CN104202766A - Selecting method and system for network probing node of wireless sensor - Google Patents

Abstract

The invention provides a selecting method for a network probing node of a wireless sensor. The method comprises the following steps: selecting the node with the maximum degree as a first probing node; positioning a shadow node set in the network by utilizing the principle of an independent detecting path according to the selected node; selecting the subsequent probing node through the improved genetic algorithm based on the rule of removing the shadow node to the maximum. The invention also provides a selecting system for the network probing node of the wireless sensor. The system comprises a first selecting unit, a calculating unit, a determining unit, an updating unit and a second selecting unit. According to the selecting method and system for the network probing node of the wireless sensor, a fault node is actively probed, and the achievable rate of the probed node in the network is raised by selecting the optimal node set.

Description

Wireless sensor network probe node choosing method and system

Technical field

The present invention relates to wireless sensor network technology field, be specifically related to wireless sensor network probe node choosing method and system.

Background technology

The node failure of wireless sensor network is divided into communication failure and data fault.Communication failure comes from the fault of node communication module or the fault of communication link (link congested cause data-bag lost or delay etc.), and data fault is mainly caused by the perception mistake of sensor assembly, the Monitoring Data that causes node collection to report is abnormal.The monitoring technology of radio sensing network fault is mainly divided into two classes: passive and active.Passive monitoring technology is applied to the detection to data fault, mainly refers to the distributed data failure detection schemes based on neighbours' cooperation.Active monitoring technology is mainly used in the detection to node communication fault, and loss of data, delay and routing state to network detect.Active probe refers to that probe node sends specific packet in network, judges the state of network specific region by the feedback situation of Monitoring Data bag.

Probe node choose as active probe implement the first step, complete choose suitable node as probe node detection is mail to all region-of-interests in network.The method of a kind of y-bends of proposition such as R.Kumar is calculated effective node set.The method is calculated the edge aggregation that can be detected by a node under all route situations, effectively reduces the interstitial content for detection of network edge set.J.D.Horton works out node method for arranging a kind of optimization and system and the integrity attribute of analysis node set overlay network on theoretical aspect and experiential basis.S.Jamin etc. propose a kind of in network the intelligent distribution method of probe node under the changeable situation of flow.

In the time there is malfunctioning node in network, will cause all the other nodes unreachable, and node selection and deployment mechanisms in said method do not considered the impact that node failure possible in network brings, therefore, can not be applied to the active probe technology of carrying out fault detect.

Summary of the invention

For the defect of prior art, wireless sensor network probe node choosing method provided by the invention carries out node failure active probe in wireless sensor network, by choosing reached at the rate of tested node in the incompatible raising network of optimized set of node.

First aspect, the invention provides wireless sensor network probe node choosing method, and the method comprises:

S1: using the set of all nodes in network as shaded nodes set, and using the node that there are the maximum number of degrees in described all nodes as current probe node;

S2: described current probe node and neighbor node thereof are removed from described shaded nodes set, and calculate the detective path of described current probe node to each node in described shaded nodes set;

S3: judge whether described current probe node is first probe node, if so, performs step S5, otherwise execution step S4;

S4: by described current probe node to the detective path of each node in described shaded nodes set with selected probe node to compare to the detective path of each node in described shaded nodes set, obtain the standalone probe path that in described shaded nodes set, each node has, remove from described shaded nodes set thering is the node that is not less than k bar standalone probe path, wherein, k represents to exist in described network the number of nodes of fault;

S5: utilize genetic algorithm to choose next probe node according to the geographical position coordinates of each node in described shaded nodes set, and using the probe node of choosing as new current probe node, return to step S2, selected the quantity of probe node to reach preset upper limit value or described shaded nodes set for empty until described.

