distance perception, angle perception, and line-of-sight perception, comprehensively measuring the sensing ability of sensors in space. Through this optimization, the positions and orientations of sensor nodes in space are further adjusted, enabling sensors to more effectively cover the monitoring area in three-dimensional space, thereby improving the coverage rate of the entire sensor network. In this way, from the optimization of the planar deployment strategy to the determination of the spatial deployment strategy, various factors of sensors in the plane and space are gradually considered throughout the process. Through two optimization processes based on the tangent search quadratic interpolation optimization algorithm, the positions and orientations of sensor nodes are continuously adjusted, enabling the sensor network to be more reasonably distributed in the plane and space, ultimately effectively improving the coverage rate of the sensor network for the monitoring area. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0055] Figure 1 It is a schematic flow chart of a method for optimizing the coverage of a wireless sensor network on the surface of a three-dimensional real terrain provided by the present invention;

[0056] Figure 2 It is a schematic flow chart of a tangent search quadratic interpolation optimization algorithm provided by the present invention;

[0057] Figure 3 It is a schematic diagram of the sensing range of a sensor node provided by the present invention;

[0058] Figure 4 It is another schematic diagram of the sensing range of a sensor node provided by the present invention;

[0059] Figure 5 It is another schematic flow chart of a method for optimizing the coverage of a wireless sensor network on the surface of a three-dimensional real terrain provided by the present invention;

[0060] Figure 6 It is a schematic diagram of the coverage effect of a sensor node on the monitoring area;

[0061] Figure 7 It is a schematic diagram of the coverage rate of the method of the present invention and the traditional method changing with iterations;

[0062] Figure 8 It is a schematic diagram of a wireless sensor network coverage optimization system for a three-dimensional real terrain surface provided by the present invention;

[0063] Figure 9 It is a schematic diagram of a computer device for implementing a method for optimizing the coverage of a wireless sensor network on a three-dimensional real terrain surface provided by the present invention. Detailed implementation manners

[0064] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0065] Currently, most coverage optimization studies focus on two-dimensional regions, usually assuming that the sensing area is an ideal plane. The coverage strategies in two-dimensional environments cannot be directly applied to complex three-dimensional terrain environments. In addition, the two-dimensional sensor node model has obvious deficiencies in sensing complex terrains and cannot meet the monitoring requirements of three-dimensional environments. A three-dimensional directed sensor network deployment strategy based on an improved differential evolution algorithm is mentioned in the prior art. The basic principle of the three-dimensional directed sensor network deployment strategy based on the improved differential evolution algorithm is as follows: This algorithm combines crossover rate sorting and polynomial-based mutation operations to enhance the search ability, population diversity and optimization performance of the differential evolution algorithm, and at the same time avoid the algorithm falling into local optimality to improve the optimization efficiency.

[0066] The above-mentioned existing node deployment strategy optimization aims at the coverage deployment problem of a three-dimensional real terrain surface, adopts a heuristic algorithm, and seeks a solution by optimizing the node positions and angles at one time. However, when dealing with large-scale scenarios, this method faces great challenges in finding the optimal solution and has high requirements for the algorithm performance. At the same time, when the heuristic algorithm solves three-dimensional problems, there is usually a problem of high complexity. In summary, the existing optimization strategies have problems such as low network coverage rate and high complexity, and the heuristic algorithm used in this strategy is relatively backward, and its optimization ability needs to be improved.

[0067] Based on this, the present invention provides a method and system for optimizing the coverage of a wireless sensor network on a three-dimensional real terrain surface, which can improve the optimization ability, achieve a higher network coverage rate with a shorter operation time, and thus optimize the network coverage rate and network life.

[0068] The following will combine with the attached drawings to detail the technical solutions provided by each embodiment of the present invention.

[0069] Figure 1 It is a schematic flowchart of a method for optimizing the coverage of a wireless sensor network on a three-dimensional real terrain surface in the present invention, which specifically includes the following steps:

[0070] S101, taking the maximum planar coverage rate as the first constraint condition, using the tangent search quadratic interpolation optimization algorithm to optimize the planar coordinates of the sensor nodes, and determining the planar deployment strategy of the sensor nodes in the monitoring area; the tangent search quadratic interpolation optimization algorithm is an algorithm in which the population update method includes an exploration method, a development method, and a tangent search method integrated.

[0071] First, before calculating the planar deployment strategy of the sensor nodes in the monitoring area, first determine the number of sensor nodes to be deployed in the monitoring area according to the sensing radius of the sensor nodes and the size of the monitoring area.

[0072] The calculation formula for the number of sensor nodes is:

[0073] R ref =R s ×sin(θ) (1)

[0074]

[0075] wherein, R ref is the reference radius, R s is the node sensing radius, θ is half of the sensing angle, N is the number of sensor nodes to be deployed in the monitoring area, AreaX is the length of the monitoring area, and AreaY is the width of the monitoring area; the sensing ability of the node model decreases as the distance increases and the angle increases. Therefore, the sensing radius R s (19.74m) and the sensing angle θ (22.5°) corresponding to when the distance sensing ability and the angle sensing ability are 0.9 are selected to calculate R ref .

[0076] In one embodiment, taking the maximum planar coverage rate as the first constraint condition, using the tangent search quadratic interpolation optimization algorithm to optimize the planar coordinates of the sensor nodes, and determining the planar deployment strategy of the sensor nodes in the monitoring area includes:

[0077] S201, initializing the population size of the search agent; the population position in the population size represents the planar coordinates of the sensor nodes.

