CN104202766B - Wireless sensor network probe node choosing method and system - Google Patents
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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 invention relates to the technical field of wireless sensor networks, in particular to a method and a system for selecting a detection node of a wireless sensor network.
Background
Node failures of a wireless sensor network are classified into communication failures and data failures. The communication fault is originated from the fault of the node communication module or the fault of the communication link (data packet loss or delay caused by the congestion of the link, etc.), and the data fault is mainly caused by the sensing error of the sensor module, so that the monitoring data collected and reported by the node is abnormal. The monitoring technology for wireless sensor network faults mainly includes two types: passive and active. The passive monitoring technology is applied to detection of data faults and mainly refers to a distributed data fault detection scheme based on neighbor cooperation. The active monitoring technology is mainly applied to detecting the communication fault of the node and detecting the data loss, delay and routing state of the network. The active detection means that a detection node sends a specific data packet to a network, and the state of a specific area of the network is judged by monitoring the feedback condition of the data packet.
The selection of the probing nodes is used as the first step of the active probing implementation, and the selection of appropriate nodes as probing nodes is completed to send probes to all interested areas in the network. Kumar et al propose a binary method to compute an effective node set. The method calculates the edge set which can be detected by one node under all routing conditions, and effectively reduces the number of nodes for detecting the network edge set. And J.D.Horton researches an optimized and systematic node arrangement method and analyzes the overall properties of the node set coverage network on the basis of theoretical level and experience. Jamin et al propose an intelligent distribution method for detecting nodes under the condition of variable traffic in the network.
When a fault node exists in the network, other nodes cannot be reached, and the node selection and deployment mechanism in the method does not consider the influence caused by the possible node fault in the network, so that the method cannot be applied to an active detection technology for fault detection.
Disclosure of Invention
Aiming at the defects of the prior art, the method for selecting the detection nodes of the wireless sensor network provided by the invention actively detects the node faults in the wireless sensor network and improves the reachable rate of the detected nodes in the network by selecting the optimized node set.
In a first aspect, the present invention provides a method for selecting a wireless sensor network detection node, where the method includes:
s1: taking a set of all nodes in a network as a shadow node set, and taking a node with the maximum degree in all the nodes as a current detection node;
s2: removing the current detection node and the neighbor nodes thereof from the shadow node set, and calculating a detection path from the current detection node to each node in the shadow node set;
s3: judging whether the current detection node is the first detection node, if so, executing a step S5, otherwise, executing a step S4;
s4: comparing a detection path from the current detection node to each node in the shadow node set with a detection path from a selected detection node to each node in the shadow node set to obtain an independent detection path from each node in the shadow node set, and removing nodes with no less than k independent detection paths from the shadow node set, wherein k represents the number of nodes with faults in the network;
s5: and selecting a next detection node by using a genetic algorithm according to the geographical position coordinates of each node in the shadow node set, taking the selected detection node as a new current detection node, and returning to the step S2 until the number of the selected detection nodes reaches a preset upper limit value or the shadow node set is empty.
Preferably, selecting the next probe node by using a genetic algorithm according to the geographical position coordinates of each node in the shadow node set comprises:
randomly selecting a plurality of nodes from the shadow node set as an initial population, and carrying out binary coding on the geographic positions of the nodes to obtain individual genes;
calculating the fitness of an individual according to the independent detection paths of the nodes in the shadow node set;
calculating the self-adaptive cross probability of the individuals and the irrelevance between the individuals according to the fitness of the individuals, establishing cross individual combination and finishing population propagation;
completing individual variation process according to the adaptive variation probability and variation digit of individuals after population reproduction to generate new individuals;
and judging whether the iteration times reach a set value, if so, selecting the individual with the highest fitness in the current population as the selected detection node, and otherwise, continuing the iteration.
Preferably, the calculating the fitness of the individual comprises:
traversing the shadow node set SN, and adding paths from the current node to be selected to all the shadow nodes into the set PS;
traversing a detection path set PT from the selected node to all the shadow nodes, and if two paths which have the same destination shadow node and are mutually independent exist in the PT and the PS, adding 1 to the accumulated independent detection path number m of the shadow node;
when m is greater than or equal to k, the number of released shadow nodes rN is added by 1, where k represents the number of failed nodes present in the network;
after the traversal is finished, f ═ rN exp (energy) is taken as the fitness value of the node, wherein energy represents the residual capacity of the node.
