CN109699091B - Wireless sensor network system - Google Patents
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- H—ELECTRICITY
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
Abstract
A wireless sensor network system comprises a node selection device and a plurality of sensor nodes; the node selection device is respectively coupled with the plurality of sensor nodes; the node selection device is suitable for selecting partial nodes from potential coding nodes in sensor nodes in the wireless sensor network as coding nodes; the potential coding nodes are sensor nodes with the number of incident edges larger than or equal to 2 and the number of emergent edges larger than or equal to 1; the selected coding node is suitable for collecting corresponding monitoring data, coding the monitoring data and transmitting the coded monitoring data to a corresponding next node; and the other sensor nodes except the coding node are suitable for acquiring corresponding monitoring data and transmitting the corresponding monitoring data to the next corresponding node. By the scheme, the data transmission efficiency of the wireless sensor network can be improved, and the network throughput can be improved.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a wireless sensor network system.
Background
Compared with a wired communication network and an internet, the wireless sensor network has a larger scale and is everywhere. However, the wireless sensor network has the characteristics of limited energy, insufficient computing power, large scale and the like which are not possessed by the traditional network, which brings a plurality of difficulties for the research and application of the sensor network. A series of research achievements are obtained by a plurality of colleges and universities and research institutes at home and abroad through about ten years of research practice.
A multicast network in a wireless sensor network is a one-to-many network in which a source node can send messages to a plurality of sink nodes. The concept of network coding is proposed by Ahlswede R et al and formally published in 2000, and theoretical research and experiments have shown the advantages of network coding in wireless sensor networks, such as performance improvement in network throughput and robustness due to the adoption of network coding in multicast networks.
However, the conventional data transmission method in the wireless sensor network has the problem of low data transmission efficiency.
Disclosure of Invention
The invention solves the technical problem of how to improve the data transmission efficiency of the wireless sensor network and improve the network throughput.
In order to achieve the above object, the present invention provides a wireless sensor network system, which includes a node selection device and a plurality of sensor nodes; the node selection device is respectively coupled with the plurality of sensor nodes;
the node selection device is suitable for selecting partial nodes from potential coding nodes in sensor nodes in the wireless sensor network as coding nodes; the potential coding nodes are sensor nodes with the number of incident edges larger than or equal to 2 and the number of emergent edges larger than or equal to 1;
the selected coding node is suitable for collecting corresponding monitoring data, coding the monitoring data and transmitting the coded monitoring data to a corresponding next node;
and the other sensor nodes except the coding node are suitable for acquiring corresponding monitoring data and transmitting the corresponding monitoring data to the next corresponding node.
Optionally, the node selection device is adapted to convert the wireless sensor network into a directed graph network, and decompose the directed graph network by using a graph decomposition algorithm; and constructing a corresponding network coding resource optimization mathematical model based on the decomposed directed graph network, and solving an optimal solution of the network coding resource optimization mathematical model to obtain the selected coding node.
Optionally, the network coding resource optimization mathematical model constructed by the node selection device is as follows:
wherein, phi (G)NCM) Representing the number of encoding edges of the decomposed directed graph network, Min (.) representing the minimum value of solution, ξijRepresenting the jth output edge of the ith potential coding node in the decomposed directed graph network, and setting ξ when the jth output edge of the ith potential coding node performs coding operationij1, otherwise, xi is setij＝0；R(s，tk) Representing source sensor node s to destination sensor node tkAchievable multicast rate of, OiRepresenting the number of outgoing edges, p, of the ith potential coding nodei(s，tk) Representing a source sensor node s to a destination sensor node t in a decomposed directed graph networkkThe ith path of (2), γi(s，tk)＝{e|e∈pi(s，tk) Denotes a path pi(s，tk) Of all links.
Optionally, the node selecting device is adapted to initialize a chromosome cluster to obtain a corresponding initial chromosome cluster; chromosomes in the chromosome population respectively correspond to one solution of the network coding resource optimization mathematical model; calculating fitness values for each chromosome based on the current location of the chromosome in the current chromosome population; updating the historical optimal solution of each chromosome of the current chromosome population and the historical optimal solution of the chromosome population based on the fitness value of each chromosome obtained by calculation to obtain the chromosome population corresponding to the current iteration; performing selection, crossing and mutation operations on chromosomes in the chromosome population obtained by performing the current iteration; and executing the next iteration until the iteration number reaches a preset number threshold, and outputting the corresponding historical optimal solution of each chromosome and the historical optimal solution of the chromosome group as the optimal solution of the network coding resource optimization mathematical model.