[0078] Initialize the population size of the search agents. Take the initialized positions of the sensor nodes as the positions of any individual in the initial search agent population, and generate the positions of the remaining individuals around the any individual.

[0079] The initialized population can be expressed as: Pos = Lb + rand × (Ub - Lb); Pos represents the population position, and Lb and Ub represent the lower bound and upper bound of the population respectively.

[0080] S202. Calculate the planar coverage rate of the search agent population according to the population position, and take the planar coverage rate as the fitness value.

[0081] First, calculate the distance between the sensor node and the grid points in the monitoring area:

[0082]

[0083] where Dis(S i ,P j ) represents the distance between the i-th sensor node and the j-th grid point, (x i , y i ) are the planar coordinates of the i-th sensor node, and (x j , y i ) are the planar coordinates of the j-th grid point.

[0084] For any sensor node and any grid point, judge the relationship between Dis(S i ,P j ) and the reference radius R ref . If Dis(S i ,P j ) <= R ref , it means that the grid point is monitored, and mark the grid point; otherwise, do not mark it. Count the number of grid points that are marked at least once, and calculate the planar coverage rate according to the number of grid points that are marked at least once and the total number of grid points in the monitoring area:

[0085]

[0086] S203. Compare the fitness values of the search agent population, and take the search agent individual with the maximum fitness value as the historical optimal position.

[0087] S204. Determine the population update method using an adaptive switching probability, and update the population positions of the search agents.

[0088] Specifically, an adaptive switching probability is adopted to determine the population update method, and the population position of the search agent is updated, including: obtaining a random number; if the random number is less than or equal to the adaptive switching probability, the exploration method is used to update the population position of the search agent; if the random number is greater than the adaptive switching probability model, it is determined whether the random number is greater than or equal to a specific value; if the random number is greater than or equal to the specific value, the exploitation method is used to update the population position of the search agent; if the random number is less than the specific value, the tangent search method is used to update the population position of the search agent.

[0089] The adaptive switching probability can be determined according to the current iteration number, the maximum iteration number, and the fitness value of the population at the current iteration number. Among them, the calculation formula of the adaptive switching probability is:

[0090]

[0091] Among them, represents the adaptive switching probability of the i-th population at the t-th iteration, T represents the maximum iteration number, represents the fitness value of the i-th population at the t-th iteration, and respectively represent the maximum and minimum fitness values at the t-th iteration.

[0092] The exploration position update formula:

[0093]

[0094] Among them, x i (t + 1) is the position of the i-th population at the t-th iteration, γ 1 is the first weight coefficient, x rand1 (t), x rand2 (t) and x rand3 (t) are the positions of different individuals randomly selected from the population at the t-th iteration, r 1 、r 2 and r 3 are random numbers between (0, 1), fit(·) represents the value of the fitness function, and the function GQI(·) outputs the minimum value of the interpolation function according to the input three positions and their fitness values. The value of the first weight coefficient γ 1 is determined by the following formula:

[0095] γ 1 =3n 1 b (8)

[0096]

[0097] Among them, n 1 obeys the standard normal distribution, and t is the current iteration number.

[0098] Develop the position update formula:

[0099]

[0100] where γ 2 is the second weight coefficient, n 2 follows the standard normal distribution, rD is a random integer in the interval [1, d], d represents the problem dimension, Lb rD and Ub rD represent the lower and upper bounds of the rD-th generation respectively, and the weight coefficient γ 2 is an adaptive development weight coefficient, and its value gradually decreases as the number of iterations increases; represents the population position of the rD-th generation.

[0101] Tangent search position update formula:

[0102]

[0103] where x i (t) is the position of the i-th population at the t-th iteration, norm() represents the Euclidean norm, d is the problem dimension, is randomly selected within the range of , and x best (t) is the historical optimal position at the t-th iteration.

[0104] S205. According to the population position of the updated search agent, recalculate the historical optimal position until the number of iterations reaches the maximum number of iterations, and then output the historical optimal position and the planar deployment strategy of the sensor nodes corresponding to the historical optimal position.

[0105] According to the population position of the search agent after updating the position, calculate the fitness value, and update the population position of the search agent after assignment according to the fitness value of the population of the search agent after updating the position to obtain a new population of search agents; compare the fitness value of the new population of search agents with the fitness value of the initial search agent, and select the search agent position corresponding to the maximum fitness value of the two as the historical optimal position of this iteration; determine whether the number of iterations reaches the maximum number of iterations; if the number of iterations reaches the maximum number of iterations, output the historical optimal position and the best planar deployment strategy of the sensor nodes corresponding to the historical optimal position; if the number of iterations does not reach the specific number of iterations, return to the step of "comparing the fitness values of the population of search agents and taking the search agent individual with the maximum fitness value as the historical optimal position".

[0106] Among them, updating the positions of the search agent population after assignment according to the fitness values of the search agent population after updating the positions specifically includes: comparing the fitness values of the search agent population after updating the positions with the fitness values of the search agents in the current generation. If it is better than the fitness value of the current generation, then retain the fitness value of the search agent population and retain the position of the search agent population; otherwise, no update is performed.

[0107] As Figure 2 shown, Figure 2 it is the flowchart of the tangent search quadratic interpolation optimization algorithm.

[0108] S102: Match the positions of the sensor nodes obtained from the planar deployment strategy of the sensor nodes with the grid point positions in the monitoring area, and fix the initial height of the sensor nodes to determine the initial spatial coordinates of the sensor nodes.

