Wireless sensor network coverage optimization method

Abstract

The invention relates to a wireless sensor network coverage optimization method, and the method comprises the steps: enabling each wireless sensor in a sensor group to correspond to each dung beetle in a one-to-one manner, and combining the division of a roll dung beetle group, a brooding roll dung beetle group, a foraging small dung beetle group and a thief dung beetle group; an improved dung beetle optimization algorithm integrated with a spiral search strategy, an optimal value guide strategy and a random disturbance strategy is executed, network coverage of a sensor group to a target area is optimized, the problem that an existing dung beetle algorithm is not uniform in distribution is solved, the algorithm is prevented from falling into a local optimal value, the generation probability of an optimal solution is greatly increased, and the network coverage rate is increased. The balance between global search and calculation precision is realized, the effective coverage rate of the network is effectively improved, and the rapid coverage of the area is realized.

Description

Technical Field

[0001] The invention relates to a wireless sensor network coverage optimization method, belonging to the technical field of network coverage optimization design. Background Art

[0002] With the in-depth research in the fields of computers, microelectronics, wireless communications and the Internet of Things, the application scope of wireless sensor networks (WSN) in various fields has gradually expanded. A wireless sensor network is a self-organizing sensor network established by a large number of low-power micro sensor nodes in a single-hop routing or multi-hop routing communication mode to perform collaborative perception. According to the needs of users, it is deployed in a specific area to perform storage, communication and calculation operations on the data information collected by the sensor nodes, thereby realizing environmental perception, real-time monitoring and data analysis in the monitoring area. It has the characteristics of random deployment, self-organization and network dynamic adaptation. According to different monitoring task requirements, wireless sensor networks usually randomly deploy large-scale sensor nodes in the monitoring area to perform information collection tasks and collect environmental data (such as ambient temperature, air humidity, light intensity, etc.). Network coverage is one of the key factors affecting the service quality of sensor networks. When wireless sensor networks are applied to complex environments, sensor nodes are usually deployed in a random scattering manner, which will lead to coverage blind spots and high node redundancy, making it difficult to achieve the expected coverage quality. The use of swarm intelligence optimization algorithms can effectively improve the network coverage.

[0003] In recent years, scholars have explored and studied the optimization of node coverage in wireless sensor networks using genetic algorithms, particle swarm algorithms, differential algorithms, gray wolf algorithms, sparrow search algorithms, etc., but there are still problems such as premature convergence and low coverage. The Dung Beetle Optimizer (DBO) simulates the behaviors of dung beetles in the biological world, such as pushing balls, reproduction, foraging, and stealing. The algorithm has the characteristics of low computational complexity and strong robustness. Although the Dung Beetle Algorithm has demonstrated superiority in solving most optimization problems, like other swarm intelligence optimization algorithms, it also has problems such as low solution accuracy and slow convergence. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a wireless sensor network coverage optimization method, introduce an improved dung beetle algorithm, overcome the low calculation accuracy and premature convergence problems of the prior art, and optimize the wireless network coverage efficiency.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions: the present invention designs a wireless sensor network coverage optimization method, for a sensor group composed of a preset number of wireless sensors, each wireless sensor corresponds to each dung beetle one by one, combined with the division of the sensor group into rolling ball dung beetle grouping, brooding ball dung beetle grouping, foraging small dung beetle grouping, and thief dung beetle grouping, executes an improved dung beetle optimization algorithm that incorporates a spiral search strategy, an optimal value guidance strategy, and a random perturbation strategy, and optimizes the network coverage of the sensor group regarding the target area.

[0006] As a preferred technical solution of the present invention: comprising the following steps A to F;

[0007] Step A. Initialize the number of iterations t=1, and randomly initialize the position of each wireless sensor in the sensor group in the target area as the position of each wireless sensor in the target area in the tth iteration, and enter step B;

[0008] Step B. According to the position of each wireless sensor in the target area at the t-th iteration, the network coverage of the sensor group to the target area is calculated to form the overall fitness of the sensor group with respect to the target area at the t-th iteration, and further obtain the individual fitness of each wireless sensor in the sensor group with respect to the target area at the t-th iteration, and obtain the sensor ranking of each wireless sensor at the t-th iteration from large to small according to its individual fitness, and then enter step C;