Optionally, the node selecting device is adapted to calculate a fitness value of each chromosome in the chromosome group obtained by performing the current iteration, and construct a fitness array corresponding to the chromosome group obtained by performing the current iteration; calculating the probability weight of each chromosome based on the maximum fitness value and the minimum fitness value of the fitness array red; calculating to obtain an accumulated probability distribution vector corresponding to the chromosome group obtained by executing the current iteration based on the calculated probability weight of each chromosome; randomly generating N random numbers between 0 and 1, and arranging the random numbers in a sequence from small to large, wherein the process corresponds to a random vector; comparing the cumulative probability distribution vector with the value of the corresponding position in the random vector cumulative probability distribution vector, and setting X when the value of the corresponding position in the cumulative probability distribution vector is determined to be larger than the value of the corresponding position in the random vector cumulative probability distribution vectori(t+1)＝Xi(t)。
Optionally, the node selecting device is adapted to obtain a corresponding current solution from chromosomes in a chromosome population obtained after performing selection, intersection, and mutation operations before performing a next iteration, and calculate to obtain a corresponding guide solution; the current solution is an optimal solution in the chromosome population obtained after selection, crossing and mutation operations are performed; determining a difference bit between the current solution and the guided solution; carrying out track search along the current solution to the guide solution to obtain a chromosome group after track search; in the process of performing track search to the guide solution along the current solution, when one difference bit corresponds to one movement of the current solution to the guide solution, generating a corresponding number of new solutions in each movement process of the current solution to the guide solution, finding out a corresponding optimal solution from the generated new solutions, and when determining that the corresponding optimal solution is superior to a worst solution in a chromosome group obtained after selection, crossing and mutation are performed, replacing the worst solution in the chromosome group obtained after selection, crossing and mutation are performed with the corresponding optimal solution until the current solution moves to the guide solution, thereby obtaining the chromosome group after track search.
Optionally, the node selecting device is adapted to set a temporary location random chromosome, and the numerical values of all locations in the temporary location random chromosome are 1; randomly generating a chromosome group comprising N chromosomes to obtain a corresponding initial chromosome group; the chromosome of each position in the initial chromosome population is a historical optimal chromosome of the position; traversing the positions in the temporary position random chromosome according to the sequence to obtain the traversed current position; setting the numerical value of the current position in the temporary position random chromosome to be 0, and keeping the numerical values of other positions unchanged to generate a new temporary position random chromosome; and when the fitness value of the generated new temporary position random chromosome is determined to be larger than the fitness value of the temporary position random chromosome, replacing the chromosome with the worst fitness value in the initialization chromosome group with the generated new temporary position random chromosome until the number of traversed positions is larger than a preset number threshold value, and obtaining a final initial chromosome group.
Compared with the prior art, the invention has the beneficial effects that:
according to the scheme, the node selection device selects part of the nodes from the potential coding nodes in the sensor nodes in the wireless sensor network as the coding nodes, and the maximum network rate which can be achieved by network coding can be achieved by using the minimum coding edge number, so that the network throughput of the wireless sensor network can be improved, and the data transmission efficiency is improved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
Fig. 1 is a schematic structural diagram of a wireless sensor network system according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating a method for transmitting data in a wireless sensor network according to an embodiment of the present invention;
fig. 3 is a flowchart illustrating a method for selecting a part of nodes from potential coding nodes in sensor nodes in a wireless sensor network as coding nodes in an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating an exploded view of a potential coding node according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a population iteration performed by the chromosome population algorithm in an embodiment of the present invention;
fig. 6 is a schematic diagram of an example of a local search algorithm in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, 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 application. The directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship between the components, the movement, etc. in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indication is changed accordingly.
In the prior art, in a conventional wireless sensor network, a multicast router transmits data in a store-and-forward mode, which causes the throughput of the wireless sensor network to be limited to a certain extent.
