Goal coverage optimization method and device based on swarm intelligence in WSN (Wireless Sensor Network)

Abstract

The invention belongs to the technical field of WSN (Wireless Sensor Network), and discloses a target coverage optimization method and device based on swarm intelligence in a WSN (Wireless Sensor Network). The invention provides an improved social spider algorithm based on an elite exclusion operator, aiming at the problems of coverage redundancy, non-uniform distribution, easy falling into local optimum, resource waste and the like caused by random putting of WSN nodes. The method comprises the following steps: firstly, constructing a WSN target coverage model and initializing a population, evaluating fitness values of individuals in a spider population through a fitness function (taking a target area coverage rate as a core), and screening out a sensor position matrix corresponding to the individual with the optimal fitness as a final deployment scheme of network coverage optimization. According to the improved algorithm, by introducing an elite exclusion mechanism, premature convergence is effectively avoided, the global optimization ability is improved, the convergence speed is increased, and the stability is enhanced.

Description

Technical Field

[0001] The present invention relates to a method and device for target coverage of a Wireless Sensor Network (WSN), and proposes a method for solving the problem of wireless sensor coverage rate by using a social spider algorithm, which can quickly find the optimal layout plan of wireless sensors and display it. Background Art

[0002] WSN is a network composed of a large number of interconnected wireless sensor nodes, which is used to monitor and collect environmental data. In the process of target coverage of WSN, the deployment and layout of nodes are very crucial, because it directly affects the performance and coverage of the network. Therefore, the WSN target coverage problem is a very important problem in the field of WSN.

[0003] The social spider algorithm is a new swarm intelligence optimization algorithm that simulates the behaviors of spiders in nature for division of labor and cooperation, information exchange, and reproduction. This algorithm has the characteristics of simple structure, strong stability, and easy to understand.

[0004] In many monitoring scenarios, such as environmental monitoring, industrial equipment operation status monitoring, security monitoring and other fields, multiple sensor nodes are often deployed to collect data and monitor the status of multiple monitored targets. How to reasonably configure the monitoring relationship between sensor nodes and monitored targets, that is, determine which sensor nodes are responsible for monitoring which monitored targets, in order to achieve the highest monitoring coverage rate, with the maximum fitness as the optimization goal, and improve the adjustment operator to increase the population diversity, 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, is the key issue to improve the performance and reliability of the monitoring system.

[0005] The coverage optimization problem of WSN is mainly described as follows: in a specified monitoring area, nodes are randomly placed in WSN, and only targeted coverage of the target area is performed, without the need for full coverage of the standard area. There may be situations of coverage redundancy and uneven node distribution, which affect the quality and reliability of network monitoring and cause a large amount of resource waste; based on the traditional coverage method, the elite strategy is not adopted. In the face of complex and large-scale monitoring scenarios, it is difficult to quickly and accurately find the optimal monitoring configuration plan, and individuals are often prone to falling into local optima, resulting in insufficient coverage of some monitored targets or waste of sensor resources. And when saving the individual fitness value, the principle of maximum fitness first is adopted, without considering whether individuals with low fitness values continue to enter the loop, resulting in a not large coverage range.

[0006] Therefore, a WSN target coverage method and device with comprehensive coverage and no coverage redundancy problem is needed. In the social spider algorithm based on the elite and exclusion operator, this scheme introduces the elite and exclusion operator to optimize the spider's search process. The elite exclusion operator adjusts the spider's search strategy and parameters according to the spider's fitness value (i.e., coverage quality), so that the algorithm can more effectively utilize the information of elite individuals during the search process, thereby improving the algorithm's convergence speed and coverage quality. Summary of the invention

[0007] Technical solution 1 is as follows:

[0008] The target coverage optimization method based on swarm intelligence in WSN includes:

[0009] The WSN target coverage model is constructed and initialized. The fitness value of each individual in the spider population in the improved social spider algorithm is calculated through the fitness function composed of the WSN target area coverage rate, and the sensor position matrix corresponding to the individual with the largest fitness value is used as the optimal deployment plan of the wireless sensor network coverage optimization model.

