CN110166933A - The method for building up of cluster head location model in forest environment monitoring based on difference algorithm - Google Patents
The method for building up of cluster head location model in forest environment monitoring based on difference algorithm Download PDFInfo
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Abstract
The method for building up of cluster head location model in the forest environment monitoring based on difference algorithm that the invention discloses a kind of, using three-dimensional forest environment as background, several intelligent optimization algorithms are compared, best cluster head combinatorial coordinates when model solution goes out sensor node utilization rate highest are finally established with differential evolution algorithm；The algorithm of sub-clustering in most suitable forest environment monitoring is not only had selected in several intellectualized algorithms in this way, and by comparing five kinds of Different Variation strategies in differential evolution algorithm and the parameter of DE is controlled, obtain the Mutation Strategy for being more suitable for model and control parameter value.The characteristic of the model is to be used in difference algorithm in the cluster head positioning of forest environment monitoring, improves the superiority of processing result, and the processing time of cluster head election is reduced by the parameter in Optimized model, and further promote result superiority.
Description
Technical field
The invention belongs to wireless sense network fields, more particularly, in the forest environment monitoring based on differential evolution algorithm
Cluster head location model foundation.
Background technique
In recent years as the function of forest and effect are increasingly by the concern of international community, people are to forestry in social economy
More expectations are expressed in the effect played in development, ecological environment and the negotiation of polygon environment pact, using a kind of new eye
It goes to recognize and protect the valuable forest reserves.Forest environment monitoring is an effective and efficient approach.Forest environment is unfolded
Monitoring, illustrates interaction mechanism between the structure of forest ecosystem and function and forest and environment, obtains long-term ring
Border factor data can help people to promote Rational Management and macro adjustments and controls to the forest reserves conscientiously.Meanwhile forest environment monitors
Real-time and accuracy also have the key effect performed meritorious deeds never to be obliterated to the prevention of the disasters such as forest plague of insects drought.
The forest monitoring means at initial stage is mainly artificial collecting sample information.Traditional artificial acquisition needs to expend a large amount of people
Power and time, accurate data degree is not high, and information continuity is not strong, it is difficult to realize long term monitoring.And due to part forest environment people
Mark is rare extremely, topography is dangerous, for example tropical rain forest depths, the acquisition of this part sample information also become extremely difficult.With forest
Environment Monitoring System it is gradually perfect, forest inventory investigation, monitoring means joined the technology using aerophotograph and topographic map.These
Though measure brings improvement, taking the photograph the defects of projection causes area distortion, rendition to cause area error due to boat also makes forest ring
Border monitoring has fallen into the awkward situation of " monitoring cycle length, ground and office work amount are all very big ".
In recent years, advancing by leaps and bounds due to silicon chip technology and production technology makes low cost, low-power consumption, multi-functional wireless
The exploitation of sensor and extensive utilization are possibly realized, laying of the wireless sense network in forest whether energy or agreement, chain
Road etc. requires reply more complex environment, and limits wireless sense network maximum bottleneck is just in forest environment monitoring
It is wireless sensor node finite energy and is difficult to be charged or be replaced, if the most of node of wireless sensor network
It breaks down, then this network will also paralyse.
Forest environment is vast in territory, physical geography condition very different, the complicated multiplicity of ecotype, different forest ecologies
System has its specific functional attributes.Forest environment type is various, and environment locating for node is usually relatively more severe, general to use
Battery power supply, and sensor node is not easy to be recharged or replaced power supply.
Summary of the invention
The technical problem to be solved by the present invention is to overcome the deficiencies of existing technologies, provide a kind of based on the gloomy of difference algorithm
The method for building up of cluster head location model in woods environmental monitoring, it is vast to can be good at adapting to forest environment topography, possesses vertical
The characteristics of layered structure.
