CN115243273A - Wireless sensor network coverage optimization method, device, equipment and medium - Google Patents

Wireless sensor network coverage optimization method, device, equipment and medium Download PDF

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CN115243273A
CN115243273A CN202211165906.XA CN202211165906A CN115243273A CN 115243273 A CN115243273 A CN 115243273A CN 202211165906 A CN202211165906 A CN 202211165906A CN 115243273 A CN115243273 A CN 115243273A
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optimization
coverage
sensor network
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wireless sensor
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CN115243273B (en
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周心博
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Kunming University of Science and Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses a method, a device, equipment and a medium for optimizing the coverage of a wireless sensor network, belonging to the field of network optimization, wherein the optimization method comprises the following steps: s1, obtaining a probability perception optimization model of a sensor node network based on a Boolean perception model; s2, constructing a wireless sensor network coverage multi-objective optimization model with sensor network coverage and node energy power consumption as optimization parameters; s3, aiming at the established model, the mixed mayflies-frog jump algorithm based on Tent chaotic map and Levy flight strategy is solved. Aiming at the effectiveness of coverage of a wireless sensor network, the method comprehensively considers the sensing attenuation and the coverage hole of the wireless sensor, and establishes a probability sensing optimization model of the sensor network; node energy consumption is further introduced, and a multi-objective optimization model of comprehensive energy and coverage rate is constructed; the traditional mayflies algorithm and the mixed frog-leap algorithm are fused, and Tent chaotic mapping and Levy flight mechanism are introduced to improve the convergence precision and the global optimization capability of the algorithm.

Description

Wireless sensor network coverage optimization method, device, equipment and medium
Technical Field
The invention belongs to the field of wireless sensor layout optimization, and particularly relates to a wireless sensor network coverage optimization method, a wireless sensor network coverage optimization device, equipment and a wireless sensor network coverage optimization medium.
Background
With the rapid development of technologies in the fields of internet of things, automation and artificial intelligence, sensor technologies have been transformed from traditional single-source information acquisition to integrated, multi-source and heterogeneous Wireless Sensor Networks (WSNs) with sensing, computing and communication capabilities. The sensor technology, the distributed information processing technology, the embedded technology, the communication technology and other advanced technologies are highly integrated, and the sensor technology, the distributed information processing technology, the embedded technology, the communication technology and other advanced technologies become one of the popular research directions in the fields of modern military industry, medical treatment, national security and the like.
The coverage optimization of the wireless sensor network is to solve the problems of low coverage rate, poor perception capability, uneven node distribution, more coverage holes and the like caused by random deployment by optimizing the deployment position of each sensor, and can directly influence the safety and accuracy of wireless network data transmission.
The research on a sensor node coverage model is mainly divided into a 0-1 perception model and a probability perception model, wherein the 0-1 perception model is ideal, and the problems that the complex application environment of a wireless sensing network under the actual condition, the perception capability of a sensor node is weakened along with the distance and the like cannot be considered comprehensively are solved.
The current article only has a few designs to the coverage optimization of the wireless sensor network, but does not design the joint optimization of the coverage rate of the sensor network and the energy consumption of the nodes.
There is no patent at present that the mixed mayfly-frog jump algorithm based on Tent chaotic mapping and Levy flight strategy is applied to the wireless sensor network coverage optimization method.
Therefore, a method for optimizing the coverage of the wireless sensor network is needed.
Disclosure of Invention
The invention aims to provide a wireless sensor network coverage optimization method, which is used for solving the problems in the prior art, reducing resource waste in the production process, improving the storage space utilization rate and the order response efficiency and obtaining a better resource planning and goods space distribution result.
In order to realize the purpose, the invention is realized by adopting the following technical scheme: designing wireless sensor nodes according to a Boolean sensing model, and establishing an objective function aiming at the coverage rate of a sensor network based on monitoring probability and a coverage hole so as to construct a probability sensing model of the sensor node network;
secondly, constructing a wireless sensor network coverage multi-objective optimization model taking the coverage rate of the sensor network and the energy consumption of nodes as optimization parameters based on the probability perception optimization model established in the step one;
and thirdly, aiming at the constructed optimization model, combining the convergence speed and the local search capability of the Mayfly Algorithm (MA) with the robustness and the global optimization capability of the hybrid frog-jump algorithm, and designing the hybrid mayfly-jump algorithm based on Tent chaotic mapping and Levy flight strategies for solving.