[0010] The WSN target coverage model is constructed according to the above, including:

[0011] Set the sensor node set F = {f1,f2,…f n}, where f i represents the i-th sensor node, the monitored target set E = {e1, e2, …, e m}, where e j Represents the jth monitored target. Define the sensing range R of the sensor node s With communication range R c And R s <R c , the sensor node f is determined by a two-dimensional coordinate system i The coordinates of With the monitored target j Location And construct the coverage relationship matrix R between the sensor node and the monitored target, where the element r e,f ∈{0,1},r e,f = 0 means that the e-th monitored target is not covered by the f-th sensor node, r e,f =1 means it is covered, that is, the distance is less than R s .

[0012] Randomly initialize N spiders, that is, N sets of sensor positions, Spider K's location:

[0013] Among them, each spider represents a sensor deployment scheme.

[0014] Calculating the fitness values of each individual in the spider population of the improved social spider algorithm according to the fitness function composed of the target area coverage rate of the wireless sensor network includes:

[0015] Initialize the selected target area size, the number of sensors, and the sensor sensing radius, determine the correspondence between the target coverage concept and the improved social spider algorithm concept, and randomly initialize the spider individual positions within the set monitoring area. Within the set monitoring area, with the coverage rate of the sensor nodes as the optimization target, obtain the spider individual fitness values and sort them; among them, the coverage matrix R of individual spider k k is as follows:

[0016] The fitness function of individual spider k is the coverage rate f k is as follows, that is, covered by at least one sensor.

[0017] Taking the sensor position matrix corresponding to the individual with the maximum fitness value as the optimal deployment scheme of the wireless sensor network coverage optimization model includes:

[0018] Calculate the distance of the spider individuals and update the spider positions; the vibration signal propagation of the spider individuals; adopt the elite and crowding strategy and retain the positions of the remaining spiders, and finally randomly generate new spiders to join the group; judge whether the set iteration threshold of the improved social spider algorithm is reached. If not, loop the above operations, otherwise, output the optimal spider individual position and the optimal fitness to obtain the optimal deployment scheme of the sensor nodes.

[0019] Calculating the distance of the spider individuals and updating the spider positions according to the above includes that the spider transmits information through vibration. The spider with high fitness attracts other spiders to approach through strong vibration, and the spider with low fitness emits weak vibration and is repelled. Assume that the position coordinate set of spider k1 is Assume that the position coordinate set of spider k2 is Consider the positions of all sensors to measure the distance between two spiders is as follows:

[0020] The position update formula is:

[0021] where x bestis the position of the spider with the current highest fitness, and α, β control the weight coefficients of the exploration and exploitation of the spider

[0022] According to the propagation of the vibration signal of the spider individual, the vibration signal intensity formula is as follows:

[0023] where is the fitness of the signal - generating spider k1 is the distance between spiders k1 and k2

[0024] According to the above - mentioned method of adopting the elite, crowding - out strategy and retaining the positions of the remaining spiders, and finally randomly generating new spiders to join the group, including:

[0025] Select spider individuals with high fitness as elites according to the elite ratio Q (0 < Q < 1), and they will not be crowded out;

[0026] Let the positions of spider k1 and spider k2 be respectively:

[0027] Then there is a similarity:

[0028] where C is the number of corresponding elements that are the same in the positions of the two spiders

[0028] Calculate the similarity between individuals, where k1 and k2 are the position vectors of two spiders respectively;

[0029] Set the crowding - out threshold: SimMAX(0 ≤ SimMAX ≤ 1)

[0030] Expel the spider individual with the highest crowding - out degree and replace it with a new spider:

[0031] In the formula, spiders that satisfy Sim(k1,k2)>SimMAX will be expelled and replaced with randomly generated new spiders