[0109] Specifically, for any sensor node, take the grid point with the closest planar distance to the sensor node as the grid point matched with the sensor node, and the initial height of the sensor node can be fixed at 23 meters, which specifically includes:

[0110]

[0111] [X, Y, Z] = [X, Y, Z point + 23] (16)

[0112] where X point , Y point and Z point represent the grid point coordinates matched with the i-th sensor node (X i , Y i ). X, Y, and Z represent the initial spatial coordinates of the i-th sensor node. In the initial state, the node height is 23 meters above the terrain surface, and this height meets the initial coverage requirements.

[0113] S103: Take the grid point matched with the sensor as the center and calculate the rotation angle of the sensor node.

[0114] Taking the grid point matched with the sensor as the center and calculating the rotation angle of the sensor node includes: for the grid point matched with any sensor node, take the matched grid point as the center and divide a grid area of a preset size; according to the grid area data, use the node rotation angle calculation method to calculate the rotation angle of the sensor node.

[0115] Specifically, taking the grid point matched with the sensor as the center and calculating the rotation angle of the sensor node includes:

[0116] For any grid point matched with a sensor node, taking the matched grid point as the center, and according to the data length of a preset size, obtain data in the four directions of up, down, left, and right to obtain grid area data of the preset size; obtain the x coordinate values of the first column and the last column, and the y coordinate values of the first row and the last row from the grid area data; respectively calculate the z coordinate means of the first column, the first row, the middle column, the middle row, the last column, and the last row according to the z coordinate values of all the data in the first column, the first row, the middle column, the middle row, the last column, and the last row in the grid area data; determine the rotation angle of the corresponding sensor node according to the x coordinate values of the first column and the last column, the y coordinate values of the first row and the last row, and the z coordinate means of the first column, the first row, the middle column, the middle row, the last column, and the last row.

[0117] Among them, dividing the grid area of the preset size, specifically, is to take the matched grid point as the center and spread m data lengths in all directions to generate grid point data (grid area data) with a side length of (2*m + 1). The number of grid areas is equal to the number of sensor nodes.

[0118] Optionally, determining the rotation angle of the corresponding sensor node according to the x coordinate values of the first column and the last column, the y coordinate values of the first row and the last row, and the z coordinate means of the first column, the first row, the middle column, the middle row, the last column, and the last row includes: judging whether the z coordinate means of the middle column and the middle row are significantly higher or lower than the z coordinate means of the first column, the first row, the last column, and the last row, and at the same time ensuring that the difference in the z coordinate means of the first column, the first row, the last column, and the last row is within the allowable range. Among them, being significantly higher or significantly lower can indicate that the difference between the z coordinate means of the middle column and the middle row and the z coordinate means of the first column, the first row, the last column, and the last row is greater than the first preset threshold or less than the second preset threshold.

[0119] If the above conditions are met, it is determined that the rotation angle of the sensor node is 0, indicating no significant rotation. Otherwise, calculate the slope angles in the x and y directions based on the difference in the z coordinate means of the first row and the first column, and use the arctangent function to convert these differences into angle values.

[0120] Using the arctangent function to convert these differences into angle values is:

[0121]

[0122] Among them, α and β are respectively the angle values of the sensor node in the x and y directions, x first 、x end respectively represent the x coordinate values of the first column and the last column, y first 、y endRepresent the y - coordinate values of the first row and the last row respectively, z first_x 、z end_x 、z first_y and z end_y represent the average Z - coordinate values of the first column, the last column, the first row and the last row respectively.

[0123] After scaling the corrected rotation angles in the x - direction and y - direction according to a certain ratio, store them in the result array, corresponding to the data unit being currently processed.

[0124] degree results_α = 0.3·α (19)

[0125] degree results_β = 0.3·β (20)

[0126] where degree results_α represents the rotation angle of the sensor node in the x - direction, degree results_β represents the rotation angle of the sensor node in the y - direction, and 0.3 is the scaling factor, whose size is determined by the terrain type.

[0127] In one embodiment, it specifically includes:

[0128] A data receiving and initializing unit, which receives a structure containing multiple data sets. Each data set is stored in an independent data unit, and each data unit contains two - dimensional coordinate information. It determines the total number of units in the data set and initializes the corresponding result array for storing the calculation results.

[0129] It should be clear that the sizes of the grid - area data obtained in the previous step are different (for the nodes near the boundary of the monitoring area, the side length of the generated grid area may not be 2*m + 1), and the grid - area data obtained in the previous step is stored centrally. The role of the data receiving unit is to split the centrally - stored grid - area data obtained in the previous step by region, and the subsequent operations process the data of each region separately, that is, each data set is stored in an independent unit, and the unit contains the coordinate information of a grid area (note: the two - dimensional coordinate data in the present invention refers to the dimensions of the array, that is, an array of size m*n, not the specific data. The data is actually three - dimensional coordinates).

[0130] The role of the initialization unit is the unit for storing the node rotation - angle data in this stage. The number of grid areas = the number of nodes = the size of the initialization data unit. Therefore, it is necessary to determine the total number of units in the data set and initialize the corresponding result array for storing the calculation results.

[0131] Process the data units one by one. For each data unit, extract its two-dimensional coordinate data and determine the number of rows and columns of the data to obtain the overall size of the data.

[0132] As mentioned above, "the sizes of the grid area data are different (for the nodes near the boundary of the monitoring area, the side length of the generated grid area may not be 2*m + 1)", so this operation needs to be carried out, which is defined as: process the data units one by one.