In 2000, the network coding proposed by rudalfahlswede, liso and the like enables the wireless sensor network to obtain the theoretical maximum throughput, and the network coding can also save energy consumption and improve the security of data transmission.
However, the network coding operation involves complex data operation, and excessive coding consumes a large amount of computer CPU and memory resources, so that on the premise of ensuring the multicast rate, how to reduce the number of network coding times to reduce the operation cost is an important research problem, thereby increasing the data transmission rate.
According to the technical scheme, the node selection device selects part of the nodes from potential coding nodes in the sensor nodes in the wireless sensor network as the coding nodes, and the maximum network rate which can be achieved by network coding can be achieved by using the minimum coding edge number, so that the network throughput of the wireless sensor network can be improved, and the data transmission efficiency can be improved.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
For ease of understanding, the structure of the wireless sensor network system in the embodiment of the present invention will be briefly described below.
Fig. 1 is a schematic structural diagram of a wireless sensor network system according to an embodiment of the present invention. Referring to fig. 1, a wireless sensor network system may include a node selection apparatus 10 and a plurality of sensor nodes 111～11N. Wherein the node selecting device 10 is respectively connected with the plurality of sensor nodes 111～11NAnd (4) coupling.
The operation of the wireless sensor network system shown in fig. 1 will be described.
Fig. 2 is a flowchart illustrating a method for transmitting data in a wireless sensor network according to an embodiment of the present invention. Referring to fig. 2, a method for transmitting data in a wireless sensor network may specifically include the following steps:
step S201: the node selection device selects part of the nodes from potential coding nodes in the sensor nodes in the wireless sensor network as coding nodes so as to enable the minimum number of coding edges to reach the maximum network rate which can be reached by network coding.
In a specific implementation, the potential coding nodes are sensor nodes in the wireless sensor network, wherein the number of incident edges is greater than or equal to 2, and the number of emergent edges is greater than or equal to 1.
Step S202: and the selected coding node collects corresponding monitoring data, codes the monitoring data and transmits the coded monitoring data to the corresponding next node.
In specific implementation, the selected coding node is a sensor node which needs to encode the monitoring data acquired by the coding node, so that when the corresponding monitoring data is acquired, the coding node firstly encodes the acquired monitoring data and then sends the encoded monitoring data to the next node in the corresponding route.
Step S203: and other sensor nodes except the coding node acquire corresponding monitoring data and transmit the corresponding monitoring data to a corresponding next node.
In specific implementation, other sensor nodes except the selected coding node do not need to encode the monitoring data acquired by the sensor node, so that when the corresponding monitoring data is acquired, the coding node directly sends the acquired monitoring data to the next node in the corresponding route.
According to the scheme, the node selection device selects part of the nodes from the potential coding nodes in the sensor nodes in the wireless sensor network as the coding nodes, and the maximum network rate which can be achieved by network coding can be achieved by using the minimum coding edge number, so that the network throughput of the wireless sensor network can be improved, and the data transmission efficiency is improved.
A method for selecting a part of nodes as coding nodes from potential coding nodes in sensor nodes in a wireless sensor network by a node selection apparatus according to an embodiment of the present invention will be described in detail below with reference to fig. 3.
Fig. 3 is a flowchart illustrating a method for selecting a part of nodes from potential coding nodes in sensor nodes in a wireless sensor network as coding nodes in an embodiment of the present invention. Referring to fig. 2, a method for selecting a part of nodes from potential coding nodes in sensor nodes in a wireless sensor network as coding nodes may specifically include:
step S301: the method comprises the steps of converting a wireless sensor network into a directed graph network, and decomposing the directed graph network by adopting a graph decomposition algorithm.
Firstly, modeling is carried out according to the structure of the wireless sensor network, the network coding resource optimization problem is converted into an algebraic problem, a directed acyclic graph G is given as (V, E) to represent the wireless sensor network, wherein V represents a network node set, E represents a network link set, and | V | and | E | respectively represent the number of nodes and the number of links. A single source multicast network coding can be represented by (G, s, T, R), where G is a directed graph, s e V is a multicast source node,
for multicast purposes, R is the achievable multicast rate. Assuming that the output information of each node is a linear combination of the input information, the set of paths from the source s to the destination node in the multicast network G is P (s, t1), P (s, t2),.. multidot.p (s, tk),. multidot.p, P (s, t)|T|) P (s, tk) denotes a set of paths from a source point s to a destination node k, P (s, t)k)＝{p1(s，tk)，p2(s，tk)，...，pR(s，tk) And (v) setting that the nodes in the multicast tree have in (v) 2 incident edges and out (v) 1 emergent edges, wherein the nodes are called potential coding nodes and the emergent edges are called coding edges.