Further, the steps include designing a wireless sensor node according to a boolean sensing model, and establishing an objective function for the coverage rate of the sensor network based on the monitoring probability and the coverage hole, so that the establishment of the probability sensing model of the sensor node network is realized by adopting the following steps:
step 1.1, suppose that the two-dimensional region S is divided into
Figure 646546DEST_PATH_IMAGE001
Mesh, set of mesh points
Figure 658365DEST_PATH_IMAGE002
Wherein N static wireless sensor nodes are arranged, and a sensor set
Figure 747543DEST_PATH_IMAGE003
Based on a Boolean sensing model, the monitoring range of each node is a circular area with R as the radius;
step 1.2, for a certain grid point in the region S
Figure 666958DEST_PATH_IMAGE004
Of wireless sensor node
Figure 157982DEST_PATH_IMAGE005
The Euclidean distance between them is:
Figure 340702DEST_PATH_IMAGE006
（1）
step 1.3, taking into account the actual conditions of sensingOptimal monitoring distance and sensing error distance of node, node
Figure 651597DEST_PATH_IMAGE007
For grid points
Figure 312386DEST_PATH_IMAGE008
The monitoring perception probability of (2) can be expressed as follows:
Figure 660846DEST_PATH_IMAGE009
（2）
Figure 14467DEST_PATH_IMAGE010
(3)
wherein,
Figure 609397DEST_PATH_IMAGE011
as sensor nodes
Figure 339455DEST_PATH_IMAGE012
Is monitored at a distance that is optimal for monitoring,
Figure 539492DEST_PATH_IMAGE013
in order to sense the error distance thereof,
Figure 64015DEST_PATH_IMAGE014
attenuation coefficient of perception ability with distance;
step 1.4, all sensor nodes in the region are paired
Figure 349502DEST_PATH_IMAGE015
The joint monitoring probability of (a) can be expressed as:
Figure 617673DEST_PATH_IMAGE016
（4）
step 1.5, suppose
Figure 468954DEST_PATH_IMAGE017
Assuming that the set of grid points that can be effectively monitored is the minimum probability threshold at which the node can be effectively monitored
Figure 898798DEST_PATH_IMAGE018
Then for any point therein the condition should be satisfied:
Figure 671582DEST_PATH_IMAGE019
（5）
for grid points not satisfying the above formula, i.e. in the region S and in the set
Figure 743443DEST_PATH_IMAGE020
The other grid points are coverage holes;
step 1.6, based on the above, in order to maximize the number of points satisfying the formula (5), establishing an objective function with the coverage rate of the wireless sensor network as an optimization target:
Figure 652494DEST_PATH_IMAGE021
（6）
therefore, a probability perception optimization model of the sensor node network is constructed.
Further, the second step is to reduce the capacity loss and optimize the network resources, and a wireless sensor network coverage multi-objective optimization model taking the sensor network coverage rate and the node energy power consumption as optimization parameters is constructed on the basis of the probability perception optimization model established in the first step;
step 2.1, because the energy reserve of the sensor node is limited, the energy consumption of the sensor node is introduced as one of optimization targets, and because partial capacity of the node needs to be amplified in the transmission process during signal transmission, the energy consumption of the node for transmitting kbit data on a link can be expressed as follows:
Figure 784398DEST_PATH_IMAGE022
（7）
Figure 572707DEST_PATH_IMAGE023
（8）
wherein
Figure 448259DEST_PATH_IMAGE024
Is the distance between the output node and the receiving node,
Figure 211815DEST_PATH_IMAGE025
is the amplification factor of the signal and is,
Figure 514621DEST_PATH_IMAGE026
is the signal amplification factor of the multi-path attenuation model,
Figure 996418DEST_PATH_IMAGE027
transmitting the energy loss of bit data for a single sensor node;
step 2.2, therefore, the overall objective function of the optimization model should be expressed as maximizing the sensor network coverage and minimizing the node energy power consumption:
Figure 410081DEST_PATH_IMAGE028
（9）
wherein
Figure 746254DEST_PATH_IMAGE029
And expressing the transmission energy consumption between each node.
