CN117062092B - Wireless sensor network deployment method - Google Patents
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Abstract
The invention provides a wireless sensor network deployment method, which comprises the following steps: generating a first sampling point population, and determining the sampling scale of the first sampling point population; obtaining a first solution set; calculating a first standard deviation and a first objective function value of the first solution set; generating a second sampling point and a second sampling point population corresponding to the first sampling point; calculating a second standard deviation and a second objective function value of the second solution set; comparing the first objective function value with the second objective function value, and taking the sampling point corresponding to the smaller objective function value as the corresponding sampling point in the new first sampling point population; iteratively updating the first sampling point population until a termination condition is reached, and taking the final first sampling point population as a point for deploying a wireless sensor in the monitoring range; the network life cycle is prolonged on the premise of ensuring the monitoring range through a reasonable networking scheme, and the monitoring precision and the accuracy of the monitoring data are improved.
Description
Technical Field
The invention relates to the technical field of sensor arrangement, in particular to a wireless sensor network deployment method.
Background
For fire prevention monitoring in mountain forest, the wireless sensor wsn node can be utilized to remotely monitor smoke, temperature and the like in mountain forest. In the prior art, wireless sensor nodes can be deployed in a throwing manner for complex mountain environments. However, the wireless sensor nodes are deployed in a throwing mode, so that the problem of unreasonable position deployment exists, the monitoring precision is low, and the monitoring data is inaccurate.
In view of the above, the invention provides a wireless sensor network deployment method, which prolongs the life cycle of the network and improves the monitoring precision and the accuracy of monitoring data on the premise of ensuring the monitoring range through a reasonable networking scheme.
Disclosure of Invention
The invention aims to provide a wireless sensor network deployment method, which comprises the following steps: generating a first sampling point population, and determining the sampling scale of the first sampling point population; blurring processing is carried out on first sampling points in the first sampling point population to obtain a first solution set; calculating a first standard deviation and a first objective function value of the first solution set; respectively taking the first sampling point as a center and the sampling scale as a radius to generate a second sampling point and a second sampling point population corresponding to the first sampling point; calculating a second standard deviation and a second objective function value for the second solution set; comparing the first objective function value with the second objective function value, and taking the sampling point corresponding to the smaller objective function value as the corresponding sampling point in the new first sampling point population; iteratively updating the first sampling point population until a termination condition is reached, and taking the final first sampling point population as a point for the wireless sensor in the monitoring range.
Further, determining an initial first sampling point population includes: determining an initial sampling scale and an initial sampling definition domain based on the monitoring range; and randomly generating a plurality of initial particles in the initial sampling definition domain, and taking the plurality of initial particles as the first sampling point population.
Further, the sampling scaleThe calculation formula of (2) is as follows:
；
wherein,representing a sampling scale; />Representing the maximum value in the distance of the node from the center of the lattice; />Representing the minimum value in the distance of the node from the center of the lattice; the grid refers to a plurality of grids obtained by dividing the monitoring range;
the calculation formula of the distance between the node and the center of the grid is as follows:
；
wherein,representing the sampling point +.>And lattice center point->Is a distance of (2); />Indicate->The individual sensors are->Coordinates in the direction; />Indicate->The individual sensors are->Coordinates in the direction; />The expression number is->Is at the center point of the lattice of (2)Coordinates in the direction; />The expression number is->The center point of the lattice of (2) is +.>Coordinates in the direction.
Further, the expression of the objective function is:
；
wherein,representing an objective function; />Representing to take the minimum value; />Representing the inside of each solutionThe number of sensor nodes participating in the construction of the network; />Representing the total number of sampling point candidate solutions; />Representing the sum of the perceived probabilities of all the cells within the monitoring range.
Further, the number of the sensor nodesThe calculation formula of (2) is as follows:
；
wherein D represents the total number of sampling points, namely the number of sensor nodes; j represents a sampling point variable;indicate->In the case where the individual sensors are selected as sampling points, and (2)>The value set is +.>When->The time indicates node ++>Is selected as the sampling point, otherwise->Indicating that it is not selected as a sampling point.
Further, the sum of the perceived probabilities of all the lattices in the monitoring rangeThe calculation formula of (2) is as follows:
；
wherein,representing the total number of lattices; />Represents a lattice variable; />Indicating all sampling points in the range of each lattice +.>Perceived probability.
