CN119997036A - A method for constructing wireless sensor network communication links with redundant nodes - Google Patents
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Abstract
The invention relates to the technical field of Internet of things and wireless communication, and discloses a method for constructing a wireless sensor network communication link of a redundant node, which comprises the steps of defining a node deployment area and setting a sensor node set in the node deployment area; initializing algorithm parameters, calculating a network maximum link by adopting a maximum flow algorithm, initially deploying the network based on a sensor node set to generate an initial solution, determining an algorithm optimization target according to the maximum flow network link, performing loop iteration of redundant node position optimization by adopting a teaching and learning optimization algorithm based on quantum entropy, obtaining a better target solution set by each iteration, outputting the solution set of the current iteration as the optimal redundant node deployment when the maximum loop iteration is reached or a stop condition is met, and otherwise repeating the previous step. The invention not only improves the stability of the network and the continuity of data transmission under the condition of partial node failure, but also realizes the effective balance of global and local search and accelerates the convergence speed of the algorithm.
Description
Technical Field
The invention relates to the technical fields of the Internet of things and wireless communication, in particular to a method for constructing a communication link of a wireless sensor network of a redundant node.
Background
The wireless sensing network (Wireless Sensor Network, WSN) is a multi-hop self-organizing information sensing, collecting and transmitting system, and can acquire detailed and accurate data in various environments. In order to ensure stable operation of WSNs, it is critical to ensure reliability of their communication links, conventional maximum flow algorithms may be used to evaluate and optimize traffic distribution in the network, but fail to adequately consider node placement within three-dimensional space and its impact on overall network performance. Furthermore, in complex environments, it is difficult to guarantee sufficient link reliability of the network by means of only existing nodes. Therefore, it is necessary to introduce additional redundant nodes and to rationally deploy the locations of the redundant nodes to improve the fault tolerance and robustness of the network.
Disclosure of Invention
The invention aims to solve the problem of insufficient communication connectivity and reliability of the existing wireless sensor network in a complex environment, and the maximum communication capacity of the network is estimated and optimized by introducing a maximum flow algorithm, and the number of network links is maximized by dynamically and reasonably adding the number of proper redundant nodes by combining teaching and learning algorithms and quantum entropy estimation technology.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
A method for constructing a communication link of a wireless sensor network of a redundant node comprises the following steps:
S1, defining a node deployment area, and setting a sensor node set in the node deployment area;
S2, initializing algorithm parameters;
s3, calculating a maximum flow network link by adopting a maximum flow algorithm;
s4, deploying a network based on the sensor node set, and generating an initial solution;
S5, determining an algorithm optimization target according to the maximum flow network link;
S6, performing cyclic iteration of redundant node position optimization by adopting a teaching and learning optimization algorithm based on quantum entropy, and obtaining a better target solution set by each iteration;
and S7, outputting the solution set of the current iteration as the optimal redundant node deployment when the maximum loop iteration is reached or the stop condition is met, otherwise, repeating the step S6.
Further, in the step S1, a node deployment area is defined asWherein l, w, h respectively represent the dividing number in the length, width and height directionsWhereinRepresenting a preset node point of the network,Representing redundant nodes to be deployed.
Further, in S4, randomly selectingMultiple location deployment redundancy nodeConstitute an initial solution set。
Further, the step S3 comprises the steps of calculating any two nodes in the sensor node set by adopting a maximum flow algorithmMaximum flow between,And obtaining the optimal communication path set, namely the maximum flow network link.
Further, in S5, the minimum value of the maximum flow network link is taken as the algorithm optimization target fit i (t), which is specifically as follows:
。
further, in the step S2, the initialization algorithm parameters comprise an initialization population size, a maximum iteration number, a random number between the initial value of the initialization teaching factor and the initial value of the learning step length of [0, 1], the initial value of the quantum entropy weight coefficient of 1 and the initial value of the iteration number of 0.