Preferably, the individual adaptive cross probability is:
wherein p ispreThe default value is 0.8; f is the fitness of the individual,is the mean fitness within the population, fmaxIs the maximum fitness within the population;
the irrelevance between individuals is:
wherein, ai、biEach bit in the gene code is represented as 1 or 0.
Preferably, the adaptive mutation probability is:
wherein f ismaxIs the maximum fitness within the population, favgIs the average fitness within the population, f' is the maximum fitness of two crossed individuals, fiThe fitness of the current individual to be mutated, ppreIs a preset value between (0, 1);
the number of variation bits is:
wherein L is constant, L/4 < L < L/3, L is chromosome length, fminIs the minimum fitness within the population.
The wireless sensor network detects node and chooses the system, this system includes:
the system comprises a first selection unit, a second selection unit and a third selection unit, wherein the first selection unit is used for taking a set of all nodes in a network as a shadow node set and taking a node with the maximum degree in all the nodes as a current detection node;
a calculating unit, configured to remove the current probe node and its neighboring nodes from the shadow node set, and calculate a probe path from the current probe node to each node in the shadow node set;
a judging unit, configured to judge whether the current probe node is a first probe node;
an updating unit, configured to compare a probe path from the current probe node to each node in the shadow node set with a probe path from a selected probe node to each node in the shadow node set, to obtain an independent probe path from each node in the shadow node set, and remove a node having no less than k independent probe paths from the shadow node set, where k represents the number of nodes with a fault in the network;
and the second selection unit is used for selecting the next detection node by using a genetic algorithm according to the geographical position coordinates of each node in the shadow node set.
Based on the technical scheme, the method for selecting the detection node of the wireless sensor network provided by the invention actively detects the node fault in the wireless sensor network, and covers the whole network as far as possible by using the minimum detection node set, so that the most reliable detection result is obtained for positioning and judging the fault node.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a method for selecting a detection node in a wireless sensor network according to an embodiment of the present invention;
FIG. 2 is a flow chart of an improved genetic algorithm provided by another embodiment of the present invention;
FIG. 3 is a flow chart of a process for calculating fitness of an individual according to another embodiment of the present invention;
fig. 4 is a schematic structural diagram of a wireless sensor network detection system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, fig. 1 is a flowchart illustrating a method for selecting a wireless sensor network detection node according to an embodiment of the present invention, where the method includes the following steps:
step S1: and taking the set of all nodes in the network as a shadow node set, and taking the node with the maximum degree in all the nodes as a current detection node.
In this embodiment, the shadow node set is initialized to the set of all nodes in the network, the candidate node set is initialized to the set of all nodes in the network, and the selected node is initialized to null.
Since the node with the maximum degree has the maximum number of one-hop neighbors, and the neighbor nodes of the node can be directly detected, the shadow nodes can be eliminated to the maximum extent.
Step S2: and removing the current detection node and the neighbor nodes thereof from the shadow node set, and calculating a detection path from the current detection node to each node in the shadow node set.
Specifically, assuming that the sink node of the management center grasps the topology structure of the whole network and the real-time electric quantity information of each node, a detection path from a node to a certain node can be calculated by using a routing algorithm, and the purpose of the step is to provide a basis for the subsequent judgment of the shadow node.
In this step, the step of calculating the detection path from the current detection node to each node in the shadow node set is as follows:
(1) calculate estimated cost to shadow node: the estimation cost is a value obtained by normalizing the node according to the distance from the node to the target area and the residual energy of the node, and the calculation mode is as follows: c (N, R) ═ ad (N, R) + (1-a) e (N). Wherein c (N, R) represents the estimation cost of the event region R of the node N, d (N, R) represents the distance from the node N to the event region, e (N) represents the residual energy of the node N, and a is a normalized proportion parameter.
(2) And after the nodes send the detection, selecting the node with the minimum replacement value information value as a next hop node in the neighbor nodes by using a greedy algorithm.
(3) And setting the own routing cost as the next hop node cost plus the own one hop communication cost.
(4) And for the routing void problem which possibly occurs, namely, when a node finds that the cost value of the neighbor node is larger than that of the node, the neighbor node with the minimum cost information is selected to forward, and the node leaves the void.