The directed graph is decomposed, and in the directed graph, nodes with in-degree in (v) ≧ 2 and out-degree out (v) ≧ 1 are called sinks (also called potential coding nodes). Assuming that a sink has in (v) incident edges and out (v) outgoing edges, in (v) input auxiliary nodes u are introduced1，...，ui，...，uin(v)And out (v) output auxiliary nodes w1，...wj，...，wout(v)Then at each pair of nodes (u)i，wj) An input stream is added in between.
Referring to fig. 4, the decomposition process for the sink of in (v) 2 and out (v) 2 is shown. A node V has two entrance edges and two exit edges, and can be decomposed into two entrance nodes U1, U2, and two exit nodes W1, W2. Then the vector may be used to represent the sink V as [ e11, e12, e21, e22], where e has a value of 0 or 1, where 0 indicates that the edge is connected, and where 1 indicates that the edge is not connected, and if w1 or w2 receives data from both u1 and u2, it indicates that the sink performs the network coding operation. Whether all sinks perform the coding operation can be represented by a vector of 0, 1, by which it can be determined how many nodes in the network perform the coding operation.
Step S302: and constructing a corresponding network coding resource optimization mathematical model based on the decomposed directed graph network, and solving an optimal solution of the network coding resource optimization mathematical model to obtain the selected coding node.
In specific implementation, when a decomposed directed graph network is obtained, the NCRM problem optimization objective function is:
and:
wherein, phi (G)NCM) Representing the number of encoding edges of the decomposed directed graph network, Min (.) representing the minimum value of solution, ξijRepresenting the jth output edge of the ith potential coding node in the decomposed directed graph network, and setting ξ when the jth output edge of the ith potential coding node performs coding operationij1, otherwise, xi is setij＝0；R(s，tk) Representing source sensor node s to destination sensor node tkAchievable multicast rate of, OiRepresenting the number of outgoing edges, p, of the ith potential coding nodei(s，tk) To representFrom source sensor node s to destination sensor node t in decomposed directed graph networkkThe ith path of (2), γi(s，tk)＝{e|e∈pi(s，tk) Denotes a path pi(s，tk) Of all links.
Thus, the network coding resource optimization problem is converted into an algebraic problem solving the optimal solution of the network coding resource optimization mathematical model by solving the optimal solution of equation (2).
In an embodiment of the present invention, when solving the optimal solution of the network coding resource optimization mathematical model, the chromosome cluster algorithm performs population iteration, and the population is screened by minimizing a fitness function value of a chromosome cluster, which may specifically include:
step S501: and performing initialization of the chromosome population to obtain a corresponding initial chromosome population.
In a specific implementation, the chromosomes in the chromosome population respectively correspond to a solution of the network coding resource optimization mathematical model.
In an embodiment of the present invention, in order to increase the iteration speed, an improved greedy initialization algorithm is used to initialize the chromosome group, which specifically includes:
first, a temporary position random chromosome X is settempSaid temporary location is a random chromosome XtempAll positions in (A) are 1, i.e. random chromosome XtempIs a vector of all 1 s.
Then, a chromosome population including N chromosomes is randomly generated, and a corresponding initial chromosome population X ═ is obtained (X)1，X2，...，Xi，...，XN) 1, 2, ·, N; the chromosome of each position in the initial chromosome population is the historically optimal chromosome of the position
Historical optimal chromosome, set-up representing ith position
Traversing the positions in the temporary position random chromosome in sequence to obtain the traversed current position, namely starting from j to 1; setting the current position in the temporary position random chromosome, namely the value of the j-th position to be 0, and keeping the values of other positions unchanged to generate a new temporary position random chromosome X'temp；
Then, judging whether the fitness value of the generated new temporary position stochastic chromosome is greater than that of the temporary position stochastic chromosome, and adopting the generated new temporary position stochastic chromosome X 'when the fitness value of the generated new temporary position stochastic chromosome is determined to be greater than that of the temporary position stochastic chromosome'tempAnd replacing the chromosome with the worst fitness value in the initialized chromosome population until the number of traversed positions is greater than a preset number threshold value, namely j is greater than D, and finishing the initialization operation of the chromosome population to obtain a final initial chromosome population. The value of D may be set according to actual needs, such as 10, and is not limited herein.