Preferably, utilizing genetic algorithm to choose next probe node according to the geographical position coordinates of each node in described shaded nodes set comprises:

From described shaded nodes set, choose several nodes as initial population at random, and binary coding is carried out in the geographical position of described node obtain individual gene;

According to the fitness of the standalone probe path computing individuality of node in described shaded nodes set;

Calculate the irrelevance between individual self adaptation crossover probability and individuality according to the fitness of described individuality, establish intersection group of individuals and merged population breeding;

According to self adaptation variation probability individual after population breeding and variation figure place, complete individual variation process, generate new individuality;

Judge whether iterations reaches set point, if choose individuality that fitness in current population is the highest as the probe node being selected, otherwise proceed iteration.

Preferably, the individual fitness of described calculating comprises:

Traversal shaded nodes S set N, adds set PS by current node to be selected to the path of all shaded nodes;

Traversal has selected node to arrive the detective path set PT of all shaded nodes, if PT and existence in PS have identical object shaded nodes and two separate paths, the accumulation standalone probe path number m of this shaded nodes adds 1;

In the time that m is more than or equal to k, d/d shaded nodes rN number adds 1, and wherein k represents the number of the malfunctioning node existing in network;

After traversal finishes, the fitness value using f=rN*EXP (energy) as this node, wherein energy represents the dump energy of this node.

Preferably, described Individual Adaptive crossover probability is:

$p_c=\left\{\begin{array}{c}{p}_{\mathrm{pre}}-\frac{(p_{\mathrm{pre}}-0.7)(f_{\max }-f)}{f_{\max }-f},f\&GreaterEqual;\stackrel{\&OverBar;}{f}\\ p_{\mathrm{pre}},f<\stackrel{\&OverBar;}{f}\end{array}\right.$

Wherein, p _prefor preset value, be defaulted as 0.8; F is individual fitness, intragroup average fitness, f _maxfor intragroup maximum adaptation degree;

Irrelevance between described individuality is:

$r(A,B)=\underset{i=1}{\overset{16}{\mathrm{\&Sigma;}}}{a}_i\&CirclePlus;b_i$

Wherein, a _i, b _irepresenting each bit in gene code, is 1 or 0.

Preferably, described self adaptation variation probability is:

$p_m=\left\{\begin{array}{c}{p}_{\mathrm{pre}}\frac{f_{\max }-f_i}{f_{\max }-f_{\mathrm{avg}}},f\&GreaterEqual;f_{\mathrm{avg}}\\ p_{\mathrm{pre}},f<f_{\mathrm{avg}}\end{array}\right.$

Wherein, f _maxfor the maximum adaptation degree in population, f _avgfor the average fitness in population, f is maximum fitness in two intersection individualities, f _ifor the current individual fitness that makes a variation, the p of waiting _prefor the preset value between (0,1);

Described variation figure place is:

$\mathrm{Bits}=\left[l\frac{f_{\max }-f_i}{f_{\max }-f_{\min }}\right]$

Wherein, l is constant, L/4<l<L/3, and L is chromosome length, f _minfor the minimum fitness in population.

Wireless sensor network probe node selecting system, this system comprises:

First chooses unit, for using the set of all nodes of network as shaded nodes set, and using the node that there are the maximum number of degrees in described all nodes as current probe node;

Computing unit, for described current probe node and neighbor node thereof are removed from described shaded nodes set, and calculates the detective path of described current probe node to each node in described shaded nodes set;

Judging unit, for judging whether described current probe node is first probe node;

Updating block, for by described current probe node to the detective path of the each node of described shaded nodes set with selected probe node to compare to the detective path of each node in described shaded nodes set, obtain the standalone probe path that in described shaded nodes set, each node has, remove from described shaded nodes set thering is the node that is not less than k bar standalone probe path, wherein, k represents to exist in described network the number of nodes of fault;

Second chooses unit, utilizes genetic algorithm to choose next probe node according to the geographical position coordinates of each node in described shaded nodes set.

Based on technique scheme, wireless sensor network probe node choosing method provided by the invention, in wireless sensor network, carry out node failure active probe, utilize minimum probe node set to cover as far as possible whole network, thereby obtain the most reliable result of detection for the location to malfunctioning node and judgement.