Further, the probability that each grid is perceived by all sampling points within the rangeThe expression of (2) is:
；
wherein D represents the total number of sampling points; j represents a sampling point variable;represents the lattice center point +.>Sampled dot +.>Probability of detection by the sensor at that location.
Further, the lattice center pointSampled dot +.>Probability of detection by the sensor at +.>The expression of (2) is:
；
wherein,representing the sampling point +.>And lattice center point->Is a distance of (2); />Representing the perceived radius of the sampling point.
Further, the termination condition is that the iteration number is greater than the maximum iteration number.
Further, the method also comprises the step of judging whether the termination condition is met, and comprises the following steps: determining a first standard deviation and a second standard deviation of the first sampling point population and the new first sampling point population respectively; judging whether the difference value of the first standard deviation and the second standard deviation is larger than the sampling scale; if the sampling scale is larger than the sampling scale, continuing to iteratively update the first sampling point population; if the sampling scale is smaller than or equal to the sampling scale, calculating the average value of the new first sampling point population; replacing the sampling point with the largest difference from the second standard deviation in the new first sampling point population with the mean value, and halving the sampling scale to obtain a new sampling scale; and repeatedly updating the first sampling point population based on the replaced first sampling point population and the new sampling scale until the iteration number is greater than the maximum iteration number.
The technical scheme of the embodiment of the invention has at least the following advantages and beneficial effects:
the invention optimizes the networking scheme, and the objective function comprises two optimization targets of coverage (monitoring) range and the number of nodes participating in work, so that the deployment of the sensor is more reasonable, and the life cycle of the network is prolonged.
Drawings
Fig. 1 is an exemplary flowchart of a wireless sensor network deployment method according to some embodiments of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Fig. 1 is an exemplary flowchart of a wireless sensor network deployment method according to some embodiments of the present invention. As shown in fig. 1, the process 100 includes the following:
step 1, generating a first sampling point seed groupAnd determining the sampling scale +.>. Wherein the sampling point population->For (+)>,/>,/>,/>) Initial sampling Point population->The size of (2) is obtained by initialization and the total number is k, including (>,/>,/>,/>) The method comprises the steps of carrying out a first treatment on the surface of the The sampling scale +.>The calculation formula of (2) is as follows:
；
wherein,representing the sampling scale (including +.>And->，/>Sample size, representing the initial sample point population, +.>Sample scale representing the population obtained by the p-th iteration,/->Representing the sampling scale of the population obtained by the (p+1) th iteration；/>Representing the maximum value in the distance of the node from the center of the lattice; />Representing the minimum value in the distance of the node from the center of the lattice; the grid refers to a plurality of grids obtained by dividing the monitoring range; wherein the initial sampling scale，/>
The calculation formula of the distance between the node and the center of the grid is as follows:
；
wherein,representing the sampling point +.>And lattice center point->Is a distance of (2); />Indicate->The individual sensors are->Coordinates in the direction; />Indicate->The individual sensors are->Coordinates in the direction; />The expression number is->Is at the center point of the lattice of (2)Coordinates in the direction; />The expression number is->The center point of the lattice of (2) is +.>Coordinates in the direction.
Wherein, in a two-dimensional monitoring range, the whole range is divided intoThe grids are respectively numberedThe center point coordinates of each lattice are expressed asWherein->The expression number is->Is +.>Coordinates in the direction, the same applies to->Is->Coordinates in the direction.
D equally-arranged sensor nodes are arranged in a monitoring range randomly in an initialization stage, and the sensing radius of the nodes is as followsNode number->，/>Is->Numbering of individual sensor nodes, node positionsWherein->Indicate->The individual sensors are->Coordinates in the direction, the same applies to->Is->Coordinates in the direction. Wherein +.>Direction and->In the direction, respectively performing one Gaussian sampling to obtainTo a set of potentially optimal coverage sampling points。
Step 2, performing blurring processing on the first sampling points in the first sampling point population to obtain a first solution set. Wherein the dimension of the solution set is DIM, < ->（/>,/>,/>,/>) Definition field is [0,1]The initial solution is (+)>,/>,/>,/>). The definition domain of each dimension of the function optimal solution Y is +.>. In the MQHOA, a new solution is obtained by taking the current solution as a center and taking the current scale as a radius for Gaussian sampling, so that in order to ensure that the new solution meets the requirement of a definition domain, a membership function shown in the following formula is used for fuzzifying Gaussian sampling points.