By adopting the chromosome population initialization algorithm, the chromosome position in the initial chromosome population obtained by initialization is closer to the final optimal position, so that the subsequent population iteration speed can be increased, and the operation resources are saved.
It is noted that, when the first iteration is performed, the chromosome population obtained in the last iteration is the initial chromosome population.
Step S502: fitness values for individual chromosomes are calculated based on the current position of the chromosomes in the current chromosome population.
In one embodiment of the present invention, the fitness value of each chromosome is calculated using the following formula:
wherein F (y) represents the fitness value of the chromosome.
Step S503: and updating the historical optimal solution of each chromosome of the current chromosome population and the historical optimal solution of the chromosome population based on the fitness value of each chromosome obtained by calculation to obtain the chromosome population corresponding to the current iteration.
In a specific implementation, the historical optimal solution of each chromosome of the current chromosome population and the historical optimal solution of the chromosome population are updated by the following formulas:
wherein, t represents the number of iterations,
representing the historical optimal solution of the ith chromosome, namely the chromosome with the minimum fitness value in the ith chromosome obtained from the 0 th iteration to the t th iteration, XgAnd (3) representing the historical optimal solution of the chromosome population, namely the chromosome with the minimum fitness value in all the historical optimal chromosomes from i to N.
Step S504: and performing selection, crossing and mutation operations on the chromosomes in the chromosome population obtained by performing the current iteration.
In an embodiment of the present invention, when performing the selection operation on the chromosome in the chromosome population obtained by performing the current iteration, the method includes the following steps:
1) and calculating a fitness value fi of each chromosome in the chromosome group obtained by executing the current iteration, and constructing a fitness array F corresponding to the chromosome group obtained by executing the current iteration [ F1, F2.
2) Maximum fitness value f based on fitness array redmaxAnd a minimum fitness value fminProbability weights for the individual chromosomes are calculated. In one embodiment of the present invention, the method is as followsCalculating to obtain probability weight f 'of each chromosome by using formula'i：
f′i＝fi/Fs (7)
And:
3) and calculating to obtain an accumulated probability distribution vector corresponding to the chromosome group obtained by executing the current iteration based on the calculated probability weight of each chromosome. In an embodiment of the present invention, the following formula is used to calculate the cumulative probability distribution vector F' corresponding to the chromosome population obtained by performing the current iteration:
F′＝[f′1，f′1+f′2，...，f′1+…+f′i，...，f′1+…+f′N] (9)
4) randomly generating N random numbers between 0 and 1 and arranging the random numbers in a sequence from small to large, wherein a random vector r corresponding to the process is [ r ═ r1，r2，...，ri，...，rN]。
5) Comparing the random vector r with a cumulative probability distribution vector F ' and determining a value (F ') of a corresponding position in the values of the corresponding position i in the cumulative probability distribution vector '1+…+f′i) A value r greater than the corresponding position i in the random vector riThen, set up Xi(t+1)＝Xi(t) until all the positions of the random vector r and the cumulative probability distribution vector F' are compared.
Then, the crossover operation is performed on the stains after the selection operation is performed up to the chromosome population. Specifically, for chromosome i, it is interchanged with the x position of the i +1 th chromosome. For example, the position is 3, Xi＝[1，0，1，0，0，1]，Xi+1＝[0，1，1，1，0，0]Then chromosome X after crossoveri＝[1，0，1，1，0，0]Wherein i is 1, 2.
