WSN target coverage method and device based on elite chaos monkey group algorithm

Abstract

The invention relates to a target coverage method and device for a focused wireless sensor network (WSN), and aims to solve the problems of incomplete coverage range and the like of the traditional method. A monitoring relation matrix M and a target coverage model are constructed with improvement of the coverage rate of sensor nodes as a target, and an elite chaos monkey swarm algorithm is used for solving. The algorithm comprises the following steps: initializing parameters and randomly generating an initial population; local search is carried out in the climbing process to update the positions and fitness of the monkeys; repeating the climbing process after the position of the better overlook point is updated in the homeographing and jumping process; transferring the search area by taking the gravity center as a fulcrum in the somersault process; updating the step length and the view length by using a chaos sequence, and reserving elite individuals to guide search; and iterating for multiple times until a preset number of times is reached, and evaluating the fitness. The method borrows elite and chaos strategies, has strong global search capability, is fast in convergence, can realize WSN complete coverage, and has an optimal simulation display coverage effect.

Description

Technical Field

[0001] The present invention relates to a wireless sensor network (WSN) target coverage method and device, in particular to a WSN target coverage method and device based on an elite chaotic monkey swarm algorithm, and belongs to the technical field of wireless sensor network coverage. Background Art

[0002] WSN is a network composed of a large number of interconnected wireless sensor nodes used to monitor and collect environmental data. The WSN target coverage problem can be defined as deploying a limited number of sensor nodes in a given area to achieve complete coverage of the area. In practical applications, the number and location of sensor nodes are limited, and maximizing the coverage of the network is extremely challenging. In the prior art, the fitness function of the WSN problem is obtained by combining the coverage efficiency of sensor nodes and the balance of network energy consumption to initialize the population. The fitness function of the optimization problem is used to calculate the fitness of each individual in the population, and the maximum fitness is taken as the optimization goal. In the subsequent migration operator of the optimization algorithm, the search strategy is expanded by integrating the particle swarm algorithm, and the adjustment operator is improved to increase the diversity of the population, accelerate the global optimization speed of the population, continuously update the population position, cover all sensor nodes in the area to be measured, and achieve coverage optimization.

[0003] However, the existing technology has the following problems: (1) Nodes are randomly deployed in WSN, and only the target area is targeted, without the need for comprehensive coverage of the standard area. There may be coverage redundancy and uneven node distribution, which affects the quality and reliability of network monitoring and causes a large amount of resource waste; (2) The traditional monkey swarm algorithm may fall into local optimality in the later stage of search due to excessive local search ability, while the chaotic monkey swarm algorithm can generate more evenly distributed search points in the entire search space by introducing chaotic sequences, making it more likely to find the global optimal solution; and when saving individual fitness values, the maximum fitness priority principle is adopted, without considering whether individuals with low fitness values ​​continue to enter the cycle, resulting in a small coverage range.

[0004] Therefore, a WSN target coverage method with comprehensive coverage and no coverage redundancy problem is needed. Summary of the Invention

[0005] A brief overview of the present invention is provided below to provide a basic understanding of certain aspects of the present invention. It should be understood that this overview is not an exhaustive overview of the present invention. It is not intended to identify key or important aspects of the present invention, nor is it intended to limit the scope of the present invention. Its purpose is simply to present certain concepts in a simplified form as a prelude to the more detailed description discussed later.

[0006] In view of this, in order to solve the problem of incomplete coverage of traditional WSN target coverage methods in the prior art, the present invention provides a WSN target coverage method based on the elite chaotic monkey swarm algorithm. Technical solution 1 is as follows:

[0007] S1. Taking the coverage of sensor nodes as the optimization target, obtain the monitoring relationship matrix M containing the monkey position to represent the monkey fitness;

[0008] Where E is the monitored target, E = 1, 2, e, ..., n, F is the sensor node, F = 1, 2, f, ..., n, x e,f Indicates that the e-th monitored target is monitored by the f-th sensor node, x e,f = 0 means that the e-th monitored target is outside the coverage of the f-th sensor node, x e,f =1 means that the e-th monitored target is within the coverage of the f-th sensor node;

[0009] The constraints of the monitoring relationship matrix M are expressed as:

[0010] When the optimization goal is the maximum total number of monitored targets that are successfully monitored, that is, the coverage rate of the sensor nodes is maximized, the function max f(x) is used to obtain the target coverage model;

[0011] The target coverage model is expressed as:

[0012] The constraints of the target coverage model are expressed as:

[0013] Among them, W e =0 means the monitored target is outside the coverage of the sensor node, W e =1 indicates that the monitored target is within the coverage of the sensor node, and X is the minimum number of sensor nodes that monitor a single monitored target.

