CN116866951A - Sensor network coverage optimization method and system based on improved artificial buzzer algorithm - Google Patents
Sensor network coverage optimization method and system based on improved artificial buzzer algorithm Download PDFInfo
- Publication number
- CN116866951A CN116866951A CN202310905421.8A CN202310905421A CN116866951A CN 116866951 A CN116866951 A CN 116866951A CN 202310905421 A CN202310905421 A CN 202310905421A CN 116866951 A CN116866951 A CN 116866951A
- Authority
- CN
- China
- Prior art keywords
- coverage
- node
- algorithm
- updating
- optimal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 86
- 238000005457 optimization Methods 0.000 title claims abstract description 66
- 230000006870 function Effects 0.000 claims abstract description 25
- 230000008447 perception Effects 0.000 claims abstract description 25
- 230000002431 foraging effect Effects 0.000 claims abstract description 21
- 238000000265 homogenisation Methods 0.000 claims abstract description 14
- 230000000739 chaotic effect Effects 0.000 claims abstract description 13
- 238000013507 mapping Methods 0.000 claims abstract description 9
- 239000011159 matrix material Substances 0.000 claims description 34
- 241000218228 Humulus Species 0.000 claims description 18
- 238000004364 calculation method Methods 0.000 claims description 15
- 238000012544 monitoring process Methods 0.000 claims description 13
- 230000035772 mutation Effects 0.000 claims description 8
- 230000001186 cumulative effect Effects 0.000 claims description 7
- 238000012163 sequencing technique Methods 0.000 claims description 6
- 230000002776 aggregation Effects 0.000 claims description 5
- 238000004220 aggregation Methods 0.000 claims description 5
- 230000006978 adaptation Effects 0.000 claims description 4
- 239000006185 dispersion Substances 0.000 claims description 4
- 238000009499 grossing Methods 0.000 claims description 4
- 108010015046 cell aggregation factors Proteins 0.000 claims description 3
- 238000012804 iterative process Methods 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 abstract description 2
- 230000008569 process Effects 0.000 description 6
- VDRGNAMREYBIHA-UHFFFAOYSA-N 2c-e Chemical compound CCC1=CC(OC)=C(CCN)C=C1OC VDRGNAMREYBIHA-UHFFFAOYSA-N 0.000 description 5
- 208000024780 Urticaria Diseases 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000002474 experimental method Methods 0.000 description 4
- 241000272878 Apodiformes Species 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000013461 design Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000013508 migration Methods 0.000 description 2
- 230000005012 migration Effects 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007667 floating Methods 0.000 description 1
- 239000004459 forage Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- 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
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
The invention discloses a sensor network coverage optimization method and a system based on an improved artificial buzzer algorithm, which relate to the fields of a group intelligent optimization algorithm and a wireless sensor network technology, and are used for obtaining a coverage optimization model of a hybrid WSN based on a Boolean perception model and a two-dimensional area coverage model; aiming at the characteristics of the hybrid WSN coverage optimization problem, a homogenization method based on thermal coverage and a Ke Xigao-step-by-step disturbance method based on nonlinear convergence factors are designed and used for updating positions of individuals in an iterative process; PWLCM chaotic mapping and Levy flying are introduced, and the PWLCM chaotic mapping and Levy flying are used for initializing individual updating positions in a population and territory foraging stage, so that algorithm convergence accuracy and global optimizing capability are improved; and finally, taking an objective function of the model as an adaptability function for improving the artificial buzzing algorithm, and solving a coverage optimization model of the hybrid WSN to obtain an optimal deployment scheme of the sensor nodes. The method can enhance the optimizing precision and the anti-stagnation capability of the standard artificial buzzing algorithm and accelerate the convergence rate.
Description
Technical Field
The invention belongs to the technical fields of intelligent optimization algorithms of groups and wireless sensor networks, and particularly relates to a sensor network coverage optimization method and system based on an improved artificial buzzer algorithm.
Background
With the rapid development of the internet, the internet of things, artificial intelligence and automation, the human society has entered a global intellectualization stage. The development of the internet of things has stimulated the progress of sensor technology, and how to efficiently deploy a complex WSN is an important difficulty facing the current situation. WSNs are often used in severe environments where manpower is difficult to reach, often adopt unmanned aerial vehicle random deployment node's mode, coverage holes appear easily, result in monitoring area information acquisition not enough, and then influence network overall performance.
The hybrid WSN is a special type of WSN, has the advantages of flexibility in deployment and deployment cost, and is widely applied to actual life. Since hybrid WSNs contain partially fixed nodes and mobile nodes, the randomness and uncertainty of deployment increases greatly. With the increase of the scale, the traditional accurate algorithm is easy to fall into a local optimal solution, the calculation cost is also increased in an exponential function, and the calculation time is unacceptable. Therefore, the application range of the accurate algorithm is narrow, and the method is not the best choice for solving the problem of coverage optimization of the hybrid WSN.