Step S3: judging whether the current detection node is the first detection node, if so, executing a step S5, otherwise, executing a step S4;
step S4: and comparing the detection path from the current detection node to each node in the shadow node set with the detection path from the selected detection node to each node in the shadow node set to obtain an independent detection path from each node in the shadow node set, and removing nodes with no less than k independent detection paths from the shadow node set, wherein k represents the number of nodes with faults in the network.
In particular, an independent probe path means that two probe paths reaching the same destination node are independent of each other if and only if there is no same forward node, i.e. they are disjoint. Based on the definition of the independent probing paths, on the premise that there are k failed nodes in the default network, nodes that are not neighbors of the probing node and contain less than k independent probing paths are called shadow nodes. Therefore, the shadow node elimination is the main purpose of probe node selection, and the number of shadow nodes is gradually reduced along with the continuous determination of the probe nodes.
Optionally, the PT represents a set of probe paths from the selected probe node to each node in the shadow node set, and the PS represents a set of probe paths from the current probe node to each node in the shadow node set, and this step includes:
traversing PT, if the traversed detection path and a certain detection path in the PS have the same target shadow node and are mutually independent, adding 1 to the number N of the independent detection paths of the shadow node;
if N is greater than or equal to k, removing the shadow node from the shadow node set; where k represents the number of nodes in the network that have a failure.
Put PS into PT.
Traversing a set of probe paths from the selected probe node to each node in the set of shadow nodes,
step S5: and selecting a next detection node by using a genetic algorithm according to the geographical position coordinates of each node in the shadow node set, taking the selected detection node as a new current detection node, and returning to the step S2 until the number of the selected detection nodes reaches a preset upper limit value or the shadow node set is empty.
In this embodiment, as shown in fig. 2, fig. 2 shows a method for selecting a next probe node by using a genetic algorithm according to the geographic location coordinates of each node in the shadow node set in step S5, where the method includes the following steps:
step 201: and randomly selecting a plurality of nodes from the shadow node set as an initial population, and carrying out binary coding on the geographic positions of the nodes to obtain individual genes.
Specifically, the method comprises the following steps of firstly, randomly selecting four nodes as an initial population, and carrying out gene coding on individuals of the initial population according to geographic coordinates, wherein the coding rule is as follows: and converting the coordinate vector (x, y) of the node into binary numbers and combining the binary numbers with high and low bits. Nodes that are geographically close have similar routing characteristics and therefore the number of shadow nodes released when acting as nodes is also similar.
Step 202: and calculating the fitness of the individual according to the independent detection paths of the nodes in the shadow node set.
The main content of the individual fitness function is to calculate the number of shadow nodes which can be eliminated by the current candidate node (individual), and the shadow nodes are weighted by the residual energy of the candidate node and then serve as the return value of the function. The design also considers the influence of energy problems on node selection.
Step 203: and calculating the self-adaptive cross probability of the individuals and the irrelevance between the individuals according to the fitness of the individuals, establishing a cross individual combination and finishing population propagation.
Specifically, in this step, a regenerative individual is first selected according to fitness. The probability that the individual with high fitness is selected is high, and the probability that the individual with low fitness is eliminated is high. The selection process uses roulette selection, which requires multiple rounds of selection in order to select mating individuals. Each round generates a [0,1] uniform random number that is used as a selection pointer to identify the selected individual. Table 1 shows the process of selecting four individuals, and the final result shows that individuals with higher fitness retain more times through four rounds of random selection. The cumulative probabilities in the table constitute four probability intervals, and the individual corresponding to each interval is retained once in which interval the random number falls.
TABLE 1 Individual selection Process
And then generating a new individual according to a certain cross probability and a certain cross method. The invention provides a self-adaptive crossing strategy, which is characterized in that: the crossing probability of each individual is determined by fitness, and the set of individuals which can be crossed is determined in a random manner. Non-crossover individuals remain directly to the next generation. The crossed individuals can be subjected to the irrelevance operation between every two individuals, and N groups with the lowest correlation are selected for crossing.
The adaptive crossover probability is defined as:
wherein p ispreThe default value is 0.8; f is the fitness of the individual,is the mean fitness within the population, fmaxIs the maximum fitness within the population. After the crossing probability of the individuals is determined, whether the individuals have crossing authority or not is determined in a random selection mode.