Further, said third step combines the convergence speed and local search capability of the Mayflies Algorithm (MA) with the robustness of the blended frog-jump algorithm and the global optimization capability for a constructed wireless sensor network coverage multi-objective optimization model to design a hybrid mayflies-jump algorithm based on Tent chaotic maps and Levy flight strategies to solve;
the algorithm searching range is expanded through a Levy flight strategy, and the algorithm can quickly jump out of local optimum; the overall flow of the algorithm is expressed as follows:
step 3.1, initializing parameters, obtaining an initial population based on a Tent chaotic mapping mechanism, and initializing individual speed;
tent mapping is a piecewise linear mapping function, has the advantages of few parameters, simple operation, uniform distribution density of results presented by mapping, good ergodicity and the like, and the initialized population of the Tent mapping function is shown as the following formula
Figure 219960DEST_PATH_IMAGE030
（10）
Wherein the individual
Figure 191983DEST_PATH_IMAGE031
From the last individual and parameter
Figure 409338DEST_PATH_IMAGE032
Determination of parameters
Figure 147487DEST_PATH_IMAGE033
；
Step 3.2, randomly dividing the initial population into mayflies
Figure 792095DEST_PATH_IMAGE034
And mayflies
Figure 45221DEST_PATH_IMAGE035
；
Step 3.3, calculating the fitness of each individual and sequencing the fitness from high to low, and dividing the male population and the female population into m sub-populations respectively based on a mixed frog-leaping algorithm, wherein:
Figure 800688DEST_PATH_IMAGE036
（11）
Figure 658922DEST_PATH_IMAGE037
（12）
and 3.4, sequencing the individuals in each sub-population of the male population and the female population according to the fitness, and respectively recording the individuals with the optimal fitness and the individuals with the worst fitness in each sub-population
Figure 208852DEST_PATH_IMAGE038
And
Figure 886958DEST_PATH_IMAGE039
；
step 3.5, for each sub-population according to equation (13) (14)
Figure 446116DEST_PATH_IMAGE040
Updating the frog leap;
Figure 158857DEST_PATH_IMAGE041
（13）
Figure 145267DEST_PATH_IMAGE042
（14）
wherein
Figure 107407DEST_PATH_IMAGE043
If, if
Figure 204676DEST_PATH_IMAGE044
The adaptability is better than
Figure 870511DEST_PATH_IMAGE045
Then remain
Figure 762243DEST_PATH_IMAGE046
Otherwise, the current global optimum is obtained
Figure 414942DEST_PATH_IMAGE047
Replacement of
Figure 315902DEST_PATH_IMAGE048
Then, updating according to the equations (13) and (14), if the evolution is not obtained yet, generating a random individual replacement
Figure 3235DEST_PATH_IMAGE049
；
Step 3.6, iterating step 3.5 repeatedly until reaching local maximum iteration times
Figure 128186DEST_PATH_IMAGE050
；
Step 3.7, recombining the updated sub-populations of frog jumps into male and female mayfly populations again, calculating the individual fitness size, and performing mayfly algorithm updating;
Figure 268180DEST_PATH_IMAGE051
（15）
Figure 707252DEST_PATH_IMAGE052
（16）
each mayflies is updated according to equation (15):
wherein
Figure 249091DEST_PATH_IMAGE053
Is the j-th dimension velocity of the individual i,
Figure 748206DEST_PATH_IMAGE054
for the historically optimal position of the mayflies,
Figure 109917DEST_PATH_IMAGE055
in order to be a global optimum of the individual positions,
Figure 352679DEST_PATH_IMAGE056
is a positive coefficient of attraction, and,
Figure 749026DEST_PATH_IMAGE057
for dance coefficient, random numbers
Figure 419042DEST_PATH_IMAGE058
Each female mayflies i is updated according to equation (17):
Figure 533628DEST_PATH_IMAGE059
（17）
Figure 583011DEST_PATH_IMAGE060
（18）
wherein
Figure 630602DEST_PATH_IMAGE061
Is a male or female individual
Figure 471519DEST_PATH_IMAGE062
The cartesian distance between the two electrodes is set to be,
Figure 73401DEST_PATH_IMAGE063
in order to be a random flight coefficient,
step 3.8, carrying out male and female mating operation according to the formulas (19) and (20) to obtain a new generation of population;
Figure 657966DEST_PATH_IMAGE064
（19）
Figure 763326DEST_PATH_IMAGE065
（20）
wherein
Figure 775144DEST_PATH_IMAGE066
；
Step 3.9, updating the position of the new generation of population based on the Levy flight strategy to avoid the population from falling into local optimum;
for each individual in the new population, levy flight updating is carried out according to a formula (21), and whether the updated individual is reserved or not is determined through fitness;
Figure 864323DEST_PATH_IMAGE067
（21）
wherein
Figure 987000DEST_PATH_IMAGE068
Is an inter (0, 2) random number, and:
Figure 9182DEST_PATH_IMAGE069
（22）
Figure 457481DEST_PATH_IMAGE070
（23）
Figure 768377DEST_PATH_IMAGE071
（24）
step 3.10, carrying out boundary processing on the position and the speed of each individual;
step 3.11, adding one to the total iteration number of the algorithm, and judging whether the maximum iteration number is reached
Figure 694744DEST_PATH_IMAGE072
If yes, outputting an optimal solution, and finishing the algorithm; otherwise, the step 3.2 is returned.
According to a first aspect of the present invention, in a second aspect, an apparatus of a coverage optimization method for a wireless sensor network includes:
the data acquisition module is used for acquiring a data set distributed by the sensors;
the data processing module is used for processing the acquired wireless network sensor data to obtain data of a wireless network sensor model;
and the data calculation module is used for carrying out quantitative calculation on the wireless network sensor model according to the processed data.
In another aspect, an electronic device includes: a processor and a memory having computer readable instructions stored thereon that, when executed by the processor, implement a wireless sensor network coverage optimization method.
In yet another aspect, a computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements a wireless sensor network coverage optimization method.