A；
Wherein,expressed as +.>New sampling point for the center, +.>Is an adjustable constant.
Step 3, calculating a first standard deviation of the first solution setAnd a first objective function value->. Wherein, the expression of the objective function is:
；
wherein,representing an objective function, including->And->，/>A target function value representing an initial sampling point population; />Expressing the objective function value of the sampling point population after the p-th iteration; />Representing the objective function value of the sampling point population after the (p+1) th iteration; />Representing to take the minimum value; />Representing the number of sensor nodes participating in constructing a network in each solution; />Representing the total number of sampling point candidate solutions; />Representing the sum of the perceived probabilities of all the cells within the monitoring range. The objective function represents the desire to use the least work node layout, i.e. the current optimal network coverage layout, on the basis that all sensor nodes are perceived. The optimal solution for solving the objective function is defined as +.>Where D is the dimension of the solution. Wherein the number of the sensor nodesThe calculation formula of (2) is as follows:
；
wherein D represents the total number of sampling points, namely the number of sensor nodes; j represents a sampling point variable;indicate->In the case where the individual sensors are selected as sampling points, and (2)>The value set is +.>When->The time indicates node ++>Is selected as the sampling point, otherwise->Indicating that it is not selected as a sampling point. Wherein the sum of the perceived probabilities of all lattices in the monitoring range is +.>The calculation formula of (2) is as follows:
；
wherein,representing the total number of lattices; />Represents a lattice variable; />Indicating all sampling points in the range of each lattice +.>Perceived probability. Wherein the probability of each grid being perceived by all sampling points in the range +.>The expression of (2) is:
；
wherein D represents the total number of sampling points; j represents a sampling point variable;represents the lattice center point +.>Sampled dot +.>Probability of detection by the sensor at that location. Wherein the lattice center point +.>Sampled dot +.>Probability of detection by the sensor at +.>The expression of (2) is:
；
wherein,representing the sampling point +.>And lattice center point->Is a distance of (2); />Representing the perceived radius of the sampling point.
Step 4, generating a second sampling point and a second sampling point population corresponding to the first sampling point by taking the first sampling point as a center and the sampling scale as a radius respectively。
Step 5, blurring the second sampling points in the second sampling point population to obtain a second sampling pointSolution set。
Step 6, calculating a second standard deviation of the second solution setAnd a second objective function value->。
And 7, comparing the first objective function value with the second objective function value, and taking the sampling point corresponding to the smaller objective function value as the corresponding sampling point in the new first sampling point population. For example, if a more optimal solution occurs, the original particles are replaced with new sampling points. The sampling replacement of k particles is completed, namely one iteration operation is completed, and k population particles after the completion of the iteration operation are @, namely @,/>,/>,/>) Is solved as (++>,/>,/>,/>). The standard deviation of the k population particles after iteration is +.>Update->And->。
And 8, iteratively updating the first sampling point population until a termination condition is reached, and taking the final first sampling point population as a point for deploying the wireless sensor in the monitoring range. The termination condition is that the iteration times are larger than the maximum iteration times. The maximum number of iterations may be maxfe=10000 x dim, the starting number of iterations fe=0 of the function, one FE added per iteration.