In order to solve the above problems, it is necessary to design a hybrid wireless sensor network coverage optimization method to realize optimal deployment of sensor nodes.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a sensor network coverage optimization method and a system based on an improved artificial buzzing algorithm, which are applied to the problem of hybrid WSN coverage optimization and realize optimal deployment of sensor nodes.
The invention is realized by the following technical scheme:
a sensor network coverage optimization method based on an improved artificial buzzer algorithm comprises the following steps:
step 1, taking a Boolean perception model as a node perception model, and establishing a hybrid WSN coverage optimization model by combining an objective function with maximized monitoring area coverage; encoding the buzzing population according to the hybrid WSN coverage optimization model, and initializing the buzzing population by using PWLCM chaotic mapping;
step 2, determining that each hummer enters a foraging stage of guiding or a foraging stage of leading the land, and then updating the positions of all the hummers in the population by combining Levy flight;
step 3, determining Euclidean distance among the buzzers updated in the step 2, and updating the positions of all the buzzers by using a homogenization method based on thermal coverage;
step 4, sequencing the humulus population according to the updated humulus fitness values in the step 3, updating the position of the inferior humulus, and updating the position of the optimal humulus by using a Ke Xigao-step-by-step disturbance method based on a nonlinear convergence factor to obtain an optimal deployment scheme of the sensor;
and 5, when the optimal sensor deployment scheme does not reach the set condition or the maximum iteration number, taking all the sensor deployment schemes obtained in the iteration as an initial population, and repeating the steps 2-4 until the optimal sensor deployment scheme reaches the set condition or the maximum iteration number.
Preferably, the method for updating the position of the buzzers in the foraging stage guided in the step 2 is as follows:
and determining a target food source according to the priority and the fitness value of the humblebirds, calculating candidate positions and candidate fitness values of the humblebirds after moving towards the target food source, and updating the positions of the humblebirds according to the candidate positions when the candidate fitness values are better than the original fitness values.
Preferably, the method for updating the position of the buzzers in the collar-to-feed stage in the step 2 is as follows:
and calculating candidate positions and candidate fitness values after the movement of the humblebirds by adopting a Levy flight solution generation method, and updating the positions of the humblebirds according to the candidate positions when the candidate fitness values are better than the original fitness values.
Preferably, the homogenization method based on thermal coverage for updating the position of each hummer in step 3 is as follows:
for a sensor node s in a buzzer i Node s i The Euclidean distance between the node and the rest node is smaller than the demarcation factor b 1 The nodes of the node (B) are screened as neighbor nodes;
when node s i With neighbor node s j Is greater than the set demarcation factor b 2 When updating node s with an aggregate operation i Is a position of (2);
when node s i With neighbor node s j Is smaller than the set demarcation factor b 2 And is greater than the demarcation factor b 3 In the time, node s is updated by a scatter operation i Is a position of (2);
when node s i With neighbor node s j Is smaller than the demarcation factor b 3 In the case, the node s is updated by a mutation operation i Is a position of (c).
Preferably, node s is updated using an aggregation operation i The method of location is as follows:
the node s i The calculation method of the moving distance of the device is as follows:
D ij ＝k 1 ×(d(s i ,s j )-d 2 )
the node s i The moving direction of the moving direction is calculated as follows:
wherein k is 1 For the aggregation factor, d (s i ,s j ) Is the Euclidean distance.
Preferably, node s is updated using a scatter operation i The method of location is as follows:
the node s i The calculation method of the moving distance of the device is as follows:
the node s i The moving direction of the moving direction is calculated as follows:
wherein k is 2 Is a dispersion coefficient.
Preferably, the node s is updated by a mutation operation i The method of location is as follows:
and calculating an accumulated coverage matrix P according to the Boolean perception model, performing smoothing operation on the accumulated coverage matrix P, converting the accumulated coverage matrix P into a thermal coverage matrix, and updating the node position to the position with the lowest heat in the thermal coverage matrix.
8. The method for optimizing coverage of a sensor network based on an improved artificial buzzer algorithm according to claim 7, wherein the method for converting the cumulative coverage matrix into the thermal coverage matrix is as follows:
representing m×l pixel points as a two-dimensional matrix form, the method of calculating the cumulative coverage matrix P is as follows:
wherein P(s) i ,c ml ) Is the boolean perceptual probability.
The method of converting the cumulative coverage matrix into the thermal coverage matrix Col is as follows:
wherein, number is the element Number in the submatrix, start and end are the starting subscript and the ending subscript of the submatrix, and the calculation method is as follows:
where arm is the arm length of the submatrix and M is the width of the monitored area.
Preferably, the method for updating the node position to the lowest heat position in the thermal coverage matrix is as follows:
x i ＝min(Col)|M
y i ＝mod(min(Col),M)
where Col is the thermal coverage matrix and M is the width of the monitored area.
Preferably, in step 4, the candidate position and the candidate fitness value of the optimal humulus are calculated by using a Ke Xigao-step-by-step perturbation method based on a nonlinear convergence factor, and when the candidate fitness value is better than the original fitness value, the original position of the optimal humulus is updated to be the candidate position.