[0032] Technical solution two is as follows: The target coverage optimization device based on swarm intelligence in WSN is used to execute the method described in technical solution one, and includes a sensing module, a processing module, a wireless communication module, and an energy supply module;

[0033] The sensing module, the processing module, and the wireless communication module are connected in sequence;

[0034] The sensing 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 of digital signals and analog signals;

[0035] The processing module includes a memory and a processor. The memory is used to store the data information collected by the sensor and computer programs, and the processor is used to execute the computer programs to implement an improved social spider algorithm-based WSN target coverage method described in Solution 1;

[0036] The wireless communication module includes an antenna and a transceiver. The antenna is used to receive electromagnetic waves and convert them into wireless signals, and the transceiver is used to convert the wireless signals received by the antenna into digital signals and convert the digital signals into wireless signals for transmission;

[0037] The power supply module is respectively connected to the sensing module, the processing module and the wireless communication module, and the power supply module is used to supply power to the sensing module, the processing module and the wireless communication module.

[0038] The beneficial effects of the present invention are as follows: By introducing an elite exclusion operator, the present invention can effectively avoid falling into local optimal solutions, enhance the search ability in complex solution spaces, thereby improving the coverage efficiency and quality of sensor nodes for monitored targets, and making the finally obtained sensor node deployment scheme closer to the global optimum. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] 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:

[0040] Figure 1 It is a schematic flow chart of an objective coverage optimization method based on swarm intelligence in WSN;

[0041] Figure 2 It is a schematic structural diagram of an objective coverage optimization device based on swarm intelligence in WSN;

[0042] Figure 3 It is a schematic diagram of simulation comparison of the coverage ranges of three WSN target coverage methods;

[0043] Figure 4 It is an abstract drawing of the objective coverage optimization method and device based on swarm intelligence in WSN. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] In order to make the technical solutions and advantages in the embodiments of the present invention clearer and more understandable, the following further describes the exemplary embodiments of the present invention in detail with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than an exhaustive list of all embodiments. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0045] Example 1: Refer to Figure 1 and Figure 3 This example will be described in detail. The target coverage optimization method based on swarm intelligence in WSN specifically includes the following steps:

[0046] Step 1: Construct the WSN target coverage model and initialize it. Set the sensor node set F = {f1, f2,... f 30}, for example, f5 represents the 5th sensor node, and the monitored target set E = {e1, e2,..., e m}, for example, e5 represents the 5th monitored target. Initialize the population size to 40, that is, 40 groups of sensor positions. Define the sensing range R s = 130m and the communication range R c = 15m0. Determine the coordinates of the sensor node f i in the two-dimensional coordinate system as and the position of the monitored target e j and construct the coverage relationship matrix R between the sensor nodes and the monitored targets, where the element r ∈ {0, 1}. For example, r e,f = 0 means that the 5th monitored target is not covered by the 5th sensor node, and r 5,5 = 1 means it is covered, that is, the distance is less than R 5,5 at this time. s .

[0047] Step 2: In the set monitoring area of 100m × 100m, take the coverage rate of the sensor nodes as the optimization goal, obtain the fitness values of the spider individuals and sort them; for example, the coverage matrix R3 of individual spider 3 is as follows:

[0048] The fitness function of individual spider 3, that is, the coverage rate, is f3 as follows, which means it is covered by at least one sensor.

[0049] Step 3: Calculate the distance of the spider individuals and update the spider positions. Assume that the position coordinate set of spider 31 is Assume that the position coordinate set of spider 42 is Then the distance between the two spiders is as follows:

[0050] The position update formula is:

[0051] Among them, xbest is the position of the spider with the current highest fitness, and α = 0.3, β = 0.4 control the weight coefficients for the exploration and exploitation of the spider.

[0052] Step 4: Propagation of the vibration signal of the spider individual, where the vibration signal intensity formula is as follows:

[0053] is the fitness of the spider 31 that generates the signal, is the distance between spiders 31 and 42.