[0133] The key coordinate point extraction unit extracts the x and y coordinates of the key starting point and ending point from the current data unit to obtain the specific values of the starting point and ending point for subsequent calculations.

[0134] This step and the next step of obtaining coordinates are for calculating the rotation angle. This step is an operation on the x and y coordinates.

[0135] The characteristics of the grid area data are as follows: the X coordinates of each column are the same, and the Y coordinates of each row are the same; extracting the x and y coordinates of the starting point and ending point from the current data unit is to extract the x coordinates of the first column and the last column and select a value at any position (taking the first column as an example, the value selected in the text is the value at the first position of the first column), extract the y coordinates of the first row and the last row. For a grid area, the number of starting points and ending points is 2 respectively (1 row starting point, 1 column starting point, totaling 2; 1 row ending point, 1 column ending point, totaling 2), and the specific values of the starting point and ending point are the extracted coordinate values.

[0136] The middle point coordinate extraction unit extracts the coordinates of the middle positions of the data in the x direction and the y direction respectively to obtain the middle point information of the data, and extracts the z coordinate values of the starting, middle, and ending positions, corresponding to the x direction and the y direction respectively.

[0137] This step is an operation on the z coordinate. The size of the z coordinate has no pattern and needs to calculate the average value. Therefore, extract the z coordinate values of the first column, row, middle column, row, and the last column, row. The number of coordinates extracted by this extraction unit is different from the previous unit. Taking the first row as an example, the extracted z coordinates are the z coordinate values of the entire first row.

[0138] Extract the coordinates of the middle positions of the data in the x direction and the y direction respectively to obtain the middle point information of the data. The x direction and the y direction belong to the column direction and the row direction respectively. Extracting the coordinates of the middle positions of the data in the x direction and the y direction is to locate the positions of the middle column and row in order to extract the z coordinates of the middle column and row.

[0139] The z coordinate value calculation and storage unit calculates the average values of the z coordinates of the starting, middle, and ending positions in the x direction and the y direction to reflect the overall height change trend.

[0140] The angle judgment and calculation unit determines whether the average value of the z - coordinates at the middle position is significantly higher or lower than the average value of the starting and ending positions, while ensuring that the difference between the average values of the starting and ending positions is within the allowable range. If the above conditions are met, the rotation angle of the node is set to zero, indicating no significant rotation; otherwise, the slope angles in the x - direction and y - direction are calculated based on the z - coordinate differences between the starting and ending positions, and these differences are converted into angle values using the arctangent function.

[0141] The result storage and output unit scales the corrected rotation angles in the x - direction and y - direction according to a certain ratio and stores them in the result array corresponding to the currently processed data unit.

[0142] After processing all data units, an array of results containing the rotation angles of all nodes is output.

[0143] S104. According to the initial spatial coordinates and rotation angles of the sensor nodes, determine the spatial coordinates of the sensor nodes, and taking the maximum comprehensive coverage rate as the second constraint condition, use the tangent search quadratic interpolation optimization algorithm to optimize the spatial coordinates of the sensor nodes to determine the spatial deployment strategy of the sensor nodes in the monitoring area; the comprehensive coverage rate is obtained by considering the distance perception and angle perception of the sensor nodes.

[0144] Optionally, determining the spatial coordinates of the sensor nodes according to the initial spatial coordinates and rotation angles of the sensor nodes includes: determining the coordinates of the center of the bottom surface of the cone covered by the sensor node in the monitoring area according to the initial spatial coordinates and rotation angles of the sensor node; determining the sensing direction of the sensor node according to the coordinates of the center of the bottom surface of the cone and the initial spatial coordinates; determining the spatial coordinates of the sensor node according to the sensing direction, the scaling factor, and the initial spatial coordinates.

[0145] The calculation formula for the spatial coordinates of the sensor node is:

[0146] New nc = Direction * τ+nc (21)

[0147] Direction = cc - nc (22)

[0148]

[0149] Among them, New nc represents the spatial coordinates of the sensor node, Direction represents the sensing direction of the sensor node, τ represents the scaling factor, nc represents the initial spatial coordinates, cc represents the coordinates of the center of the bottom surface of the cone of the sensor node, cc x 、cc y and cc z respectively represent the coordinates of the center of the bottom surface of the cone on the x - axis, y - axis, and z - axis, nc xand nc y represent the x - coordinate value and y - coordinate value in the initial spatial coordinates respectively, Rs represents the sensing radius, θ represents half of the sensing range, degree results_α and degree results_β represent the rotation angle of the sensor node in the horizontal direction and the rotation angle in the vertical direction respectively.

[0150] As Figure 3 shown, Figure 3 is a schematic diagram of the sensing range of the sensor node. The sensing direction of the sensor is , and the sensing direction of the sensor node is vertically downward from the horizontal plane. Therefore, the coordinates of the center of the bottom surface of the cone of the sensor node are the coordinates where it intersects the monitoring area vertically downward from the horizontal plane.

[0151] In the height optimization stage, the tangent search quadratic interpolation optimization algorithm is used for optimization. The solution space of the population is: between the initial position coordinates of the node and the coordinates of the center of the bottom surface of the node model cone (i.e., the node only moves in the current sensing direction after rotation). This can reduce the activity space of the population and improve the convergence of the solution; and in the process of optimizing the spatial coordinates of the sensor node using the tangent search quadratic interpolation optimization algorithm, the algorithm optimizes the scaling factor τ.

[0152] Optionally, the calculation method of the comprehensive coverage rate specifically includes:

[0153] For any grid point, calculate the distance perception of each sensor for the grid point according to the spatial coordinates of the sensor node and the coordinates of the grid point.