Further, the step S6 includes:
S61, generating a new solution, namely a redundant node position set with a larger link number, by mutual cooperation of a teacher stage and a learning stage of the teaching and learning optimization algorithm:
In the teacher stage, for each of the initial solutions Generating a new solution:
;
Wherein, Is the optimal solution among the initial solutions,Is the average of all the solutions in the initial solution,Is the learning step size of the learning step,Is a teaching factor;
During the learning phase, two solutions are randomly selected from the initial solutions AndLearning each other to generate new solution:
;
Wherein f is a fitness function;
s62, calculating Euclidean probability p (x ' i) of a new solution x ' i through Euclidean distance measurement:
;
Wherein, Representing a new solutionAndA Euclidean distance metric between N represents the number of individuals in the population;
s63, collecting all new solutions into a new solution set Calculate the new solution set after each round of iterationQuantum entropy of (2):
;
Wherein, W q is a quantum entropy weight coefficient, W q linearly decreases with the number of iterations, and the decreasing formula is:
;
Wherein, Is the initial value of the initial quantum entropy weight coefficient, and the weight is set to be 1; is the final value of the quantum entropy weight coefficient, and the weight is 0.1; Maxitr is the maximum iteration number;
S64, dynamically optimizing teaching factors and learning step sizes through quantum entropy:
dynamic optimization teaching factor :
;
Wherein round is a rounding function;
dynamic optimization of learning step size :
;
S65, updating the optimized teaching factors and learning step sizes, and performing loop iteration to obtain a solution set meeting the algorithm optimization target after each iteration, namely the node layout scheme with the best current iteration。
Further, in the step S7, when the maximum iteration number is reached or the stop condition that the entropy of the continuous 50 iterations has no significant change is satisfied, the solution set of the current iteration is output as the optimal redundant node deployment, otherwise, the step S6 is repeated to continue the iteration.
Compared with the prior art, the invention has the following beneficial effects:
1. The invention enhances the connectivity and reliability of the network by introducing a maximum flow algorithm to evaluate the connectivity of the existing network and combining with the addition of a strategy of redundant nodes. The invention enhances the number of communication links of the wireless sensor network in a complex environment. The enhancement not only improves the success rate of data transmission, but also increases the tolerance of the network to faults, and ensures that the whole network can still maintain high-efficiency operation even if part of nodes fail.
2. The invention realizes the effective balance of global and local search, and can automatically adjust the balance between global exploration and local development in the search process by combining teaching and learning optimization algorithm and quantum entropy optimization technology. Specifically, in the early stage of the algorithm, the higher quantum entropy encourages wide exploration behaviors, helps to avoid trapping in a local optimal solution, gradually weakens the influence of the quantum entropy along with the increase of iteration times, turns to finer local search, accelerates the convergence speed and improves the probability of finding a global optimal solution. The method effectively solves the problem that the traditional optimization algorithm is easy to converge in premature ripening, and provides a more robust solution.
3. The invention improves the resource utilization efficiency, pays attention to reasonable deployment of redundant nodes, and aims to maximize the number of links. The influence of the position of the redundant node set on the construction of the whole network link is calculated, and the quality of the solution is evaluated according to the quantum entropy, so that the newly added redundant node can be ensured to be capable of improving the network connectivity most effectively. In addition, the method is also beneficial to prolonging the service life of the network, reducing the maintenance cost and providing powerful support for large-scale deployment in practical application.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present invention more apparent. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the method for constructing a communication link of a wireless sensor network of a redundant node provided by the invention is characterized by comprising the following steps:
S1, defining a node deployment area, and setting a sensor node set in the node deployment area;
S2, initializing algorithm parameters;
S3, calculating a network maximum link by adopting a maximum flow algorithm;
s4, initially deploying a network based on the sensor node set, and generating an initial solution;
S5, determining an algorithm optimization target according to the maximum flow network link;
S6, performing cyclic iteration of redundant node position optimization by adopting a teaching and learning optimization algorithm based on quantum entropy, and obtaining a better target solution set by each iteration;
and S7, outputting the solution set of the current iteration as the optimal redundant node deployment when the maximum loop iteration is reached or the stop condition is met, otherwise, repeating the step S6.