[0014] S2. Initialization: Set the initial population, number of iterations T, climbing step length a, visual field length b and flip interval [c, d]. Let N be the size of the population, and the position of the i-th monkey can be expressed as: X i =(x i ,y i ), the actual position of each monkey represents a decision vector for the optimization problem.

[0015] MA uses random initialization uniformly distributed in the solution space, generated by the following formula: xi =x min +r i1 (x max -x min ) y i =y min +r i2 (y max -y min )

[0016] Among them, x max and x min Represents x i The upper and lower limits of y max and y min Represents y i The upper and lower limits, r i1 and r i2 is a uniformly distributed random number generated between [0,1].

[0017] S3. Climbing process: The climbing process of MA mainly changes the monkey's position through continuous iteration, thereby gradually improving the objective function value. The specific steps are as follows:

[0018] (1) For each monkey i (i=1, 2, ..., N) at the current position X i Conduct a local search nearby.

[0019] (2) Generate in x i and y i Displacement Δx in the direction i and Δy i .

[0020] (3) Update the monkey's position:

[0021] (4) Calculate the new position Fitness

[0022] S4. Hopping process: At this point, each monkey has reached the highest point of the mountain in its current area, which means that the objective function value has reached a local optimum. Next, the monkey needs to look around, mainly observing whether there is a point better than the current position within its field of view. If there is a point better than the current position after observation, it needs to jump away from the current position and repeat the climbing process. The specific steps are as follows:

[0023] (1) In the field of view (x i -b,x i +b) and (y i -b,y i+b) range, randomly generate two real numbers x′ i and y′ i , where Y i =(x′ i ,y′ i )(i=1,2,…,N), b is the visual field length, which means the farthest distance the monkey can see at the current position;

[0024] (2) If vector Y i =(x′ i ,y′ i )(i=1,2,…,N) satisfies the constraint condition, that is, x′ i ∈[x min ,x max ],y′ i ∈[y min ,y max ], and I(Y i )>I(X i ), then X i Update to Y i , otherwise repeat step (1) until a point Y that meets the conditions is found i , or meet a certain number of hope-hop times T w until;

[0025] (3) Then use the updated X i As the initial position, repeat the climbing process.

[0026] S5. Somersault process: To prevent the monkeys from falling into a local optimum after the climbing and looking-jumping processes, a somersault process is needed to transfer the monkey's current search area to a new area. Each monkey uses the center of gravity of all monkeys as a fulcrum and performs a somersault process in the direction or opposite direction of the fulcrum to reach a new area for search. The specific steps are as follows:

[0027] (1) A real number θ is randomly generated in the interval [c, d] so that the distance generated by the monkey during the somersault can increase with the increase of θ, thereby avoiding the process from falling into the trend of local optimality, where c is the lower limit of the monkey's somersault interval and d is the upper limit of the somersault interval.

[0028] (2) Calculate the monkey group's fulcrum P according to the formula:

[0029] (3) Use the formula to realize the flipping process: x i =x i +θ(x p -x i),i=1,2,…,N y i =y i +θ(y p -y i ),i=1,2,…,N If θ>0, the monkey will climb over in the direction of the fulcrum, otherwise it will climb over in the opposite direction.

[0030] (4) If vector Y i =(x′ i ,y′ i ) in the variable range [x min ,x max ] and [y min ,y max ] and satisfy I(Y i )>I(X i ), then update X i Y i Otherwise, repeat steps (1) to (3) until a Y that meets the conditions is found. i until.

[0031] S6. Update: Based on the evaluation results, update the population and retain the better solutions. The randomness and ergodicity of the algorithm are increased by introducing chaotic sequences, and chaotic sequences are generated using chaotic mapping.