Brief description of the drawings

In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, to the accompanying drawing of required use in embodiment or description of the Prior Art be briefly described below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, do not paying under the prerequisite of creative work, can also obtain according to these figure other accompanying drawing.

Fig. 1 is the flow chart of the wireless sensor network probe node choosing method that provides of one embodiment of the invention;

Fig. 2 is the flow chart of the improved genetic algorithm that provides of another embodiment of the present invention;

Fig. 3 is the flow chart of the computational process of the ideal adaptation degree that provides of another embodiment of the present invention;

The structural representation of the wireless sensor network detection system that Fig. 4 one embodiment of the invention provides.

Embodiment

Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiment.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtaining under creative work prerequisite, belong to the scope of protection of the invention.

As shown in Figure 1, Fig. 1 shows the flow chart of the wireless sensor network probe node choosing method that one embodiment of the invention provides, and the method comprises the steps:

Step S1: using the set of all nodes in network as shaded nodes set, and using the node that there are the maximum number of degrees in described all nodes as current probe node.

In the present embodiment, the set of initialization shaded nodes is the set of all nodes in network, and the set of initialization both candidate nodes is the set of all nodes of network, and it is empty that node has been selected in initialization.

There is maximum hop neighbor numbers owing to thering is the node of the maximum number of degrees, and the neighbor node of node can be arrived by direct detection, therefore can eliminate to greatest extent shaded nodes.

Step S2: described current probe node and neighbor node thereof are removed from described shaded nodes set, and calculate the detective path of described current probe node to each node in described shaded nodes set.

Specifically, suppose the administrative center sink node grasp topological structure of the whole network and the real-time information about power of each node, utilize routing algorithm can calculate the detective path of node to certain node, the object of this step is to provide foundation for the follow-up judgement of carrying out shaded nodes.

In this step, calculate described current probe node as follows to the step of the detective path of each node in described shaded nodes set:

(1) calculate the estimate cost of shaded nodes: estimate cost is node according to self to the numerical value after the distance in object region and the dump energy two parts normalization of self, its account form is as follows: c (N, R)=ad (N, R)+(1-a) e (N).Wherein, c (N, R) represents the estimate cost of node N event area R, and d (N, R) represents the distance of node N to event area, and e (N) represents the dump energy of node N, and a is normalization scale parameter.

(2) after node sends and surveys, the node that utilization greedy algorithm is chosen cost information value minimum in neighbor node is as next-hop node.

(3) the route cost of oneself is set as next-hop node cost add self one jump communication cost.

(4) to the route cavity problem that may occur, certain node finds that the cost value of its neighbor node is all large than self, and the neighbor node of choosing cost information minimum forwards, and walks out cavity.

Step S3: judge whether described current probe node is first probe node, if so, performs step S5, otherwise execution step S4;

Step S4: by described current probe node to the detective path of each node in described shaded nodes set with selected probe node to compare to the detective path of each node in described shaded nodes set, obtain the standalone probe path that in described shaded nodes set, each node has, remove from described shaded nodes set thering is the node that is not less than k bar standalone probe path, wherein, k represents to exist in described network the number of nodes of fault.

Specifically, standalone probe path refers to and reaches two detective paths of same destination node separate and if only if there is no identical forward direction node, and the two is non-intersect.Based on the definition in standalone probe path, in default network, exist under the prerequisite of k malfunctioning node, be not the neighbours of probe node and comprising be less than k bar independently the node of detective path be called shaded nodes.Therefore, eliminating shaded nodes is exactly to carry out the main purpose that probe node is chosen, and along with constantly determining of probe node, the number of shaded nodes also progressively reduces.

Alternatively, represent to select the set of probe node to the detective path of each node in described shaded nodes set with PT, PS represents the set of current probe node to the detective path of each node in described shaded nodes set, and this step comprises:

Traversal PT, if the detective path traveling through has identical object shaded nodes and separate with certain detective path in PS, the standalone probe number of path N that this shaded nodes has adds 1;

If N is more than or equal to k, this shaded nodes is removed from shaded nodes set; Wherein, k represents to exist in described network the number of nodes of fault.