In order to solve the above technical problems, the cluster head that the present invention is provided in a kind of forest environment monitoring based on difference algorithm is fixed
The method for building up of bit model, which comprises the steps of:
S1: according to the layered structure of trees in forest, general sensor nodes is constructed and cluster head sensor node is equal at random
The three-dimensional space model of even spreading；
S2: all general sensor nodes and the unique ID of cluster head sensor node of system distribution are given, CSMA/CA MAC layer is used
Their ID of protocol broadcast, cluster head sensor node integrate out local network according to the ID of its general sensor nodes being collected into
Network information simultaneously issues base station, and the general sensor nodes in the cluster of any one cluster head sensor node in communication range are regarded
For effective node, it is added in forest environment monitoring wireless sense network；
S3: in order not to cause the waste of general sensor nodes and provide a possibility that more for the subsequent sub-clustering stage,
Construct following fitness function:
Wherein T (j) indicates that the common monitoring node in each cluster head sensor node communication range is total
Number, C represents the total number of the common monitoring node of whole cluster head sensor node coverings, due to the capacity usage ratio in communication
On, the common monitoring node sum in each cluster head sensor node communication range is prior influence factor, so setting ratio
Example coefficient a and b are respectively 0.91 and 0.09, and the cluster head sensor node coordinate of the bigger explanation of fitness function value at this time more meets
It is required that N indicates the number of all general sensor nodes；J indicates the number of cluster head sensor node, j ∈ { 1,2 ... M }, M generation
The number of all cluster head sensor nodes of table；
S4: setting general sensor nodes have N0A, cluster head sensor node has M0A, NP represents population scale, takes NP=
6, random to generate NP=6 group cluster head sensor node coordinate individual, each individual includes 3*M0A data, each ordinary node cluster
The maximum value of interior communication range is Rmax, fixed algorithm iteration number is N0It is secondary, zoom factor F=0.5, crossover probability CR=
0.1, M is selected using differential evolution algorithm0A cluster head, compares DE/rand/1, DE/rand/2, DE/best/1, DE/best/2
With the value of the number of iterations and fitness function when five kinds of Mutation Strategy convergences of DE/rand-to-best/1；
S5: fixed population scale NP=6, maximum number of iterations N0It is secondary, crossover probability CR=0.1, using DE/rand/2
Variation rule, increases the value of F constantly from 0 to 2 with 0.1 step-length, and when observation F takes different value, fitness function is with iteration time
Several situations of change；
S6: fixed F=0.8 is slowly increased crossover probability CR from 0 to 1 with 0.1 step-length, when observation CR takes different value,
Fitness function with the number of iterations situation of change；
S7: setting the variation range of zoom factor F to [0.75,0.85], and crossover probability variation range is set as
[0.75,0.85], step-length are reduced into 0.01, observe fitness function with the situation of change of the number of iterations；
S8: the chromosome number of initial population is 50 in setting genetic algorithm, differential evolution algorithm, genetic algorithm and simulation
The number of iterations of annealing algorithm is set as 400, compares the convergency value and the number of iterations of three kinds of algorithm fitness functions.
Further, in the step S1, sensor uses wireless sensor, and type function includes monitoring Soil Temperature And Moisture
Degree, aerial temperature and humidity, intensity of illumination and rainfall, the three-dimensional space model of forest are determined as a long xm=500m, wide ym=
500m, high zmThe three-dimensional space of=200m.
Further, in the step S2, effective node is dist (s (xi,yi,zi),g(xj,yj,zj))≤RmaxWhen
Ordinary node, wherein dist (s (xi,yi,zi),g(xj,yj,zj)) indicate distance between ordinary node and cluster head node, s (xi,
yi,zi) indicate the coordinate of common monitoring node, xi,yi,ziIndicate the coordinate element of common monitoring node, g (xj,yj,zj) represent
The coordinate of cluster head node, xj,yj,zjIndicate that the coordinate element of cluster head node, i ∈ { 1,2 ..., N }, N indicate all common
The number of node；J ∈ { 1,2 ... M }, M represents the number of all cluster head nodes.
Further, which is characterized in that in the step 3, coefficient a and b are set to 0.91 and 0.09.
Further, in the step 4, setting all kinds of common monitoring sensor nodes has N=200, therefrom selects M
=20 cluster head nodes, the communication range R in each ordinary node clustermax=120m.