[0154]

[0155] Among them, represents the distance perception of the i - th sensor node for the j - th grid point, dist represents the Euclidean distance between the i - th sensor node and the j - th grid point, (x i , y i , z i ) are the spatial coordinates of the i - th sensor node, (x j , y j , z j ) are the spatial coordinates of the j - th grid point, R l , R m , R u are custom threshold factors respectively, which can be determined according to historical experience; the parameters ν 1 , ν 2 , ω 1 and ω 2 are used to represent the sensing characteristics of the node. Different types of sensors can be simulated by adjusting the values of these parameters, satisfying ν 1+ν 2 = 1, and ν 1 , ν 2 ∈ [0, 1].

[0156] Calculate the angle perception of each sensor node with respect to the grid point based on the horizontal deflection angle and vertical deflection angle between the sensor node and the grid point.

[0157]

[0158] Wherein, represents the angle perception of the i-th sensor node with respect to the j-th grid point, represents the combined deflection angle of the horizontal deflection angle and the vertical deflection angle, represents the horizontal deflection angle, represents the vertical deflection angle, θ l , θ m , θ u are custom threshold factors respectively, and the parameters λ 1 , λ 2 , μ 1 , μ 2 , σ PAN and σ TILT represent the sensing characteristics of the node. By adjusting the values of these parameters, different types of sensors can be simulated to satisfy λ 1 + λ 2 = 1, and λ 1 , λ 2 ∈ [0, 1]; the greater the angle between the node sensing direction and the vector direction determined by the two coordinate points of the node and the grid point, the smaller its sensing probability.

[0159]

[0160] Wherein, new nc x (S i ), new nc y (S i ) and New nc z (S i ) represent the x-coordinate value, y-coordinate value and z-coordinate value of the sensor node S i in the space coordinate respectively, and X point (P j ), Y point (P j ) and Z point (P j ) represent the x-coordinate value, y-coordinate value and z-coordinate value of the grid point P j in the monitoring area respectively. cc x (S i ), cc y(S i ) and cc z (S i ) represent the coordinates of the center of the conical bottom surface of the sensor node S i on the x-axis, y-axis, and z-axis respectively, and Direction(S i ) represents the sensing direction of the sensor node S i .

[0161] Based on the distance perception and angle perception of each sensor node for the grid points, the overall perception probability of each grid point is obtained; the formula for calculating the overall perception probability is:

[0162] P OV (S i , P j ) = P Dist (S i , P j ) × P Agl (S i , P j ) (32)

[0163]

[0164] Among them, P state (P j ) represents the overall perception probability of the jth grid point, P th represents the threshold factor, which can be determined according to historical experience; N represents the number of sensor nodes.

[0165] Based on the overall perception probability of each grid point, the comprehensive coverage rate is determined; the formula for calculating the comprehensive coverage rate is:

[0166]

[0167] Among them, CR represents the overall perception probability, and M represents the total number of grid points in the monitoring area.

[0168] It should be noted that there may be occlusion in the sensing range of the sensor node. Therefore, when calculating the overall perception probability of the sensor node, the line-of-sight perception of the sensor can also be considered. As Figure 4 shown, let the set of target area points that the ith sensor node S i can sense be P Area . Since area A and area B are non-adjacent areas, there are differences in their coordinate information, and the coordinates within each area are continuous numerically. Therefore, the set P Area can be divided into P AreaA = {P 1 , P 2 , …, P u} and PAreaB = {P 1 , P 2 , …, P v}, two subsets, respectively representing the point sets of two regions. By calculating the average distance from the two sets to the node, it is determined whether the region can be monitored. The point set with a relatively closer average distance can be monitored and is regarded as LOS, otherwise it is NLOS; as shown in formula (36).

[0169]

[0170] Therefore, considering the line-of-sight perception, the calculation formula for the overall perception probability is as follows:

[0171] P OV (S i , P j ) = P LOS (S i , P j ) × P Dist (S i , P j ) × P Agl (S i , P j ) (37)

[0172] In one embodiment, the maximum comprehensive coverage rate is used as the fitness function. Under the constraint of the fitness function, the tangent search quadratic interpolation optimization algorithm is used to iteratively update the positions of the search agent population a specific number of times; after the iteration ends, the optimal spatial deployment strategy of the sensor nodes corresponding to the search agent of the tangent search quadratic interpolation optimization algorithm is output. The optimal spatial deployment strategy of the sensor nodes includes the spatial coordinates of each sensor node in the monitoring area.

[0173] The tangent search quadratic interpolation optimization algorithm in this embodiment is the same as the tangent search quadratic interpolation optimization algorithm for optimizing the plane coordinates described above, and will not be elaborated here in this embodiment.

[0174] S105, deploy sensors on the monitoring area corresponding to the spatial deployment strategy of the sensor nodes.

[0175] The coverage rate of the sensor network for the covered area is the optimized fitness function. By maximizing this function, an ideal deployment method is obtained. To more effectively find the global optimal solution, the adaptive switching probability increases the exploration intensity in the initial stage of the algorithm to ensure that the population can widely cover the entire search space, thereby enhancing the diversity of solutions. In the later stage of the algorithm, local search is performed near the known optimal solution. At the same time, the tangent search improves the convergence of the algorithm and speeds up the convergence speed, making it easier to find the optimal solution.