A system for performing a sensor network coverage optimization method based on an improved artificial buzzer algorithm, comprising:
the model building module is used for taking the Boolean perception model as a node perception model and building a coverage optimization model of the hybrid WSN by combining an objective function with maximized area coverage;
the initialization module is used for encoding the humulus population according to the coverage optimization model of the hybrid WSN and initializing the humulus population by PWLCM chaotic mapping;
the updating module is used for determining that each hummer enters a leading foraging stage or a territory foraging stage, and then updating the positions of all the hummers by combining Levy flight; determining the Euclidean distance between the updated buzzes, and updating the positions of all the buzzes by combining a homogenization method based on thermal coverage; sequencing the hive population according to the updated adaptation value of the hive, updating the position of the inferior hive, and updating the position of the optimal hive by using a Ke Xigao-step-by-step disturbance method based on a nonlinear convergence factor;
the output module is used for re-determining the sensor deployment schemes by taking all the sensor deployment schemes obtained in the iteration as initial population when the optimal sensor deployment scheme does not reach the set condition or the maximum iteration number, until the optimal sensor deployment scheme reaches the set condition or the maximum iteration number;
the deployment module is used for obtaining the optimal deployment scheme of the sensor after the optimal deployment scheme of the sensor reaches a set condition or reaches the maximum iteration number, and moving the sensor to an optimal position according to the optimal deployment scheme.
Compared with the prior art, the invention has the following beneficial technical effects:
according to the sensor network coverage optimization method and system based on the improved artificial buzzing algorithm, a coverage optimization model of the hybrid WSN is obtained based on the Boolean perception model and the two-dimensional area coverage model; firstly, aiming at the characteristic of the problem of hybrid WSN coverage optimization, a homogenization method based on thermal coverage is designed and is used for updating the position of an individual in the iterative process; secondly, a Ke Xigao S optimal individual dimension-by-dimension disturbance method based on nonlinear convergence factors is designed, and the anti-stagnation capacity of an algorithm is improved; in addition, a PWLCM chaotic map and a solution generation method based on Levy flight are introduced, and the method is used for initializing individual updating positions in a population and territory foraging stage, so that algorithm convergence accuracy and global optimizing capability are improved; finally, taking an objective function of the model as an adaptation function for improving an artificial buzzing algorithm (Artificial Hummingbird Algorithm with Thermal Homogenization and Dimensional Perturbation, THPAHA), solving a coverage optimization model of the hybrid WSN to obtain an optimal deployment scheme of the sensor node.
Further, a homogenization method based on thermal coverage is combined with an artificial buzzing algorithm and applied to the problem of hybrid WSN coverage optimization, the homogenization method based on thermal coverage firstly selects neighbor nodes according to Euclidean distances among the nodes, calculates the distance and the direction of the nodes to be moved to the neighbor nodes, and then updates the node positions. The method is characterized in that when nodes in the neighborhood are concentrated, the nodes can perform scattered operation, and redundant coverage is avoided; when nodes in the adjacent domains are scattered, the nodes can perform aggregation operation, and coverage holes are avoided. In addition, when the distance between two nodes in the neighborhood is too close, a great amount of redundant coverage occurs in the neighborhood by the nodes moving anyway, and the nodes need to be mutated into other neighbors through mutation operation. In the mutation process, firstly, the accumulated coverage matrix of the region is calculated, then, the thermal coverage matrix is calculated by using a smoothing operation, and finally, the node is updated to the neighbor with the lowest thermal power. Finally, according to the Ke Xigao-step optimal individual dimension-by-dimension disturbance method based on the nonlinear convergence factor, disturbance operation of an optimal individual in the early stage of iteration is biased to be distributed in a more dispersed Cauchy mode, disturbance operation in the later stage of iteration is biased to be distributed in a more concentrated Gaussian mode, and the problem that the optimal individual is prone to being locally stagnated under the condition that the optimal individual is not guided is avoided.
Drawings
FIG. 1 is an overall flow chart of a hybrid wireless sensor network coverage optimization method of the present invention;
FIG. 2 is a flowchart of the THPAHA algorithm used in the method of the present invention;
FIG. 3 is a comparison of coverage schemes before and after optimization of the method of the present invention with different numbers of nodes;
wherein, the a diagram is a comparison diagram of coverage schemes before and after optimization of 30 nodes; b, a comparison chart of the coverage schemes before and after 35 nodes are optimized; c, a comparison chart of coverage schemes before and after optimization of 40 nodes; d, the graph is a comparison graph of coverage schemes before and after optimization of 45 nodes; e, the figure is a comparison figure of coverage schemes before and after 50 nodes are optimized; f, the graph is a comparison graph of coverage schemes before and after optimization of 55 nodes;
FIG. 4 is an iteration diagram of the coverage rate of the optimization process for different node numbers in the method of the present invention;
Detailed Description
The invention will now be described in further detail with reference to the accompanying drawings, which illustrate but do not limit the invention.
