CN104202766B - Wireless sensor network probe node choosing method and system - Google Patents
Wireless sensor network probe node choosing method and system Download PDFInfo
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Abstract
The present invention provides wireless sensor network probe node choosing methods, this method comprises: choosing has the node of maximum degree as first probe node；According to the principle that having selected node, the shaded nodes set in standalone probe path positioning network is utilized；To eliminate shaded nodes to greatest extent as criterion, subsequent probe node is chosen using Revised genetic algorithum.The present invention also provides wireless sensor network probe node selecting system, which includes the first selection unit, computing unit, judging unit, updating unit and the second selection unit.The present invention is based on the active probes to malfunctioning node, and the reachable rate that node is tested in network is improved by choosing the node set optimized.
Description
Technical field
The present invention relates to wireless sensor network technology fields, and in particular to wireless sensor network probe node selection side
Method and system.
Background technique
The node failure of wireless sensor network is divided into communication failure and data fault.Communication failure communicates mould derived from node
The failure of block or the failure (congestion of link leads to data-bag lost or delay etc.) of communication link, and data fault is mainly by passing
The perception mistake of sensor module causes, and the monitoring data for causing node acquisition to report are abnormal.To the prison of wireless sensor network failure
Survey technology is broadly divided into two classes: passive and active.Passive monitoring technology is applied to the detection to data fault, refers mainly to based on neighbour
Occupy the distributed data failure detection schemes of cooperation.Active monitoring technology is mainly used in the detection to node communication failure, right
Loss of data, delay and the routing state of network are detected.Active probe refers to that probe node is sent specifically into network
Data packet judges the state of network specific region by the feedback situation of monitoring data packet.
The first step that the selection of probe node is implemented as active probe completes to choose node appropriate as probe node
All interest regions in network are sent to will detect.A kind of method that R.Kumar etc. proposes y-bend calculates effective node set.
This method calculating can be effectively reduced by the edge aggregation of a nodal test for detecting network under all routing situations
The interstitial content of edge aggregation.J.D.Horton develops a kind of optimization and system inserting knot method and in theoretic
With the integrity attribute of analysis node set overlay network on experiential basis.S.Jamin etc. proposes that a kind of flow in a network is changeable
The intelligent distribution method of probe node under situation.
When unreachable there are will lead to remaining node when malfunctioning node in network, and node selection and portion in the above method
Administration's mechanism is there is no considering that possible node failure bring influences in network, therefore, can not be applied to carry out fault detection
Active probing technique.
Summary of the invention
In view of the drawbacks of the prior art, wireless sensor network probe node choosing method provided by the invention, wireless
Node failure active probe is carried out in sensor network, and node is tested in network to improve by choosing the node set optimized
Reachable rate.
In a first aspect, the present invention provides wireless sensor network probe node choosing methods, this method comprises:
S1: using the set of nodes all in network as shaded nodes set, and will there is maximum in all nodes
The node of degree is as current probe node；
S2: the current probe node and its neighbor node are removed from the shaded nodes set, and described in calculating
The detective path of current probe node each node into the shaded nodes set；
S3: judge whether the current probe node is otherwise first probe node is held if so, thening follow the steps S5
Row step S4；
S4: the current probe node detective path of each node and detection has been selected into the shaded nodes set
Node detective path of each node into the shaded nodes set is compared, and is obtained each in the shaded nodes set
The standalone probe path that node has will have the node not less than k standalone probe path from the shaded nodes set
It removes, wherein k indicates the number of nodes in the network there are failure；
S5: next using genetic algorithm selection according to the geographical position coordinates of each node in the shaded nodes set
Probe node, and using the probe node of selection as new current probe node, return step S2, until described selected detection to save
The quantity of point reaches preset upper limit value or the shaded nodes collection is combined into sky.
Preferably, under being chosen according to the geographical position coordinates of each node in the shaded nodes set using genetic algorithm
One probe node includes:
Several nodes are chosen from the shaded nodes set at random as initial population, and to the geography of the node
Position carries out binary coding and obtains the gene of individual；
According to the fitness of the standalone probe path computing individual of the shaded nodes set interior joint；
The irrelevance between the adaptive crossover mutation and individual of individual is calculated according to the fitness of the individual, is established and is handed over
It pitches group of individuals and merges completion Population breeding；
According to self-adaptive mutation individual after Population breeding and variation digit, individual variation process is completed, is generated new
Individual；
Judge whether the number of iterations reaches setting value, if then choosing the highest individual conduct of fitness in current population
The probe node being selected, otherwise continues iteration.