Assuming that the number of individuals in the population is N and the number of individuals not subjected to crossover is R, N-R next individuals need to be generated by crossover. And for N-R individuals to be crossed, calculating the irrelevance between every two individuals and sorting according to the irrelevance, and selecting N groups with the lowest relativity for crossing to obtain N-R next-generation individuals.
Individual independence is defined as:
wherein,for XOR operator, n is the individual code length, ai、biEach bit in the gene code is represented as 1 or 0. The irrelevance represents the degree of difference between individuals.
And after selecting the individual combination for crossing, carrying out crossing operation according to the following uniform crossing operator to obtain the next generation of individuals. Unlike traditional single-point and multi-point crossover, uniform crossover is more generalized, and each gene node serves as a potential crossover point. A 0-1 mask is randomly generated that is equal in length to the individuals, with the segments in the mask indicating which parent provides the variable values to the children. And selecting corresponding bits by using the mask to combine to generate a new individual.
The specific steps are as follows:
1) calculating an adaptive cross probability p for an individualc。
2) D individuals with lower crossing probability are selected for crossing, and pairing before crossing is carried out through pairwise combination.
3) And calculating the irrelevance of each group of pairings, and selecting the D/2 combination with the lowest correlation for crossing.
4) And calculating mask samples aiming at each crossing group, and exchanging genes at the appointed positions of the parents according to the mask samples to complete crossing. Consider two individuals with 16-bit variables:
parent 1: 0100110010010101
Parent individual 2: 1001101001100001
Mask samples (1 indicates parent 1 provides variable values, 0 indicates parent 2 provides variable values):
sample 1: 0110001101011010
Sample 2: 1001110010100101
The two new individuals after crossover were:
sub-individuals 11101100000110001
Sub-individuals 20000111011000101
Step 204: and finishing the individual variation process according to the self-adaptive variation probability and variation digit of the individuals after population propagation to generate new individuals.
Wherein, new individuals are generated according to a certain mutation probability and a mutation method. The invention provides a method for determining the number of variation digits according to the quality condition of an individual, namely, the individual with low fitness varies a plurality of genes, and the individual with high fitness adopts less variation or no variation, so that a plurality of variation combinations can be formed, and the search space is enlarged. The adaptive mutation probability is defined as follows:
wherein f ismaxIs the maximum fitness within the population, favgIs the average fitness within the population, f' is the maximum fitness of two crossed individuals, fiThe fitness of the current individual to be mutated, ppreIs a preset value between (0, 1); the number of variation bits is given by:
wherein L is constant, L/4 < L < L/3, L is chromosome length, fminIs the minimum fitness within the population; f. ofmax-fminIndicating the fitness range of the current population; f. ofmax-fiIndicating the distance between the fitness of the individual and the maximum fitness;indicating the degree of goodness of the individual in the current population.
The method comprises the following specific steps:
1) generating a random number s if s is less than pmContinuing the step 2; otherwise, directly returning to the gene sequence X of the current individual, and ending the variation.
2) A full 1 sequence of Bits length is generated, then 0 is randomly filled into the gaps between the existing Bits, and L-Bits turns are filled in total, and finally a mutation mask M is generated, as shown in table 2, assuming that Bits is 5 and L is 16.
Table 2 variant mask generation procedure
| Round | Current sequence | Insert position |
| 0 | 11111 | 11↓111 |
| 1 | 110111 | 110111↓ |
| 2 | 1101110 | 11011↓10 |
| … | … | … |
| 11 | 0100101000100010 | \ |
3) And carrying out XOR operation on the generated mutation mask M and the X to obtain a final mutation gene sequence, thereby completing the mutation process. Wherein X represents the gene sequence of an individual.
Step 205: and judging whether the iteration number reaches a set value, if so, turning to a step 206, and otherwise, turning to a step 203 to continue the iteration.
Step 206: and selecting the individual with the highest fitness in the current population as the selected detection node.
Specifically, the individual with the highest fitness, that is, the node capable of releasing the most shaded nodes, is selected from the current population as the node selected in the current round.