Referring to fig. 1 and 2, a sensor network coverage optimization method based on an improved artificial buzzer algorithm includes the following steps:
step 1, taking a Boolean perception model as a node perception model and combining an objective function with maximized area coverage, and establishing a coverage optimization model of the hybrid WSN, wherein the coverage optimization model comprises the following specific steps of:
s1.1, the symbols made on the coverage optimization model are explained as follows: in an area of M× L M 2 In the monitoring area of (1), n sensor nodes are deployed, denoted as set s= { S 1 ,s 2 ,...,s i ,...,s n And, wherein the first dim sensor nodes are mobile nodes. s is(s) i The position coordinates are (x i ,y i ) I=1, 2, n, the sensing radius of the sensor nodes is R. Discretizing the monitoring area into M×L pixel points, wherein the pixel point set is C= { C 1 ,c 2 ,...,c j ,...,c M×L }，c j The position coordinates are (x j ,y j ),j＝1,2,...,M×L。
S1.2, a Boolean perception model is adopted as a node perception model, namely the perception capability of the node does not gradually fade along with the increase of the distance, and the perception probability of the node is calculated as shown in a formula (1):
wherein d(s) i ,c j ) For the sensor node s i And pixel point c j The euclidean distance of (2) is calculated as shown in formula (2):
and S1.3, designing an objective function to maximize the coverage rate of the region, wherein the objective function is expressed as the ratio of the sum of joint perception probabilities of the node set S to the pixel point set C to the total number of the pixel points. The design constraint condition is that the coordinate ranges of the sensor nodes and the pixel points do not exceed the range of the monitoring area, a hybrid WSN coverage optimization model is established, and the expression is shown in the formula (3):
wherein P(s) i ,c j ) For the sensor node s i For pixel point c j Is determined by the perceptual probability of the (c).
Step 2, according to the optimization problem characteristics of the hybrid WSN coverage optimization model, constructing a coding structure of a buzzer individual, and representing dim mobile nodes as a buzzer individual, wherein the two-dimensional rectangular form of the coding structure of the buzzer individual is shown as a formula (4):
wherein the rows represent mobile nodes s i Column indicates s i Two-dimensional coordinates (x) i ,y i )。
Step 3, establishing an fitness function and a boundary constraint function of the THPAHA according to an objective function and constraint conditions of the hybrid WSN coverage optimization model, wherein the fitness function and the boundary constraint function are specifically as follows:
s3.1, designing a fitness function to maximize the coverage rate of a monitoring area, in the calculation process, firstly, splicing a deployment scheme of a mobile node and a deployment scheme of a fixed node into a complete deployment scheme, then calculating the coverage rate according to the complete deployment scheme, and designing the fitness function to maximize the coverage rate of the monitoring area according to an objective function, wherein the coverage rate is shown in a formula (5):
s3.2, according to constraint conditionsCalculating boundary constraint function if node s i And updating the node coordinates to legal states by using the remainder operation when the coordinates exceed the monitoring area range, wherein the node coordinates are shown in the formula (6):
and 4, setting a hybrid WSN coverage optimization model and related parameters of THPAHA. In this embodiment, the maximum iteration number is 200, the population number is 30, the migration foraging parameter is 0.1, and the area of the monitoring area is 50×50m 2 The total number of the sensor nodes is 30-55, the number of the fixed nodes is 16, and the node perception radius is 5m.
Step 5, initializing a population (namely, a buzzer population consisting of mobile nodes) by using PWLCM chaotic mapping, and randomly initializing coordinates of fixed nodes, wherein the specific process is as follows:
s5.1, randomly generating a first mobile node, then generating a PWLCM chaotic sequence of an x coordinate according to a formula (7), and carrying out the same principle as a y coordinate:
wherein r is a random floating point number from 0 to 1, mod is a remainder operation, a is a PWLCM chaotic parameter, and a is 0.4 in the embodiment;
s5.2, mapping the x coordinate and the y coordinate of the mobile node into a monitoring area according to two PWLCM chaotic sequences, wherein the x coordinate and the y coordinate are shown as a formula (8):
x i ＝x i ×M,y i ＝y i ×L (8)
and 6, inputting the initial population obtained in the step 5, entering the main iteration of the algorithm, and outputting the optimal sensor node deployment scheme after the maximum iteration times are reached in the steps 7-11 which are a complete iteration process.
And 7, traversing all the buzzes in the population, updating the fitness value of the population, generating random numbers for each buzzes in the population, entering a guide foraging stage if the random numbers are larger than 0.5, and otherwise entering a territory foraging stage.