PS is put into PT.

Traversal has been selected the detective path set of probe node to each node in described shaded nodes set,

Step S5: utilize genetic algorithm to choose next probe node according to the geographical position coordinates of each node in described shaded nodes set, and using the probe node of choosing as new current probe node, return to step S2, selected the quantity of probe node to reach preset upper limit value or described shaded nodes set for empty until described.

In the present embodiment, as shown in Figure 2, Fig. 2 shows in the step S5 that another embodiment of the present invention provides and utilizes genetic algorithm to choose the method for next probe node according to the geographical position coordinates of each node in described shaded nodes set, and the method comprises the steps:

Step 201: choose several nodes as initial population at random from described shaded nodes set, and binary coding is carried out in the geographical position of described node obtain individual gene.

Specifically, this step is chosen four nodes first at random as initial population, and the individuality of initial population is carried out to gene code according to its geographical coordinate, and coding rule is: the coordinate vector (x, y) of node is converted into binary number and carries out high-low-position combination.The close node in geographical position has similar routing characteristic, and the shaded nodes number therefore discharging when as node is also close.

Step 202: according to the fitness of the standalone probe path computing individuality of node in described shaded nodes set.

Wherein, ideal adaptation degree function to the effect that calculates the shaded nodes number that current both candidate nodes (individuality) can be eliminated, and is used the return value as function after the dump energy weighting of this both candidate nodes.The impact of energy problem for node selection also considered in this design simultaneously.

Step 203: calculate the irrelevance between individual self adaptation crossover probability and individuality according to the fitness of described individuality, establish intersection group of individuals and merged population breeding.

Specifically, first this step selects regeneration individual according to fitness.The selected probability of individuality that fitness is high is high, and the probability that the individuality that fitness is low is eliminated is high.Selection course adopts roulette wheel selection, for assortative mating individuality, need to carry out many wheels and select.Each is taken turns and produces [0, a 1] uniform random number, determines selected individuality using this random number as select finger.Table 1 is depicted as the process that four individualities are selected, and chooses at random through four-wheel, and end product shows that the number of times of the individuality reservation with higher fitness is more.Cumulative probability in table forms four probability intervals, and which interval is random number fall into, and just the individuality of this interval correspondence retained once.

Table 1 individual choice process

Next according to certain crossover probability and cross method, generate new individuality.The present invention proposes a kind of adaptive Crossover Strategy, and its main idea is: determine the crossover probability of each individuality by fitness, and determine in random mode the individual collections that can intersect.The individuality that can not intersect directly remains to the next generation.The irrelevance computing of the individuality that can intersect between carrying out between two, selects the N group that correlation is minimum and intersects.

Self adaptation crossover probability is defined as:

$p_c=\left\{\begin{array}{c}{p}_{\mathrm{pre}}-\frac{(p_{\mathrm{pre}}-0.7)(f_{\max }-f)}{f_{\max }-f},f\&GreaterEqual;\stackrel{\&OverBar;}{f}\\ p_{\mathrm{pre}},f<\stackrel{\&OverBar;}{f}\end{array}\right.$

Wherein, p _prefor preset value, be defaulted as 0.8; F is individual fitness, intragroup average fitness, f _maxfor intragroup maximum adaptation degree.Determining after individual crossover probability, determining the individual authority whether with intersection by the mode of choosing at random.

Suppose that in population, individual amount is N, the individual amount not intersecting is R, and N-R of the generation that need to intersect is next individual.Wait to intersect individual for N-R, by the irrelevance between calculating between two and sort according to irrelevance, select the minimum n group of correlation to intersect, obtain N-R individuality of future generation.

Individual irrelevance is defined as:

$r(A,B)=\underset{i=1}{\overset{16}{\mathrm{\&Sigma;}}}{a}_i\&CirclePlus;b_i$

Wherein, for xor operator, n is individual code length, a _i, b _irepresenting each bit in gene code, is 1 or 0.Irrelevance embodies the difference degree between individuality.