[0176] The present invention adopts a distributed optimization strategy based on the tangent search quadratic interpolation optimization algorithm, combined with three key steps of plane optimization, angle optimization, and height optimization. First, the quadratic interpolation optimization algorithm is improved, and the tangent search quadratic interpolation optimization algorithm is proposed. By introducing an adaptive switching probability and tangent search technology, the search speed and convergence of the algorithm are significantly improved. The algorithm is used to optimize the plane of the nodes so that they are reasonably distributed in the monitoring area. Secondly, a scheme for calculating the horizontal deflection angle and vertical deflection angle of the nodes is proposed for node angle optimization. Finally, the tangent search quadratic interpolation optimization algorithm is used to adjust the height of the nodes again to achieve optimal coverage. The present invention effectively solves the problems of low efficiency and insufficient coverage rate in the deployment of wireless sensor network nodes under three-dimensional real terrain.

[0177] In one embodiment, the present invention also provides a method for optimizing the coverage of a wireless sensor network on the surface of a three-dimensional real terrain, as Figure 5 shown. This embodiment includes the following steps:

[0178] S501. Determine the number of sensors according to the planar size of the monitoring area, and calculate the planar coverage rate based on the sensor nodes and grid points.

[0179] S502. Take the maximum planar coverage rate as the fitness function.

[0180] S503. Under the constraint of the fitness function, use the tangent search quadratic interpolation optimization algorithm to iteratively update the positions of the search agent population a specific number of times.

[0181] S504. Match the positions of the sensor nodes obtained from the best planar deployment strategy of the sensor nodes with the positions of the grid points, and fix the initial height of the sensor nodes.

[0182] S505. Take the matched grid points as the center and divide a grid area of a specific size.

[0183] S506. According to the grid area data, use the node rotation angle calculation method to calculate the rotation angle of the sensor nodes.

[0184] S507. Calculate the comprehensive coverage rate based on the positions, angles, and grid points of the sensor nodes.

[0185] S508. Take the maximum comprehensive coverage rate as the fitness function.

[0186] S509. Under the constraint of the fitness function, use the tangent search quadratic interpolation optimization algorithm to iteratively update the positions of the search agent population a specific number of times.

[0187] S510. After the iteration ends, output the best spatial deployment strategy of the sensor nodes corresponding to the search agent of the tangent search quadratic interpolation optimization algorithm.

[0188] In the embodiments of the present invention, a tangent search quadratic interpolation optimization algorithm, a node rotation angle calculation method, and a comprehensive coverage rate evaluation method are adopted, effectively optimizing the coverage of wireless sensor networks on the three-dimensional real terrain surface, achieving a higher network coverage rate, significantly shortening the operation time, and improving the optimization efficiency. In summary, the present invention provides an efficient, reliable, and highly adaptable wireless sensor network coverage optimization method and system, significantly improving the network coverage rate, and having broad application prospects and practical value.

[0189] To further illustrate the above problems, a simulation of the distributed optimization strategy (Distributed Optimization Strategy Based on the Tangent Search Quadratic Interpolation Optimization Algorithm, TS-QIODOS) used in the present invention for the wireless sensor network coverage problem is carried out. It is considered that 64 sensors are deployed in an area of 100m×100m. The population size is 50, and the sensor sensing radius R S = 25m. The number of iterations is 100 times. The grid size is considered to be 1m×1m, that is, 10,000 grid points are used to calculate the coverage rate.

[0190] When the monitored area is 100m×100m and the number of nodes is 64, the coverage effect is as Figure 6 shown, where the red solid circles represent the sensing nodes. Figure 6 is the convergence curve graph of the coverage rate with the comparative algorithm. It can be seen from Figure 6 the distribution of the nodes that the distribution of the sensor nodes after executing TS-QIODOS is more uniform and the coverage effect is better.

[0191] To compare the convergence performance of the algorithms, by comparing the coverage rate iteration of TS-QIODOS with the quadratic interpolation optimization algorithm (Quadratic Interpolation Optimization Algorithm, QIO), the differential evolution algorithm based on CR sorting and polynomial mutation (CC DE-based algorithm with CR-sort and polynomial-based mutation, CCDEXSPM), and the Archimedes optimization algorithm (Archimedes Optimization Algorithm, AOA), as Figure 7As shown, the abscissa is the number of iterations, and the ordinate is the coverage rate. From Figure 7 it can be seen that the convergence rate of TS-QIODOS is faster than before the improvement, and the coverage rate has increased significantly.

[0192] Table 1 shows the comparison of the running times of four algorithms under different terrains. The running time of TS-QIODOS on the plain terrain is 16396.35 seconds, 20683.22 seconds on the hilly terrain and 14821.26 seconds on the mountainous terrain. The running time of TS-QIODOS is reduced by 61.89% compared with the QIO algorithm and by 27.5% compared with the CCDEXSPM algorithm. The running time is significantly lower than that of the QIO and CCDEXSPM algorithms.

[0193] Table 2 shows the comparison of the energy consumption of four algorithms under different terrains. In the plain, hilly and mountainous terrains, the corresponding energy consumption of TS-QIODOS is 34.8016 J, 42.0692 J and 43.1638 J respectively. Taking the plain terrain as an example, compared with the QIO algorithm, the energy consumption of TS-QIODOS is reduced by 67.66%; compared with the CCDEXSPM algorithm, it is reduced by 97.37%; compared with the AOA algorithm, it is reduced by 36.15%. These data indicate that TS-QIODOS effectively shortens the moving distance of the nodes and reduces the rotation angle, thereby reducing the moving energy consumption, enabling more energy to be used for the monitoring task, and extending the service life of the network as a whole.