S7.1, in the stage of guiding foraging, firstly selecting the buzzers with highest priority in the access table as target food sources, if a plurality of buzzers with highest priority exist at the same time, selecting the buzzers with the optimal fitness value as the target food sources, then calculating candidate positions and candidate fitness values after the buzzers move to the target food sources, and carrying out boundary constraint on the candidate positions. And if the candidate fitness value is better than the original fitness value, updating the original position and the original fitness value of the humming bird into the candidate position and the candidate fitness value, and updating the access table. The new x coordinate calculation after the humming bird moves towards the target food source is shown in the formula (9), and the y coordinates are the same as the above:
x new ＝x tar +cauchy(0,1)×(|x old -x tar |) (9)
wherein x is tar As a target food source, cauchy (0, 1) is a random number meeting the standard cauchy distribution;
s7.2, in the territory foraging stage, the humblers randomly move nearby to forage. And calculating candidate positions and candidate fitness values after the humming birds move by utilizing Levy flight, and carrying out boundary constraint on the candidate positions. And if the candidate fitness value is better than the original fitness value, updating the original position and the original fitness value of the humming bird into the candidate position and the candidate fitness value, and updating the access table. The new x coordinate calculation of the foraging stage of the collar is shown as a formula (10), and the y coordinates are the same:
x new ＝x old +Levy(t)×gauss(0,1) (10)
where Levy (t) represents a vector conforming to Levy distribution, t is the current iteration number, and gauss (0, 1) is a random number conforming to standard gaussian distribution.
Step 8, traversing all the buzzers after the movement in the step 7, and setting a demarcation factor b 1 、b 2 And b 3 . Splicing a mobile node deployment scheme and a fixed node deployment scheme represented by a buzzer into a complete deployment scheme, and calculating Euclidean distance d(s) between every two nodes in the complete deployment scheme i ,s j ) According to Euler' sThe distance and the homogenization method based on thermal coverage update the position of the mobile node, and carry out boundary constraint, and the specific process is as follows:
s8.1, screening and node S i The Euclidean distance of b 1 The node in the node is used as a neighbor node, and the neighbor node is traversed;
s8.2 for neighbor node S j When d (s i ,s j ) Greater than b 2 When it indicates that coverage holes, s, may occur i An aggregation operation is performed. Calculating s according to formula (11) i S is calculated according to equation (12) i Is a moving direction of the (c);
D ij ＝k 1 ×(d(s i ,s j )-d 2 ) (11)
wherein k is 1 For the aggregation factor, s is determined i Aggregation level with neighboring nodes. k (k) 1 The larger s i The more aggregated the nodes in the neighborhood;
s8.3 for neighbor node S j When d (s i ,s j ) Less than b 2 And is greater than b 3 When it is indicated that redundant coverage may occur, s i A scatter operation is performed. Calculating s according to formula (13) i S is calculated according to equation (14) i Is a moving direction of the (c);
wherein k is 2 For the dispersion coefficient, s is determined i Degree of dispersion with neighboring nodes. k (k) 2 The larger s i The more dispersed the nodes within the neighborhood.
S8.4 for the neighborhoodResiding node s j When d (s i ,s j ) Less than b 3 At this time, two nodes are shown to be close to overlapping, at which time s i There is a large amount of redundant coverage to move to whichever position in the neighborhood, requiring a mutation operation.
The mutation operation is specifically as follows: first, M×L pixel points are expressed as M×L two-dimensional matrix form, and element P in cumulative coverage matrix P, P is calculated ml Representing pixel point c ml Is calculated as shown in equation (15):
wherein P(s) i ,c ml ) Is s i For pixel point c ml The boolean probability of perception of (2) is calculated as shown in equation (1). And then carrying out smoothing operation on the P by utilizing convolution to obtain the thermal coverage matrix. The edge information of the accumulated coverage matrix is lost in the traditional convolution mode, so that the edge pixel point is also subjected to convolution operation, specifically: determining a convolution kernel, p ml As a center point, a matrix with arm length arm is extended in four directions, and p is calculated according to the formula (16) ml Convolving to obtain a thermal coverage matrix Col:
wherein Number is the Number of elements in the submatrix, start and end are the start subscript and the end subscript of the submatrix, and the calculation is shown in formula (17):
wherein arm is the submatrix arm length, and the calculation is shown in formula (18):
arm＝kernel|2 (18)
the thermal coverage matrix Col can thus represent the monitoring in its entiretyCoverage thermodynamic diagrams of the area. Finally, finding the place with the lowest heat degree in Col, and updating s according to the formula (19) i Coordinates of (c):
wherein min (Col) is the total index covering the minimum value in the thermodynamic matrix, and rounding it to obtain x i The coordinates are subjected to residual taking operation to obtain y i Coordinates.
And 9, calculating the fitness values of all the updated hives in the step 8, sequencing the hive population from good to bad, selecting a certain proportion of bad hives to migrate and feed according to the migration and feed-seeking coefficients, updating the positions of the bad hives, carrying out boundary constraint, and finally updating the access table. In this embodiment, the number of inferior hives is 10% of the population, the new x coordinate calculation of each dimension of the hives is shown in formula (20), and the y coordinates are the same:
x new ＝cauchy(0,1)×(f best -f old ) (20)
wherein f best For best fitness value, f old Is the fitness value of the current humming bird.
And step 10, selecting an optimal humulus from the well-ordered population in the step 9, calculating candidate positions by using a Ke Xigao-step dimension-by-dimension disturbance method based on a nonlinear convergence factor, carrying out boundary constraint, calculating candidate fitness values of the candidate positions, if the candidate fitness values are better than the original fitness values, updating the original positions and the original fitness values of the optimal humulus into the candidate positions and the candidate fitness values, and updating an access table. The new x coordinate calculation of the optimal humming bird is shown in the formula (21), and the y coordinates are the same as the above:
wherein beta is 1 And beta 2 For the nonlinear convergence factor, the values shown in the formulas (22) and (23) are calculated:
β 2 ＝1-β 1 (23)
wherein t is the current iteration number, iteraion is the maximum iteration number, b is a constant, and 3, tanh is a hyperbolic tangent function in this patent.