[0032] The chaotic sequence is used to adjust the climbing step length a and the visual field length b to increase the randomness of the search. Each monkey moves randomly in the search space to find a better position. The wild length b is adjusted by the chaotic sequence, and the specific formula is as follows:

[0033] Where a0 and b0 are the initial step size and field of view length, k is the current iteration number, and T is the total number of iterations. The random adjustment of the chaos operator helps the algorithm escape from the local optimum and improve the global search capability.

[0034] S7. Iteration: Repeat the climbing process, looking process, jumping process and flipping process until the predetermined number of iterations or the target population fitness is reached. After each iteration, use f cover Evaluate the fitness of the current population. Target coverage evaluation function f cover It is used to quantify the coverage effect of sensor nodes on the target area. The specific formula is as follows:

[0035] Among them, I(x i ,y j ) indicates that the i-th sensor is at position (x i,y j ), n and m are the width and height of the target area respectively.

[0036] At the end of each iteration, the monkey (solution) with the highest fitness is selected as the elite individual. The location information of the elite individual is copied to the next generation of the population to ensure that the optimal solution is retained. In subsequent iterations, the location information of the elite individual is used to guide the search direction of other monkeys.

[0037] Technical solution 2 is as follows: A WSN target coverage method and device based on elite chaotic monkey swarm algorithm, used to execute the method described in technical solution 1, including a sensor module, a processor module, a communication module and a power supply module.

[0038] The sensor module, processor module and communication module are connected in sequence; The sensor module includes a sensor and an analog-to-digital conversion module. The sensor is used to collect data information of the monitored target, and the analog-to-digital conversion module is used to realize the mutual conversion between analog signals and digital signals. The processor module includes a processor configured to execute the computer program to implement the method described in Example 1; the communication module includes an antenna configured to receive electromagnetic waves and convert them into wireless signals, and a wireless module configured to convert the wireless signals received by the antenna into digital signals and then convert the digital signals into wireless signals for transmission; The power supply module is connected to the sensor module, the processor module and the communication module respectively, and is used to supply power to the sensor module, the processor module and the communication module.

[0039] The deployment implementation can be connected to the actual WSN system, and the deployment plan can be sent to the corresponding sensor node execution device. The deployed network is verified and feedback is provided to ensure that the node deployment meets expectations and can achieve efficient target coverage.

[0040] The beneficial effects of the present invention are as follows: the present invention adopts the elite chaotic monkey swarm algorithm to establish a target coverage model to search for sensor node deployment plans; the elite strategy is adopted to enhance the algorithm's ability to find the optimal individual; the chaotic strategy is adopted to update the current search position of the monkey individual, expand the population search range, and avoid falling into local optimality during later iterative operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0042] Figure 1The figure is a flowchart of the WSN target coverage method based on the elite chaotic monkey swarm algorithm;

[0043] Figure 2 Schematic diagram of coverage simulation comparison of three WSN target coverage methods;

[0044] Figure 3 It is the functional diagram of infinite sensor nodes; Figure 4 Schematic diagram of the core process of WSN target coverage based on the elite chaotic monkey swarm algorithm. DETAILED DESCRIPTION Example 1: WSN target coverage method and device based on elite operator chaotic monkey swarm algorithm

[0045] This embodiment describes in detail a WSN target coverage method based on the elite operator chaotic monkey swarm algorithm. The specific steps are as follows:

[0046] S1. Taking the coverage of sensor nodes as the optimization target, obtain the monitoring relationship matrix M containing the monkey position to represent the monkey fitness;

[0047] Where E is the monitored target, E = 1, 2, e, ..., n, F is the sensor node, F = 1, 2, f, ..., n, x e,f Indicates that the e-th monitored target is monitored by the f-th sensor node, x e,f = 0 means that the e-th monitored target is outside the coverage of the f-th sensor node, x e,f =1 means that the e-th monitored target is within the coverage of the f-th sensor node;

[0048] The constraints of the monitoring relationship matrix M are expressed as:

[0049] When the optimization goal is the maximum total number of monitored targets that are successfully monitored, that is, the coverage rate of sensor nodes is maximized, the target coverage model is obtained using the function maxf(x);

[0050] The target coverage model is expressed as:

[0051] The constraints of the target coverage model are expressed as: Where, indicates that the monitored target is outside the coverage of the sensor node, indicates that the monitored target is within the coverage of the sensor node, and X is the minimum number of sensor nodes that monitor a single monitored target.

