CN112333723B - Wireless sensor node deployment method, storage medium and computing device - Google Patents
Wireless sensor node deployment method, storage medium and computing device Download PDFInfo
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Abstract
The invention discloses a wireless sensor node deployment method, a storage medium and computing equipment, wherein a good point set is adopted to perform population initialization on fireflies in a target monitoring interval, so that fireflies individuals are uniformly distributed in a target area, and the solution space ergodicity of the fireflies individuals is enhanced; a modified firefly algorithm with a sigmoid function curve having a nonlinear exponential decreasing characteristic is introduced, so that balance is kept between the capability of searching for a new high-brightness individual in a target area and the capability of searching for a new high-brightness individual near a current optimal solution; finally, micro-disturbance is carried out through Gaussian distribution, and the defect that the individual gradually gets close to the individual with better fitness value in the later iteration stage of the firefly algorithm is overcome, so that the individual gets trapped in a premature trap is overcome.
Description
Technical Field
The invention belongs to the technical field of wireless sensor networks, and particularly relates to a wireless sensor node deployment method, a storage medium and computing equipment based on an optimal point set self-adaptive optimized firefly algorithm.
Background
Wireless Sensor Networks (WSNs) are multi-hop wireless networks which are composed of a plurality of sensor nodes and are formed in a self-organizing manner through wireless communication. Each node in the network has the capabilities of sensing communication, calculating and storing data and processing and transmitting data, and the wireless sensing network is widely applied to the fields of military industry, agriculture and forestry, aerospace, structural health monitoring and the like due to the advantages of flexible deployment, high reliability and the like. The target area coverage rate of the sensor nodes is one of indexes for measuring the service quality of the WSN, and in order to provide a new idea for the coverage optimization problem of the WSN, a group intelligent algorithm is applied to the field to improve the comprehensive performance of the WSNs.
The existing method has the problems that a blind search phenomenon exists in the later evolution stage, fine local search in the later stage is difficult to jump out of local optimum, and the hundred percent coverage of a target area is not realized. Although effective in WSN node deployment, WSN coverage needs to be improved to meet the requirements of practical applications.
Therefore, the gaze is transferred to an optimal point set and a self-adaptive inertial weight strategy optimization standard firefly algorithm (GPSAFA), and the position of a sensor node is determined for a target monitoring area based on the algorithm, so that the network coverage rate is maximized, the coverage redundancy and the coverage hole are minimized, and the searching speed and the searching precision are improved.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a wireless sensor node deployment method, a storage medium and a computing device based on an optimal point set adaptive optimization firefly algorithm, aiming at the above deficiencies in the prior art, and to solve the problems of coverage holes and coverage redundancy lack of an effective method in the existing wireless sensor network.
The invention adopts the following technical scheme:
a wireless sensor node deployment method comprises the following steps:
s1, setting a light absorption coefficient, a maximum attraction force, a step factor, a population scale, iteration times and dimension parameters;
s2, initializing the population of the target monitoring interval by adopting an optimal point set method according to the parameters set in the step S1 to obtain the position information and the corresponding fitness value of each firefly;
s3, optimizing the position information and the corresponding fitness value of each firefly in the step S2 by using the deformed sigmoid function curve as an inertia weight and variable step strategy, realizing iterative update of the position information and the corresponding fitness value of the firefly individuals from the time t to the time t +1, and determining a current global optimum value and a current local extreme value in the population;
s4, updating the inertia weight and the step factor respectively by using an improved inertia weight updating formula and an improved step updating formula according to the current global optimum value and the current local extreme value of the population determined in the step S3 to update the position of each firefly;
s5, according to the current global optimum value and the current local extreme value in the population determined in the step S3, carrying out Gaussian disturbance on the firefly individual fitness value updated in the step S4, comparing the fitness values of the individual positions before and after updating, reserving the individual positions of which the individual position fitness value is larger than a set threshold value, and determining the maximum iteration number when the firefly individual fitness value is updated to the optimal coverage rate;
and S6, if the current iteration times are larger than the maximum iteration times obtained in the step S5, ending and outputting the optimal coverage rate and the sensor node position graph to finish the deployment of the wireless sensor node.