[0009] Step C. According to the one-to-one correspondence between each wireless sensor and each dung beetle, and according to the number of wireless sensors in the sensor group, the preset rolling ball dung beetle group, brooding ball dung beetle group, foraging small dung beetle group, and thief dung beetle group, the number of dung beetles corresponding to each preset rolling ball dung beetle group, brooding ball dung beetle group, foraging small dung beetle group, and thief dung beetle group are sequentially divided in the t-th iteration for each wireless sensor in the sensor sorting in the t-th iteration, and then the process proceeds to step D;

[0010] Step D. Based on the improved dung beetle optimization algorithm incorporating the spiral search strategy, the optimal value guidance strategy, and the random perturbation strategy, the rolling ball dung beetle group, the brooding ball dung beetle group, the foraging small dung beetle group, and the thief dung beetle group are sequentially updated with the position of the first dung beetle in the group as the target, and the updated positions of each dung beetle in the group under the t-th iteration are formed in combination with the position of the first dung beetle in the group. Then, for the group, according to the method of step B, the individual fitness corresponding to the updated position of each dung beetle in the group under the t-th iteration is calculated; and then the individual fitness corresponding to the updated position of each dung beetle in each group under the t-th iteration is obtained, and then the process proceeds to step E;

[0011] Step E. For each wireless sensor in the sensor sorting at the t-th iteration, determine whether the individual fitness of the wireless sensor is less than the individual fitness of the corresponding dung beetle update position. If yes, adjust the position of the wireless sensor to the corresponding dung beetle update position; otherwise, keep the position of the wireless sensor unchanged, and then form the update position of each wireless sensor in the sensor group at the t-th iteration. Then, calculate and obtain the individual fitness corresponding to the update position of each wireless sensor in the sensor group at the t-th iteration in the manner of step B, and obtain the maximum individual fitness therein as the target fitness at the t-th iteration, and then enter step F;

[0012] Step F. Determine whether t is equal to the preset maximum number of iterations T. If yes, obtain the maximum value of the target fitness under each iteration, and use the updated position of each wireless sensor corresponding to the maximum value, and optimize the network coverage of the sensor group in the target area according to the position distribution of each wireless sensor in the sensor group in the target area; otherwise, obtain the maximum value of the target fitness under each iteration, and use the updated position of each wireless sensor corresponding to the maximum value as the position of each wireless sensor in the target area under the t+1th iteration, then add 1 to update t, and return to step B.

[0013] As a preferred technical solution of the present invention: in the step D, for the grouping of brooding ball dung beetles in the t-th iteration, a spiral search strategy is adopted, with the position of the first dung beetle in the grouping of brooding ball dung beetles as the target, and the positions of other dung beetles in the group are sequentially targeted, according to the following formula:

[0014]

[0015] Calculate the updated position of the dung beetle, where k 1,i (t) represents the position of the i-th dung beetle in the brooding ball dung beetle group under the corresponding sensor sorting in the t-th iteration, k1' ,i (t) represents k 1,i (t)’s updated position, represents the position of the first dung beetle in the brooding ball dung beetle group in the tth iteration, l, b1, b2 are random numbers (0,1), r represents the spiral search parameter under the spiral search strategy, e represents the natural constant, Ub * , Lb * They respectively represent the upper and lower limits of the moving distance of the preset dung beetles in the brooding ball dung beetle group under a single position update.

[0016] As a preferred technical solution of the present invention: in the step D, for the foraging dung beetle group in the t-th iteration, the optimal value guidance strategy is adopted, with the position of the first dung beetle in the foraging dung beetle group as the target, and the positions of other dung beetles in the group are sequentially targeted, according to the following formula:

[0017]

[0018] Calculate the updated position of the dung beetle, where k 2,i (t) represents the position of the i-th dung beetle in the corresponding sensor sorting in the foraging dung beetle group at the t-th iteration, k' 2,i (t) represents k 2,i (t)’s updated position, represents the position of the first dung beetle in the foraging dung beetle group at the tth iteration, C1~N(0,1), C1 represents a random number that obeys the normal distribution, C2 represents a 1×D random vector belonging to (0,1), and D represents the dimension of the problem to be optimized. λ represents the optimal value guidance parameter under the optimal value guidance strategy, UB2 and LB1 represent the upper and lower limits of the moving distance of the foraging dung beetles under a single position update in the preset grouping of dung beetles, respectively.