Further, in the S8, in 400 iterative process, differential evolution algorithm best performance, final adaptation
Degree functional value is up to 1.357；Genetic algorithm is taken second place；Simulated annealing effect in three kinds of algorithms is worst, but convergency value and something lost
Propagation algorithm differs only by 0.009.Compare three kinds of intellectualized algorithms on forest environment monitoring cluster head orientation problem convergence time
Difference finds that genetic algorithm and simulated annealing are not much different, and required time is 5 seconds or so, and needed for differential evolution algorithm
The time to be expended is longest, is twice or more of genetic algorithm and simulated annealing, it is seen then that differential evolution algorithm is being transported
Row is inferior to genetic algorithm and simulated annealing on the time.
Advantageous effects of the invention:
By comparing five kinds of Different Variation strategies of differential evolution algorithm, the variation plan of most suitable forest environment has been selected
Slightly, fitness function is true with the discussion of the situation of change of the number of iterations when and by taking different value to parameter in differential evolution algorithm
Make the parameter of most suitable forest environment.One group of suitable cluster head node coordinate set is determined in experiment, more effectively extends nothing
The service life of line sensor node.And simulation results show, time, solving result are being handled when differential evolution algorithm solves the problems, such as this
Superiority in terms of have biggish advantage.
Detailed description of the invention
Fig. 1 is five kinds of Mutation Strategy fitness function value schematic diagrames；
Fig. 2 is fitness function with zoom factor and the number of iterations variation three-dimensional figure；
Fig. 3 is fitness function with crossover probability and the number of iterations variation three-dimensional figure；
Fig. 4 is fitness function with crossover probability and the number of iterations variation three-dimensional figure；
Fig. 5 is three kinds of algorithm fitness function values；
Fig. 6 is three kinds of algorithm cluster head orientation problem convergence times.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.Following embodiment is only used for clearly illustrating the present invention
Technical solution, and not intended to limit the protection scope of the present invention.
A kind of method for building up of cluster head location model in the forest environment monitoring based on difference algorithm, including walks as follows
It is rapid:
S1 constructs wireless sensor node (general sensor nodes and cluster head section according to the layered structure of trees in forest
Point) three-dimensional space model that uniformly dispenses at random, wherein the function of wireless sensor includes that monitoring soil temperature and humidity, air are warm and humid
Degree, intensity of illumination and rainfall etc., ordinary sensors need itself collected information passing to cluster head in the form of data packet
Sensor node, for cluster head sensor node, they have the ability identify the data packet being collected into, process, handling；
S2 distributes unique ID to all general sensor nodes and cluster head, broadcasts it using CSMA/CA mac-layer protocol
ID, it is concurrent to integrate out local network information according to the ID of its general sensor nodes being collected into cluster head sensor node
To base station, by the communication range R in the cluster of any one cluster head sensor nodemaxInterior general sensor nodes are considered as effectively
Node is added in forest environment monitoring wireless sense network, that is, works as: dist (s (xi,yi,zi),g(xj,yj,zj))≤RmaxWhen, it should
Node is considered as effective node, is added in forest environment wireless sense network, wherein s (xi,yi,zi) indicate general sensor nodes
Coordinate, g (xj,yj,zj) coordinate of cluster head sensor node is represented, i ∈ { 1,2 ..., N }, N indicate all ordinary sensors sections
The number of point；J ∈ { 1,2 ... M }, M represent the number of all cluster head sensor nodes；
S3, in order not to cause the waste of general sensor nodes and provide a possibility that more for the subsequent sub-clustering stage,
Fitness function is designed are as follows:
Wherein TjIndicate the general sensor nodes sum in each cluster head sensor node communication range,C represents the total number of the general sensor nodes of whole cluster head sensor node coverings, coefficient a and b difference
It is set as 0.91 and 0.09, the cluster head sensor node coordinate of the bigger explanation of fitness function value at this time more meets the requirements；
S4, setting all kinds of general sensor nodes has N=200, therefrom selects M=20 cluster head sensor node,
Random to generate NP=6 group cluster head sensor node coordinate individual, each individual includes D=3*M data, each ordinary sensors