[0052] S2. Initialization: Set the initial population N = 40, the number of iterations T = 100, the sensor perception radius to 12m, the climbing step length a0 = 5, the visual field length b0 = 10, and the flip interval to [-20, 20]. Let N be the population size, N be the spatial dimension of the monkey group's location, and the location of the i-th monkey can be expressed as: X i =(x i ,y i ), the actual position of each monkey represents a decision vector for the optimization problem.

[0053] Population initialization has a significant impact on the algorithm's global convergence and optimization performance. If the initialization deviates significantly from the optimal solution range, convergence will slow down, resulting in low accuracy of the global optimal solution. If the initialization is within a small range, it is easy to fall into a local optimum. To ensure that the initial population is as evenly distributed as possible in the feasible solution space, MA uses random initialization that is evenly distributed in the solution space, generated by the following formula: x i =x min +r i1 (x max -x min ) y i =y min +r i2 (y max -y min )

[0054] Among them, x max =1 and x min =5 respectively represent x i The upper and lower limits of y max =1 and y min =8 respectively represent y i The upper and lower limits, r i1 and r i2 is a uniformly distributed random number generated between [0,1].

[0055] S3. Climbing process: The climbing process of MA mainly changes the monkey's position through continuous iteration, thereby gradually improving the objective function value. The specific steps are as follows:

[0056] (1) For each monkey i (i=1, 2, ..., N) at the current position X i Conduct a local search nearby.

[0057] (2) Generate in x i1 and y i Displacement Δx in the direction i and Δy i .

[0058] (3) Update the monkey's position: in, K is the current number of iterations, T=100 is the total number of iterations

[0059] (4) Calculate the new position Fitness Among them, N covered Indicates the number of coverage targets, N total Indicates the total number of targets;

[0060] S4. Looking: After completing the climbing process a set number of times, each monkey has reached the highest point of the mountain in its current area, which means that the objective function value has reached a local optimum. Next, the monkey needs to look around, mainly observing whether there is a point better than the current position within its field of view. If there is a point better than the current position after observation, it needs to jump away from the current position and repeat the climbing process. The specific steps are as follows:

[0061] (1) In the field of view (x i -b,x i +b) and (y i -b,y i +b) range, randomly generate two real numbers x′ i and y′ i , where Y i =(x′ i ,y′ i )(i=1,2,…,N), b is the visual field length, which means the farthest distance the monkey can see at the current position;

[0062] (2) If vector Y i =(x′ i ,y′ i )(i=1,2,…,N) satisfies the constraint condition, that is, x′ i , y′ i ∈[x min ,x max ], and I(Y i )>I(X i ), then X i Update to Y i , otherwise repeat step (1) until a point Y that meets the conditions is found i , or meet the expected jump number T w =10;

[0063] (3) Then use the updated X i As the initial position, repeat the climbing process.

[0064] S5. Somersault process: To prevent the monkeys from falling into a local optimum after the climbing and looking-jumping processes, a somersault process is needed to transfer the monkey's current search area to a new area. Each monkey uses the center of gravity of all monkeys as a fulcrum and performs a somersault process in the direction or opposite direction of the fulcrum to reach a new area for search. The specific steps are as follows:

[0065] A real number θ is randomly generated in the interval [c, d] so that the distance generated by the monkey during the somersault can increase with the increase of θ, thereby avoiding the process from falling into the trend of local optimality, where c is the lower limit of the monkey's somersault interval and d is the upper limit of the somersault interval.

[0066] (2) Calculate the monkey group's fulcrum P according to the formula:

[0067] (3) Use the formula to realize the flipping process: x1=x1+θ(x p -x1) y1=y1+θ(y p -y1) x2=x2+θ(x p -x2) y2=y2+θ(y p -y2) … x 40 =x 40 +θ(x p -x 40 ) y 40 =y 40 +θ(y p -y 40 ) If θ>0, the monkey will climb over in the direction of the fulcrum, otherwise it will climb over in the opposite direction.

[0068] (4) If vector Y i =(x′ i ,y′ i ) in the variable range [x min ,x max ] and [y min ,y max ] and satisfy I(Y i )>I(X i ), then update X i Y i Otherwise, repeat steps (1) to (3) until a Y that meets the conditions is found. i until.

[0069] S6. Update: Based on the evaluation results, update the population and retain the better solutions. The randomness and ergodicity of the algorithm are increased by introducing chaotic sequences, and chaotic sequences are generated using chaotic mapping.