Preferably, the fitness for calculating individual includes:
Shaded nodes set SN is traversed, set PS is added in current node to be selected to the path of all shaded nodes；
Traversal has selected node to the detective path set PT of all shaded nodes, if PT exists with PS with identical purpose
Shaded nodes and mutually independent two paths, then the accumulation standalone probe path number m of the shaded nodes adds 1；
When m is greater than or equal to k, the shaded nodes number rN being released adds 1, and wherein k indicates failure present in network
The number of node；
After traversal, fitness value by f=rN*EXP (energy) as the node, wherein energy indicates the section
The remaining capacity of point.
Preferably, the Individual Adaptive crossover probability are as follows:
Wherein, ppreFor preset value, it is defaulted as 0.8；F is the fitness of individual,For intragroup average fitness, fmax
For intragroup maximum adaptation degree；
Irrelevance between the individual are as follows:
Wherein, ai、biIt indicates each of gene coding bit, is 1 or 0.
Preferably, the self-adaptive mutation are as follows:
Wherein, fmaxFor the maximum adaptation degree in population, favgFor the average fitness in population, f' is two intersection individuals
In maximum fitness, fiFor currently to the fitness of variation individual, ppreFor the preset value between (0,1)；
The variation digit are as follows:
Wherein, l is constant, and L/4 < l < L/3, L are chromosome length, fminFor the minimum fitness in population.
Wireless sensor network probe node selecting system, the system include:
First selection unit, for using the set of nodes all in network as shaded nodes set, and will be described all
Have the node of maximum degree as current probe node in node；
Computing unit, for the current probe node and its neighbor node to be removed from the shaded nodes set,
And calculate the detective path of the current probe node each node into the shaded nodes set；
Judging unit, for judging whether the current probe node is first probe node；
Updating unit, for by the detective path of the current probe node each node into the shaded nodes set
It has selected probe node detective path of each node into the shaded nodes set to be compared, has obtained the shaded nodes
The standalone probe path that each node has in set will have the node not less than k standalone probe path from the shade
It is removed in node set, wherein k indicates the number of nodes in the network there are failure；
Second selection unit utilizes genetic algorithm according to the geographical position coordinates of each node in the shaded nodes set
Choose next probe node.
Based on the above-mentioned technical proposal, wireless sensor network probe node choosing method provided by the invention is wirelessly passing
Node failure active probe is carried out in sensor network, whole network is covered as far as possible using the smallest probe node set, to take
Detection result the most reliable is obtained for the positioning and judgement to malfunctioning node.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
Other attached drawings are obtained according to these figures.
Fig. 1 is the flow chart for the wireless sensor network probe node choosing method that one embodiment of the invention provides；
Fig. 2 be another embodiment of the present invention provides Revised genetic algorithum flow chart；
Fig. 3 be another embodiment of the present invention provides individual adaptation degree calculating process flow chart；
The structural schematic diagram for the wireless sensor network detection system that Fig. 4 one embodiment of the invention provides.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
As shown in Figure 1, Fig. 1 shows the wireless sensor network probe node choosing method of one embodiment of the invention offer
Flow chart, this method comprises the following steps:
Step S1: using the set of nodes all in network as shaded nodes set, and will have in all nodes
The node of maximum degree is as current probe node.
In the present embodiment, initialization shaded nodes collection is combined into the set of all nodes in network, initializes candidate node set
It is combined into the set of all nodes of network, initialization has selected node for sky.
Since the node with maximum degree has a most hop neighbor numbers, and the neighbor node of node can be visited directly
It measures, therefore shaded nodes can be eliminated to the maximum extent.
Step S2: the current probe node and its neighbor node are removed from the shaded nodes set, and calculated
The detective path of the current probe node each node into the shaded nodes set.
Specifically, it is assumed that administrative center's sink node grasps the topological structure of the whole network and the real time electrical quantity letter of each node
Breath, can calculate egress to the detective path of certain node using routing algorithm, the purpose of the step is for subsequent carry out shade
The judgement of node provides foundation.
In this step, current probe node detective path of each node into the shaded nodes set is calculated
Steps are as follows:
(1) calculate arrive shaded nodes estimate cost: estimate cost be node according to itself to destination region distance and
Numerical value after the dump energy two parts normalization of itself, calculation are as follows: c (N, R)=ad (N, R)+(1-a) e (N).