Then, the execution is handed overAnd performing mutation operation on the chromosome group after the fork operation. The specific process is as follows: setting a gene mutation parameter p, and then randomly generating N random numbers between 0 and 1, wherein r is [ r ═ r [ [ r ]1，r2，...，ri，...，rN]For riIf the number of the chromosome is less than p, mutation operation is carried out on the chromosome, otherwise, no operation is carried out on the chromosome. In an embodiment of the present invention, the position and velocity of the chromosome in the mutated chromosome population are obtained by the following formula:
Vid(t+1)＝wXid(t)+F(Xrd1(t)-Xrd2(t)) (12)
wherein, Xid(t +1) denotes the d-dimensional position of the i-th chromosome after t +1 iterations, Vid(t +1) represents the d-dimensional velocity of the i-th chromosome after the t +1 iteration, w represents the weight of the t-th chromosome, Xr1(t) represents the d-dimensional position, X, of a randomly selected chromosome from the t-th generation chromosome populationr2(t) represents the d-dimensional position w of another randomly selected chromosome in the t-th generation chromosome population, F is a preset coefficient, r1 and r2 are random numbers between 1 and N, t represents the iteration number, and d represents the d-dimensional position in the chromosome.
In an embodiment of the present invention, in order to further increase the iteration speed of the chromosome population, before performing the next iteration, a local search algorithm is performed on the chromosome population obtained after performing the selection, intersection, and mutation operations, so as to further increase the convergence speed, which specifically includes:
(a) and obtaining a corresponding current solution from chromosomes in the chromosome group obtained after the selection, crossing and mutation operations are performed, and calculating to obtain a corresponding guide solution. Wherein the current solution is an optimal solution in the chromosome population obtained after the selection, crossing and mutation operations are executed, and the guide solution is obtained according to the following formula:
Sgui＝(s(a1)，...，s(ad)，...s(aD)) (8)
wherein S isguiIndicating a guided solution.
(b) Determining difference bits between the current solution and the guided solution. The difference bits between the current solution and the guiding solution refer to the number of positions with different numerical values, which are identical in position to the guiding solution and are not shown in the figure. For example, when the value of a position in the current solution is 0 and the value of the position in the guided solution is 1, the position corresponds to a difference bit.
(d) And carrying out track search along the current solution to the guide solution to obtain a chromosome group after track search. When a difference bit corresponds to one movement of the current solution to the guide solution in the process of performing track search to the guide solution along the current solution, generating a corresponding number of new solutions in each movement of the current solution to the guide solution, finding out a corresponding optimal solution from the generated new solutions, and when determining that the corresponding optimal solution is superior to a worst solution in a chromosome group obtained after performing selection, crossing and mutation operations, replacing the worst solution in the chromosome group obtained after performing the selection, crossing and mutation operations with the corresponding optimal solution until the current solution moves to the guide solution, thereby obtaining the chromosome group after track search.
In a specific implementation, executing the local search algorithm generates a corresponding optimal new solution XnewIf the optimal new solution XnewPreference to worst solution X in chromosome populationgThen use the optimal new solution XnewReplacing the worst solution in the chromosome population, otherwise, maintaining the worst solution XgRemain unchanged. In an embodiment of the present invention, in order to reduce the algorithm operation time, as long as the new solution searched by the local search algorithm is better than the global optimal solution of the chromosome group, the search is ended.
Referring to FIG. 6, let the initial solution be SiniGuided solution is SguiAt SiniAnd SguiThere are 3 different bits (bits) between, then in the first move, three new solutions S1, S2, S3 are generated, then evaluated to find the best solution, if S2, S2 is chosen for the second action, and so on, and finally point to the guided solution SguiIf the new solution generated during the movement is better than the worst solution of the chromosome population, the worst solution of the chromosome population is replaced with the optimal solution.
Step S505: judging whether the iteration times reach a preset time threshold value or not; if yes, go to step S506; otherwise, step S507 may be performed.
Step S506: and outputting the corresponding historical optimal solution of each chromosome and the historical optimal solution of the chromosome group as the optimal solution of the network coding resource optimization mathematical model.
After one iteration is executed, if the iteration times do not reach a preset time threshold value, outputting the corresponding historical optimal solution of each chromosome and the historical optimal solution of the chromosome group, namely obtaining the optimal solution of the network coding resource optimization mathematical model.
Step S507: the next iteration is performed.