[0070] The chaotic sequence is used to adjust the climbing step length a and the visual field length b to increase the randomness of the search. Each monkey moves randomly in the search space to find a better position. The wild length b is adjusted by the chaotic sequence, and the specific formula is as follows:

[0071] Where a0 and b0 are the initial step size and field of view length, k is the current iteration number, and T is the total number of iterations. The random adjustment of the chaos operator helps the algorithm escape from the local optimum and improve the global search capability.

[0072] S7. Iteration: Repeat the climbing process, looking process, jumping process and flipping process until the predetermined number of iterations or the target population fitness is reached. After each iteration, use f cover Evaluate the fitness of the current population. Target coverage evaluation function f cover It is used to quantify the coverage effect of sensor nodes on the target area. The specific formula is as follows:

[0073] Among them, I(x i ,y j ) indicates that the i-th sensor is at position (x i ,y j ), n and m are the width and height of the target area respectively.

[0074] At the end of each iteration, the monkey (solution) with the highest fitness is selected as the elite individual. The location information of the elite individual is copied to the next generation of the population to ensure that the optimal solution is retained. In subsequent iterations, the location information of the elite individual is used to guide the search direction of other monkeys.

[0075] In the WSN target coverage process, a limited number of sensor nodes are deployed within a given monitoring area. By treating each sensor node as a monkey, the optimal node deployment solution is found. Each monkey has its own position and fitness, and the fitness represents the quality of the monkey's solution. Monkeys can exchange information through mutual attraction and repulsion, and the monkey's position is updated in each iteration. Through multiple iterations, the optimal node deployment solution can be found by combining chaos strategy and elitism strategy, thus achieving complete coverage of the WSN. It has global search capabilities and a high convergence speed, which can avoid the problem of falling into the local optimal solution.

[0076] Example 1: The target area size of the WSN target coverage method based on the elite chaotic monkey swarm algorithm is set to 600×600m, the number of monitored targets is 200, and the locations of the monitored targets and sensor nodes are randomly distributed. Each monitored target requires at least 3 sensor nodes to monitor simultaneously, and each sensor node can simultaneously sense up to 5 targets within the coverage area. The number of sensors is 150, the sensor sensing radius is 60m, the monkey swarm size is 40, and the number of iterations is 100.

[0077] refer to Figure 2 The WSN target coverage method based on the elite chaotic monkey swarm algorithm was compared with the basic particle swarm algorithm and the monkey swarm algorithm. The population size of each algorithm was 40. The maximum individual velocity of the particle swarm algorithm was 5, and its individual and social cognition parameters were set to c1 = c2 = 1.5. The initial number of fruits for the WSN target coverage method based on the elite chaotic monkey swarm algorithm and the monkey swarm algorithm was 20. Simulation curves show that the WSN target coverage method based on the elite chaotic monkey swarm algorithm has the best coverage effect. Compared with the particle algorithm, which converged after about 25 iterations, the monkey swarm algorithm converged after about 45 iterations, and the proposed algorithm converged after 75 iterations, avoiding the local optimum problem. Furthermore, the coverage rate of the elite chaotic monkey swarm algorithm, 0.81, is higher than the 0.72 of the particle swarm algorithm and the 0.52 of the monkey swarm algorithm.

[0078] Example 2: Reference Figure 3 Detailed description of this embodiment, the WSN target coverage device of the elite chaotic monkey swarm algorithm is used to execute the method described in embodiment 1, including a sensor module, a processor module, a communication module and a power module;

[0079] The sensor module, processor module and communication module are connected in sequence;

[0080] The sensor module includes a sensor and an analog-to-digital conversion module. The sensor is used to collect data information of the monitored target, and the analog-to-digital conversion module is used to realize the mutual conversion between analog signals and digital signals.

[0081] The processor module includes a processor configured to execute the computer program to implement the method described in Example 1; the communication module includes an antenna configured to receive electromagnetic waves and convert them into wireless signals, and a wireless module configured to convert the wireless signals received by the antenna into digital signals and then convert the digital signals into wireless signals for transmission;

[0082] The power supply module is connected to the sensor module, the processor module and the communication module respectively, and is used to supply power to the sensor module, the processor module and the communication module.