Wherein, c (N, R) indicates that the estimate cost of node N event area R, d (N, R) indicate distance of the node N to event area, e (N)
Indicate the dump energy of node N, a is normalization scale parameter.
(2) after node sends detection, cost information is chosen in neighbor node with greedy algorithm and is worth the smallest node work
For next-hop node.
(3) routing cost of oneself is set as next-hop node cost plus the cost of itself a jump communication.
(4) to the routing cavity problem being likely to occur, i.e. certain node finds that the cost value of its neighbor node is all bigger than itself,
It then chooses the smallest neighbor node of cost information to be forwarded, walks out cavity.
Step S3: judging whether the current probe node is first probe node, if so, S5 is thened follow the steps, it is no
Then follow the steps S4；
Step S4: the current probe node detective path of each node and has been selected into the shaded nodes set
Probe node detective path of each node into the shaded nodes set is compared, and is obtained in the shaded nodes set
The standalone probe path that each node has will have the node not less than k standalone probe path from the shaded nodes collection
It is removed in conjunction, wherein k indicates the number of nodes in the network there are failure.
Specifically, standalone probe path refer to reach two detective paths of same destination node independently of each other when and only
When no identical forward direction node, i.e. the two is non-intersecting.Based on the definition in standalone probe path, there are k in default network
Under the premise of malfunctioning node, it is not the neighbours of probe node and includes to be known as shade less than the node of the independent detective path of k item
Node.Therefore, eliminating shaded nodes is exactly to carry out the main purpose of probe node selection, with the continuous determination of probe node,
The number of shaded nodes also gradually reduces.
Optionally, indicate to have selected the collection of probe node detective path of each node into the shaded nodes set with PT
It closes, PS indicates the set of current probe node detective path of each node into the shaded nodes set, then this step packet
It includes:
PT is traversed, if the detective path traversed and certain detective path purpose shaded nodes having the same and phase in PS
Mutually independent, then the standalone probe number of path N that the shaded nodes have adds 1；
If N is more than or equal to k, which is removed from shaded nodes set；Wherein, k is indicated in the network
There are the number of nodes of failure.
PS is put into PT.
Traversal has selected the detective path set of probe node each node into the shaded nodes set,
Step S5: under being chosen according to the geographical position coordinates of each node in the shaded nodes set using genetic algorithm
One probe node, and using the probe node of selection as new current probe node, return step S2, until described selected spy
The quantity for surveying node reaches preset upper limit value or the shaded nodes collection is combined into sky.
In the present embodiment, as shown in Fig. 2, Fig. 2 shows another embodiment of the present invention provides step S5 according to institute
The method that the geographical position coordinates of each node in shaded nodes set utilize genetic algorithm to choose next probe node is stated, it should
Method includes the following steps:
Step 201: choosing several nodes from the shaded nodes set at random as initial population, and to the section
The geographical location of point carries out binary coding and obtains the gene of individual.
Specifically, this step randomly selects four nodes as initial population first, and to initial population individual according to
Gene coding, and coding rule are carried out according to its geographical coordinate are as follows: are converted binary number for the coordinate vector (x, y) of node and gone forward side by side
Row height bit combination.Node similar in geographical location has similar routing characteristic, therefore the yin discharged when as node
Film festival points are also similar.
Step 202: according to the fitness of the standalone probe path computing individual of the shaded nodes set interior joint.
Wherein, individual adaptation degree function to the effect that calculates the shaded nodes that current candidate node (individual) can be eliminated
Number, as the return value of function after it being used the dump energy of the both candidate nodes weight.It is this to design while having also contemplated energy
Influence of the amount problem for node selection.
Step 203: being calculated according to the fitness of the individual uncorrelated between individual adaptive crossover mutation and individual
Property, it establishes and intersects group of individuals merging completion Population breeding.
Specifically, this step is first depending on fitness selection regeneration individual.The selected probability of the high individual of fitness
Height, the probability that the low individual of fitness is eliminated are high.Selection course uses roulette wheel selection, for assortative mating individual, needs
Carry out more wheel selections.Each round generates [0, a 1] uniform random number, and by the random number, alternatively pointer is selected to determine
Individual.Table 1 show the process that four individuals are carried out with selection, randomly selects by four-wheel, and final result is shown with higher
The number that the individual of fitness retains is more.Cumulative probability in table constitutes four probability intervals, which section is random number fall into,
Just the corresponding individual in the section is retained primary.