[0054] Step 5: Adopt the elite and crowding strategy and retain the positions of the remaining spiders. Finally, randomly generate new spiders to join the group. In each iteration, select the spider individuals with high fitness as elites according to the elite ratio Q (0 < Q < 1), and they do not participate in crowding. For individuals other than the elite individuals, for example, the positions of spiders 3 and 4 are then there is a similarity where C is the number of corresponding elements that are the same in the positions of the two spiders. Let the crowding threshold be SimMAX (0 ≤ SimMAX ≤ 1), and the spider individual with the highest crowding degree will be crowded out and replaced with a new spider. For example, the spider that satisfies Sim(31, 42) > SimMAX will be crowded out.

[0055] Step 5: Combine the elite individuals and the remaining individuals after the crowding operation to form a new generation of population. The new generation of population will inherit the excellent genes in the previous generation of population and maintain a certain degree of diversity.

[0056] Step 6: Determine whether the set iteration threshold of the elite crowding spider algorithm is reached, that is, when (the maximum number of iterations is reached, the coverage rate reaches the preset threshold, or a solution that meets the requirements is found), the algorithm ends and outputs the optimal deployment plan. If the algorithm threshold is not reached, calculate the fitness of the newly generated spiders, then re-substitute to calculate the attraction of the spiders in order, and loop the above operations.

[0057] Through simulation and analysis, comparing the genetic algorithm in the algorithm with a mutation rate of 0.1, the population size and the number of nodes are the same as those of the improved social spider algorithm. Comparing the values of the iteration number and the coverage ratio, for the WSN target coverage method of the improved social spider algorithm compared with the genetic algorithm, the method of the present invention effectively improves the coverage area of the sensor nodes, and has a faster running convergence speed and a stable optimization ability. The compared genetic algorithm converges at the 89th generation and the coverage value is lower than the algorithm we proposed.

[0058] Example 2: Refer to Figure 2Specifically describe this embodiment. The target coverage optimization device based on swarm intelligence in WSN is used to execute the method described in Embodiment 1, and includes a sensing module, a processing module, a wireless communication module, and an energy supply module;

[0059] The sensing module, the processing module, and the wireless communication module are connected in sequence;

[0060] The sensing 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 digital signals and analog signals;

[0061] The processing module includes a memory and a processor. The memory is used to store the data information collected by the sensor and computer programs, and the processor is used to execute the computer programs to realize the method described in Embodiment 1;

[0062] The wireless communication module includes an antenna and a transceiver. The antenna is used to receive electromagnetic waves and convert them into wireless signals, and the transceiver 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;

[0063] The energy supply module is respectively connected to the sensing module, the processing module, and the wireless communication module. The energy supply module is used to supply power to the sensing module, the processing module, and the wireless communication module.

[0064] Although the present invention is described based on a limited number of embodiments, those skilled in the art in this technical field will understand that other embodiments can be conceived within the scope of the present invention thus described. In addition, it should be noted that the language used in this specification is mainly selected for readability and teaching purposes, rather than for the purpose of explaining or limiting the subject matter of the present invention. Therefore, many modifications and changes are obvious to those of ordinary skill in the art in this technical field without departing from the scope and spirit of the appended claims. For the scope of the present invention, the disclosure of the present invention is illustrative, not restrictive, and the scope of the present invention is defined by the appended claims.

Claims

1. A target coverage optimization method and device based on swarm intelligence in WSN, characterized in that: It includes the following steps: Construct a WSN target coverage model and initialize it. Calculate the fitness values of each individual in the spider population of the improved social spider algorithm through a fitness function composed of the coverage rate of the target area of the wireless sensor network, and use the sensor position matrix corresponding to the individual with the maximum fitness value as the optimal deployment plan for the wireless sensor network coverage optimization model.

2. The construction of the WSN target coverage model and initialization according to claim 1, characterized in that The size of the target area, the number of sensors, and the sensing radius of the sensors are selected, the corresponding relationship between the target coverage concept and the improved social spider algorithm concept is determined, and the positions of the spider individuals are randomly initialized within the set monitoring area; specifically: the deployment plan of each group of sensor nodes corresponds to the corresponding spider individual, and the coverage rate corresponds to the fitness value of the spider individual.