Communication range R in node clustermax=120m.Fixed algorithm iteration number is 200 times, zoom factor F=0.5, crossover probability CR
=0.1,20 cluster heads are selected using differential evolution algorithm,
The specific implementation step of differential evolution algorithm is as follows:
Step 1: the initialization population by the way of real coding, specific formula is as follows:
Wherein, xa,b(0) b-th of gene of a-th of individual of initial population is indicated.Wherein a represents individual in population
Sequence number, value range a=1,2 ..., NP, NP represent population scale；B=1,2 ... D represent dimension, also represent individual
Gene number, rand indicate [0,1] section in random real number.WithThe minimum value and maximum value of variable parameter are represented,
The population combination obtained after G iteration is denoted as X (G)；
Step 2: mutation operation has been directed to difference strategy in 5:
A:DE/rand/1
vi(G+1)=xt1(G)+F·(xt2(G)-xt3(G))
Wherein vi(G+1) filial generation variation individual, t are indicated1、t2、t3For the random integers in section [1, NP], and t1≠t2≠
t3, xt(G) variation individual is indicated；
B:DE/rand/2
vi(G+1)=xt1(G)+F·(xt2(G)-xt3(G))+F·(xt4(G)-xt5(G))
Wherein t1、t2、t3、t4、t5For the random integers in section [1, NP], and it is not mutually equal；
C:DE/best/1
vi(G+1)=xbest(G)+F·(xt2(G)-xt3(G))
Wherein xbestIndicate fitness value optimum individual, t1、t2, best be random integers in section [1, NP], and t1、
t2, best is not mutually equal；
D:DE/best/2
vi(G+1)=xbest(G)+F·(xt1(G)-xt2(G))+F·(xt3(G)-xt4(G))
Wherein t1、t2、t3、t4, best is not mutually equal
E:DE/rand-to-best/1
vi(G+1)=xi(G)+F'·(xbest(G)-xi(G))+F·(xt1(G)-xt2(G))
Wherein [0,2] F' ∈ usually takes F'=F best, and t1、t2, best is not mutually equal.
Step 3: crossover operation, by variation individual vi(G) and xi(G) parameter mixes, and using Bernoulli Jacob's experiment method, generates
Experimental subjects ui(G).The operation object of crossover operation for entire individual, is not carried out by the component of each individual vector
's.The formula of crossover operation is as follows:
In formula, CR is crossover probability, is a constant in section [0,1].randni,jIt is a random real number, takes
Being worth range is [0,1]；And jrandIt is the random integers for belonging to set { 1,2 ..., D }.Crossover probability factor CR determines test
Body ui(G+1) component is by target vector x ini,j(G) it provides or by filial generation variation individual vi,j(G+1) it provides.
Step 5: selection operation, after variation intersects, the test individual u of generationi(G+1) with target individual xi(G) reference
Following tournament selection generates next-generation individual vector.
F () represents fitness function in formula.The set X (G+1) of filial generation target individual will be in next iteration (i.e. G+
2 times) it is middle as the further iteration optimization of target population participation, until reaching termination condition, and compares five kinds of Mutation Strategies and receive
The value of the number of iterations and fitness function when holding back；
S5, fixed population scale NP=6, maximum number of iterations are 200 times, crossover probability CR=0.1, using DE/rand/
2 variation rules, increase the value of F constantly from 0 to 2 with 0.1 step-length, and when observation F takes different value, fitness function is with iteration time
Several situations of change；
S6, fixed F=0.8, is slowly increased crossover probability CR from 0 to 1 with 0.1 step-length.When observation CR takes different value,
Fitness function with the number of iterations situation of change；
S7 sets the variation range of zoom factor F to [0.75,0.85], and crossover probability variation range is set as
[0.75,0.85], step-length are reduced into 0.01, observe fitness function with the situation of change of the number of iterations；
S8, the chromosome number that initial population in genetic algorithm is arranged is 50, differential evolution algorithm, genetic algorithm and simulation
The number of iterations of annealing algorithm is set as 400, compares the convergency value and the number of iterations of three kinds of algorithm fitness functions.At 400 times
In iterative process, differential evolution algorithm best performance, final fitness function value is up to 1.357；Genetic algorithm is taken second place；Simulation
Annealing algorithm effect in three kinds of algorithms is worst, but convergency value and genetic algorithm differ only by 0.009.Compare three kinds of intelligent calculations
Difference of the method on forest environment monitoring cluster head orientation problem convergence time, finds genetic algorithm and simulated annealing difference not
Greatly, required time is 5 seconds or so, and the time expended required for differential evolution algorithm is longest, is genetic algorithm and simulation
Twice or more of annealing algorithm, it is seen then that differential evolution algorithm is inferior to genetic algorithm and simulated annealing in terms of run time.