The invention has the beneficial effects that:
the invention relates to a wireless sensor network coverage optimization method based on a mixed mayfly-frog leap algorithm, aiming at the effectiveness of wireless sensor network coverage, comprehensively considering the sensing attenuation and the coverage hole of a wireless sensor, and establishing a sensor network probability sensing optimization model; further introducing node energy consumption, and constructing a multi-objective optimization model of comprehensive energy and coverage rate; the traditional mayflies algorithm and the mixed frog-leap algorithm are fused, and Tent chaotic mapping and Levy flight mechanism are introduced to improve the convergence precision and the global optimization capability of the algorithm.
Drawings
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described with reference to the accompanying drawings, in which:
FIG. 1 is a schematic diagram of the effective monitoring area and coverage holes of a wireless sensor for a wireless sensor network coverage optimization method based on the mayflies-leapfrog algorithm provided in the invention;
FIG. 2 is a flowchart of a mixed mayfly-frog jump algorithm based on Tent chaotic map and Levy flight strategy for a wireless sensor network coverage optimization method based on the mixed mayfly-frog jump algorithm provided by the invention.
Detailed Description
The present invention will be described in detail with reference to fig. 1-2, and the technical solutions in the embodiments of the present invention will be clearly and completely described, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Step one, designing wireless sensor nodes according to a Boolean sensing model, and establishing and obtaining an objective function aiming at the coverage rate of a sensor network based on monitoring probability and a coverage hole so as to construct a probability sensing model of the sensor node network.
The method comprises the following steps of designing a wireless sensor node according to a Boolean sensing model, and establishing an objective function aiming at the coverage rate of the sensor network based on monitoring probability and a coverage hole, so that the probability sensing model of the sensor node network is established by adopting the following steps:
step 1.1, suppose that the two-dimensional region S is divided into
Figure 40275DEST_PATH_IMAGE073
Individual grid, set of grid points
Figure 125387DEST_PATH_IMAGE002
In which N static wireless sensor nodes are arranged, a sensor set
Figure 923579DEST_PATH_IMAGE003
Based on a Boolean sensing model, the monitoring range of each node is a circular area with R as the radius;
step 1.2 for a certain grid point in the region S
Figure 653637DEST_PATH_IMAGE004
Of wireless sensor node
Figure 853675DEST_PATH_IMAGE005
The Euclidean distance between them is:
Figure 378197DEST_PATH_IMAGE006
（1）
step 1.3, considering the optimal monitoring distance and sensing error distance of the sensor node under the actual condition, the node
Figure 460422DEST_PATH_IMAGE007
For grid points
Figure 728593DEST_PATH_IMAGE008
The monitoring perception probability of (2) can be expressed as follows:
Figure 783136DEST_PATH_IMAGE009
（2）
Figure 744139DEST_PATH_IMAGE010
(3)
wherein,
Figure 251344DEST_PATH_IMAGE011
as sensor nodes
Figure 57626DEST_PATH_IMAGE012
The optimum monitoring distance of (a) is,
Figure 232255DEST_PATH_IMAGE013
for the purpose of sensing the error distance,
Figure 98580DEST_PATH_IMAGE014
is the attenuation coefficient of perception ability with distance.
Step 1.4, all sensor node pairs in the region
Figure 358660DEST_PATH_IMAGE015
The joint monitoring probability of (a) can be expressed as:
Figure 968633DEST_PATH_IMAGE016
(4)
step 1.5, suppose
Figure 797436DEST_PATH_IMAGE017
Assuming that the set of grid points that can be effectively monitored is the minimum probability threshold at which the node can be effectively monitored
Figure 100242DEST_PATH_IMAGE018
Then for any point therein the condition should be satisfied:
Figure 582038DEST_PATH_IMAGE019
（5）
for grid points not satisfying the above formula, i.e. in the region S and in the set
Figure 995702DEST_PATH_IMAGE074
The other grid points are coverage holes;
step 1.6, based on the above, in order to maximize the number of points satisfying the formula (5), establishing an objective function with the coverage rate of the wireless sensor network as an optimization target:
Figure 879345DEST_PATH_IMAGE021
（6）
therefore, a probability perception optimization model of the sensor node network is constructed.
And step two, further considering the energy consumption problem of each sensor, and constructing a wireless sensor network coverage multi-objective optimization model taking the sensor network coverage rate and the node energy power consumption as optimization parameters based on the probability perception optimization model established in the step one in order to reduce the capacity loss and optimize the network resources.