Pick out after the individuality combination intersecting, carry out interlace operation according to following uniformity crossover, obtain of future generation individual.Intersect differently from traditional single-point and multiple spot, evenly intersect generalization more, each gene node is as potential crosspoint.Produce randomly and individual isometric 0-1 mask, the fragment in mask shows which father's individuality provides variate-value to sub-individuality.After utilizing mask to select corresponding positions to combine, can generate new individuality.

Concrete steps are as follows:

1) calculate individual self adaptation crossover probability p _c.

2) the D individuality of selecting to have a lower crossover probability intersects, and carries out prechiasmal pairing by combination of two.

3) calculate the right irrelevance of every assembly, choose D/2 the combination that correlation is minimum and intersect.

4) for each intersection group, calculate mask sample, and exchange accordingly the gene of the individual assigned address of father, complete intersection.Consider the individuality of following two 16 bit variables:

The individual 1:0 100110010010101 of father

The individual 2:1 001101001100001 of father

Mask sample (1 represents that father's individuality 1 provides variate-value, and 0 represents that father's individuality 2 provides variate-value):

Sample 1:0 110001101011010

Sample 2:1 001110010100101

Intersecting latter two new individuality is:

Son individual 11101100000110001

Son individual 20000111011000101

Step 204: according to self adaptation variation probability individual after population breeding and variation figure place, complete individual variation process, generate new individuality.

Wherein, according to certain variation probability and variation method, generate new individuality.The present invention's individual good and bad situation of giving chapter and verse determines its variation figure place, the multiple genes of individual variation that fitness is low, and the individuality that fitness is high adopts few variation or does not make a variation, and can form so multiple variation combination, has expanded search volume.Self adaptation variation definition of probability is as follows:

$p_m=\left\{\begin{array}{c}{p}_{\mathrm{pre}}\frac{f_{\max }-f_i}{f_{\max }-f_{\mathrm{avg}}},f\&GreaterEqual;f_{\mathrm{avg}}\\ p_{\mathrm{pre}},f<f_{\mathrm{avg}}\end{array}\right.$

Wherein, f _max is the maximum adaptation degree in population, f _avgfor the average fitness in population, f is maximum fitness in two intersection individualities, f _ifor the current individual fitness that makes a variation, the p of waiting _prefor the preset value between (0,1); Variation figure place is provided by following formula:

$\mathrm{Bits}=\left[l\frac{f_{\max }-f_i}{f_{\max }-f_{\min }}\right]$

Wherein, l is constant, L/4<l<L/3, and L is chromosome length, f _minfor the minimum fitness in population; f _max-f _minshow the fitness scope of current population; f _max-f _ishow the distance between individual fitness and fitness maximum; show individual good and bad degree in current population.

Concrete steps are as follows:

1) generate random number s, if s is less than p _m, continue the 2nd step; Otherwise directly return to the gene order X of current individuality, variation finishes.

2) generate complete 1 sequence that length is Bits, then fill 0 toward the space between already present position is random, altogether fill L-Bits round, finally generate variation mask M, example is as shown in table 2, supposes Bits=5, L=16.

The table 2 mask product process that makes a variation

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 are carried out to XOR, obtain final mutant gene sequence, thereby complete mutation process.Wherein, X represents individual gene order.

Step 205: judge whether iterations reaches set point, if go to step 206, proceed iteration otherwise go to step 203.

Step 206: choose individuality that fitness in current population is the highest as the probe node being selected.

Specifically, from current population, choose and there is the individuality of high fitness, can discharge the node that the node of maximum shaded nodes is chosen as epicycle.

Specifically, as shown in Figure 3, Fig. 3 shows the flow chart that calculates individual fitness in step 202, comprises the steps:

Step 201: the shaded nodes number rN that initialization discharges is the number of degrees of current node S to be selected.

Step 202: traversal shaded nodes S set N, calculates the detective path of current node to be selected to all shaded nodes, and adds set PS.