Specifically, in step S2, the step of calculating the optimal point set specifically includes:
s201, setting V D Is a unit cube in D-dimensional Euclidean space, namely x ∈ V D ，x∈(x 1 ,x 2 ,…x D )，0≤x i ≤1，i＝1,2,…,D；
S202, if r ∈ V D In the shape of
The deviation phi (n) satisfies that phi (n) is less than or equal to V (r, epsilon) n -1+ε Balance P n (i) Is a set of good points, r is a good point, V (r, ε) is a normal number associated only with r and ε, ε is an arbitrarily small positive number;
s203, solving the optimal point set r by using a cyclotomic field method, and taking r =2cos (2 k pi/t), k is more than or equal to 1 and less than or equal to D, and t is the minimum prime number meeting the requirement, or taking r = e k K is more than or equal to 1 and less than or equal to D, and r is the good point.
Specifically, in step S3, the positional information X of the firefly i at the time t +1 i (t + 1) is specifically as follows:
X i (t+1)＝w(t)X i (t)+β ij (r ij )(X i (t)-X j (t))+α(t+1)(rand-0.5)
where t is the current iteration number, w (t) is a weight coefficient, w (t) is an element [0,1]](ii) a α (t + 1) is the variable step size, rand is [0,1]]Subject to uniformly distributed random numbers, beta ij Is an attractive force between two fireflies, r ij Is the Euclidean distance between two fireflies.
Specifically, in step S4, the inertia weight and the step factor are respectively updated by using an inertia weight updating formula and a step updating formula, specifically:
α(t+1)＝max(α min ,min(A(t),α max ))
wherein t is the current iteration number; t is max Is the maximum number of iterations; w (t) is a weight coefficient, w (t) is ∈ [0,1]](ii) a α (t + 1) is the variable step size, f gbest Is the adaptive value of the current individual global optimum solution, f pbest Fitness value, alpha, of the current optimal solution of the current individual max 、α min Respectively, the upper and lower limits of the step size.
Specifically, in step S5, the location Gbest after Gaussian disturbance t+1 The calculation is as follows:
wherein, the first and the second end of the pipe are connected with each other,
and f is the fitness value of the current position.
Further, the disturbed position Gbest new Comprises the following steps:
Gbest new ＝Gbest×(1+Gaussian(μ,σ 2 ))
wherein Gbest is the current optimal position, mu is the mean value, and sigma is 2 Is the variance.
Specifically, in step S6, if the current iteration count is less than or equal to the maximum iteration count, the process returns to step S3.
Another aspect of the invention is a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods.
Another aspect of the present invention is a computing device, including:
one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods.
Compared with the prior art, the invention at least has the following beneficial effects:
the invention relates to a wireless sensor node deployment method, which is characterized in that a firefly population is initialized by utilizing a good point set method to be uniformly distributed in a target area, and improved X is utilized i (t + 1) carrying out iterative optimization in space by using a formula to find out the current global optimum value and the current local extreme value of the firefly population at the next moment; in order to balance local search and global search to find a better optimal solution set, each firefly position is updated by utilizing an improved w (t) formula and an improved alpha (t + 1) formula, and the firefly individual is subjected to rough search and then local search when traversing the whole space of a target region by the aid of the non-linear decreasing w (t) and the gradually decreasing alpha (t + 1), so that the individual is prevented from missing a position with higher fitness in the space during evolution iteration; avoids the firefly individuals from being trapped in the early-maturing trap, the positions of the firefly individuals are not stopped, the fitness value of the firefly individuals is not changed any more, gaussian disturbance is carried out on the current global optimum, the situation is broken or avoided, part of individuals jump out of the trap to search a potential global optimum solution with higher fitness than the current moment,until reaching the maximum iteration times in the step six; and determining the positions of the firefly individuals with the fitness values arranged in the first N according to the number N of the constraint condition sensor nodes, and indirectly determining the deployment positions of the sensor nodes, thereby realizing the maximum coverage rate of the WSN.