[0194] Table 1

[0195]

[0196] Table 2

[0197]

[0198] When applying the wireless sensor network coverage optimization method for the three-dimensional real terrain surface provided by the present invention, it is not necessary to execute according to Figure 1 the order of the steps shown. The specific execution order of each step can be determined as needed, and the present invention does not limit this.

[0199] The above is the wireless sensor network coverage optimization method for the three-dimensional real terrain surface provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding wireless sensor network coverage optimization system for the three-dimensional real terrain surface, as Figure 8 shown.

[0200] Figure 8Schematic diagram of a wireless sensor network coverage optimization system for a three-dimensional real terrain surface provided by the present invention. The system 800 includes:

[0201] A planar optimization algorithm module 801, which is used to optimize the planar coordinates of sensor nodes with the maximum planar coverage rate as the first constraint condition by using the tangent search quadratic interpolation optimization algorithm, and determine the planar deployment strategy of sensor nodes in the monitoring area; the tangent search quadratic interpolation optimization algorithm is an algorithm in which the population update method includes an exploration method, a development method, and a tangent search method integrated.

[0202] An angle optimization module 802, which is used to match the positions of sensor nodes obtained from the planar deployment strategy of the sensor nodes with the positions of grid points in the monitoring area, fix the initial height of the sensor nodes, and determine the initial spatial coordinates of the sensor nodes; taking the grid points matched with the sensor nodes as the center, calculate the rotation angle of the sensor nodes.

[0203] A height optimization module 803, which is used to determine the spatial coordinates of the sensor nodes according to the initial spatial coordinates and rotation angle of the sensor nodes, and optimize the spatial coordinates of the sensor nodes by using the tangent search quadratic interpolation optimization algorithm with the maximum comprehensive coverage rate as the second constraint condition, and determine the spatial deployment strategy of sensor nodes in the monitoring area; the comprehensive coverage rate is obtained by considering the distance perception and angle perception of sensor nodes.

[0204] A deployment module 804, which is used to deploy sensors on the monitoring area corresponding to the spatial deployment strategy of the sensor nodes.

[0205] Optionally, the system 800 further includes a data processing module and a control and management module.

[0206] The data processing module is used to receive and import the three-dimensional terrain data, sensor node parameters, and grid point distribution information of the monitoring area, divide the grid area of a specific size according to the terrain data, and provide a basis for subsequent coverage rate calculation.

[0207] The control and management module is used to manage the iteration times and termination conditions of the optimization algorithm, ensure that the algorithm runs within a predetermined number of iterations, record the optimal solution of each iteration, and ensure that the finally output deployment strategy is optimal.

[0208] For the specific limitations of the wireless sensor network coverage optimization device for the three-dimensional real terrain surface, reference can be made to the limitations of the wireless sensor network coverage optimization method for the three-dimensional real terrain surface in the above text, which will not be elaborated here. Each module in the above-mentioned wireless sensor network coverage optimization device for the three-dimensional real terrain surface can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor in the computer device in hardware form or be independent of it, or can be stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0209] The present invention also provides a computer-readable storage medium, which stores a computer program that can be used to execute the above Figure 1 provided wireless sensor network coverage optimization method for the three-dimensional real terrain surface.

[0210] The present invention also provides Figure 9 a schematic structural diagram of the computer device shown in, as Figure 9 shown, at the hardware level, this computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include other hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the above Figure 1 provided wireless sensor network coverage optimization method for the three-dimensional real terrain surface.

[0211] Those of ordinary skill in the art can understand that all or part of the processes in the above method embodiments can be completed by instructing relevant hardware through a computer program. 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 method embodiments. Among them, any reference to the memory, storage, database, or other media used in the various embodiments provided by the present invention can include at least one of non-volatile and volatile memories. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. The volatile memory can include random access memory (RAM) or an external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0212] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope recorded by the present invention.

Claims

1. A wireless sensor network coverage optimization method for a three-dimensional real terrain surface, characterized in that: include: Taking the maximum plane coverage as the first constraint, the tangent search quadratic interpolation optimization algorithm is used to optimize the plane coordinates of the sensor nodes and determine the plane deployment strategy of the sensor nodes in the monitoring area; the tangent search quadratic interpolation optimization algorithm is an algorithm that integrates the population update method, including the exploration method, the development method and the tangent search method; Matching the sensor node positions obtained by the plane deployment strategy of the sensor node with the grid point positions of the monitoring area, fixing the initial height of the sensor node, and determining the initial spatial coordinates of the sensor node; The grid point matching the sensor node is taken as the center to calculate the rotation angle of the sensor node; According to the initial spatial coordinates and rotation angle of the sensor node, the spatial coordinates of the sensor node are determined, and the maximum comprehensive coverage rate is used as the second constraint condition, and the spatial coordinates of the sensor node are optimized by using the tangent search quadratic interpolation optimization algorithm to determine the spatial deployment strategy of the sensor node in the monitoring area; the comprehensive coverage rate is obtained by considering the distance perception and angle perception of the sensor node; Sensors are correspondingly deployed in the monitoring area according to the spatial deployment strategy of the sensor nodes.