Step 203: judge and select whether node is empty to the detective path set PT of all shaded nodes, if so, go to step 210, otherwise go to step 204.

Step 204: traversal has selected node to arrive the detective path set PT of all shaded nodes.

Step 205: for each traversal object in PT, judge that whether it exists with PS and have identical object shaded nodes and separate path, if so, goes to step 406, otherwise goes to step 204.

Step 206: the accumulation standalone probe path number m of this shaded nodes adds 1;

Step 207: judge that whether m is more than or equal to k, if so, goes to step 208; Otherwise go to step 204.Wherein, k represents the number of the malfunctioning node existing in network

Step 208: d/d shaded nodes rN number adds 1.

Step 209: judge that whether PT traversal completes, and if so, goes to step 210, otherwise goes to step 204.

Step 210: the fitness value using f=rN*EXP (energy) as this node, wherein energy represents the dump energy of this node, EXP represents the exponential function taking natural constant e the end of as, and EXP (energy) represents the energy power of natural constant e.

In view of this, the wireless sensor network probe node choosing method that the present embodiment provides, in wireless sensor network, carry out node failure active probe, utilize minimum probe node set to cover as far as possible whole network, obtain the most reliable result of detection for the location to malfunctioning node and judgement.The method is chosen reached at the rate of tested node in the incompatible raising network of optimized set of node by genetic algorithm.

As shown in Figure 4, Fig. 4 shows the structure chart of the wireless sensor network probe node selecting system that one embodiment of the invention provides, and this system comprises that first chooses unit 401, computing unit 402, judging unit 403, updating block 404 and second and choose unit 405.

First chooses unit 401, for using the set of all nodes of network as shaded nodes set, and using the node that there are the maximum number of degrees in described all nodes as current probe node;

Computing unit 402, for described current probe node and neighbor node thereof are removed from described shaded nodes set, and calculates the detective path of described current probe node to each node in described shaded nodes set;

Judging unit 403, for judging whether described current probe node is first probe node;

Updating block 404, for by described current probe node to the detective path of the each node of described shaded nodes set with selected probe node to compare to the detective path of each node in described shaded nodes set, obtain the standalone probe path that in described shaded nodes set, each node has, remove from described shaded nodes set thering is the node that is not less than k bar standalone probe path, wherein, k represents to exist in described network the number of nodes of fault;

Second chooses unit 405, utilizes genetic algorithm to choose next probe node according to the geographical position coordinates of each node in described shaded nodes set.

Above embodiment only, in order to technical scheme of the present invention to be described, is not intended to limit; Although the present invention is had been described in detail with reference to previous embodiment, those of ordinary skill in the art is to be understood that; Its technical scheme that still can record aforementioned each embodiment is modified, or part technical characterictic is wherein equal to replacement; And these amendments or replacement do not make the essence of appropriate technical solution depart from the spirit and scope of various embodiments of the present invention technical scheme.

Claims (6)

1. wireless sensor network probe node choosing method, is characterized in that, the method comprises:

S1: using the set of all nodes in network as shaded nodes set, and using the node that there are the maximum number of degrees in described all nodes as current probe node;

S2: described current probe node and neighbor node thereof are removed from described shaded nodes set, and calculate the detective path of described current probe node to each node in described shaded nodes set;

S3: judge whether described current probe node is first probe node, if so, performs step S5, otherwise execution step S4;

S4: by described current probe node to the detective path of each node in described shaded nodes set with selected probe node to compare to the detective path of each node in described shaded nodes set, obtain the standalone probe path that in described shaded nodes set, each node has, remove from described shaded nodes set thering is the node that is not less than k bar standalone probe path, wherein, k represents to exist in described network the number of nodes of fault;

S5: utilize genetic algorithm to choose next probe node according to the geographical position coordinates of each node in described shaded nodes set, and using the probe node of choosing as new current probe node, return to step S2, selected the quantity of probe node to reach preset upper limit value or described shaded nodes set for empty until described.