1 individual choice process of table
Next according to certain crossover probability and cross method, new individual is generated.The present invention proposes a kind of adaptive
Crossover Strategy, main idea is: the crossover probability of each individual is determined by fitness, and determination can be into a random way
The individual collections that row intersects.The individual that can not intersect directly retains to the next generation.Between the individual that can be intersected carries out two-by-two
Irrelevance operation is selected the minimum N group of correlation and is intersected.
Adaptive crossover mutation is defined as:
Wherein, ppreFor preset value, it is defaulted as 0.8；F is the fitness of individual,For intragroup average fitness, fmax
For intragroup maximum adaptation degree.After the crossover probability for determining individual, determine whether individual has by way of randomly selecting
There is the permission of intersection.
Assuming that individual amount is N in population, the individual amount without intersection is R, then it is a next to need to intersect generation N-R
Individual.For N-R individuals to be intersected, it is ranked up by the irrelevance between calculating two-by-two and according to irrelevance, selects
It selects the minimum n group of correlation to be intersected, obtains N-R next-generation individual.
Individual irrelevance is defined as:
Wherein,For xor operator, n is individual UVR exposure length, ai、biIt indicates each of gene coding bit, is
1 or 0.Irrelevance embodies the difference degree between individual.
After picking out the individual combination intersected, crossover operation is carried out according to following uniformity crossover, under acquisition
Generation individual.Different from traditional single-point and multiple point crossover, uniform crossover more generalization, each gene node is used as potential
Crosspoint.The 0-1 mask isometric with individual is randomly generated, the segment in mask shows which father's individual is provided to sub- individual
Variate-value.It is selected using mask and produces new individual after corresponding positions are combined.
Shown in specific step is as follows:
1) the adaptive crossover mutation p of individual is calculatedc。
2) there is the D individual of lower crossover probability to be intersected for selection, and carry out prechiasmal pairing by combination of two.
3) irrelevance for calculating every group of pairing is chosen D/2 minimum combination of correlation and is intersected.
4) it is directed to each intersection group, calculates mask sample, and exchanges the gene of father's individual designated position accordingly, completes to hand over
Fork.Consider the individual of following two 16 bit variables:
Father's individual 1:0 100110010010101
Father's individual 2:1 001101001100001
Mask sample (1 indicates that father's individual 1 provides variate-value, and 0, which represents father's individual 2, provides variate-value):
Sample 1:0 110001101011010
Sample 2:1 001110010100101
Intersect latter two new individual are as follows:
Sub- individual 11101100000110001
Sub- individual 20000111011000101
Step 204: according to self-adaptive mutation individual after Population breeding and variation digit, completing individual variation mistake
Journey generates new individual.
Wherein, according to certain mutation probability and variation method, new individual is generated.The present invention is proposed according to the excellent of individual
Bad situation determines its digit that makes a variation, i.e. multiple genes of the low individual variation of fitness, and the high individual of fitness is then using few position
Whether variation or not, can form a variety of altered compositions in this way, expand search space.Self-adaptive mutation is defined as follows:
Wherein, fmaxFor the maximum adaptation degree in population, favgFor the average fitness in population, f' is two intersection individuals
In maximum fitness, fiFor currently to the fitness of variation individual, ppreFor the preset value between (0,1)；The digit that makes a variation is by following formula
It provides:
Wherein, l is constant, and L/4 < l < L/3, L are chromosome length, fminFor the minimum fitness in population；fmax-
fminShow the fitness range of current population；fmax-fiShow the distance between fitness and the fitness maximum value of individual；Then show superiority and inferiority degree of the individual in current population.
Specific step is as follows:
1) random number s is generated, if s is less than pm, continue step 2；Otherwise the gene order X for directly returning to current individual, becomes
Different end.
2) it generates length and is complete 1 sequence of Bits, then fill 0 at random toward the gap between already present position, fill in total
L-Bits round, ultimately produces variation mask M, and example is as shown in table 2, it is assumed that Bits=5, L=16.
The variation mask product process of table 2
| Round | Current sequence | Insert position |
| 0 | 11111 | 11↓111 |
| 1 | 110111 | 110111↓ |
| 2 | 1101110 | 11011↓10 |
| … | … | … |
| 11 | 0100101000100010 | \ |
3) the variation mask M of generation and X is subjected to XOR operation, final mutant gene sequence is obtained, to complete to become
Different process.Wherein, X indicates the gene order of individual.