And 11, if the maximum iteration number or the coverage rate of the optimal coverage scheme reaches the preset requirement, ending the algorithm, outputting the optimal hummingbirds (namely the optimal deployment scheme) in the population, evaluating the performance of the algorithm through indexes described by the formula (24) and the formula (25), otherwise, inputting the population obtained in the current iteration as the initial population of the next iteration, and repeating the steps 7-11.
Example 1
Assume that sensor nodes are deployed in a 50m×50m square monitoring area, with a node sensing radius of 5m. As shown in fig. 3 and 4, 50 independent experiments were performed with the THPAHA algorithm with the number of sensor nodes being 30, 35, 40, 45, 50, 55, respectively, and the optimization effects were compared. Algorithm performance was evaluated by calculating the mean coverage and standard deviation of the 50 runs. The higher the average coverage rate is, the stronger the optimizing capability of the algorithm is; the smaller the standard deviation, the better the stability of the algorithm. The average coverage is calculated as shown in formula (24), and the standard deviation is calculated as shown in formula (25):
wherein exp is total experiment times, f i The maximum coverage obtained for the ith experiment.
As shown in table 1, the average coverage ratio before and after optimization for the number of equivalent nodes was counted. The average coverage before optimization varies from 56.08% to 78.82% because the coverage area must be slightly increased as the number of nodes increases. After THPAHA optimization, the average coverage rate is improved to 81.7 to 99.7 percent, and the average coverage rate is improved by 24.48 percent. Under the condition of the same number of nodes, the THPAHA has remarkable optimization effect.
Table 1THPAHA optimized coverage comparison for different node numbers for hybrid WSN coverage models
The invention also provides a system for the sensor network coverage optimization method based on the improved artificial buzzing algorithm, which comprises the following steps:
the model building module is used for taking the Boolean perception model as a node perception model and building a coverage optimization model of the hybrid WSN by combining an objective function with maximized area coverage;
the initialization module is used for encoding the humulus population according to the coverage optimization model of the hybrid WSN and initializing the humulus population by PWLCM chaotic mapping;
the updating module is used for determining that each hummer enters a leading foraging stage or a territory foraging stage, and then updating the positions of all the hummers by combining Levy flight; determining the Euclidean distance between the updated buzzes, and updating the positions of all the buzzes by combining a homogenization method based on thermal coverage; sequencing the hive population according to the updated adaptation value of the hive, updating the position of the inferior hive, and updating the position of the optimal hive by using a Ke Xigao-step-by-step disturbance method based on a nonlinear convergence factor;
the output module is used for re-determining the sensor deployment schemes by taking all the sensor deployment schemes obtained in the iteration as initial population when the optimal sensor deployment scheme does not reach the set condition or the maximum iteration number, until the optimal sensor deployment scheme reaches the set condition or the maximum iteration number;
the deployment module is used for obtaining the optimal deployment scheme of the sensor after the optimal deployment scheme of the sensor reaches a set condition or reaches the maximum iteration number, and moving the sensor to an optimal position according to the optimal deployment scheme.
According to the sensor network coverage optimization method and system based on the improved artificial buzzing algorithm, a coverage optimization model of the hybrid WSN is obtained based on the Boolean perception model and the two-dimensional area coverage model; firstly, aiming at the characteristic of the problem of hybrid WSN coverage optimization, a homogenization method based on thermal coverage is designed and is used for updating the position of an individual in the iterative process; secondly, a Ke Xigao S optimal individual dimension-by-dimension disturbance method based on nonlinear convergence factors is designed, and the anti-stagnation capacity of an algorithm is improved; in addition, a PWLCM chaotic map and a solution generation method based on Levy flight are introduced, and the method is used for initializing individual updating positions in a population and territory foraging stage, so that algorithm convergence accuracy and global optimizing capability are improved; and finally, taking an objective function of the model as an adaptability function for improving the artificial buzzing algorithm, and solving a coverage optimization model of the hybrid WSN to obtain an optimal deployment scheme of the sensor nodes. The optimizing ability and stability of the proposed method were checked according to the average coverage and standard deviation of 50 experiments. Experimental results show that the method can enhance the optimizing precision and anti-stagnation capability of the standard artificial buzzing algorithm, quicken the convergence rate, provide a solution for the problem of hybrid WSN coverage optimization, and enlarge the application scene of the standard artificial buzzing algorithm.