Secondly, reducing the capacity loss and optimizing network resources, and constructing a wireless sensor network coverage multi-objective optimization model with the sensor network coverage rate and the node energy power consumption as optimization parameters based on the probability perception optimization model established in the first step;
step 2.1, because the energy reserve of the sensor node is limited, the energy consumption of the sensor node is introduced as one of optimization targets, and because partial capacity of the node needs to be amplified in the transmission process during signal transmission, the energy consumption of the node for transmitting kbit data on a link can be expressed as follows:
Figure 87472DEST_PATH_IMAGE022
（7）
Figure 56565DEST_PATH_IMAGE023
（8）
wherein
Figure 8341DEST_PATH_IMAGE075
Is output asThe distance between the node and the receiving node,
Figure 808806DEST_PATH_IMAGE025
is the amplification factor of the signal and is,
Figure 453414DEST_PATH_IMAGE026
is the signal amplification factor of the multi-path attenuation model,
Figure 909803DEST_PATH_IMAGE027
transmitting the energy loss of bit data for a single sensor node;
step 2.2, therefore, the overall objective function of the optimization model should be expressed as maximizing the sensor network coverage and minimizing the node energy power consumption:
Figure 399691DEST_PATH_IMAGE028
（9）
wherein
Figure 992346DEST_PATH_IMAGE029
And expressing the transmission energy consumption between each node.
And thirdly, aiming at the constructed optimization model, combining the convergence speed and local search capacity of the mayflies algorithm MA with the robustness and global optimization capacity of the mixed-frog jump algorithm, designing the mixed-mayflies-jump algorithm based on Tent chaotic maps and Levy flight strategies for solution.
Although the traditional mayflies algorithm has better optimizing capability, the problems of low convergence speed, low stability, easy falling into local optimum and the like still exist, and the traditional mayflies algorithm is combined with a mixed frog-leap algorithm with stronger global optimizing capability to balance the global searching capability of the algorithm. Meanwhile, a Tent chaotic mapping mechanism and a Levy flight strategy are introduced, a high-quality initial solution is generated by using the Tent chaotic mapping, and the diversity of the initial solution is improved, so that the algorithm convergence speed and the solving precision are improved; the algorithm searching range is expanded through a Levy flight strategy, and the algorithm can quickly jump out local optimum. The overall flow of the algorithm is expressed as follows.
The third step is to design a hybrid mayflies-frog jump algorithm based on Tent chaotic map and Levy flight strategy to solve for the constructed wireless sensor network coverage multi-objective optimization model by combining the convergence speed and local search capacity of the Mayflies Algorithm (MA) with the robustness and global optimization capacity of the hybrid frog jump algorithm;
the algorithm searching range is expanded through a Levy flight strategy, and the algorithm can quickly jump out of local optimum; the overall algorithm flow is expressed as follows:
step 3.1, initializing parameters, obtaining an initial population based on a Tent chaotic mapping mechanism, and initializing individual speed;
tent mapping is a piecewise linear mapping function, has the advantages of few parameters, simple operation, uniform distribution density of results presented by mapping, good ergodicity and the like, and the initialized population of the Tent mapping function is shown as the following formula
Figure 807855DEST_PATH_IMAGE030
（10）
Wherein the individual
Figure 748611DEST_PATH_IMAGE031
From the last individual and parameter
Figure 104506DEST_PATH_IMAGE032
Determination of parameters
Figure 817247DEST_PATH_IMAGE033
；
Step 3.2 randomly dividing the initial population into mayflies
Figure 538078DEST_PATH_IMAGE034
And female dayflies
Figure 969060DEST_PATH_IMAGE035
；
Step 3.3, calculating the fitness of each individual and sequencing the fitness from high to low, and dividing the male population and the female population into m sub-populations respectively based on a mixed frog-leaping algorithm, wherein:
Figure 800749DEST_PATH_IMAGE036
（11）
Figure 633576DEST_PATH_IMAGE037
（12）
and 3.4, sequencing the individuals in each sub-population of the male population and the female population according to the fitness, and respectively recording the individuals with the optimal fitness and the individuals with the worst fitness in each sub-population
Figure 525309DEST_PATH_IMAGE038
And
Figure 443586DEST_PATH_IMAGE039
；
step 3.5, for each sub-population according to equation (13) (14)
Figure 141284DEST_PATH_IMAGE040
Updating the frog leap;
Figure 563038DEST_PATH_IMAGE041
（13）
Figure 891251DEST_PATH_IMAGE042
（14）
wherein
Figure 765666DEST_PATH_IMAGE043
If, if
Figure 470317DEST_PATH_IMAGE044
The adaptability is better than
Figure 15086DEST_PATH_IMAGE045
Then remain
Figure 514201DEST_PATH_IMAGE046
Otherwise, the current global optimum is obtained
Figure 938229DEST_PATH_IMAGE047
Replacement of
Figure 180991DEST_PATH_IMAGE048