Step 205: judging whether the number of iterations reaches setting value, if then going to step 206, otherwise go to step 203
Continue iteration,.
Step 206: choosing the highest individual of the fitness in current population as the probe node being selected.
Specifically, the individual with highest fitness is chosen from current population, can discharge most shaded nodes
The node chosen as epicycle of node.
Specifically, as shown in figure 3, Fig. 3 shows the flow chart for calculating the fitness of individual in step 202, including such as
Lower step:
Step 201: the shaded nodes number rN for initializing release is the degree of current node S to be selected.
Step 202: traversal shaded nodes set SN calculates the detective path of current node to be selected to all shaded nodes,
And set PS is added.
Step 203: whether the detective path set PT that node has been selected in judgement to all shaded nodes is empty, if so, turning
To step 210, step 204 is otherwise gone to.
Step 204: traversal has selected node to the detective path set PT of all shaded nodes.
Step 205: for traverse object each in PT, judge its whether there is with PS have identical purpose shaded nodes and
Otherwise mutually independent path goes to step 204 if so, going to step 406.
Step 206: the accumulation standalone probe path number m of the shaded nodes adds 1；
Step 207: judging whether m is more than or equal to k, if so, going to step 208；Otherwise step 204 is gone to.Wherein, k
Indicate the number of malfunctioning node present in network
Step 208: the shaded nodes number rN being released adds 1.
Step 209: judging whether PT traversal is completed, if so, going to step 210, otherwise go to step 204.
Step 210: the fitness value by f=rN*EXP (energy) as the node, wherein energy indicates the node
Remaining capacity, EXP indicates that using natural constant e as the exponential function at bottom, then EXP (energy) indicates natural constant e
Energy power.
From this, wireless sensor network probe node choosing method provided in this embodiment, in wireless sensor network
Node failure active probe is carried out in network, is covered whole network as far as possible using the smallest probe node set, is obtained the most reliable
Detection result for positioning and judgement to malfunctioning node.This method by genetic algorithm choose optimize node set come
Improve the reachable rate that node is tested in network.
As shown in figure 4, Fig. 4 shows the wireless sensor network probe node selecting system of one embodiment of the invention offer
Structure chart, the system include the first selection unit 401, computing unit 402, judging unit 403, updating unit 404 and second
Selection unit 405.
First selection unit 401, for using the set of nodes all in network as shaded nodes set, and by the institute
There is the node in node with maximum degree as current probe node；
Computing unit 402, for moving the current probe node and its neighbor node from the shaded nodes set
It removes, and calculates the detective path of the current probe node each node into the shaded nodes set；
Judging unit 403, for judging whether the current probe node is first probe node；
Updating unit 404, for by the detection of the current probe node each node into the shaded nodes set
Path and probe node detective path of each node into the shaded nodes set is selected to be compared, has obtained the shade
The standalone probe path that each node has in node set will have the node not less than k standalone probe path from described
It is removed in shaded nodes set, wherein k indicates the number of nodes in the network there are failure；
Second selection unit 405 utilizes heredity according to the geographical position coordinates of each node in the shaded nodes set
The next probe node of algorithm picks.
The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although with reference to the foregoing embodiments
Invention is explained in detail, those skilled in the art should understand that；It still can be to aforementioned each implementation
Technical solution documented by example is modified or equivalent replacement of some of the technical features；And these modification or
Replacement, the spirit and scope for technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution.