As shown in Figure 1, five kinds of Mutation Strategy fitness function values, it can be seen that, in the cluster head positioning of forest environment monitoring
In problem, the convergency value of the Mutation Strategy fitness function of DE/rand/2 is maximum, has reached 1.314, illustrates that forest environment monitors
Background under more preferably use DE/rand/2 Mutation Strategy；
As shown in Fig. 2, the value of fitness function reaches maximum value at the 200th time, and observes fitness letter when F takes 0.8
Number change curve, there are also change trends for fitness function.It has also been discovered that, under the problem model, zoom factor is 0 or 1 simultaneously
When, fitness function convergent is worst.It can be concluded that under the model of forest environment monitoring cluster head positioning, the conjunction of zoom factor
Reason value is 0.8 or so；
As shown in figure 3, reaching maximum value 1.334 in 200 iteration as CR=0.8, it is seen then that supervised in forest environment
It surveys under cluster head location model, the relatively figure of merit of crossover probability CR is 0.8；
As shown in Figure 4, it is seen then that work as F=0.77, when CR=0.83, fitness function reaches maximum value so far
1.35, fitness function is bigger, illustrates that the cluster head acquired combination is more excellent, it can be seen that, take control parameter zoom factor F and friendship
When fork probability CR is 0.77 and 0.83, it is more advantageous to the optimization of result；
As shown in Figure 5, it is seen that in 400 iterative process, differential evolution algorithm best performance, final fitness function
Value up to 1.357, genetic algorithm is taken second place；
As shown in fig. 6, visible differential evolution algorithm is slightly inferior to simulated annealing and genetic algorithm in time.
The present invention establishes the 3-D wireless monitored based on forest environment with reference to the characteristic and detection demand of practical forest environment
Pessimistic concurrency control is sensed, the type of the index monitored as needed, configuration is various can be with monitoring temperature, humidity, intensity of illumination, rainfall
The equipment of the environment attributes such as amount, wind direction, communication frequency is high, and transmission range is short, and without having data fusion ability, data fusion exists
It is carried out in cluster head；Its communication range can be made to cover all common sensings by the cluster head sensor node that cluster head location model determines
Device node avoids the waste of the energy；Cluster head sensor node possesses additional energy, convenient for its receive local data, convergence and
Think the movable progress of base station transmission pooled data；Based on Network Synchronization, all nodes are waken up simultaneously per hour, keep with for the moment
Between section radio, then simultaneously be switched to sleep state to realize sustainable sensing.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the technical principles of the invention, several improvement and deformations can also be made, these improvement and deformations
Also it should be regarded as protection scope of the present invention.
Claims (6)
1. a kind of method for building up of the cluster head location model in forest environment monitoring based on difference algorithm, which is characterized in that packet
Include following steps:
S1: according to the layered structure of trees in forest, general sensor nodes is constructed and cluster head sensor node is uniformly spread at random
The three-dimensional space model of cloth；
S2: all general sensor nodes and the unique ID of cluster head sensor node of system distribution are given, CSMA/CA mac-layer protocol is used
Their ID is broadcasted, cluster head sensor node integrates out local network letter according to the ID of its general sensor nodes being collected into
Base station is ceased and issues, the general sensor nodes in the cluster of any one cluster head sensor node in communication range, which are considered as, to be had
Node is imitated, is added in forest environment monitoring wireless sense network；
S3: in order not to cause the waste of general sensor nodes and provide a possibility that more for the subsequent sub-clustering stage, building
Following fitness function:
Wherein T (j) indicates the common monitoring node sum in each cluster head sensor node communication range, C
The total number for representing the common monitoring node of whole cluster head sensor nodes coverings, due on the capacity usage ratio of communication, often
Common monitoring node sum in a cluster head sensor node communication range is prior influence factor, so setting proportionality coefficient
A and b is respectively 0.91 and 0.09, and the cluster head sensor node coordinate of the bigger explanation of fitness function value at this time more meets the requirements, N