In a specific implementation, after one iteration is performed, if the iteration number does not reach the preset number threshold, the next iteration is performed, that is, the execution is restarted from step S502 until the iteration number reaches the preset number threshold.
The method of the present invention is described in detail above, and a system corresponding to the method is described below.
By adopting the scheme in the embodiment of the invention, the node selection device selects part of the nodes from the potential coding nodes in the sensor nodes in the wireless sensor network as the coding nodes, and the maximum network rate which can be reached by network coding can be reached by using the minimum coding edge number, so that the network throughput of the wireless sensor network can be improved, and the data transmission efficiency can be improved.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the foregoing description only for the purpose of illustrating the principles of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the invention as defined by the appended claims, specification, and equivalents thereof.
Claims (3)
1. A wireless sensor network system is characterized by comprising a node selection device and a plurality of sensor nodes; the node selection device is respectively coupled with the plurality of sensor nodes;
the node selection device is suitable for selecting part of nodes from potential coding nodes in sensor nodes in a wireless sensor network as coding nodes, and specifically comprises the following steps: converting a wireless sensor network into a directed graph network, and decomposing the directed graph network by adopting a graph decomposition algorithm; constructing a corresponding network coding resource optimization mathematical model based on the decomposed directed graph network, and solving an optimal solution of the network coding resource optimization mathematical model to obtain a selected coding node; wherein, the constructed mathematical model for optimizing the network coding resources is as follows:
and:
wherein, phi (G)NCM) Representing the number of encoding edges of the decomposed directed graph network, Min (.) representing the minimum value of solution, ξijRepresenting ith potential in decomposed directed graph networkAt the jth output edge of the coding node, when the jth output edge of the ith potential coding node executes the coding operation, xi is setij1, otherwise, xi is setij＝0；R(s，tk) Representing source sensor node s to destination sensor node tkAchievable multicast rate of, OiRepresenting the number of outgoing edges, p, of the ith potential coding nodei(s，tk) Representing a source sensor node s to a destination sensor node t in a decomposed directed graph networkkThe ith path of (2), γi(s，tk)＝{e|e∈pi(s，tk) Denotes a path pi(s，tk) A set of all links of (a); the step of searching fox to solve the optimal solution of the network coding resource optimization mathematical model comprises the following steps: initializing a chromosome population to obtain a corresponding initial chromosome population; chromosomes in the chromosome population respectively correspond to one solution of the network coding resource optimization mathematical model; calculating fitness values for each chromosome based on the current location of the chromosome in the current chromosome population; updating the historical optimal solution of each chromosome of the current chromosome population and the historical optimal solution of the chromosome population based on the fitness value of each chromosome obtained by calculation to obtain the chromosome population corresponding to the current iteration; and performing selection, crossing and mutation operations on the chromosomes in the chromosome population obtained by performing the current iteration, wherein the step of performing selection on the chromosomes in the chromosome population obtained by performing the current iteration comprises the following steps: calculating the fitness value of each chromosome in the chromosome group obtained by executing the current iteration, and constructing a fitness array corresponding to the chromosome group obtained by executing the current iteration; calculating the probability weight of each chromosome based on the maximum fitness value and the minimum fitness value of the fitness array red; calculating to obtain an accumulated probability distribution vector corresponding to the chromosome group obtained by executing the current iteration based on the calculated probability weight of each chromosome; randomly generating N random numbers between 0 and 1, and arranging the random numbers in a sequence from small to large, wherein the process corresponds to a random vector; comparing the cumulative probability distribution vector with the value of the corresponding position in the random vector cumulative probability distribution vector, and determining the valueSetting X when the numerical value of the corresponding position in the cumulative probability distribution vector is larger than the numerical value of the corresponding position in the cumulative probability distribution vector of the random vectori(t+1)＝Xi(t); executing the next iteration until the iteration number reaches a preset number threshold, and outputting the corresponding historical optimal solution of each chromosome and the historical optimal solution of the chromosome group as the optimal solution of the network coding resource optimization mathematical model; the potential coding nodes are sensor nodes with the number of incident edges larger than or equal to 2 and the number of emergent edges larger than or equal to 1;
the selected coding node is suitable for collecting corresponding monitoring data, coding the monitoring data and transmitting the coded monitoring data to a corresponding next node;
and the other sensor nodes except the coding node are suitable for acquiring corresponding monitoring data and transmitting the corresponding monitoring data to the next corresponding node.