[0083] Although the present invention has been described with respect to a limited number of embodiments, it will be apparent to those skilled in the art, having benefit of the foregoing description, that other embodiments are contemplated within the scope of the invention thus described. Furthermore, it should be noted that the language used in this specification has been selected primarily for readability and didactic purposes, rather than for the purpose of explaining or limiting the subject matter of the present invention. Consequently, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is intended to be illustrative rather than restrictive of the scope of the invention, which is defined by the appended claims.

Claims

1. A WSN target coverage method and device based on elite chaotic monkey swarm algorithm, characterized in that: The following steps are involved: S01 selects the target area size, the number of sensors, and the sensor perception radius, determines the correspondence between the target coverage concept and the concept of the elite chaotic monkey swarm algorithm, and establishes a target coverage model. Within the set monitoring area, the monkey positions are randomly initialized. Specifically, the sensor nodes correspond to the monkeys, and the sensor node coverage rate corresponds to the monkey's fitness; S02 uses the monkey group algorithm to obtain monkeys with high fitness through the processes of climbing, looking and jumping, and somersaults. It uses the chaos strategy to update the step length of the climbing process and the field of view of the looking and jumping process; it uses the elite strategy to eliminate monkeys with poor fitness and obtain the optimal monkey fitness; S03 determines whether the set elite chaotic monkey group algorithm iteration threshold is reached. If not, return to step S02 to loop the above operation. Otherwise, output the optimal monkey position and optimal fitness to obtain the optimal deployment plan of the sensor nodes.

2. The WSN target coverage method and device based on the elite chaotic monkey swarm algorithm according to claim 1 is characterized in that: In said S01, the coverage of the sensor nodes is taken as the optimization target, and a monitoring relationship matrix M including the monkey position representing the monkey fitness is obtained; The monitoring relationship matrix M is expressed as: Where E is the monitored target, E = 1, 2, e, ..., n, F is the sensor node, F = 1, 2, f, ..., n, x e,f Indicates that the e-th monitored target is monitored by the f-th sensor node, x e,f = 0 means that the e-th monitored target is outside the coverage of the f-th sensor node, x e,f =1 means that the e-th monitored target is within the coverage of the f-th sensor node; The constraints of the monitoring relationship matrix M are expressed as: When the optimization goal is the maximum total number of monitored targets that are successfully monitored, that is, the coverage rate of the sensor nodes is maximized, the function max f(x) is used to obtain the target coverage model; The target coverage model is expressed as: The constraints of the target coverage model are expressed as: Among them, W e =0 means the monitored target is outside the coverage of the sensor node, W e =1 means that the monitored target is within the coverage of the sensor node, and X is the minimum number of sensor nodes that monitor a single monitored target; Initialization: Set the initial population, number of iterations T, climbing step length a, visual field length b, and somersault interval [c, d]; Let N be the size of the population, and the position of the i-th monkey can be expressed as: X i =(x i ,y i ), the actual position of each monkey represents a decision vector for the optimization problem. A random initialization uniformly distributed in the solution space is used, generated by the following formula: x i =x min +r i1 (x max -x min ) and i =and min +r i2 (and max -and min ) Among them, x max and x min Represents x i1 The upper and lower limits of y max and y min Represents y i The upper and lower limits, r i1 and r i2 is a uniformly distributed random number generated between [0,1].

3. The WSN target coverage method and device based on the elite chaotic monkey swarm algorithm according to claim 1 is characterized in that: In step S02, the climbing process is as follows: the monkey is at the current position X i Perform local search near x and generate i and y i Displacement Δx in the reverse direction i and Δy i , update the monkey's position: Calculate the fitness after moving (1) For each monkey i (i=1, 2, ..., N) at the current position X i Conduct a local search nearby; (2) Generate in x i1 and x i2 Displacement Δx in the reverse direction i and Δy i ; (3) Update the monkey's position: (4) Calculate the new position 's adaptability.