Indicate the number of all general sensor nodes；J indicates the number of cluster head sensor node, and j ∈ { 1,2 ... M }, M represent all
The number of cluster head sensor node；
S4: setting general sensor nodes have N0A, cluster head sensor node has M0A, NP represents population scale, takes NP=6, with
Machine generates NP=6 group cluster head sensor node coordinate individual, and each individual includes 3*M0A data, in each ordinary node cluster
The maximum value of communication range is Rmax, fixed algorithm iteration number is N0It is secondary, zoom factor F=0.5, crossover probability CR=0.1,
M is selected using differential evolution algorithm0A cluster head, compares DE/rand/1, DE/rand/2, DE/best/1, DE/best/2 and DE/
The value of the number of iterations and fitness function when five kinds of Mutation Strategy convergences of rand-to-best/1；
S5: fixed population scale NP=6, maximum number of iterations N0Secondary, crossover probability CR=0.1 is made a variation using DE/rand/2
Rule increases the value of F constantly from 0 to 2 with 0.1 step-length, and when observation F takes different value, fitness function is with the number of iterations
Situation of change；
S6: fixed F=0.8 is slowly increased crossover probability CR from 0 to 1 with 0.1 step-length, when observation CR takes different value, adapts to
Function is spent with the situation of change of the number of iterations；
S7: setting the variation range of zoom factor F to [0.75,0.85], crossover probability variation range be set as [0.75,
0.85], step-length is reduced into 0.01, observes fitness function with the situation of change of the number of iterations；
S8: the chromosome number of initial population is 50 in setting genetic algorithm, differential evolution algorithm, genetic algorithm and simulated annealing
The number of iterations of algorithm is set as 400, compares the convergency value and the number of iterations of three kinds of algorithm fitness functions.
2. the foundation side of the cluster head location model in the forest environment monitoring according to claim 1 based on difference algorithm
Method, which is characterized in that in the step S1, sensor use wireless sensor, type function include monitoring soil temperature and humidity,
Aerial temperature and humidity, intensity of illumination and rainfall, the three-dimensional space model of forest are determined as a long xm=500m, wide ym=500m is high
zmThe three-dimensional space of=200m.
3. the foundation side of the cluster head location model in the forest environment monitoring according to claim 1 based on difference algorithm
Method, which is characterized in that in the step S2, effective node is dist (s (xi,yi,zi),g(xj,yj,zj))≤RmaxWhen it is common
Node, wherein dist (s (xi,yi,zi),g(xj,yj,zj)) indicate distance between ordinary node and cluster head node, s (xi,yi,zi)
Indicate the coordinate of common monitoring node, xi,yi,ziIndicate the coordinate element of common monitoring node, g (xj,yj,zj) represent cluster head section
The coordinate of point, xj,yj,zjIndicate the coordinate element of cluster head node, i ∈ { 1,2 ..., N }, N indicate all common ordinary nodes
Number；J ∈ { 1,2 ... M }, M represents the number of all cluster head nodes.
4. the foundation side of the cluster head location model in the forest environment monitoring according to claim 1 based on difference algorithm
Method, which is characterized in that in the step 3, coefficient a and b are set to 0.91 and 0.09.
5. the foundation side of the cluster head location model in the forest environment monitoring according to claim 1 based on difference algorithm
Method, which is characterized in that in the step 4, setting all kinds of common monitoring sensor nodes has N=200, therefrom selects M=
20 cluster head nodes, the communication range R in each ordinary node clustermax=120m.
6. the foundation side of the cluster head location model in the forest environment monitoring according to claim 1 based on difference algorithm
Method, which is characterized in that in the S8, in 400 iterative process, differential evolution algorithm best performance, final fitness
Functional value is up to 1.357；Genetic algorithm is taken second place；Simulated annealing effect in three kinds of algorithms is worst, but convergency value and heredity
Algorithm differs only by 0.009, compares area of three kinds of intellectualized algorithms on forest environment monitoring cluster head orientation problem convergence time
Not, genetic algorithm and simulated annealing are not much different, and required time is 5 seconds or so, and is expended required for differential evolution algorithm
Time be longest, be twice or more of genetic algorithm and simulated annealing, it is seen then that differential evolution algorithm at runtime between
On be inferior to genetic algorithm and simulated annealing.
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