2. method according to claim 1, is characterized in that, utilizes genetic algorithm to choose next probe node comprise according to the geographical position coordinates of each node in described shaded nodes set:

From described shaded nodes set, choose several nodes as initial population at random, and binary coding is carried out in the geographical position of described node obtain individual gene;

According to the fitness of the standalone probe path computing individuality of node in described shaded nodes set;

Calculate the irrelevance between individual self adaptation crossover probability and individuality according to the fitness of described individuality, establish intersection group of individuals and merged population breeding;

According to self adaptation variation probability individual after population breeding and variation figure place, complete individual variation process, generate new individuality;

Judge whether iterations reaches set point, if choose individuality that fitness in current population is the highest as the probe node being selected, otherwise proceed iteration.

3. method according to claim 2, is characterized in that, the individual fitness of described calculating comprises:

Traversal shaded nodes S set N, adds set PS by current node to be selected to the path of all shaded nodes;

Traversal has selected node to arrive the detective path set PT of all shaded nodes, if PT and existence in PS have identical object shaded nodes and two separate paths, the accumulation standalone probe path number m of this shaded nodes adds 1;

In the time that m is more than or equal to k, d/d shaded nodes rN number adds 1, and wherein k represents the number of the malfunctioning node existing in network;

After traversal finishes, the fitness value using f=rN*EXP (energy) as this node, wherein energy represents the dump energy of this node.

4. method according to claim 2, is characterized in that, described Individual Adaptive crossover probability is:

$p_c=\left\{\begin{array}{c}{p}_{\mathrm{pre}}-\frac{(p_{\mathrm{pre}}-0.7)(f_{\max }-f)}{f_{\max }-f},f\&GreaterEqual;\stackrel{\&OverBar;}{f}\\ p_{\mathrm{pre}},f<\stackrel{\&OverBar;}{f}\end{array}\right.$

Wherein, p _prefor preset value, be defaulted as 0.8; F is individual fitness, intragroup average fitness, f _maxfor intragroup maximum adaptation degree;

Irrelevance between described individuality is:

$r(A,B)=\underset{i=1}{\overset{16}{\mathrm{\&Sigma;}}}{a}_i\&CirclePlus;b_i$

Wherein, a _i, b _irepresenting each bit in gene code, is 1 or 0.

5. method according to claim 2, is characterized in that, described self adaptation variation probability is:

$p_m=\left\{\begin{array}{c}{p}_{\mathrm{pre}}\frac{f_{\max }-f_i}{f_{\max }-f_{\mathrm{avg}}},f\&GreaterEqual;f_{\mathrm{avg}}\\ p_{\mathrm{pre}},f<f_{\mathrm{avg}}\end{array}\right.$

Wherein, f _maxfor the maximum adaptation degree in population, f _avgfor the average fitness in population, f is maximum fitness in two intersection individualities, f _ifor the current individual fitness that makes a variation, the p of waiting _prefor the preset value between (0,1);

Described variation figure place is:

$\mathrm{Bits}=\left[l\frac{f_{\max }-f_i}{f_{\max }-f_{\min }}\right]$

Wherein, l is constant, L/4<l<L/3, and L is chromosome length, f _minfor the minimum fitness in population.

6. wireless sensor network probe node selecting system, is characterized in that, this system comprises:

First chooses unit, for using the set of all nodes of network as shaded nodes set, and using the node that there are the maximum number of degrees in described all nodes as current probe node;

Computing unit, for described current probe node and neighbor node thereof are removed from described shaded nodes set, and calculates the detective path of described current probe node to each node in described shaded nodes set;

Judging unit, for judging whether described current probe node is first probe node;

Updating block, for by described current probe node to the detective path of the each node of described shaded nodes set with selected probe node to compare to the detective path of each node in described shaded nodes set, obtain the standalone probe path that in described shaded nodes set, each node has, remove from described shaded nodes set thering is the node that is not less than k bar standalone probe path, wherein, k represents to exist in described network the number of nodes of fault;

Second chooses unit, utilizes genetic algorithm to choose next probe node according to the geographical position coordinates of each node in described shaded nodes set.