2. The method according to claim 1, characterized in that The method uses the maximum plane coverage as the first constraint condition, uses the tangent search quadratic interpolation optimization algorithm to optimize the plane coordinates of the sensor nodes, and determines the plane deployment strategy of the sensor nodes in the monitoring area, including: Initializing a population size of a search agent; a population position in the population size represents a plane coordinate of a sensor node; According to the population position, the plane coverage rate of the search agent population is calculated, and the plane coverage rate is used as the fitness value; Compare the fitness values ​​of the search agent population, and take the search agent individual with the largest fitness value as the historical optimal position; Adopting adaptive switch probability to determine the population update method and update the population position of the search agent; The historical optimal position is recalculated according to the updated population position of the search agent until the number of iterations reaches the maximum number of iterations, and the historical optimal position and the plane deployment strategy of the sensor nodes corresponding to the historical optimal position are output.

3. The method according to claim 2, characterized in that The method of using the adaptive switch probability to determine the population update mode and updating the population position of the search agent includes: Get a random number; If the random number is less than or equal to the adaptive switch probability, the population position of the search agent is updated in an exploratory manner; If the random number is greater than the adaptive switch probability model, determining whether the random number is greater than or equal to a specific value; If the random number is greater than or equal to a specific value, the population position of the search agent is updated in a development manner; If the random number is less than a specific value, the population position of the search agent is updated using the tangent search method.

4. The method according to claim 1, characterized in that: Before calculating the planar deployment strategy of the sensor nodes in the monitoring area, the method further includes: The number of sensor nodes to be deployed in the monitoring area is determined according to the sensing radius of the sensor node and the size of the monitoring area.

5. The method according to claim 1, characterized in that: The method of calculating the rotation angle of the sensor node by taking the grid point matched with the sensor as the center includes: For the grid point that matches any sensor node, take the matched grid point as the center, obtain data in four directions of up, down, left and right according to the preset data length, and obtain the grid area data of the preset size; Obtain the x-coordinate values ​​of the first and last columns, and the y-coordinate values ​​of the first and last rows from the grid area data; According to the z coordinate values ​​of all data in the first column, first row, middle column, middle row, last column and last row in the grid area data, calculate the mean z coordinate values ​​of the first column, first row, middle column, middle row, last column and last row respectively; The rotation angle of the corresponding sensor node is determined according to the x-coordinate values ​​of the first and last columns, the y-coordinate values ​​of the first and last rows, and the z-coordinate averages of the first column, first row, middle column, middle row, last column, and last row.

6. The method according to claim 1, characterized in that Determining the spatial coordinates of the sensor node according to the initial spatial coordinates and the rotation angle of the sensor node includes: Determine the coordinates of the center of the cone bottom surface of the sensor node covering the monitoring area according to the initial spatial coordinates and the rotation angle of the sensor node; Determine the sensing direction of the sensor node according to the coordinates of the center of the cone bottom and the initial space coordinates; The spatial coordinates of the sensor node are determined according to the sensing direction, the scaling factor and the initial spatial coordinates.

7. The method according to claim 6, characterized in that The calculation formula of the spatial coordinates of the sensor node is: New nc=Direction*τ+nc; Direction = cc-nc; Among them, New nc represents the spatial coordinates of the sensor node, Direction represents the sensing direction, τ represents the scaling factor, nc represents the initial spatial coordinates, cc represents the coordinates of the center of the cone bottom of the sensor node, and cc x ,cc y and cc z Respectively represent the coordinates of the center of the cone base on the x-axis, y-axis, and z-axis, nc x and nc y They represent the x-coordinate value and y-coordinate value in the initial space coordinates, Rs represents the perception radius, θ represents half of the perception range, and degree results_α 、degree results_β Represents the horizontal rotation angle and vertical rotation angle of the sensor node.

8. The method according to claim 7, characterized in that In the process of optimizing the spatial coordinates of the sensor node using the tangent search quadratic interpolation optimization algorithm, the algorithm optimizes the scaling factor τ.

9. The method according to claim 1, characterized in that: The calculation method of the comprehensive coverage ratio includes: For any grid point, the distance perception of each sensor to the grid point is calculated based on the spatial coordinates of the sensor node and the coordinates of the grid point; According to the horizontal deflection angle and vertical deflection angle between the sensor node and the grid point, the angle perception of each sensor node to the grid point is calculated; According to the distance perception and angle perception of each sensor node to the grid point, the overall perception probability of each grid point is obtained, and the comprehensive coverage rate is determined according to the overall perception probability of each grid point.

10. A wireless sensor network coverage optimization system for a three-dimensional real terrain surface, characterized in that: include: The plane optimization algorithm module is used to optimize the plane coordinates of the sensor nodes using the tangent search quadratic interpolation optimization algorithm with the maximum plane coverage as the first constraint condition, and determine the plane deployment strategy of the sensor nodes in the monitoring area; the tangent search quadratic interpolation optimization algorithm is an algorithm that integrates the population update method including the exploration method, the development method and the tangent search method; An angle optimization module, used to match the sensor node position obtained by the plane deployment strategy of the sensor node with the grid point position of the monitoring area, fix the initial height of the sensor node, determine the initial spatial coordinates of the sensor node; take the grid point matched with the sensor node as the center, and calculate the rotation angle of the sensor node; A height optimization module is used to determine the spatial coordinates of the sensor node according to the initial spatial coordinates and rotation angle of the sensor node, and to optimize the spatial coordinates of the sensor node using a tangent search quadratic interpolation optimization algorithm with the maximum comprehensive coverage as the second constraint condition, so as to determine the spatial deployment strategy of the sensor node in the monitoring area; the comprehensive coverage is obtained by considering the distance perception and angle perception of the sensor node; The deployment module is used to deploy sensors corresponding to the monitoring area according to the spatial deployment strategy of the sensor nodes.

Images