2. The wireless sensor network system according to claim 1, wherein the node selecting device is adapted to obtain a corresponding current solution from chromosomes in a chromosome population obtained after performing the selecting, crossing and mutation operations and calculate a corresponding guide solution before performing the next iteration; the current solution is an optimal solution in the chromosome population obtained after selection, crossing and mutation operations are performed; determining a difference bit between the current solution and the guided solution; carrying out track search along the current solution to the guide solution to obtain a chromosome group after track search; in the process of performing track search to the guide solution along the current solution, when one difference bit corresponds to one movement of the current solution to the guide solution, generating a corresponding number of new solutions in each movement process of the current solution to the guide solution, finding out a corresponding optimal solution from the generated new solutions, and when determining that the corresponding optimal solution is superior to a worst solution in a chromosome group obtained after selection, crossing and mutation are performed, replacing the worst solution in the chromosome group obtained after selection, crossing and mutation are performed with the corresponding optimal solution until the current solution moves to the guide solution, thereby obtaining the chromosome group after track search.
3. The wireless sensor network system according to claim 1 or 2, wherein the node selecting means is adapted to set a temporary location random chromosome, and the numerical values of all locations in the temporary location random chromosome are 1; randomly generating a chromosome group comprising N chromosomes to obtain a corresponding initial chromosome group; the chromosome of each position in the initial chromosome population is a historical optimal chromosome of the position; traversing the positions in the temporary position random chromosome according to the sequence to obtain the traversed current position; setting the numerical value of the current position in the temporary position random chromosome to be 0, and keeping the numerical values of other positions unchanged to generate a new temporary position random chromosome; and when the fitness value of the generated new temporary position random chromosome is determined to be larger than the fitness value of the temporary position random chromosome, replacing the chromosome with the worst fitness value in the initialization chromosome group with the generated new temporary position random chromosome until the number of traversed positions is larger than a preset number threshold value, and obtaining a final initial chromosome group.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103297983A (en) * | 2013-05-06 | 2013-09-11 | 南京邮电大学 | Wireless sensor network node dynamic deployment method based on network flow |
| EP2659471A4 (en) * | 2010-12-30 | 2014-07-23 | Sensys Networks Inc | Wireless and wireline sensor nodes, micro-radar, networks and systems |
| CN104219154A (en) * | 2014-09-22 | 2014-12-17 | 西南交通大学 | Resource optimization method under network coding environment based on ant colony optimization algorithm |
| CN107294658A (en) * | 2017-07-03 | 2017-10-24 | 电子科技大学 | A kind of coding nodes choosing method in network control coding |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090248598A1 (en) * | 2008-03-31 | 2009-10-01 | Burns Daniel J | Hardware acceleration of DNA codeword searching |
-
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- 2019-01-28 CN CN201910084121.1A patent/CN109699091B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2659471A4 (en) * | 2010-12-30 | 2014-07-23 | Sensys Networks Inc | Wireless and wireline sensor nodes, micro-radar, networks and systems |
| CN103297983A (en) * | 2013-05-06 | 2013-09-11 | 南京邮电大学 | Wireless sensor network node dynamic deployment method based on network flow |
| CN104219154A (en) * | 2014-09-22 | 2014-12-17 | 西南交通大学 | Resource optimization method under network coding environment based on ant colony optimization algorithm |
| CN107294658A (en) * | 2017-07-03 | 2017-10-24 | 电子科技大学 | A kind of coding nodes choosing method in network control coding |
Non-Patent Citations (3)
| Title |
|---|
| An Optimization Algorithm Based on GA for Multicast Network;Along Xu，Linghua Zhang;《2018 IEEE International Conference on Automation, Electronics and Electrical Engineering》;20181118;摘要，部分2、3，图1 * |
| On Minimizing Network Coding Resource: A Modified Particle Swarm Optimization Approach;Huanlai Xing,Fuhong Song;《2016 12th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)》;20161218;全文 * |
| 基于遗传算法优化的WSN覆盖控制算法;李向峰，席志红，李爽;《无线电工程》;20180517;全文 * |
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