Then, updating according to the equations (13) and (14), if the evolution is not obtained yet, generating a random individual replacement
Figure 577338DEST_PATH_IMAGE049
；
Step 3.6, iterating step 3.5 repeatedly until reaching local maximum iteration times
Figure 247353DEST_PATH_IMAGE050
；
Step 3.7 recombining the updated sub-populations of frog jumps into the male and female mayflies, calculating the individual fitness size and performing the updating of the mayflies;
Figure 96361DEST_PATH_IMAGE051
（15）
Figure 877235DEST_PATH_IMAGE076
（16）
each mayflies is updated according to equation (15):
wherein
Figure 393667DEST_PATH_IMAGE053
Is the j-th dimension velocity of the individual i,
Figure 31322DEST_PATH_IMAGE054
for the historically optimal position of the mayflies,
Figure 633204DEST_PATH_IMAGE055
in order to be a global optimum of the individual positions,
Figure 217769DEST_PATH_IMAGE056
in order to be a positive coefficient of attraction,
Figure 588708DEST_PATH_IMAGE077
for dance coefficient, random numbers
Figure 334947DEST_PATH_IMAGE058
Each female mayflies is updated according to equation (17):
Figure 155617DEST_PATH_IMAGE059
（17）
Figure 543873DEST_PATH_IMAGE078
（18）
wherein
Figure 769318DEST_PATH_IMAGE061
Is the Cartesian distance between the male and female individuals i,
Figure 952037DEST_PATH_IMAGE063
in order to be a random flight coefficient,
step 3.8, carrying out male and female mating operation according to the formulas (19) and (20) to obtain a new generation of population;
Figure 325250DEST_PATH_IMAGE064
（19）
Figure 251618DEST_PATH_IMAGE065
（20）
wherein
Figure 331569DEST_PATH_IMAGE066
；
Step 3.9, updating the position of the new generation of population based on the Levy flight strategy to avoid the population falling into local optimum;
for each individual in the new population, levy flight updating is carried out according to the formula (21), and whether the updated individual is reserved or not is determined through fitness;
Figure 75403DEST_PATH_IMAGE067
（21）
wherein
Figure 873595DEST_PATH_IMAGE068
Is an inter (0, 2) random number, and:
Figure 603653DEST_PATH_IMAGE069
（22）
Figure 538111DEST_PATH_IMAGE070
（23）
Figure 65563DEST_PATH_IMAGE071
（24）
step 3.10, carrying out boundary processing on the position and the speed of each individual;
step 3.11, adding one to the total iteration number of the algorithm, and judging whether the maximum iteration number is reached
Figure 351051DEST_PATH_IMAGE072
If yes, outputting an optimal solution, and finishing the algorithm; otherwise, the step 3.2 is returned.
According to a first aspect of the present invention, in a second aspect, an apparatus of a coverage optimization method for a wireless sensor network includes:
the data acquisition module is used for acquiring a data set distributed by the sensors;
the data processing module is used for processing the acquired wireless network sensor data to obtain data of a wireless network sensor model;
and the data calculation module is used for carrying out quantitative calculation on the wireless network sensor model according to the processed data.
In another aspect, an electronic device includes: a processor and a memory having computer readable instructions stored thereon that, when executed by the processor, implement a wireless sensor network coverage optimization method.
In yet another aspect, a computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements a wireless sensor network coverage optimization method.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by hardware related to instructions of a program, and the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, read-only memories (ROMs), random Access Memories (RAMs), magnetic or optical disks, and the like. It should be noted that the above detailed description is exemplary and is intended to provide further explanation of the disclosure.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular is intended to include the plural unless the context clearly dictates otherwise. Furthermore, it will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
It should be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.

Claims (7)

1. A wireless sensor network coverage optimization method is applied to wireless sensor network coverage optimization in various wireless communication technical fields and other intelligent fields, and is characterized in that: the method comprises the following steps:
designing wireless sensor nodes according to a Boolean sensing model, and establishing a target function aiming at the coverage rate of a sensor network based on monitoring probability and a coverage hole so as to construct a probability sensing model of the sensor node network;
secondly, constructing a wireless sensor network coverage multi-objective optimization model taking the coverage rate of the sensor network and the energy consumption of nodes as optimization parameters based on the probability perception optimization model established in the step one;
and thirdly, aiming at the constructed optimization model, combining the convergence speed and local search capacity of the mayflies algorithm MA with the robustness and global optimization capacity of the mixed-frog jump algorithm, designing the mixed-mayflies-jump algorithm based on Tent chaotic maps and Levy flight strategies for solution.