Specifically, as shown in fig. 3, fig. 3 shows a flowchart for calculating fitness of an individual in step 202, comprising the following steps:
step 201: and the number rN of the shadow nodes which are initially released is the degree of the current node S to be selected.
Step 202: and traversing the shadow node set SN, calculating detection paths from the current node to be selected to all the shadow nodes, and adding the set PS.
Step 203: and judging whether the detection path set PT from the selected node to all the shadow nodes is empty, if so, turning to a step 210, and otherwise, turning to a step 204.
Step 204: and traversing the probe path set PT from the selected node to all the shadow nodes.
Step 205: and for each traversal object in the PT, judging whether a path which has the same destination shadow node with the PS and is independent of the PS exists, if so, turning to a step 406, otherwise, turning to the step 204.
Step 206: adding 1 to the accumulated number m of independent probing paths of the shadow node;
step 207: judging whether m is greater than or equal to k, if so, turning to step 208; otherwise go to step 204. Where k represents the number of failed nodes present in the network
Step 208: the number of shadow nodes released rN is increased by 1.
Step 209: and judging whether the PT traversal is finished, if so, turning to a step 210, and otherwise, turning to a step 204.
Step 210: taking f (rN) EXP (energy) as the fitness value of the node, where energy represents the remaining power of the node, EXP represents an exponential function with a natural constant e as a base, and EXP (energy) represents the energy of the natural constant e to the power.
Therefore, the method for selecting the detection node of the wireless sensor network provided by the embodiment actively detects the node fault in the wireless sensor network, covers the whole network as far as possible by using the minimum detection node set, and obtains the most reliable detection result for positioning and judging the fault node. The method selects an optimized node set through a genetic algorithm to improve the reachable rate of the tested nodes in the network.
As shown in fig. 4, fig. 4 is a structural diagram illustrating a wireless sensor network detecting node selecting system according to an embodiment of the present invention, where the system includes a first selecting unit 401, a calculating unit 402, a determining unit 403, an updating unit 404, and a second selecting unit 405.
A first selecting unit 401, configured to use a set of all nodes in a network as a shadow node set, and use a node with a maximum degree in all nodes as a current probe node;
a calculating unit 402, configured to remove the current probe node and its neighboring nodes from the shadow node set, and calculate a probe path from the current probe node to each node in the shadow node set;
a determining unit 403, configured to determine whether the current probe node is a first probe node;
an updating unit 404, configured to compare a probe path from the current probe node to each node in the shadow node set with a probe path from a selected probe node to each node in the shadow node set, to obtain an independent probe path from each node in the shadow node set, and remove a node having no less than k independent probe paths from the shadow node set, where k represents the number of nodes with a fault in the network;
and a second selecting unit 405, selecting a next detection node by using a genetic algorithm according to the geographical position coordinates of each node in the shadow node set.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art; the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (5)
1. The method for selecting the detection node of the wireless sensor network is characterized by comprising the following steps:
s1: taking a set of all nodes in a network as a shadow node set, and taking a node with the maximum degree in all the nodes as a current detection node;
s2: removing the current detection node and the neighbor nodes thereof from the shadow node set, and calculating a detection path from the current detection node to each node in the shadow node set;
s3: judging whether the current detection node is the first detection node, if so, executing a step S5, otherwise, executing a step S4;
s4: comparing a detection path from the current detection node to each node in the shadow node set with a detection path from a selected detection node to each node in the shadow node set to obtain an independent detection path from each node in the shadow node set, and removing nodes with no less than k independent detection paths from the shadow node set, wherein k represents the number of nodes with faults in the network;
s5: selecting a next detection node by using a genetic algorithm according to the geographical position coordinates of each node in the shadow node set, taking the selected detection node as a new current detection node, and returning to the step S2 until the number of the selected detection nodes reaches a preset upper limit value or the shadow node set is empty;
wherein the selecting a next probe node using a genetic algorithm according to the geographic location coordinates of each node in the set of shadow nodes comprises:
randomly selecting a plurality of nodes from the shadow node set as an initial population, and carrying out binary coding on the geographic positions of the nodes to obtain individual genes;
calculating the fitness of an individual according to the independent detection paths of the nodes in the shadow node set;
calculating the self-adaptive cross probability of the individuals and the irrelevance between the individuals according to the fitness of the individuals, establishing cross individual combination and finishing population propagation;
completing individual variation process according to the adaptive variation probability and variation digit of individuals after population reproduction to generate new individuals;
judging whether the iteration times reach a set value, if so, selecting an individual with highest fitness in the current population as a selected detection node, and otherwise, continuing to perform iteration;
the independent detection paths refer to that two detection paths reaching the same destination node are independent of each other and only if there is no same forward node, that is, the two detection paths are not intersected.