4. The WSN target coverage method and device based on the elite chaotic monkey swarm algorithm according to claim 1 is characterized in that: In step S02, the jump process is as follows: the monkey searches for a position with the best fitness within the current visual field. If the fitness of the position is better than the current position, the monkey jumps to the position and updates the visual field. (1) In the field of view (x i -b,x i +b) and (y i -b,y i +b) within the range, randomly generate two real numbers x′ i and y′ i , where Y i =(x′ i ,y′ i )(i=1,2,…,N), b is the visual field length, which means the farthest distance the monkey can see at the current position; (2) If vector Y i =(x′ i ,y′ i )(i=1,2,…,N) satisfies the constraint condition, that is, x′ i ∈[x min ,x max ],y′ i ∈[y min ,y max ], there is I(Y i )>I(X i ), then X i Update to Y i , otherwise repeat step (1) until a point Y that meets the conditions is found i , or meet a certain number of hope-hop times T w until; (3) Then use the updated X i As the initial position, repeat the climbing process.

5. The WSN target coverage method and device based on the elite chaotic monkey swarm algorithm according to claim 1 is characterized in that: In step S02, the somersault process specifically includes: randomly changing the monkey's position with a certain probability to increase the randomness of the algorithm and the ability to escape from the local optimum; (1) Randomly generate a real number θ in the interval [c, d] so that the distance generated by the monkey during the somersault can increase with the increase of θ, thus avoiding the process from falling into the trend of local optimality. Where c is the lower limit of the monkey's somersault interval, and d is the upper limit of the somersault interval; (2) Calculate the monkey group's fulcrum P according to the formula: (3) Use the formula to realize the flipping process: x i =x i +θ(x p -x i ),i=1,2,…,N and i =and i +θ(and p -and i ),i=1,2,…,N If θ>0, the monkey will climb over in the direction of the fulcrum, otherwise it will climb over in the opposite direction. (4) If vector Y i =(x′ i ,y′ i ) in the variable range [x min ,x max ] and [y min ,y max ] and satisfy I(Y i )>I(X i ), then update X i Y i Otherwise, repeat steps (1) to (3) until a Y that meets the conditions is found. i until.

6. The WSN target coverage method based on the elite chaotic monkey swarm algorithm according to claim 1 is characterized in that: In said S02, the randomness and ergodicity of the algorithm are increased by introducing a chaotic sequence, and a chaotic sequence is generated by using a chaotic map; A chaotic sequence is used to adjust the climbing step length a and the visual field length b to increase the randomness of the search; each monkey moves randomly in the search space to find a better position. The step length a and the visual field length b of the climbing process are adjusted by the chaotic sequence. The specific formula is as follows: Among them, a0 and b0 are the initial step size and field of view length, k is the current number of iterations, and T is the total number of iterations. The random adjustment of the chaos operator helps the algorithm escape from the local optimum and improve the global search capability.

7. The WSN target coverage method based on the elite chaotic monkey swarm algorithm according to claim 1 is characterized in that: In S03, the climbing process, the looking process, the jumping process and the flipping process are repeatedly executed until a predetermined number of cycles is reached; After each iteration, use f cover Evaluate the fitness of the current population; target coverage evaluation function f cover It is used to quantify the coverage effect of sensor nodes on the target area. The specific formula is as follows:

8. The WSN target coverage method and device based on the elite chaotic monkey swarm algorithm according to claim 1 is characterized in that: in, I(x i ,y j ) indicates that the i-th sensor is at position (x i ,y j ), n and m are the width and height of the target area respectively; at the end of each iteration, the monkey (solution) with the highest fitness is selected as the elite individual; the position information of the elite individual is copied to the next generation population to ensure that the optimal solution is retained; in subsequent iterations, the position information of the elite individual is used to guide the search direction of other monkeys.

9. A WSN target coverage method and device based on elite chaotic monkey swarm algorithm, characterized in that: Used to perform the method according to any one of claims 1 to 8, comprising a sensor module, a processor module, a communication module and a power supply module; The sensor module, processor module and communication module are connected in sequence; The sensor module includes a sensor and an analog-to-digital conversion module. The sensor is used to collect data information of the monitored target, and the analog-to-digital conversion module is used to realize the mutual conversion between analog signals and digital signals. The processor module includes a processor, and the processor is used to execute the computer program to implement the method according to any one of claims 1 to 8; The communication module includes an antenna and a wireless module. The antenna is used to receive electromagnetic waves and convert them into wireless signals. The wireless module is used to convert the wireless signals received by the antenna into digital signals and convert the digital signals into wireless signals and send them out. The power supply module is connected to the sensor module, the processor module and the communication module respectively, and is used to supply power to the sensor module, the processor module and the communication module.

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