The above is only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited by this, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (10)
1. The sensor network coverage optimization method based on the improved artificial buzzing algorithm is characterized by comprising the following steps of:
step 1, taking a Boolean perception model as a node perception model, and establishing a hybrid WSN coverage optimization model by combining an objective function with maximized monitoring area coverage; encoding the buzzing population according to the hybrid WSN coverage optimization model, and initializing the buzzing population by using PWLCM chaotic mapping;
step 2, determining that each hummer enters a foraging stage of guiding or a foraging stage of leading the land, and then updating the positions of all the hummers in the population by combining Levy flight;
step 3, determining Euclidean distance among the buzzers updated in the step 2, and updating the positions of all the buzzers by using a homogenization method based on thermal coverage;
step 4, sequencing the humulus population according to the updated humulus fitness values in the step 3, updating the position of the inferior humulus, and updating the position of the optimal humulus by using a Ke Xigao-step-by-step disturbance method based on a nonlinear convergence factor to obtain an optimal deployment scheme of the sensor;
and 5, when the optimal sensor deployment scheme does not reach the set condition or the maximum iteration number, taking all the sensor deployment schemes obtained in the iteration as an initial population, and repeating the steps 2-4 until the optimal sensor deployment scheme reaches the set condition or the maximum iteration number.
2. The sensor network coverage optimization method based on the improved artificial hive algorithm of claim 1, wherein the hive location update method for guiding the foraging stage in step 2 is as follows:
and determining a target food source according to the priority and the fitness value of the humblebirds, calculating candidate positions and candidate fitness values of the humblebirds after moving towards the target food source, and updating the positions of the humblebirds according to the candidate positions when the candidate fitness values are better than the original fitness values.
3. The sensor network coverage optimization method based on the improved artificial buzzing algorithm according to claim 1, wherein the buzzing position updating method in the territory-seeking stage in the step 2 is as follows:
and calculating candidate positions and candidate fitness values after the movement of the humblebirds by adopting a Levy flight solution generation method, and updating the positions of the humblebirds according to the candidate positions when the candidate fitness values are better than the original fitness values.
4. The improved artificial buzzer algorithm-based sensor network coverage optimization method of claim 1, wherein the thermal coverage-based homogenization method for updating each buzzer location in step 3 is as follows:
for a sensor node s in a buzzer i Node s i The Euclidean distance between the node and the rest node is smaller than the demarcation factor b 1 The nodes of the node (B) are screened as neighbor nodes;
when node s i With neighbor node s j Is greater than the set demarcation factor b 2 When updating node s with an aggregate operation i Is a position of (2);
when node s i With neighbor node s j Is smaller than the set demarcation factor b 2 And is greater than the demarcation factor b 3 In the time, node s is updated by a scatter operation i Is a position of (2);
when node s i With neighbor node s j Is smaller than the demarcation factor b 3 In the case, the node s is updated by a mutation operation i Is a position of (c).
5. The improved artificial buzzer algorithm-based sensor network coverage optimization method of claim 4, wherein nodes s are updated using an aggregation operation i The method of location is as follows:
the node s i The calculation method of the moving distance of the device is as follows:
D ij ＝k 1 ×(d(s i ,s j )-d 2 )
the node s i The moving direction of the moving direction is calculated as follows:
wherein k is 1 For the aggregation factor, d (s i ,s j ) Is the Euclidean distance.
6. The improved artificial buzzer algorithm-based sensor network coverage optimization method of claim 4, wherein nodes s are updated using decentralized operations i The method of location is as follows:
the node s i The calculation method of the moving distance of the device is as follows:
the node s i The moving direction of the moving direction is calculated as follows:
wherein k is 2 Is a dispersion coefficient.
7. The improved artificial buzzer algorithm-based sensor network coverage optimization method of claim 4, wherein the nodes s are updated by mutation operations i The method of location is as follows:
and calculating an accumulated coverage matrix P according to the Boolean perception model, performing smoothing operation on the accumulated coverage matrix P, converting the accumulated coverage matrix P into a thermal coverage matrix, and updating the node position to the position with the lowest heat in the thermal coverage matrix.
8. The method for optimizing coverage of a sensor network based on an improved artificial buzzer algorithm according to claim 7, wherein the method for converting the cumulative coverage matrix into the thermal coverage matrix is as follows:
representing m×l pixel points as a two-dimensional matrix form, the method of calculating the cumulative coverage matrix P is as follows:
wherein P(s) i ,c ml ) Is Boolean perception probability;
the method of converting the cumulative coverage matrix into the thermal coverage matrix Col is as follows:
wherein, number is the element Number in the submatrix, start and end are the starting subscript and the ending subscript of the submatrix, and the calculation method is as follows:
where arm is the arm length of the submatrix and M is the width of the monitored area.
9. The sensor network coverage optimization method based on the improved artificial hive algorithm according to claim 1, wherein in the step 4, a Ke Xigao-step-by-step disturbance method based on a nonlinear convergence factor is used for calculating candidate positions and candidate fitness values of the optimal hive, and when the candidate fitness values are better than the original fitness values, the home positions of the optimal hive are updated to the candidate positions.