2. The method of claim 1 for optimizing coverage in a wireless sensor network, wherein: the method comprises the following steps that firstly, wireless sensor nodes are designed according to a Boolean perception model, and a target function for the coverage rate of the sensor network is established and obtained based on the monitoring probability and the coverage hole, so that the probability perception model of the sensor node network is established and realized by the following steps:
step 1.1, suppose that the two-dimensional region S is divided into
Figure 525418DEST_PATH_IMAGE001
Individual grid, set of grid points
Figure 640005DEST_PATH_IMAGE002
In which N static wireless sensor nodes are arranged, a sensor set
Figure 158229DEST_PATH_IMAGE003
Based on a Boolean sensing model, the monitoring range of each node is a circular area with R as the radius;
step 1.2, for a certain grid point in the region S
Figure 674661DEST_PATH_IMAGE004
Of wireless sensor node
Figure 515578DEST_PATH_IMAGE005
The Euclidean distance between them is:
Figure 914199DEST_PATH_IMAGE006
（1）
step 1.3, considering the optimal monitoring distance and sensing error distance of the sensor node under the actual condition, the node
Figure 498764DEST_PATH_IMAGE007
For grid points
Figure 869702DEST_PATH_IMAGE008
The monitoring perception probability of (2) can be expressed as follows:
Figure 881521DEST_PATH_IMAGE009
（2）
Figure 705120DEST_PATH_IMAGE010
(3)
wherein,
Figure 827797DEST_PATH_IMAGE011
as sensor nodes
Figure 53242DEST_PATH_IMAGE012
The optimum monitoring distance of (a) is,
Figure 235962DEST_PATH_IMAGE013
for the purpose of sensing the error distance,
Figure 812436DEST_PATH_IMAGE014
attenuation coefficient of perception ability with distance;
step 1.4, all sensor node pairs in the region
Figure 535542DEST_PATH_IMAGE015
The joint monitoring probability of (a) can be expressed as:
Figure 615493DEST_PATH_IMAGE016
（4）
step 1.5, suppose
Figure 231764DEST_PATH_IMAGE017
Assuming that the set of grid points that can be effectively monitored is the minimum probability threshold at which the node can be effectively monitored
Figure 29955DEST_PATH_IMAGE018
Then for any point therein the condition should be satisfied:
Figure 494435DEST_PATH_IMAGE019
（5）
for grid points not satisfying the above formula, i.e. in the region S and in the set
Figure 428893DEST_PATH_IMAGE020
The other grid points are coverage holes;
step 1.6, based on the above, in order to maximize the number of points satisfying the formula (5), establishing an objective function with the coverage rate of the wireless sensor network as an optimization target:
Figure 218994DEST_PATH_IMAGE021
（6）
therefore, a probability perception optimization model of the sensor node network is constructed.
3. The method of claim 1 for optimizing coverage of a wireless sensor network, wherein the method comprises the following steps: secondly, reducing the capacity loss and optimizing network resources, and constructing a wireless sensor network coverage multi-objective optimization model with the sensor network coverage rate and the node energy power consumption as optimization parameters based on the probability perception optimization model established in the first step;
step 2.1, because the energy reserve of the sensor node is limited, the energy consumption of the sensor node is introduced as one of optimization targets, and because partial capacity of the node needs to be amplified in the transmission process during signal transmission, the energy consumption of the node for transmitting kbit data on the link can be expressed as follows:
Figure 238903DEST_PATH_IMAGE022
（7）
Figure 772652DEST_PATH_IMAGE023
（8）
wherein
Figure 623934DEST_PATH_IMAGE024
Which is the distance between the output node and the receiving node,
Figure 319357DEST_PATH_IMAGE025
is the amplification factor of the signal and is,
Figure 92141DEST_PATH_IMAGE026
is the signal amplification factor of the multi-path attenuation model,
Figure 164002DEST_PATH_IMAGE027
transmitting the energy loss of bit data for a single sensor node;
step 2.2, therefore, the overall objective function of the optimization model should be expressed as maximizing the sensor network coverage and minimizing the node energy power consumption:
Figure 807473DEST_PATH_IMAGE028
（9）
wherein
Figure 939377DEST_PATH_IMAGE029
And expressing the transmission energy consumption between each node.