Further, the random initialization in the group intelligent algorithm causes the individual aggregation of the group, which results in the local extreme value in the later evolution process and the inability to traverse the target monitoring area, thereby affecting the final optimization result of the firefly algorithm; the individual in the population can be uniformly distributed in the target area by the optimal point set method, so that higher population diversity can be maintained, the quality of the solution is indirectly improved, and the possibility that the individual in the population falls into a local extreme value is reduced.
Furthermore, the population individuals need larger step length to traverse the whole target area as soon as possible in the early stage of evolution, and need smaller step length to search carefully in the later stage of evolution. The global rough search and the local fine search are in a coupling relationship, so the relationship between the global rough search and the local fine search needs to be balanced as much as possible, and the oscillation phenomenon and the premature phenomenon are prevented.
Furthermore, in order to balance the global rough search and the local fine search, a firefly algorithm improved by a function with a decreasing nonlinear index and a variable step length strategy is provided, so that the convergence speed and the search precision of the algorithm can be improved, and early convergence in the later period of trapping in a local extreme value in the earlier period can be avoided.
Furthermore, in the later stage of the firefly algorithm evolution, the firefly gradually moves towards other individuals with high brightness and good adaptability, so that the algorithm is very likely to get into a trap of a local extreme value and has no jumping capability. In order to avoid the occurrence of the event, gaussian disturbance is carried out on the current global optimum of each generation of the population, so that part of individuals jump out of traps to search other potential global optimum solutions.
Further, part of individuals jumping out of the early-maturing traps randomly scatter in each place of the target area, if the fitness value of the scattered objects is larger than that of the individuals before, fine search is conducted to find a larger local extreme value, the local extreme value is compared with the global optimal value, and if the fitness value is larger than that of the scattered objects, the local extreme value is reserved; otherwise, the operation is abandoned.
In conclusion, the invention optimizes the wireless sensor node deployment problem by using the improved firefly algorithm and realizes the maximization of the coverage rate of the target monitoring area by using a small number of sensor nodes. The method is characterized in that the problem of optimizing the coverage of a target area is converted into the problem of solving the maximum fitness value, namely the problem of the maximum brightness, the optimization process of the sensor node position in the WSN coverage is specifically the biological behavior among firefly populations, which is mutually attracted due to the intensity of the brightness, and the position of a firefly set with higher brightness in the populations is the node deployment position.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention;
fig. 2 is a graph comparing the initialized distribution of the chaos-optimal point set when N =400, where (a) is 400 points generated by the chaos cubic mapping, and (b) is 400 points generated by the optimal point set method;
in FIG. 3, T is N =100 max =1000, a =100m × 100m sensor node random deployment graph;
in FIG. 4, T is N =100 max Sensor random deployment iteration-coverage map when a =100m × 100m, = 1000;
in FIG. 5, T is N =40 max When =300 and A =50m × 50m, the ASFA and GPSAFA algorithm nodes deploy a distribution contrast graph;
in FIG. 6, T is N =40 max The ASFA and GPSAFA algorithms optimize a coverage rate-iteration comparison graph when A =50m multiplied by 50m when 300 is measured;
in FIG. 7, T is N =40 max When =80, A =80m × 80m, the HAPSO and GPSAFA algorithm nodes deploy a distribution contrast diagram;
in FIG. 8, T is N =40 max When =80, a =80m × 80m, the HAPSO, GPSAFA algorithms optimize the coverage-iterative contrast map;
in FIG. 9, T is N =100 max =1000,a = 100mx100m, ctfa, GPSAFA algorithm node deployment distribution contrast graph;
in fig. 10, when N =100, T max ＝1000，A＝100m×100m，CTFA、GPSAFA algorithm coverage-iteration contrast.
Detailed Description
The invention provides a wireless sensor node deployment method, a storage medium and computing equipment based on an optimal point set self-adaptive optimization firefly algorithm, wherein firstly, the optimal point set carries out population initialization on fireflies in a target monitoring interval, so that fireflies individuals are uniformly distributed in a target area, and the solution space ergodicity of the fireflies individuals is enhanced; a modified firefly algorithm with a sigmoid function curve having a nonlinear exponential decreasing characteristic is introduced, so that balance is kept between the capability of searching for a new high-brightness individual in a target area and the capability of searching for a new high-brightness individual near a current optimal solution; finally, micro-disturbance is carried out through Gaussian distribution, and the defect that the individuals get into premature traps because the individuals gradually get close to the individuals with better fitness values in the iteration later stage of the firefly algorithm is overcome. According to simulation results, the improved algorithm has no phenomenon of bundle accumulation in a target monitoring area, and has few coverage holes and node redundancy. Compared with an ASFA algorithm, an HSPSO algorithm and a CGSO algorithm, the coverage rate of the algorithm is respectively improved by 14%, 11% and 1.13%, and under the condition of the same coverage rate, the number of the sensor nodes used by the algorithm is less, so that the deployment cost is saved.
Referring to fig. 1, the invention relates to a wireless sensor node deployment method based on an optimal point set adaptive optimization firefly algorithm, wherein the optimal point set initializes firefly population, adaptively updates weight factors to balance local and global search, jumps out of a premature trap due to gaussian disturbance, judges the condition of algorithm ending cycle, ends the algorithm and outputs an optimal solution set if the condition is satisfied, and returns to related steps if the condition is not satisfied, and the specific steps are as follows:
s1, initializing an algorithm and setting relevant parameters such as an algorithm light absorption coefficient, maximum attraction, a step factor, a population scale, iteration times, dimensionality and the like;
in a target monitoring area with the area of A (m multiplied by n), the light absorption coefficients gamma and the step factor alpha of n fireflies are initialized randomly, the circulation variable is increased from 1, and the circulation is circulated to the maximum iteration time T max 。
S2, initializing the population by adopting a good point set method to obtain the position information and the fitness value of each firefly;
the firefly population is initialized by using the optimal point set method, so that the population is more uniformly distributed in a target monitoring area, the diversity of the population is improved, and the quality of the global optimal value is improved.
The basic method for finding the optimal point set is as follows:
s201, setting V D Is a unit cube in D-dimensional Euclidean space, namely x ∈ V D ，x∈(x 1 ,x 2 ,…x D ) Wherein 0 is less than or equal to x i ≤1，i＝1,2,…,D；
S202, if r ∈ V D In the shape of
The deviation phi (n) satisfies that phi (n) is less than or equal to V (r, epsilon) n -1+ε Then is called P n (i) Is the set of sweet spots, r is the sweet spot, V (r, ε) is the normal number only related to r and ε, ε is any small positive number;
s203, the optimal point set r is obtained by a general method of the cyclotomic field, r =2cos (2 k pi/t), 1 ≦ k ≦ D, t is the minimum prime number met, or r = e k K is more than or equal to 1 and less than or equal to D, and r is the good point.
S3, utilizing the characteristic that a deformed sigmoid function curve has nonlinear exponential decreasing as the global searching and local searching capabilities of an inertia weight and variable step length strategy balance algorithm;
the global search and local search capability of the firefly algorithm is balanced by using the self-adaptive inertia weight w (t) and the variable step length alpha (t + 1), so that the global search and the local search of a target monitoring area are realized, and the method specifically comprises the following steps:
X i (t+1)＝w(t)X i (t)+β ij (r ij )(X i (t)-X j (t))+α(t+1)(rand-0.5)
wherein t is the current iteration number; w (t) is a weight coefficient, w (t) belongs to [0,1]; α (t + 1) is a variable step size.
S4, respectively updating the inertia weight and the step length factor according to the inertia weight updating formula and the step length updating formula to update the position of each firefly;
the inertia weight and the step length factor are respectively updated by using a w (t) updating formula and an alpha (t + 1) updating formula, and the w (t) is exponentially decreased along with the increase of the evolution times, so that the moving distance requirements of the firefly at different evolution stages are met, the firefly individual is enabled to rapidly move to a high-brightness and high-quality area, and the algorithm searching capability is increased; alpha (t + 1) is exponentially decreased along with the increase of the evolution times, so that the search is from global rough search to local fine search, and the progress of the algorithm is improved.
α(t+1)＝max(α min ,min(A(t),α max ))
Wherein t is the current iteration number; t is max Is the maximum number of iterations; w (t) is a weight coefficient, w (t) is ∈ [0,1]](ii) a α (t + 1) is the variable step size, f gbest Is the adaptive value of the current individual global optimum solution, f pbest Fitness value, alpha, of the current optimal solution of the current individual max 、α min Respectively the upper and lower limits of the step size.
Calculating the fitness value f of each firefly individual in the population after the position is updated, and finding out the position of the firefly with the maximum current overall brightness of the population;
wherein, A area (S all ) Is the coverage area of the sensor node set, A is the target monitoring area, S all Is a set of sensor nodes.
S5, carrying out Gaussian disturbance on the updated firefly individuals, comparing fitness values of individual positions before and after updating, keeping the individual positions of which the fitness values of the individual positions are larger than a set threshold, determining the maximum iteration times when the updating reaches the optimal coverage rate, and otherwise discarding the disturbed positions;
and a Gaussian disturbance strategy is adopted to carry out disturbance updating on the positions of the firefly individuals after the positions are updated, so that the firefly individuals are prevented from being trapped into a local optimal solution, and the convergence is early, so that the ability of the algorithm to jump out of the local optimal solution is improved.
Gbest new ＝Gbest×(1+Gaussian(μ,σ 2 ))
Wherein, gbest new For the perturbed position, gbest is the current optimal position, μ represents the mean, σ 2 The variance is indicated.
The new global optimal position is updated as follows:
wherein the content of the first and second substances,
and f is the fitness value of the current position.
S6, judging whether the algorithm reaches T max (ii) a If T > T is satisfied max The algorithm is ended, and an optimal coverage rate and a sensor node position graph (namely the globally optimal position of the firefly) are output; otherwise, the process returns to step S3.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, 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. Thus, the following detailed description of the embodiments of the present invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
Referring to fig. 2, fig. 2 shows initialized population distribution with a population scale of 400 generated by the chaos logic sequence method and the optimal point set method, and as can be seen from the graphs (a) and (b), the optimal point set method is more uniform and stable in layout than the chaos logic sequence method, and can traverse each place of the interval, so that firefly individuals find the positions of individuals with higher brightness (i.e., high fitness) than the firefly individuals.
Simulation experiments were performed in a MATLAB2017a environment. 100 sensor nodes are randomly deployed in a square plane A with a target monitoring area of 100m multiplied by 100 m. Each sensor node represents a firefly in the population, the sensing radius of the node is 7m, the communication radius is 14m, and each basic parameter alpha, beta of the initialization algorithm 0 ,γ,t,n,D。
The experimental results are shown in fig. 3 and 4, where fig. 3 and 4 are respectively a deployment condition of a node and a coverage rate-iteration number graph when a target monitoring area is covered by a randomly deployed sensor node, a point represents position information of the sensor node, and a circle represents an area covered by the node.
TABLE 1 Algorithm comparison Table
Table 2 comparison table of coverage rate results of random deployment, ASFA algorithm, FA algorithm and GPSAFA algorithm
TABLE 3 comparison table of coverage results of random deployment, HSPSO algorithm, FA algorithm and GPSAFA algorithm
Carrying out simulation experiments in an MATLAB2017a environment, setting simulation experiment parameters, wherein a target monitoring area is a two-dimensional plane of 100m multiplied by 100m, isomorphic sensor nodes are 40, the sensing radius is 5m, the communication radius is 10m, and the iteration frequency is 300.
Fig. 6 is a coverage-iteration curve diagram of the AFSA algorithm and the IFA algorithm, fig. 5 is a deployment diagram of optimized sensor nodes, and table 2 is a comparison of coverage optimization results of the random distribution and the AFSA algorithm and the IFA algorithm. As can be seen from fig. 6 and table 2, under the same condition, the network coverage of the IFA algorithm reaches 90.90%, and compared with the random distribution and AFSA algorithms, the coverage is respectively improved by 24.67% and 14%, so that the coverage redundancy is reduced, and the node aggregation phenomenon in the target area is improved. In addition, as can be seen from fig. 6, the coverage of IFA algorithm reaches 76.90% of the existing method at the 45 th generation. The improved strategy is shown to effectively improve the convergence rate and can quickly realize the optimization of the target area.
Carrying out simulation experiments in an MATLAB2017a environment, setting simulation experiment parameters, wherein a target monitoring area is a two-dimensional plane of 80m multiplied by 80m, the number of isomorphic sensor nodes is 40, the sensing radius is 8m, the communication radius is 16m, and the iteration frequency is 80.
Fig. 7 and 8 are a coverage-iteration graph of the HSPSO algorithm and the IFA algorithm in the monitoring interval and a sensor node deployment diagram after optimization, respectively, and table 3 is a comparison of coverage optimization results of the random tossing, the HSPSO algorithm, and the IFA algorithm. As can be seen from the analysis of fig. 7, fig. 8 and table 3, the coverage rate of the sensor nodes with random distribution is only 69.60%, which results in a large amount of coverage redundancy and holes in the monitored area. Sensor nodes are deployed by adopting the HSPSO algorithm, and the algorithm is in local optimum in the early stage of iteration, so that the coverage rate is not before. The IFA algorithm is used for WSN coverage optimization, sensor nodes are uniformly distributed in a monitoring area, the coverage rate reaches 90.60%, and compared with a node random deployment algorithm and an HSPSO algorithm, the coverage rate is respectively improved by 24% and 10.81%. And multiple experiments show that the coverage rate average value of the IFA algorithm adopting 32 sensors reaches 80.40%, compared with the HSPSO algorithm, the IFA algorithm can reduce 8 sensors and save the deployment cost of the sensor nodes.
TABLE 4 comparison table of coverage results of random deployment, CTFA algorithm, FA algorithm, GPSAFA algorithm
The simulation experiment is carried out in an MATLAB2017a environment, simulation experiment parameters are set, a monitoring area is a two-dimensional plane of 100m multiplied by 100m, the number of isomorphic sensor nodes is 100, the sensing radius is 7m, the communication radius is 14m, and the iteration number is 1000. Fig. 9 and fig. 10 are an iteration graph of the CTFA algorithm and the IFA algorithm during coverage optimization and an optimized sensor node deployment diagram, respectively, and table 4 is a comparison of coverage optimization results of the random tossing, the CTFA algorithm, and the IFA algorithm. As can be seen from the analysis of fig. 7 and table 4, under the same simulation parameters, the coverage rate of the IFA algorithm in WSN coverage optimization is 98.26%, which is 23.64% and 1.13% higher than that of the node random distribution and CTFA algorithms, respectively. Through multiple tests, the coverage rate of the IFA algorithm is slightly higher than that of the CTFA algorithm, and the coverage rate of the IFA algorithm can reach 97.84% by adopting 90 sensor nodes, so that 10 sensors are saved compared with the CGSO algorithm, the deployment cost can be reduced, and the utilization rate of the sensors is improved.
As can be seen from tables 1 to 3, the average coverage rate of the firefly sensor node deployment strategy after the optimization set self-adaptive optimization is higher than that of other algorithms under other equal experimental conditions, the required effective coverage range can be achieved by using a smaller number of nodes, the utilization rate of the nodes, the algorithm convergence rate and the algorithm solving quality are higher than those of other algorithms, and the node utilization rate and the network coverage rate can be effectively improved by the gps sa algorithm through comparison discovery.
In summary, according to the wireless sensor node deployment method, the storage medium and the computing device, nodes obtained by population initialization optimized based on the optimal point set method are uniformly distributed in a target monitoring area in a covering mode, so that the coverage redundancy and the coverage holes are reduced sharply.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (4)
1. A wireless sensor node deployment method is characterized by comprising the following steps:
s1, setting a light absorption coefficient, a maximum attraction force, a step size factor, a population scale, iteration times and dimension parameters;
s2, initializing the population of the target monitoring interval by adopting a good point set method according to the parameters set in the step S1 to obtain the position information and the corresponding fitness value of each firefly, and calculating the good point set specifically as follows:
s201, setting V D Is a unit cube in D-dimensional Euclidean space, i.e. x ∈ V D ，0≤x i ≤1，i＝1,2,…,D；
S202, if r ∈ V D In the shape of
The deviation phi (n) satisfies that phi (n) is less than or equal to V (r, epsilon) n -1+ε Balance P n (i) Is a set of good points, r is a good point, V (r, ε) is a normal number associated only with r and ε, ε is an arbitrarily small positive number;
s203, solving the optimal point set r by using a cyclotomic field method, and taking r =2cos (2 k pi/t), k is more than or equal to 1 and less than or equal to D, and t is the minimum prime number meeting the requirement, or taking r = e k K is more than or equal to 1 and less than or equal to D, and r is a good point;
s3, optimizing the position information and the corresponding fitness value of each firefly in the step S2 by using the deformed sigmoid function curve as an inertia weight and variable step strategy, realizing iterative update of the position information and the corresponding fitness value of the firefly individuals from the time t to the time t +1, determining a current global optimum value and a current local extreme value in the population, and determining the position information X of the firefly i at the time t +1 i (t + 1) is specifically as follows:
X i (t+1)＝w(t)X i (t)+β ij (r ij )(X i (t)-X j (t))+α(t+1)(rand-0.5)
where t is the current iteration number, w (t) is a weight coefficient, w (t) is an element [0,1]](ii) a α (t + 1) is the variable step size, rand is [0,1]]Subject to uniformly distributed random numbers, beta ij To be an attractive force between two fireflies, r ij Is the Euclidean distance between two fireflies;
s4, according to the current global optimum value and the current local extreme value of the population determined in the step S3, updating the inertia weight and the step factor by using an improved inertia weight updating formula and an improved step updating formula respectively to update the position of each firefly, and updating the inertia weight and the step factor by using the inertia weight updating formula and the step updating formula respectively, specifically:
α(t+1)＝max(α min ,min(A(t),α max ))
wherein t is the current iteration number; t is a unit of max Is the maximum number of iterations; w (t) is a weight coefficient, w (t) is E [0,1](ii) a α (t + 1) is the variable step size, f gbest Is the adaptive value of the current individual global optimal solution, f pbest Fitness value, alpha, of the current optimal solution of the current individual max 、α min Upper and lower limits of step size, respectively;
s5, according to the current global optimum value and the current local extreme value in the population determined in the step S3, gaussian disturbance is carried out on the firefly individual fitness value updated in the step S4, the fitness values of the individual positions before and after updating are compared, the individual position with the individual position fitness value larger than a set threshold value is reserved, the maximum iteration frequency when the firefly individual fitness value is updated to the optimal coverage rate and the Gaussian disturbed position Gbest t+1 The calculation is as follows:
wherein the content of the first and second substances,
f is the fitness value of the current position;
post-disturbance position Gbest new Comprises the following steps:
Gbest new ＝Gbest×(1+Gaussian(μ,σ 2 ))
wherein Gbest is the current optimal position, mu is the mean value, and sigma is 2 Is the variance;
and S6, if the current iteration times are larger than the maximum iteration times obtained in the step S5, ending and outputting the optimal coverage rate and the sensor node position graph to finish the deployment of the wireless sensor node.
2. The deployment method of the wireless sensor nodes according to claim 1, wherein in step S6, if the current iteration number is less than or equal to the maximum iteration number, the step S3 is returned to.
3. A computer readable storage medium storing one or more programs, wherein the one or more programs comprise instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1 or 2.
4. A computing device, comprising:
one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1 or 2.
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