Claims (5)
1. wireless sensor network probe node choosing method, which is characterized in that this method comprises:
S1: using the set of nodes all in network as shaded nodes set, and will there is maximum degree in all nodes
Node as current probe node；
S2: the current probe node and its neighbor node are removed from the shaded nodes set, and are calculated described current
The detective path of probe node each node into the shaded nodes set；
S3: judge whether the current probe node is otherwise first probe node if so, thening follow the steps S5 executes step
Rapid S4；
S4: the current probe node detective path of each node and probe node has been selected into the shaded nodes set
Into the shaded nodes set, the detective path of each node is compared, and obtains each node in the shaded nodes set
To there is the node not less than k standalone probe path to remove from the shaded nodes set in the standalone probe path having,
Wherein, k indicates the number of nodes in the network there are failure；
S5: next detection is chosen using genetic algorithm according to the geographical position coordinates of each node in the shaded nodes set
Node, and using the probe node of selection as new current probe node, return step S2, until described selected probe node
Quantity reaches preset upper limit value or the shaded nodes collection is combined into sky；
Wherein, the geographical position coordinates according to each node in the shaded nodes set are chosen next using genetic algorithm
A probe node includes:
Several nodes are chosen from the shaded nodes set at random as initial population, and to the geographical location of the node
It carries out binary coding and obtains the gene of individual；
According to the fitness of the standalone probe path computing individual of the shaded nodes set interior joint；
The irrelevance between the adaptive crossover mutation and individual of individual is calculated according to the fitness of the individual, establishes intersection
Body group, which merges, completes Population breeding；
According to self-adaptive mutation individual after Population breeding and variation digit, individual variation process is completed, generates new
Body；
Judge whether the number of iterations reaches setting value, if the fitness then chosen in current population is highest individual as selected
The probe node taken, otherwise continues iteration；
Wherein, the standalone probe path, which refers to, reaches two detective paths of same destination node independently of each other and if only if not having
There is identical forward direction node, i.e. the two is non-intersecting.
2. the method according to claim 1, wherein the fitness for calculating individual includes:
Shaded nodes set SN is traversed, set PS is added in current node to be selected to the path of all shaded nodes；
Traversal has selected node to the detective path set PT of all shaded nodes, if PT exists with PS with identical purpose shade
Node and mutually independent two paths, then the accumulation standalone probe path number m of the shaded nodes adds 1；
When m is greater than or equal to k, the shaded nodes number rN being released adds 1, and wherein k indicates malfunctioning node present in network
Number；
After traversal, fitness value by f=rN*EXP (energy) as the node, wherein energy indicates the node
Remaining capacity.
3. the method according to claim 1, wherein the Individual Adaptive crossover probability are as follows:
Wherein, ppreFor preset value, it is defaulted as 0.8；F is the fitness of individual,For intragroup average fitness, fmaxFor group
Intracorporal maximum adaptation degree；
Irrelevance between the individual are as follows:
Wherein, ai、biIt indicates each of gene coding bit, is 1 or 0.
4. the method according to claim 1, wherein the self-adaptive mutation are as follows:
Wherein, fmaxFor the maximum adaptation degree in population, favgFor the average fitness in population, f' be during two intersection are individual most
Big fitness, fiFor currently to the fitness of variation individual, ppreFor the preset value between (0,1)；
The variation digit are as follows:
Wherein, l is constant, and L/4 < l < L/3, L are chromosome length, fminFor the minimum fitness in population.
5. wireless sensor network probe node selecting system, which is characterized in that the system includes:
First selection unit, for using the set of nodes all in network as shaded nodes set, and by all nodes
In have the node of maximum degree as current probe node；
Computing unit for removing the current probe node and its neighbor node from the shaded nodes set, and is counted
Calculate the detective path of the current probe node each node into the shaded nodes set；
Judging unit, for judging whether the current probe node is first probe node；
Updating unit, for by the current probe node into the shaded nodes set detective path of each node and
It selects probe node detective path of each node into the shaded nodes set to be compared, obtains the shaded nodes set
In the standalone probe path that has of each node, will have node not less than k standalone probe path from the shaded nodes
It is removed in set, wherein k indicates the number of nodes in the network there are failure；
Second selection unit is chosen according to the geographical position coordinates of each node in the shaded nodes set using genetic algorithm
Next probe node；
Wherein, the geographical position coordinates according to each node in the shaded nodes set are chosen next using genetic algorithm
A probe node includes:
Several nodes are chosen from the shaded nodes set at random as initial population, and to the geographical location of the node
It carries out binary coding and obtains the gene of individual；
According to the fitness of the standalone probe path computing individual of the shaded nodes set interior joint；
The irrelevance between the adaptive crossover mutation and individual of individual is calculated according to the fitness of the individual, establishes intersection
Body group, which merges, completes Population breeding；
According to self-adaptive mutation individual after Population breeding and variation digit, individual variation process is completed, generates new
Body；
Judge whether the number of iterations reaches setting value, if the fitness then chosen in current population is highest individual as selected
The probe node taken, otherwise continues iteration；
Wherein, the standalone probe path, which refers to, reaches two detective paths of same destination node independently of each other and if only if not having
There is identical forward direction node, i.e. the two is non-intersecting.
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| CN104954263B (en) * | 2015-06-24 | 2018-07-31 | 广东中标数据科技股份有限公司 | A kind of searching method and device of complex network destination node |
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