4. The method of claim 1 for optimizing coverage of a wireless sensor network, wherein the method comprises the following steps: aiming at the constructed wireless sensor network coverage multi-target optimization model, combining the convergence speed and local search capability of the Mayfly Algorithm (MA) with the robustness and global optimization capability of the hybrid frog-jump algorithm, designing a hybrid mayfly-frog-jump algorithm based on Tent chaotic mapping and Levy flight strategy for solving;
the algorithm searching range is expanded through a Levy flight strategy, and the algorithm can quickly jump out of local optimum; the overall flow of the algorithm is expressed as follows:
step 3.1, initializing parameters, obtaining an initial population based on a Tent chaotic mapping mechanism, and initializing individual speed;
tent mapping is a piecewise linear mapping function, has the advantages of few parameters, simple operation, uniform distribution density of results presented by mapping, good ergodicity and the like, and the initialized population of the Tent mapping function is shown as the following formula
Figure 933878DEST_PATH_IMAGE030
（10）
Wherein the individual
Figure 809430DEST_PATH_IMAGE031
From the last individual and parameter
Figure 841496DEST_PATH_IMAGE032
Determination of parameters
Figure 878722DEST_PATH_IMAGE033
；
Step 3.2, randomly dividing the initial population into mayflies
Figure 422836DEST_PATH_IMAGE034
And female dayflies
Figure 836500DEST_PATH_IMAGE035
；
Step 3.3, calculating the fitness of each individual and sequencing the fitness from high to low, and dividing the male population and the female population into m sub-populations respectively based on a mixed frog-leaping algorithm, wherein:
Figure 720142DEST_PATH_IMAGE036
（11）
Figure 928270DEST_PATH_IMAGE037
（12）
and 3.4, sequencing the individuals in each sub-population of the male population and the female population according to the fitness, and respectively recording the individuals with the optimal fitness and the individuals with the worst fitness in each sub-population
Figure 631783DEST_PATH_IMAGE038
And
Figure 849138DEST_PATH_IMAGE039
；
step 3.5, for each sub-population according to equation (13) (14)
Figure 587287DEST_PATH_IMAGE040
Updating the frog leap;
Figure 28633DEST_PATH_IMAGE041
（13）
Figure 485022DEST_PATH_IMAGE042
（14）
wherein
Figure 240488DEST_PATH_IMAGE043
If, if
Figure 98723DEST_PATH_IMAGE044
The adaptability is better than
Figure 914232DEST_PATH_IMAGE045
Then remain
Figure 323829DEST_PATH_IMAGE046
Otherwise, the current global optimum is obtained
Figure 882986DEST_PATH_IMAGE047
Replacement of
Figure 595727DEST_PATH_IMAGE048
Then, updating according to the formulas (13) and (14), if evolution is not obtained, generating a random individual replacement
Figure 582138DEST_PATH_IMAGE049
；
Step 3.6, iterating step 3.5 repeatedly until reaching local maximum iteration times
Figure 13119DEST_PATH_IMAGE050
；
Step 3.7 recombining the updated sub-populations of frog jumps into the male and female mayflies, calculating the individual fitness size and performing the updating of the mayflies;
Figure 907126DEST_PATH_IMAGE051
（15）
Figure 474374DEST_PATH_IMAGE052
（16）
each mayflies is updated according to equation (15):
wherein
Figure 631685DEST_PATH_IMAGE053
Is the j-th dimension velocity of the individual i,
Figure 284384DEST_PATH_IMAGE054
for the optimal position of the mayflies in history,
Figure 185344DEST_PATH_IMAGE055
in order to be a global optimum of the individual positions,
Figure 607098DEST_PATH_IMAGE056
is a positive coefficient of attraction, and,
Figure 935311DEST_PATH_IMAGE057
for dance coefficient, random numbers
Figure 75305DEST_PATH_IMAGE058
Each female mayflies i is updated according to equation (17):
Figure 514377DEST_PATH_IMAGE059
（17）
Figure 855884DEST_PATH_IMAGE060
（18）
wherein
Figure 354998DEST_PATH_IMAGE061
Is a male or female individual
Figure 982289DEST_PATH_IMAGE062
The cartesian distance between the two electrodes is set to be,
Figure 225051DEST_PATH_IMAGE063
in order to have a random coefficient of flight,
step 3.8, carrying out male and female mating operation according to the formulas (19) and (20) to obtain a new generation of population;
Figure 621397DEST_PATH_IMAGE064
（19）
Figure 291413DEST_PATH_IMAGE065
（20）
wherein
Figure 140420DEST_PATH_IMAGE066
；
Step 3.9, updating the position of the new generation of population based on the Levy flight strategy to avoid the population from falling into local optimum;
for each individual in the new population, levy flight updating is carried out according to a formula (21), and whether the updated individual is reserved or not is determined through fitness;
Figure 983612DEST_PATH_IMAGE067
（21）
wherein is an inter (0, 2) random number, and:
Figure 500044DEST_PATH_IMAGE068
（22）
Figure 340961DEST_PATH_IMAGE069
（23）
Figure 677264DEST_PATH_IMAGE070
（24）
step 3.10, carrying out boundary processing on the position and the speed of each individual;
step 3.11, adding one to the total iteration number of the algorithm, and judging whether the maximum iteration number is reached
Figure 261829DEST_PATH_IMAGE071
If yes, outputting an optimal solution, and finishing the algorithm; otherwise, the step 3.2 is returned.
5. A device of a wireless sensor network coverage optimization method is used for the wireless sensor network coverage optimization method based on the hybrid mayfly-frog leap algorithm, characterized in that: the device of the method for optimizing the coverage of the wireless sensor network based on the hybrid mayflies-frog jump algorithm comprises the following steps:
the data acquisition module is used for acquiring a data set distributed by the sensors;
the data processing module is used for processing the acquired wireless network sensor data to obtain data of a wireless network sensor model;
and the data calculation module is used for carrying out quantitative calculation on the wireless network sensor model according to the processed data.
6. An electronic device, comprising: a processor and a memory, the memory having stored thereon computer readable instructions, which when executed by the processor, implement a wireless sensor network coverage optimization method as claimed in any one of claims 1 to 4.
7. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements a wireless sensor network coverage optimization method according to any one of claims 1 to 4.
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