10. A system for performing the improved artificial hive algorithm-based sensor network coverage optimization method of any one of claims 1-9, comprising:
the model building module is used for taking the Boolean perception model as a node perception model and building a coverage optimization model of the hybrid WSN by combining an objective function with maximized area coverage;
the initialization module is used for encoding the humulus population according to the coverage optimization model of the hybrid WSN and initializing the humulus population by PWLCM chaotic mapping;
the updating module is used for determining that each hummer enters a leading foraging stage or a territory foraging stage, and then updating the positions of all the hummers by combining Levy flight; determining the Euclidean distance between the updated buzzes, and updating the positions of all the buzzes by combining a homogenization method based on thermal coverage; sequencing the hive population according to the updated adaptation value of the hive, updating the position of the inferior hive, and updating the position of the optimal hive by using a Ke Xigao-step-by-step disturbance method based on a nonlinear convergence factor;
the output module is used for re-determining the sensor deployment schemes by taking all the sensor deployment schemes obtained in the iteration as initial population when the optimal sensor deployment scheme does not reach the set condition or the maximum iteration number, until the optimal sensor deployment scheme reaches the set condition or the maximum iteration number;
the deployment module is used for obtaining the optimal deployment scheme of the sensor after the optimal deployment scheme of the sensor reaches a set condition or reaches the maximum iteration number, and moving the sensor to an optimal position according to the optimal deployment scheme.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310905421.8A CN116866951A (en) | 2023-07-21 | 2023-07-21 | Sensor network coverage optimization method and system based on improved artificial buzzer algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310905421.8A CN116866951A (en) | 2023-07-21 | 2023-07-21 | Sensor network coverage optimization method and system based on improved artificial buzzer algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116866951A true CN116866951A (en) | 2023-10-10 |
Family
ID=88232115
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310905421.8A Pending CN116866951A (en) | 2023-07-21 | 2023-07-21 | Sensor network coverage optimization method and system based on improved artificial buzzer algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116866951A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117572295A (en) * | 2024-01-12 | 2024-02-20 | 山东和兑智能科技有限公司 | Multi-mode on-line monitoring and early warning method for high-voltage sleeve |
| CN118211735A (en) * | 2024-05-20 | 2024-06-18 | 山东工商学院 | Desert golden mole rat optimization algorithm and application thereof |
-
2023
- 2023-07-21 CN CN202310905421.8A patent/CN116866951A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117572295A (en) * | 2024-01-12 | 2024-02-20 | 山东和兑智能科技有限公司 | Multi-mode on-line monitoring and early warning method for high-voltage sleeve |
| CN117572295B (en) * | 2024-01-12 | 2024-04-12 | 山东和兑智能科技有限公司 | Multi-mode on-line monitoring and early warning method for high-voltage sleeve |
| CN118211735A (en) * | 2024-05-20 | 2024-06-18 | 山东工商学院 | Desert golden mole rat optimization algorithm and application thereof |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116866951A (en) | Sensor network coverage optimization method and system based on improved artificial buzzer algorithm | |
| Mo et al. | Research of biogeography particle swarm optimization for robot path planning | |
| CN111176807A (en) | Multi-satellite cooperative task planning method | |
| CN111982125A (en) | Path planning method based on improved ant colony algorithm | |
| Wang | A comparative study of cuckoo algorithm and ant colony algorithm in optimal path problems | |
| CN112462803B (en) | Unmanned aerial vehicle path planning method based on improved NSGA-II | |
| Wang et al. | Rank-driven salp swarm algorithm with orthogonal opposition-based learning for global optimization | |
| CN112788699B (en) | Method and system for determining network topology of self-organizing network | |
| CN113985888B (en) | Forklift path planning method and system based on improved ant colony algorithm | |
| CN112484732B (en) | IB-ABC algorithm-based unmanned aerial vehicle flight path planning method | |
| CN110442940A (en) | A kind of parallel intelligent Layout Design method of efficient ship multi-pipeline | |
| CN112484733B (en) | Reinforced learning indoor navigation method based on topological graph | |
| CN111565372A (en) | Directed sensor network optimized deployment system and method | |
| CN115167502A (en) | Unmanned aerial vehicle collaborative flight path planning method and device based on immune clone algorithm | |
| CN116227767A (en) | Multi-unmanned aerial vehicle base station collaborative coverage path planning method based on deep reinforcement learning | |
| CN114662638A (en) | Mobile robot path planning method based on improved artificial bee colony algorithm | |
| CN114237282B (en) | Unmanned aerial vehicle flight path intelligent planning method for intelligent industrial park monitoring | |
| CN110059848B (en) | WSN charging service site setting method and charging equipment driving path planning method | |
| CN115408929A (en) | Data processing method and device for tunnel driving construction prediction | |
| Cuevas et al. | Metaheuristic algorithms for wireless sensor networks | |
| CN107483309B (en) | Distributed network system minimum weight vertex coverage solving method based on game theory | |
| CN115620046A (en) | Multi-target neural architecture searching method based on semi-supervised performance predictor | |
| Shankar et al. | Integrated cuckoo and monkey search algorithm for energy efficient clustering in wireless sensor networks | |
| CN113688971A (en) | Key node set determination method based on group intelligence | |
| CN112344934B (en) | GNG network-based construction method for deletable environment topology map |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |