CN109922478A - A kind of water quality sensor network optimization dispositions method based on improvement cuckoo algorithm - Google Patents
A kind of water quality sensor network optimization dispositions method based on improvement cuckoo algorithm Download PDFInfo
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Abstract
The invention proposes a kind of based on the water quality sensor network optimization dispositions method for improving cuckoo algorithm.It is mesh point by monitoring region discretization firstly, establishing water quality sensor network coverage model, the ratio for defining the total the number of grids of mesh point number Zhan covered by sensor is coverage rate, to improve the network coverage as optimization aim.Secondly, realizing the Optimization deployment to whole network using improved cuckoo algorithm.The present invention, can be by less the number of iterations by improving to cuckoo algorithm, the covering performance for being optimal water quality sensor network.
Description
Technical field
The present invention relates to environmental monitoring and sensor network field more particularly to a kind of water based on improvement cuckoo algorithm
The research of matter sensor network Optimization deployment method.
Background technique
Water is that Source of life and the mankind are rely the necessary resource of breeding.However, recent decades, with national economy
Fast-developing and living standards of the people continuous improvements, the contradiction of supply and demand for the water resource become increasingly conspicuous.According to " 2016 Chinese environmental situations
Bulletin ", in the Ground water Quality Survey situation of 6124 monitoring points of national 225 ground level boroughs in 2016, water quality is
The monitoring point ratio of excellent grade is only 10.1%, and the observation point accounting of poor grade reaches 45.4%.For entire surface water, by
It is higher to the bad V class water body proportion seriously polluted, the whole nation about 8.6%.
In recent years, scientifically monitoring water environment is more and more paid attention to.During to monitoring water environment, sensing
Device network occupies highly important status.Due to water quality sensor higher cost, it is desirable to be able to the emphasis in monitoring environment
The more sensors of regional deployment are to improve monitoring quality, save the cost.Therefore, it is necessary to find in waters to be monitored to need emphasis
The region of monitoring, and the deployment to sensor network is realized by effective sensor deployment strategy, to carry out accurate water ring
Border monitoring provides substantial theoretical foundation.
Summary of the invention
It is a kind of based on the water quality sensor network optimization deployment side for improving cuckoo algorithm it is an object of the invention to propose
Method can provide fundamental basis for the deployment of water quality sensor network, can be widely applied to monitoring water environment, water pollution prediction and
The fields such as improvement.
In order to achieve the above objectives, the present invention proposes a kind of based on the water quality sensor network optimization portion for improving cuckoo algorithm
Arranging method specifically includes two basic steps of Optimization deployment for establishing water quality sensor network coverage model and sensor network.
Step 1, in one embodiment of the invention, the water quality sensor network coverage model of establishing further are wrapped
It includes:
To monitoring waters carry out sliding-model control, by its it is discrete turn to m mesh point, wherein any one mesh point pjSeat
It is designated as (xj, yj), one group of sensor node with same perceived radius r is randomly placed in monitoring region, if s={ s1, s2,
s3…snThe set of the sensor node is represented, wherein any one sensor node siCoordinate be (xi, yi)；Calculate siIt arrives
Point pjEuclidean distance is defined as:
Then monitor some mesh point p in regionjThe case where being covered by sensor node be
P(si,pj)=1 illustrates that the mesh point can be covered by sensor node；Mesh point monitored for one, by its quilt
All the sensors node in entire monitoring region monitor definition of probability is combined monitoring probability, mesh point pjJoint prison
It surveys shown in the following formula of probability:
Statistical monitoring probability is equal to 1 number of grid, covers with ratio, that is, entire water quality monitoring network of total grid number m
Lid rate；
Step 2, in one embodiment of the invention, the Optimization deployment of the sensor network further comprises:
It is uniformly disposed based on the network for improving cuckoo algorithm.Cuckoo algorithm ties up flight using Lay and carries out global search,
With good global optimizing ability；Cuckoo algorithm combines the random walk of global search and the random walk of part,
In, the random walk of global search is as follows:
Wherein, xg,iIndicate a Bird's Nest in the Bird's Nest position in g generation；Indicate step size controlling amount:
Wherein, xbestFor current optimal solution；L (β) indicates that Lay ties up random search path, obeys Lay and ties up probability distribution:
L é vy~u=t-β(1≤β≤3)
β is a parameter, and value is 1.5 herein；It calculates for convenience in practice, it is random to generate Lay dimension using following equation
Number:
I.e. the location update formula of cuckoo can be expressed as follows:
Wherein, u, v all Normal Distributions,For constant；
It presses probability PaAfter abandoning part solution, the new explanation of identical quantity is regenerated using local random walk:
xg+1,i=xg,i+r(xg,j-xg,k)
Wherein, r is zoom factor, is the uniform random number in (0,1) section, xG, i, xG, kIndicate two of g generation with
Machine number；
Eliminate probability PaWhat is indicated is the probability that cuckoo Bird's Nest is found by host, that is, the probability of new explanation is generated, initial
Its in cuckoo algorithm is a fixed value, takes PaIt is 0.25.In practical searching process, with the continuous increasing of the number of iterations
Add, as a result increasingly drawn close to optimal value, if eliminating probability at this time still keeps original radix, can be eliminated a large amount of excellent
The solution of matter destroys the optimizing performance of algorithm.Therefore by introducing variable function, make to eliminate probability PaAs a meeting with iteration
The value of number variation.Introduce formula:
Wherein, pa_newFor new superseded probability, PaFor the superseded probability before improvement, P is takenaIt is most for 0.25, N_iter
Big the number of iterations, n1For bird egg number.From the above equation, we can see that the quality of solution is not as the increase of the number of iterations is recycled into the later period
Disconnected to improve, individual is found and the probability being eliminated is lower and lower, so that improved cuckoo algorithm possesses convergence speed faster
Degree and better optimizing effect.
Fig. 2 show the initial random distribution map of water quality sensor network, and the circle in figure represents water quality sensor node, outermost
The box in face is region to be monitored, and the grid in box is the mesh point of discretization.By the successive ignition of cuckoo algorithm, seek
It is excellent, sensor node deployment can be made to make the maximum position of the network coverage, as shown in Figure 3.Fig. 4 show proposed in this paper change
Into cuckoo algorithm and particle swarm algorithm under identical primary condition, the number of iterations required for the network coverage is maximum is realized
Comparison.In figure, PSO represents particle swarm algorithm, and IMCS represents improved cuckoo algorithm, and as seen from the figure, improved cuckoo is calculated
Method has just found optimal solution after the number of iterations reaches 65 times, and particle swarm algorithm needs just may be used for iteration up to 145 times
Reach its optimal optimum results.It can be seen that the search speed of improved cuckoo algorithm is better than particle swarm algorithm.Also, scheme
Middle particle swarm algorithm has several sections of smooth curves before not up to optimal, illustrates that particle swarm algorithm is easily trapped into local optimum
Solution.Therefore the deployment effect of water quality sensor network can be improved with higher efficiency using improved cuckoo algorithm.
The implementation above uniform deployment of water quality sensor network can obtain the water quality number in monitoring waters on this basis
According to, for collected water area monitoring data, each factor is analyzed using Principal Component Analysis, to water quality parameter carry out
Dimension-reduction treatment extracts the representative component of water quality assessment, mathematical model are as follows:
Wherein, i is number of samples；J is factor number；N is the principal component number after principal component analysis；a1j, a2j..., anj
It is load of the original variable matrix in each principal component；Xi1, Xi2..., XijIt is original variable matrix by standardization
Value；zi1, zi2... ..., zinIndicate the value of each principal component after principal component analysis；
By the principal component z for each sample that principal component analysis obtainsinValue, can obtain corresponding principal component evaluation function
Zi, as the data basis for judging emphasis monitoring point:
Wherein, ZiIt is the corresponding principal component evaluation score value of each sample；λi1,λi2…λinIt is matrix [Xi1, Xi2... ...,
Xij] the corresponding variance contribution ratio of initial characteristic values；
Analysis by principal component analysis to certain region water quality parameter, it can be deduced that synthesis of the region in time change
Water quality assessment score Zi, the variance yields of these scores is sought, the water quality data stabilization in the waters or the feelings of fluctuation can be evaluated
Condition similarly can acquire the variance yields of the comprehensive water quality assessment score in each waters, the variance yields size in more each waters will be square
The maximum monitoring point of difference monitors region as emphasis, and the smallest monitoring point of variance monitors region as non-emphasis, then will be non-
The sensor in emphasis monitoring region is moved to emphasis monitoring region, realizes and redeploys to emphasis monitoring waters, effectively improves
The monitoring efficiency of network.
It is proposed by the present invention it is a kind of based on improve cuckoo algorithm water quality sensor network optimization dispositions method, it can be achieved that
Effective monitoring to emphasis monitoring waters provides substantial theoretical foundation for the effective monitoring and comprehensive treatment of water environment.
Detailed description of the invention
Fig. 1 is a kind of based on the water quality sensor network optimization dispositions method for improving cuckoo algorithm of the embodiment of the present invention
Flow chart；
Fig. 2 is the initial random distribution map of water quality sensor network of the embodiment of the present invention；
Fig. 3 is the Optimization deployment result figure of the water quality sensor network of the embodiment of the present invention；
Fig. 4 present invention is proposed method and is realized the number of iterations comparison diagram of network optimization deployment based on particle swarm algorithm.
Specific embodiment
The embodiment of the present invention is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar meaning.The embodiments described below are exemplary, and are only used for explaining
The present invention, and be not construed as limiting the claims.
The present invention be directed to during monitoring water environment, for complicated water environment, one kind of proposition is based on improving cloth
The water quality sensor network optimization dispositions method of paddy bird algorithm.
In order to have clearer understanding to the present invention, it is briefly described herein.The present invention includes two basic steps
Rapid: step 1 establishes water quality sensor network coverage model；Step 2, the Optimization deployment of sensor network.
Specifically, Fig. 1 show a kind of based on the water quality sensor network for improving cuckoo algorithm of the embodiment of the present invention
The flow chart of Optimization deployment method, comprising the following steps:
Step S101 establishes water quality sensor network coverage model.
In one embodiment of the invention, sliding-model control is carried out to monitoring waters, its discrete is turned into m grid
Point, wherein any one mesh point pjCoordinate be (xj, yj), randomly placing one group in monitoring region has same perceived radius r
Sensor node, if s={ s1, s2, s3…snThe set of the sensor node is represented, wherein any one sensor node si
Coordinate be (xi, yi)；Calculate siTo point pjEuclidean distance is defined as:
Then monitor some mesh point p in regionjThe case where being covered by sensor node are as follows:
P(si,pj)=1 illustrates that the mesh point can be covered by sensor node；Mesh point monitored for one, by its quilt
All the sensors node in entire monitoring region monitor definition of probability is combined monitoring probability, mesh point pjJoint prison
It surveys shown in the following formula of probability:
Statistical monitoring probability is equal to 1 number of grid, ratio, that is, entire water quality monitoring network with total grid number m × n
Coverage rate；
Step S102 is uniformly disposed based on the network for improving cuckoo algorithm.
Cuckoo algorithm ties up flight using Lay and carries out global search, has good global optimizing ability；Cuckoo algorithm
Combine the random walk of global search and the random walk of part, wherein the random walk of global search such as formula (4) institute
Show:
Wherein, xg,iIndicate a Bird's Nest in the Bird's Nest position in g generation；Indicate step size controlling amount:
Wherein, xbestFor current optimal solution；L (β) indicates that Lay ties up random search path, obeys Lay and ties up probability distribution:
L é vy~u=t-β(1≤β≤3) (6)
β is a parameter, and value is 1.5 herein；It calculates for convenience in practice, it is random to generate Lay dimension using following equation
Number:
I.e. the location update formula of cuckoo can be expressed as follows:
Wherein, u, v all Normal Distributions,For constant；
It presses probability PaAfter abandoning part solution, the new explanation of identical quantity is regenerated using local random walk:
xg+1,i=xg,i+r(xg,j-xg,k) (10)
Wherein, r is zoom factor, is the uniform random number in (0,1) section, xG, i, xG, kIndicate two of g generation with
Machine number；
Eliminate probability PaWhat is indicated is the probability that cuckoo Bird's Nest is found by host, that is, the probability of new explanation is generated, initial
Its in cuckoo algorithm is a fixed value, takes PaIt is 0.25.In practical searching process, with the continuous increasing of the number of iterations
Add, as a result increasingly drawn close to optimal value, if eliminating probability at this time still keeps original radix, can be eliminated a large amount of excellent
The solution of matter destroys the optimizing performance of algorithm.Therefore by introducing variable function, make to eliminate probability PaAs a meeting with iteration
The value of number variation.Introduce formula:
Wherein, pa_newFor new superseded probability, PaFor the superseded probability before improvement, P is takenaIt is most for 0.25, N_iter
Big the number of iterations, n1For bird egg number.By formula (11) it is found that as the increase of the number of iterations is recycled into later period, the matter of solution
Amount is continuously improved, and individual is found and the probability being eliminated is lower and lower, so that improved cuckoo algorithm possesses faster receipts
Hold back speed and better optimizing effect.
By the successive ignition of cuckoo algorithm, optimizing, sensor node deployment can be made to keep the network coverage maximum
Position.
Step S103 realizes effective covering to emphasis monitoring section domain.
For collected water area monitoring data, each factor is analyzed using Principal Component Analysis, water quality is joined
Number carries out dimension-reduction treatment, extracts the representative component of water quality assessment, mathematical model are as follows:
Wherein, i is number of samples；J is factor number；N is the principal component number after principal component analysis；a1j, a2j..., anj
It is load of the original variable matrix in each principal component；Xi1, Xi2..., XijIt is original variable matrix by standardization
Value；zi1, zi2... ..., zinIndicate the value of each principal component after principal component analysis；
By the principal component z for each sample that principal component analysis obtainsinValue, can obtain corresponding principal component evaluation function
Zi, as the data basis for judging emphasis monitoring point:
Wherein, ZiIt is the corresponding principal component evaluation score value of each sample；λi1,λi2…λinIt is matrix [Xi1, Xi2... ...,
Xij] the corresponding variance contribution ratio of initial characteristic values.
Analysis by principal component analysis to certain region water quality parameter, it can be deduced that synthesis of the region in time change
Water quality assessment score Zi, the variance yields of these scores is sought, the water quality data stabilization in the waters or the feelings of fluctuation can be evaluated
Condition similarly can acquire the variance yields of the comprehensive water quality assessment score in each waters, the variance yields size in more each waters will be square
The maximum monitoring point of difference monitors region as emphasis, and the smallest monitoring point of variance monitors region as non-emphasis, then will be non-
The sensor in emphasis monitoring region is moved to emphasis monitoring region, realizes and redeploys to emphasis monitoring waters, effectively improves
The monitoring efficiency of network.
What is proposed through the invention is a kind of based on the water quality sensor network optimization dispositions method for improving cuckoo algorithm, can
Realize to the Optimization deployment of water quality sensor network, for water environment effective monitoring and comprehensive treatment provide substantial theory according to
According to.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations.Ability
The those of ordinary skill in domain is it is understood that it can still modify to technical solution documented by previous embodiment or right
Part of technical characteristic is equivalently replaced, and these are modified or replaceed, and it does not separate the essence of the corresponding technical solution
The spirit and scope of technical solution of various embodiments of the present invention, the scope of the present invention is by appended claims and its equivalent limits.