2. The method of claim 1, wherein calculating the fitness of the individual comprises:
traversing the shadow node set SN, and adding paths from the current node to be selected to all the shadow nodes into the set PS;
traversing a detection path set PT from the selected node to all the shadow nodes, and if two paths which have the same destination shadow node and are mutually independent exist in the PT and the PS, adding 1 to the accumulated independent detection path number m of the shadow node;
when m is greater than or equal to k, the number of released shadow nodes rN is added by 1, where k represents the number of failed nodes present in the network;
after the traversal is finished, f ═ rN exp (energy) is taken as the fitness value of the node, wherein energy represents the residual capacity of the node.
3. The method of claim 1, wherein the individual adaptive cross probabilities are:
wherein p ispreThe default value is 0.8; f is the fitness of the individual,is the mean fitness within the population, fmaxIs the maximum fitness within the population;
the irrelevance between individuals is:
wherein, ai、biEach bit in the gene code is represented as 1 or 0.
4. The method of claim 1, wherein the adaptive mutation probability is:
wherein f ismaxIs the maximum fitness within the population, favgIs the average fitness within the population, f' is the maximum fitness of two crossed individuals, fiThe fitness of the current individual to be mutated, ppreIs a preset value between (0, 1);
the number of variation bits is:
wherein L is constant, L/4 < L < L/3, L is chromosome length, fminIs the minimum fitness within the population.
5. Wireless sensor network surveys node and chooses system, its characterized in that, this system includes:
the system comprises a first selection unit, a second selection unit and a third selection unit, wherein the first selection unit is used for taking a set of all nodes in a network as a shadow node set and taking a node with the maximum degree in all the nodes as a current detection node;
a calculating unit, configured to remove the current probe node and its neighboring nodes from the shadow node set, and calculate a probe path from the current probe node to each node in the shadow node set;
a judging unit, configured to judge whether the current probe node is a first probe node;
an updating unit, configured to compare a probe path from the current probe node to each node in the shadow node set with a probe path from a selected probe node to each node in the shadow node set, to obtain an independent probe path from each node in the shadow node set, and remove a node having no less than k independent probe paths from the shadow node set, where k represents the number of nodes with a fault in the network;
the second selection unit is used for selecting the next detection node by using a genetic algorithm according to the geographical position coordinate of each node in the shadow node set;
wherein the selecting a next probe node using a genetic algorithm according to the geographic location coordinates of each node in the set of shadow nodes comprises:
randomly selecting a plurality of nodes from the shadow node set as an initial population, and carrying out binary coding on the geographic positions of the nodes to obtain individual genes;
calculating the fitness of an individual according to the independent detection paths of the nodes in the shadow node set;
calculating the self-adaptive cross probability of the individuals and the irrelevance between the individuals according to the fitness of the individuals, establishing cross individual combination and finishing population propagation;
completing individual variation process according to the adaptive variation probability and variation digit of individuals after population reproduction to generate new individuals;
judging whether the iteration times reach a set value, if so, selecting an individual with highest fitness in the current population as a selected detection node, and otherwise, continuing to perform iteration;
the independent detection paths refer to that two detection paths reaching the same destination node are independent of each other and only if there is no same forward node, that is, the two detection paths are not intersected.
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| CN102111789A (en) * | 2010-12-24 | 2011-06-29 | 北京邮电大学 | Method for repairing fault of wireless sensor network |
| CN103945508A (en) * | 2014-02-24 | 2014-07-23 | 浙江理工大学 | Wireless-sensing-network topology construction method based on probability comparison |
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| CN102111789A (en) * | 2010-12-24 | 2011-06-29 | 北京邮电大学 | Method for repairing fault of wireless sensor network |
| CN103945508A (en) * | 2014-02-24 | 2014-07-23 | 浙江理工大学 | Wireless-sensing-network topology construction method based on probability comparison |
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