3. The construction of the WSN target coverage model and initialization according to claim 1, characterized in that Set the sensor node set F = {f1,f2,…f n }, where f i represents the i-th sensor node, the monitored target set E = {e1, e2, …, e m }, where e j Represents the jth monitored target; defines the sensing range R of the sensor node s With communication range R c And R s <R c , the sensor node f is determined by a two-dimensional coordinate system i The coordinates of With the monitored target j Location And construct the coverage relationship matrix R between the sensor node and the monitored target, where the element r e,f ∈{0,1},r e,f = 0 means that the e-th monitored target is not covered by the f-th sensor node, r e,f =1 means it is covered, that is, the distance is less than R s ; Randomly initialize N spiders, that is, N groups of sensor positions, Spider K's location: where each spider represents a sensor deployment plan.

4. The calculation of the fitness values of each individual in the spider population of the improved social spider algorithm through a fitness function composed of the coverage rate of the target area of the wireless sensor network according to claim 1, characterized in that In the set monitoring area, the coverage rate of sensor nodes is taken as the optimization target, and the fitness values ​​of individual spiders are obtained and sorted; the coverage matrix R of individual spider k is k As shown below, The fitness function of individual spider k, that is, the coverage rate, is f k As shown below, it is covered by at least one sensor.

5. The use of the sensor position matrix corresponding to the individual with the maximum fitness value as the optimal deployment plan for the wireless sensor network coverage optimization model according to claim 1, characterized in that Calculate the distance of the spider individuals and update the positions of the spiders; the propagation of the vibration signals of the spider individuals; adopt the elite and crowding strategy and retain the positions of the remaining spiders, and finally randomly generate new spiders to join the group; determine whether the set iteration threshold of the improved social spider algorithm is reached. If not, loop the above operations. Otherwise, output the optimal spider individual position and the optimal fitness to obtain the optimal deployment plan of the sensor nodes.

6. The calculation of the distance of the spider individuals and the update of the positions of the spiders according to claim 5, characterized in that Assume that the position coordinates of spider k1 are Assume that the position coordinates of spider k2 are Considering the positions of all sensors to measure the distance between the two spiders As shown below: The position update formula is: where x best is the position of the spider with the highest current fitness, and α and β control the weight coefficients of the spider's exploration and development.

7. The spider individual vibration signal propagation according to claim 5, characterized in that: The vibration signal intensity formula is as follows: To generate the fitness of the signal spider k1, is the distance between spiders k1 and k2.

8. The adoption of the elite and crowding strategy and the retention of the positions of the remaining spiders, and finally the random generation of new spiders to join the group according to claim 5, characterized in that Select spider individuals with high fitness as elites according to the elite ratio Q (0 < Q < 1) and they will not be crowded out; Let the positions of spider k1 and spider k2 be: Then there is a similarity: where C is the number of corresponding elements that are the same in the positions of the two spiders; Calculate the similarity between individuals, where k1 and k2 are the position vectors of the two spiders respectively; Let the crowding threshold be: SimMAX (0 ≤ SimMAX ≤ 1) Eliminate the spider individual with the highest crowding degree and replace it with a new spider: In the formula, the spiders that satisfy Sim(k1, k2) > SimMAX will be eliminated and replaced with randomly generated new spiders.

9. A WSN target coverage optimization device for implementing the method described in claims 1-8, characterized in that: It includes: Initialization module: Configure the monitoring area parameters and generate the initial spider population; Fitness calculation module: Calculate the individual fitness based on the coverage rate, uniformity, and connectivity; Position update module: Execute the spider movement rules and correct the individuals that cross the boundary; Selection optimization module: implement elite retention and similarity exclusion strategies; Output module: Generates and stores the optimal sensor deployment plan.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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

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