Further comprising determining whether a termination condition is reached, including: determining the first sampling point population and the new first sampling point population respectively,/>,/>,/>) A first standard deviation and a second standard deviation of (2); judging the first standard deviation->And the second standard deviation +.>Whether or not it is greater than said sampling scale +.>The method comprises the steps of carrying out a first treatment on the surface of the If the current size is larger than the sampling size, indicating that the population does not reach a diffusion stable state under the current size, and continuing to iteratively update the first sampling point population; if the sampling scale is smaller than or equal to the sampling scale, the dispersion of population particles is stabilized under the primary scale, and calculation is performedThe mean value of the new first sampling point population +.>The method comprises the steps of carrying out a first treatment on the surface of the Sample points in the new first sample point population which are most different from the second standard deviation +.>Replacing with the mean value and halving the sampling scale +.>Obtaining a new sampling scale; repeatedly updating the first sampling point population based on the replaced first sampling point population and the new sampling scale until the iteration number is greater than the maximum iteration number +.>. When the algorithm is finished, outputting a sensor sampling point with optimal objective function value +.>And optimal objective function value->。
Also included is determining an initial first sampling point population, comprising: determining an initial sampling scale based on the monitoring rangeAnd an initial sample definition field->The method comprises the steps of carrying out a first treatment on the surface of the And randomly generating a plurality of initial particles in the initial sampling definition domain, and taking the plurality of initial particles as the first sampling point population.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A wireless sensor network deployment method, comprising:
generating a first sampling point population, and determining the sampling scale of the first sampling point population; the sampling scaleThe calculation formula of (2) is as follows:
；
wherein,representing a sampling scale; />Representing the maximum value in the distance of the node from the center of the lattice; />Representing the minimum value in the distance of the node from the center of the lattice; the grids are a plurality of grids obtained by dividing the monitoring range;
the calculation formula of the distance between the node and the center of the grid is as follows:
；
wherein,representing the sampling point +.>And lattice center point->Is a distance of (2); />Indicate->The individual sensors are->Coordinates in the direction; />Indicate->The individual sensors are->Coordinates in the direction; />The expression number is->The center point of the lattice of (2) is +.>Coordinates in the direction; />The expression number is->The center point of the lattice of (2) is +.>Coordinates in the direction;
blurring processing is carried out on first sampling points in the first sampling point population to obtain a first solution set;
calculating a first standard deviation and a first objective function value of the first solution set;
respectively taking the first sampling point as a center and the sampling scale as a radius to generate a second sampling point and a second sampling point population corresponding to the first sampling point;
blurring processing is carried out on second sampling points in the second sampling point population to obtain a second solution set;
calculating a second standard deviation and a second objective function value for the second solution set;
comparing the first objective function value with the second objective function value, and taking the sampling point corresponding to the smaller objective function value as the corresponding sampling point in the new first sampling point population; the expression of the objective function is:
；
wherein,representing an objective function; />Representing to take the minimum value; />Representing the number of sensor nodes participating in constructing a network in each solution; />Representing the total number of sampling point candidate solutions; />Representing the sum of perceived probabilities of all grids in the monitoring range;
iteratively updating the first sampling point population until a termination condition is reached, and taking the final first sampling point population as a point for the wireless sensor in the monitoring range; the termination condition is that the iteration number is larger than the maximum iteration number;
further comprising determining whether a termination condition is reached, including:
determining a first standard deviation and a second standard deviation of the first sampling point population and the new first sampling point population respectively;
judging whether the difference value of the first standard deviation and the second standard deviation is larger than the sampling scale;
if the sampling scale is larger than the sampling scale, continuing to iteratively update the first sampling point population;
if the sampling scale is smaller than or equal to the sampling scale, calculating the average value of the new first sampling point population;
replacing the sampling point with the largest difference from the second standard deviation in the new first sampling point population with the mean value, and halving the sampling scale to obtain a new sampling scale;
and repeatedly updating the first sampling point population based on the replaced first sampling point population and the new sampling scale until the iteration number is greater than the maximum iteration number.
2. The wireless sensor network deployment method of claim 1, further comprising determining an initial first sampling point population, comprising:
determining an initial sampling scale and an initial sampling definition domain based on the monitoring range;
and randomly generating a plurality of initial particles in the initial sampling definition domain, and taking the plurality of initial particles as the first sampling point population.
3. The wireless sensor network deployment method of claim 1, wherein the number of sensor nodesThe calculation formula of (2) is as follows:
；
wherein D represents the total number of sampling points, namely the number of sensor nodes; j represents a sampling point variable;indicate->In the case where the individual sensors are selected as sampling points, and (2)>The value set is +.>When->The time indicates node ++>Is selected as the sampling point, otherwise->Indicating that it is not selected as a sampling point.
4. The wireless sensor network deployment method of claim 1, wherein the sum of all grid perceived probabilities within the monitoring rangeThe calculation formula of (2) is as follows:
；
wherein,representing the total number of lattices; />Represents a lattice variable; />Representing all sampling points within each lattice quiltPerceived probability.
5. The deployment method of wireless sensor network of claim 4, wherein each grid is perceived by all sampling points within a range with a probability of being perceived by the sampling pointsThe expression of (2) is:
；
wherein D represents the total number of sampling points; j represents a sampling point variable;represents the lattice center point +.>Sampled pointProbability of detection by the sensor at that location.
6. The wireless sensor network deployment method of claim 5, wherein the lattice center pointSampled dot +.>Probability of detection by the sensor at +.>The expression of (2) is:
；
wherein,representing the sampling point +.>And lattice center point->Is a distance of (2); />Representing the perceived radius of the sampling point.
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