[0019] As a preferred technical solution of the present invention: in the step D, for the grouping of dung beetles in the t-th iteration, a random perturbation strategy is adopted, with the position of the first dung beetle in the grouping of dung beetles as the target, and the positions of other dung beetles in the group are sequentially targeted, according to the following formula:

[0020]

[0021] Calculate the updated position of the dung beetle, where k 3,i (t) represents the position of the i-th dung beetle in the corresponding sensor sorting in the thief dung beetle group at the t-th iteration, k3' ,i (t) represents k 3,i (t)’s updated position, represents the position of the first dung beetle in the group of foraging dung beetles at the tth iteration, k * (t) represents the position of the first dung beetle in the rolling ball dung beetle group at the tth iteration, g represents a random vector of size 1×D, R=rand(1,D)×2×δ-δ, R represents the random perturbation term under the random perturbation strategy, δ represents the preset constant, D represents the dimension of the problem to be optimized, and S represents the preset constant value.

[0022] As a preferred technical solution of the present invention: in the step A, Tent mapping is used to perform a chaotic initialization operation to initialize the position of each wireless sensor in the sensor group in the target area as the position of each wireless sensor in the target area in the tth iteration.

[0023] As a preferred technical solution of the present invention: in the step B, based on the grid division of the target area, the following steps B1 to B3 are performed to obtain the overall fitness of the sensor group with respect to the target area in the tth iteration;

[0024] Step B1. According to the following formula:

[0025]

[0026] The probability of a single grid in the target area being sensed by a single wireless sensor is obtained, where 1≤i≤I, I represents the number of wireless sensors in the sensor group, 1≤j≤J, J represents the number of grids in the target area, P(k i ,h j ) represents the probability that the jth grid in the target area is sensed by the i-th wireless sensor, d(k i ,h j ) represents the distance between the jth grid and the i-th wireless sensor in the target area, h j represents the position of the jth grid in the target area, Respectively represent the horizontal and vertical coordinates of the j-th grid position in the target area, k i represents the location of the i-th wireless sensor, Respectively represent the horizontal and vertical coordinates of the i-th wireless sensor, R p Indicates the sensing radius of the wireless sensor;

[0027] Step B2. According to the following formula:

[0028]

[0029] Get the probability that a single grid in the target area is sensed by the sensor group, where P(K,h j ) represents the probability that the jth grid in the target area is sensed by the sensor group, and K represents the sensor group;

[0030] Step B3. According to the following formula:

[0031]

[0032] The network coverage of the sensor group to the target area is obtained, which constitutes the overall fitness of the sensor group with respect to the target area in the tth iteration.

[0033] Accordingly, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the wireless sensor network coverage optimization method when executing the computer program.

[0034] The wireless sensor network coverage optimization method described in the present invention adopts the above technical solution and has the following technical effects compared with the prior art:

[0035] The invention designs a wireless sensor network coverage optimization method, which corresponds each wireless sensor in a sensor group to each dung beetle one by one, combines the division of rolling ball dung beetle grouping, brooding ball dung beetle grouping, foraging small dung beetle grouping, and thief dung beetle grouping, executes an improved dung beetle optimization algorithm integrating a spiral search strategy, an optimal value guidance strategy, and a random perturbation strategy, optimizes the network coverage of the sensor group to the target area, overcomes the uneven distribution problem of the existing dung beetle algorithm, and avoids the algorithm from falling into a local optimal value, greatly increases the probability of generating the optimal solution, achieves a balance between global search and calculation accuracy, effectively improves the effective coverage rate of the network, and realizes rapid coverage of the area. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a flow chart of the method for optimizing the coverage of a wireless sensor network designed by the present invention;

[0037] Figure 2 The present invention is in 20×20m 2 Node distribution diagram under monitoring coverage scenario; among them, Figure 2 (a) is the node coverage diagram of the DBO algorithm. Figure 2 (b) is the PSO algorithm node coverage diagram. Figure 2 (c) is the node coverage graph of the SSA algorithm. Figure 2 (d) is the coverage map of IDBO algorithm;

[0038] Figure 3 The present invention is 100×100m 2 Node distribution diagram under monitoring coverage scenario; among them, Figure 3 (a) is the node coverage diagram of the DBO algorithm. Figure 3 (b) is the PSO algorithm node coverage diagram. Figure 3 (c) is the node coverage graph of the SSA algorithm. Figure 3 (d) is the coverage map of IDBO algorithm;

[0039] Figure 4 This is a comparison chart of network coverage between the technical solution of the present invention and the corresponding embodiments of the other three algorithms. DETAILED DESCRIPTION

[0040] The specific implementation modes of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0041] The present invention designs a wireless sensor network coverage optimization method. For a sensor group composed of a preset number of wireless sensors, each wireless sensor corresponds to each dung beetle one by one. Combined with the division of the sensor group into rolling ball dung beetle grouping, brooding ball dung beetle grouping, foraging small dung beetle grouping, and thief dung beetle grouping, an improved dung beetle optimization algorithm integrating a spiral search strategy, an optimal value guidance strategy, and a random perturbation strategy is executed to optimize the network coverage of the sensor group on a target area.

[0042] In practical applications, such as Figure 1 As shown, the specific design executes the following steps A to F.

[0043] Step A: Initialize the number of iterations to t=1, and use Tent mapping to perform a chaotic initialization operation to initialize the position of each wireless sensor in the sensor group in the target area as the position of each wireless sensor in the target area in the tth iteration, and enter step B.

[0044] Step B. According to the position of each wireless sensor in the target area at the t-th iteration, the network coverage of the sensor group to the target area is calculated to form the overall fitness of the sensor group with respect to the target area at the t-th iteration, and further obtain the individual fitness of each wireless sensor in the sensor group with respect to the target area at the t-th iteration, and obtain the sensor sorting of each wireless sensor at the t-th iteration from large to small according to its individual fitness, and then enter step C.

[0045] In practical applications, in the above step B, based on the grid division of the target area, the following steps B1 to B3 are performed to obtain the overall fitness of the sensor group with respect to the target area in the tth iteration.

[0046] Step B1. According to the following formula:

[0047]

[0048] The probability of a single grid in the target area being sensed by a single wireless sensor is obtained, where 1≤i≤I, I represents the number of wireless sensors in the sensor group, 1≤j≤J, J represents the number of grids in the target area, P(k i ,h j ) represents the probability that the jth grid in the target area is sensed by the i-th wireless sensor, d(k i ,h j) represents the distance between the jth grid and the i-th wireless sensor in the target area, h j represents the position of the jth grid in the target area, Respectively represent the horizontal and vertical coordinates of the j-th grid position in the target area, k i represents the location of the i-th wireless sensor, Respectively represent the horizontal and vertical coordinates of the i-th wireless sensor, R p Indicates the sensing radius of the wireless sensor.

[0049] Step B2. According to the following formula:

[0050]

[0051] Get the probability that a single grid in the target area is sensed by the sensor group, where P(K,h j ) represents the probability that the jth grid in the target area is sensed by the sensor group, and K represents the sensor group.

[0052] Step B3. According to the following formula:

[0053]

[0054] The network coverage of the sensor group to the target area is obtained, which constitutes the overall fitness of the sensor group with respect to the target area in the tth iteration.

[0055] Step C. According to the one-to-one correspondence between each wireless sensor and each dung beetle, and according to the number of wireless sensors in the sensor group, the preset ball-rolling dung beetle group, the brooding ball dung beetle group, the foraging small dung beetle group, and the thief dung beetle group correspond to the number of dung beetles respectively, for each wireless sensor in the sensor sorting under the t-th iteration, the ball-rolling dung beetle group, the brooding ball dung beetle group, the foraging small dung beetle group, and the thief dung beetle group are divided in sequence under the t-th iteration, and then the process proceeds to step D.

[0056] Step D. Based on the improved dung beetle optimization algorithm that incorporates the spiral search strategy, the optimal value guidance strategy, and the random perturbation strategy, the rolling ball dung beetle group, the brooding ball dung beetle group, the foraging small dung beetle group, and the thief dung beetle group under the t-th iteration are sequentially updated with the position of the first dung beetle in the group as the target, and the updated positions of each dung beetle in the group under the t-th iteration are combined with the position of the first dung beetle in the group to form the updated positions of each dung beetle in the group under the t-th iteration, and then for the group, according to the method of step B, the individual fitness corresponding to the updated positions of each dung beetle in the group under the t-th iteration is calculated; and then the individual fitness corresponding to the updated positions of each dung beetle in each group under the t-th iteration is obtained, and then step E is entered.

[0057] In practical application, the above step D sequentially updates the positions of other dung beetles in the group of rolling ball dung beetles, group of brooding ball dung beetles, group of foraging small dung beetles, and group of thief dung beetles in the t-th iteration in the following specific manner.

[0058] Regarding the rolling ball dung beetle grouping, for the rolling ball dung beetle grouping in the t-th iteration, taking the position of the first dung beetle in the rolling ball dung beetle grouping as the target, the positions of other dung beetles in the grouping are sequentially calculated to obtain the updated positions of the dung beetles.

[0059] Regarding the grouping of brooding ball dung beetles, for the grouping of brooding ball dung beetles in the t-th iteration, a spiral search strategy is adopted, with the position of the first dung beetle in the brooding ball dung beetle group as the target, and the positions of other dung beetles in the group are sequentially targeted according to the following formula:

[0060]

[0061] The updated position of the dung beetle is calculated. The application of the spiral search strategy here can prevent the algorithm from falling into the local optimal value. 1,i (t) represents the position of the i-th dung beetle in the brooding ball dung beetle group under the corresponding sensor sorting in the t-th iteration, k1' ,i (t) represents k 1,i (t)’s updated position, represents the position of the first dung beetle in the brooding ball dung beetle group in the tth iteration, l, b1, b2 are random numbers (0,1), r represents the spiral search parameter under the spiral search strategy, e represents the natural constant, Ub * , Lb * They respectively represent the upper and lower limits of the moving distance of the preset dung beetles in the brooding ball dung beetle group under a single position update.

[0062] Regarding the grouping of foraging dung beetles, for the grouping of foraging dung beetles in the tth iteration, the optimal value guidance strategy is adopted, with the position of the first dung beetle in the foraging dung beetle group as the target, and the positions of other dung beetles in the group are sequentially targeted according to the following formula:

[0063]

[0064] The updated position of the dung beetle is calculated. Here, the adoption of the optimal value guidance strategy greatly increases the probability of generating the optimal solution; where k 2,i (t) represents the position of the i-th dung beetle in the corresponding sensor sorting in the foraging dung beetle group at the t-th iteration, k' 2,i (t) represents k 2,i (t)’s updated position, represents the position of the first dung beetle in the foraging dung beetle group at the tth iteration, C1~N(0,1), C1 represents a random number that obeys the normal distribution, C2 represents a 1×D random vector belonging to (0,1), and D represents the dimension of the problem to be optimized. λ represents the optimal value guidance parameter under the optimal value guidance strategy, UB2 and LB1 represent the upper and lower limits of the moving distance of the foraging dung beetles under a single position update in the preset grouping of dung beetles, respectively.

[0065] Regarding the grouping of dung beetles, for the grouping of dung beetles in the tth iteration, a random perturbation strategy is adopted, with the position of the first dung beetle in the dung beetle group as the target, and the positions of other dung beetles in the group are sequentially targeted according to the following formula:

[0066]

[0067] The updated position of the dung beetle is calculated. The addition of the random perturbation strategy here can prevent the algorithm from falling into the local optimum in the later stage and improve the convergence. 3,i (t) represents the position of the i-th dung beetle in the corresponding sensor sorting in the thief dung beetle group at the t-th iteration, k3' ,i (t) represents k 3,i (t)’s updated position, represents the position of the first dung beetle in the group of foraging dung beetles at the tth iteration, k * (t) represents the position of the first dung beetle in the rolling ball dung beetle group at the tth iteration, g represents a random vector of size 1×D, R=rand(1,D)×2×δ-δ, R represents the random perturbation term under the random perturbation strategy, δ represents the preset constant, D represents the dimension of the problem to be optimized, and S represents the preset constant value.

[0068] Step E. For each wireless sensor in the sensor sorting at the t-th iteration, determine whether the individual fitness of the wireless sensor is less than the individual fitness of the corresponding dung beetle update position. If so, adjust the position of the wireless sensor to the corresponding dung beetle update position; otherwise, keep the position of the wireless sensor unchanged, and then form the update position of each wireless sensor in the sensor group at the t-th iteration. Then, according to the method of step B, calculate and obtain the individual fitness corresponding to the update position of each wireless sensor in the sensor group at the t-th iteration, and obtain the maximum individual fitness as the target fitness at the t-th iteration, and then enter step F.

[0069] Step F. Determine whether t is equal to the preset maximum number of iterations T. If yes, obtain the maximum value of the target fitness under each iteration, and use the updated position of each wireless sensor corresponding to the maximum value, and optimize the network coverage of the sensor group in the target area according to the position distribution of each wireless sensor in the sensor group in the target area; otherwise, obtain the maximum value of the target fitness under each iteration, and use the updated position of each wireless sensor corresponding to the maximum value as the position of each wireless sensor in the target area under the t+1th iteration, then add 1 to update t, and return to step B.

[0070] Accordingly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of a wireless sensor network coverage optimization method when executing the computer program.

[0071] The wireless sensor network coverage optimization method designed by the present invention is applied in practice, and the coverage optimization problem of the wireless sensor network is studied using the dung beetle optimization algorithm (DBO), the particle swarm optimization algorithm (PSO) and the sparrow search algorithm (SSA), and the results are compared with the improved dung beetle optimization algorithm (IDBO) designed by the present invention. Among them, the particle swarm optimization algorithm (PSO) parameter settings: learning factor c1=2, c2=2, inertia weight factor ω1=0.9, ω2=0.4, and the number of particles in the initial population is 35. The sparrow search algorithm (SSA) settings: the sparrow population size is 35, the control parameter ST is the proportion of warning sparrows, ST=0.6, PD is the proportion of discoverers, PD=0.7, SD is the proportion of awareness of danger, SD=0.2. When performing simulation experiments, all algorithms are run independently 40 times, and the average coverage is calculated as a comparison. The maximum number of iterations of the experiment is 500 times.

[0072] like Figure 2 Shown 20×20m 2 Node coverage diagram under the detection area, where Figure 2 (a) to Figure 2 (d) shows the deployment distribution diagram of nodes after the wireless sensor network coverage is optimized by the dung beetle optimization algorithm (DBO), particle swarm optimization algorithm (PSO), sparrow search algorithm (SSA) and the improved dung beetle optimization algorithm (IDBO) designed by the present invention. Figure 2 As shown in (a), after DBO is used to optimize coverage, a large number of coverage blind spots appear in the middle of the detection area, and the coverage of nodes around the detection area is relatively small. Figure 2 (b) After PSO optimization is used to optimize coverage, the nodes in the detection area are mostly concentrated in the lower left corner of the area, and the nodes in the upper right corner are sparsely distributed, resulting in a large number of coverage blind spots. Figure 2 (c) After SSA optimization is used, the nodes are unevenly distributed and a large amount of coverage redundancy occurs. Figure 2 (d) shows that IDBO is used to optimize coverage, with even node distribution, less coverage redundancy in blind areas, and a coverage rate of 93.19%. In this scenario, the IDBO algorithm is more evenly distributed in node deployment, reduces node redundancy, and significantly improves network coverage.

[0073] like Figure 3 Shown is 100×100m 2 Node coverage diagram under the detection area, where Figure 3 (a) to Figure 3 (d) shows the deployment distribution diagram of nodes after the wireless sensor network coverage is optimized by the dung beetle optimization algorithm (DBO), particle swarm optimization algorithm (PSO), sparrow search algorithm (SSA) and the improved dung beetle optimization algorithm (IDBO) designed by the present invention; Figure 3 As shown in (a), after DBO is used to optimize coverage, a coverage blind spot appears on the right side of the detection area, and most nodes are concentrated on the left side of the area. Figure 3 (b) After PSO is used to optimize coverage, a large number of coverage blind spots appear on the lower right side of the detection area. Figure 3 (c) After SSA is used to optimize coverage, the nodes are distributed in a disorderly manner, resulting in a large number of coverage redundancies and coverage blind spots. Figure 3 (d) shows that IDBO is used to optimize coverage, with even node distribution, less coverage redundancy in blind areas, and a coverage rate of 94.81%. In this scenario, the IDBO algorithm is more evenly distributed in node deployment, reduces node redundancy, and significantly improves network coverage.

[0074] like Figure 4 The network coverage of the algorithm designed by the present invention is compared with the other three algorithms. It can be seen that the convergence speed of the wireless sensor network coverage optimization method designed by the present invention is better than that of the other three algorithms. Figure 4 As shown in the figure, as the number of iterations increases, the network coverage rate gradually increases. That is, under the same environment, the design scheme of the present invention can find the optimal solution with fewer iterations compared with the particle swarm algorithm, sparrow search algorithm, and dung beetle optimization algorithm. Compared with other algorithms, the response time is shortened, and the stability and other aspects are substantially improved.

[0075] In summary, the wireless sensor network coverage optimization method designed in the present invention corresponds each wireless sensor in the sensor group to each dung beetle one by one, combines the division of rolling ball dung beetle grouping, brooding ball dung beetle grouping, foraging small dung beetle grouping, and thief dung beetle grouping, and executes an improved dung beetle optimization algorithm that incorporates a spiral search strategy, an optimal value guidance strategy, and a random perturbation strategy to optimize the network coverage of the sensor group to the target area, overcomes the uneven distribution problem of the existing dung beetle algorithm, and avoids the algorithm from falling into a local optimal value, greatly increases the probability of generating the optimal solution, and achieves a balance between global search and calculation accuracy, effectively improves the effective coverage rate of the network, and achieves rapid coverage of the area.

[0076] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

Claims

1. A wireless sensor network coverage optimization method, characterized in that: For a sensor group composed of a preset number of wireless sensors, each wireless sensor corresponds to each dung beetle one by one. Combined with the division of the sensor group into rolling ball dung beetles, brooding ball dung beetles, foraging small dung beetles and thief dung beetles, an improved dung beetle optimization algorithm integrating spiral search strategy, optimal value guidance strategy and random perturbation strategy is executed to optimize the network coverage of the sensor group on the target area.

2. A wireless sensor network coverage optimization method according to claim 1, characterized in that: The method comprises the following steps A to F; Step A. Initialize the number of iterations t=1, and randomly initialize the position of each wireless sensor in the sensor group in the target area as the position of each wireless sensor in the target area in the tth iteration, and enter step B; Step B. According to the position of each wireless sensor in the target area at the t-th iteration, the network coverage of the sensor group to the target area is calculated to form the overall fitness of the sensor group with respect to the target area at the t-th iteration, and further obtain the individual fitness of each wireless sensor in the sensor group with respect to the target area at the t-th iteration, and obtain the sensor ranking of each wireless sensor at the t-th iteration from large to small according to its individual fitness, and then enter step C; Step C. According to the one-to-one correspondence between each wireless sensor and each dung beetle, and according to the number of wireless sensors in the sensor group, the preset rolling ball dung beetle group, brooding ball dung beetle group, foraging small dung beetle group, and thief dung beetle group respectively correspond to the number of dung beetles, for each wireless sensor in the sensor sorting under the t-th iteration, the rolling ball dung beetle group, the brooding ball dung beetle group, the foraging small dung beetle group, and the thief dung beetle group under the t-th iteration are sequentially divided, and then the process proceeds to step D; Step D. Based on the improved dung beetle optimization algorithm incorporating the spiral search strategy, the optimal value guidance strategy, and the random perturbation strategy, the rolling ball dung beetle group, the brooding ball dung beetle group, the foraging small dung beetle group, and the thief dung beetle group are sequentially updated with the position of the first dung beetle in the group as the target, and the updated positions of each dung beetle in the group under the t-th iteration are formed in combination with the position of the first dung beetle in the group. Then, for the group, according to the method of step B, the individual fitness corresponding to the updated position of each dung beetle in the group under the t-th iteration is calculated; and then the individual fitness corresponding to the updated position of each dung beetle in each group under the t-th iteration is obtained, and then the process proceeds to step E; Step E. For each wireless sensor in the sensor sorting at the t-th iteration, determine whether the individual fitness of the wireless sensor is less than the individual fitness of the corresponding dung beetle update position. If yes, adjust the position of the wireless sensor to the corresponding dung beetle update position; otherwise, keep the position of the wireless sensor unchanged, and then form the update position of each wireless sensor in the sensor group at the t-th iteration. Then, calculate and obtain the individual fitness corresponding to the update position of each wireless sensor in the sensor group at the t-th iteration in the manner of step B, and obtain the maximum individual fitness therein as the target fitness at the t-th iteration, and then enter step F; Step F. Determine whether t is equal to the preset maximum number of iterations T. If yes, obtain the maximum value of the target fitness under each iteration, and use the updated position of each wireless sensor corresponding to the maximum value, and optimize the network coverage of the sensor group in the target area according to the position distribution of each wireless sensor in the sensor group in the target area; otherwise, obtain the maximum value of the target fitness under each iteration, and use the updated position of each wireless sensor corresponding to the maximum value as the position of each wireless sensor in the target area under the t+1th iteration, then add 1 to update t, and return to step B.

3. A wireless sensor network coverage optimization method according to claim 2, characterized in that: In step D, for the brooding ball dung beetle group in the t-th iteration, a spiral search strategy is adopted, with the position of the first dung beetle in the brooding ball dung beetle group as the target, and the positions of other dung beetles in the group are sequentially targeted, according to the following formula: Calculate the updated position of the dung beetle, where k 1,i (t) represents the position of the i-th dung beetle in the brooding ball dung beetle group under the corresponding sensor sorting in the t-th iteration, k' 1,i (t) represents k 1,i (t)’s updated position, represents the position of the first dung beetle in the brooding ball dung beetle group in the tth iteration, l, b1, b2 are random numbers (0,1), r represents the spiral search parameter under the spiral search strategy, e represents the natural constant, Ub * , Lb * They respectively represent the upper and lower limits of the moving distance of the preset dung beetles in the brooding ball dung beetle group under a single position update.

4. A wireless sensor network coverage optimization method according to claim 2, characterized in that: In step D, for the foraging dung beetle group in the t-th iteration, the optimal value guidance strategy is adopted, with the position of the first dung beetle in the foraging dung beetle group as the target, and the positions of other dung beetles in the group are sequentially targeted according to the following formula: k' 2,i (t)=k 2,i (t)+C1·(k 2,i (t)-LB1)+C2·(k 2,i (t)-UB2)+λ(k2 * (t)-k 2i, (t)) Calculate the updated position of the dung beetle, where k 2,i (t) represents the position of the i-th dung beetle in the corresponding sensor sorting in the foraging dung beetle group at the t-th iteration, k' 2,i (t) represents k 2,i (t)’s updated position, represents the position of the first dung beetle in the foraging dung beetle group at the tth iteration, C1~N(0,1), C1 represents a random number that obeys the normal distribution, C2 represents a 1×D random vector belonging to (0,1), and D represents the dimension of the problem to be optimized. λ represents the optimal value guidance parameter under the optimal value guidance strategy, UB2 and LB1 represent the upper and lower limits of the moving distance of the foraging dung beetles under a single position update in the preset grouping of dung beetles, respectively.

5. The wireless sensor network coverage optimization method according to claim 2, characterized in that: In the step D, for the thief dung beetle group in the t-th iteration, a random perturbation strategy is adopted, with the position of the first dung beetle in the thief dung beetle group as the target, and the positions of other dung beetles in the group are sequentially targeted, according to the following formula: Calculate the updated position of the dung beetle, where k 3,i (t) represents the position of the i-th dung beetle in the corresponding sensor sorting in the thief dung beetle group at the t-th iteration, k' 3,i (t) represents k 3,i (t)’s updated position, represents the position of the first dung beetle in the group of foraging dung beetles at the tth iteration, k * (t) represents the position of the first dung beetle in the rolling ball dung beetle group at the tth iteration, g represents a random vector of size 1×D, R=rand(1,D)×2×δ-δ, R represents the random perturbation term under the random perturbation strategy, δ represents the preset constant, D represents the dimension of the problem to be optimized, and S represents the preset constant value.

6. A wireless sensor network coverage optimization method according to claim 2, characterized in that: In the step A, Tent mapping is used to perform a chaotic initialization operation to initialize the position of each wireless sensor in the sensor group in the target area as the position of each wireless sensor in the target area in the tth iteration.

7. A wireless sensor network coverage optimization method according to claim 2, characterized in that: In the step B, based on the grid division of the target area, the following steps B1 to B3 are performed to obtain the overall fitness of the sensor group with respect to the target area in the tth iteration; Step B1. According to the following formula: The probability of a single grid in the target area being sensed by a single wireless sensor is obtained, where 1≤i≤I, I represents the number of wireless sensors in the sensor group, 1≤j≤J, J represents the number of grids in the target area, P(k i ,h j ) represents the probability that the jth grid in the target area is sensed by the i-th wireless sensor, d(k i ,h j ) represents the distance between the jth grid and the i-th wireless sensor in the target area, h j represents the position of the jth grid in the target area, Respectively represent the horizontal and vertical coordinates of the j-th grid position in the target area, k i represents the location of the i-th wireless sensor, Respectively represent the horizontal and vertical coordinates of the i-th wireless sensor, R p Indicates the sensing radius of the wireless sensor; Step B2. According to the following formula: Get the probability that a single grid in the target area is sensed by the sensor group, where P(K,h j ) represents the probability that the jth grid in the target area is sensed by the sensor group, and K represents the sensor group; Step B3. According to the following formula: The network coverage of the sensor group to the target area is obtained, which constitutes the overall fitness of the sensor group with respect to the target area in the tth iteration.

8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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