Claims (1)
1. a kind of based on the water quality sensor network optimization dispositions method for improving cuckoo algorithm, it is characterised in that: including establishing
Two basic steps of Optimization deployment of water quality sensor network coverage model and sensor network；
The water quality sensor network coverage model of establishing includes: to carry out sliding-model control to monitoring waters, its discrete is turned to m
A mesh point, wherein any one mesh point pjCoordinate be (xj, yj), randomly placing one group in monitoring region has the phase same feeling
The sensor node of radius r is known, if s={ s1, s2, s3…snThe set of the sensor node is represented, wherein any one is sensed
Device node siCoordinate be (xi, yi)；Calculate siTo point pjEuclidean distance is defined as:
Then monitor some mesh point p in regionjThe case where being covered by sensor node are as follows:
P(si,pj)=1 illustrates that the mesh point can be covered by sensor node；Mesh point monitored for one, it is entire
Monitoring region in all the sensors node monitor definition of probability be combined monitoring probability, mesh point pjCombined monitoring it is general
Shown in the following formula of rate:
Statistical monitoring probability is equal to 1 number of grid, ratio, that is, entire water quality monitoring network covering with total grid number m
Rate；
The Optimization deployment of the sensor network includes:
(1) it is uniformly disposed based on the network for improving cuckoo algorithm
Cuckoo algorithm ties up flight using Lay and carries out global search, has good global optimizing ability；Cuckoo algorithm combines
The random walk of global search and the random walk of part, wherein shown in the random walk of global search such as formula (4):
Wherein, xg,iIndicate a Bird's Nest in the Bird's Nest position in g generation；Indicate step size controlling amount:
Wherein, xbestFor current optimal solution；L (β) indicates that Lay ties up random search path, obeys Lay and ties up probability distribution:
L é vy~u=t-β(1≤β≤3) (6)
β is a parameter, and value is 1.5 herein；It calculates for convenience in practice, Lay is generated using following equation and ties up random number:
I.e. the location update formula of cuckoo can be expressed as follows:
Wherein, u, v all Normal Distributions,For constant；
It presses probability PaAfter abandoning part solution, the new explanation of identical quantity is regenerated using local random walk:
xg+1,i=xg,i+r(xg,j-xg,k) (10)
Wherein, r is zoom factor, is the uniform random number in (0,1) section, xG, i, xG, kTwo of expression g generation are random
Number；
Eliminate probability PaWhat is indicated is the probability that cuckoo Bird's Nest is found by host, that is, the probability of new explanation is generated, in initial cuckoo
Its in algorithm is a fixed value, takes PaIt is 0.25；In practical searching process, with being continuously increased for the number of iterations, as a result
It is increasingly drawn close to optimal value, if eliminating probability at this time still keeps original radix, a large amount of good solutions can be eliminated,
Destroy the optimizing performance of algorithm；Therefore by introducing variable function, make to eliminate probability PaAs a meeting as the number of iterations becomes
The value of change；Introduce formula:
Wherein, pa_newFor new superseded probability, PaFor the superseded probability before improvement, P is takenaIt is greatest iteration for 0.25, N_iter
Number, n1For bird egg number, by formula (11) it is found that the quality of solution is continuous as the increase of the number of iterations is recycled into the later period
It improves, individual is found and the probability being eliminated is lower and lower, so that improved cuckoo algorithm possesses faster convergence rate
With better optimizing effect；
By the successive ignition of cuckoo algorithm, optimizing, sensor node deployment can be made to make the maximum position of the network coverage；
(2) effective covering to emphasis monitoring section domain is realized
The implementation above uniform deployment of water quality sensor network can obtain the water quality data in monitoring waters on this basis, will
Obtained data carry out principal component analysis and variance analysis, and defining the smallest region of data wave momentum is that non-emphasis monitors region,
The maximum region of data wave momentum is that emphasis monitors region, and the sensor in non-emphasis monitoring region is then moved to emphasis monitoring
Region is realized and is redeployed to emphasis monitoring waters, effectively increases the monitoring efficiency of network.
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