The method not only improves the stability of the network and the continuity of data transmission under the condition of partial node failure, but also realizes the effective balance of global and local search, accelerates the algorithm convergence speed and improves the probability of finding the global optimal solution. In addition, the invention pays attention to the utilization efficiency of resources, ensures that the newly added redundant node can most effectively improve the network performance, and simultaneously minimizes the energy consumption and other resource costs, thereby prolonging the service life of the network and reducing the maintenance cost.
The invention discloses a three-dimensional space node deployment, wherein the S1 is a three-dimensional space node deployment area which is divided into l multiplied by w multiplied by h pixel points to form a point set R a, sensor nodes monitor the area in the node deployment area,Wherein l, w, h respectively represent the dividing number in the length, width and height directionsWhereinRepresenting a preset node point of the network,Representing redundant nodes to be deployed.
The invention S2 is the initialization of algorithm parameters, namely initializing population size PS, maximum iteration number Maxitr and initializing teaching factorsInitial value of 1, learning step lengthThe initial value of (2) is a random number between [0,1], and the quantum entropy weight coefficientThe initial value of (1) and the initial value of the iteration number t is 0. Teaching factorAnd generating a new solution for the teacher stage, wherein the value of the new solution changes along with the iteration times. Learning step sizeThe new solution for the learning phase is generated with values that also vary with the number of iterations. Quantum entropy weight coefficientThe value of (2) decreases linearly with the number of iterations to achieve a natural transition from exploration to development.
The S4 is the initial deployment of the network and the generation of the initial solution, namely, the initial solution is deployed in the networkThe existing nodes, i.e. preset nodesRandomly selectingMultiple location deployment redundancy nodeConstitute an initial solution setThereby ensuring the maximum connectivity of the network.
The invention S3 is the calculation of the maximum flow network link, which adopts the maximum flow algorithm (Edmonds-Karp method) to calculate any two nodes in the sensor node setMaximum flow between,And obtaining the optimal communication path set, namely the maximum flow network link. Thereby calculating the overall connectivity of the network.
The S5 is the determination of an algorithm optimization target, wherein the overall optimization target takes the minimum value of the maximum current value aggregate between any two nodes as an optimization target fit i (t), and the method specifically comprises the following steps:
。
The calculating of the solution set with the highest fitness value in the S6 of the invention comprises the following steps:
S61, generating a new solution, namely a redundant node position set with a larger link number, by mutual cooperation of a teacher stage and a learning stage of the teaching and learning optimization algorithm:
In the teacher stage, for each of the initial solutions Generating a new solution:
;
Wherein, Is the optimal solution among the initial solutions,Is the average of all the solutions in the initial solution,Is the learning step size of the learning step,Is a teaching factor;
During the learning phase, two solutions are randomly selected from the initial solutions AndLearning each other to generate new solution:
;
Wherein f is a fitness function;
s62, calculating Euclidean probability p (x ' i) of a new solution x ' i through Euclidean distance measurement:
;
Wherein, Representing a new solutionAndA Euclidean distance metric between N represents the number of individuals in the population;
The probability is calculated based on the Euclidean distance metric between the solutions. If two solutions are very close, they may be considered similar and given a lower probability, whereas a farther solution is given a higher probability to encourage exploration of a wider solution space.
S63, collecting all new solutions into a new solution setCalculate the new solution set after each round of iterationQuantum entropy of (2):
;
Wherein, W q is a quantum entropy weight coefficient, W q linearly decreases with the number of iterations, and the decreasing formula is:
;
Wherein, Is the initial value of the initial quantum entropy weight coefficient, and the weight is set to be 1; is the final value of the quantum entropy weight coefficient, and the weight is 0.1; Maxitr is the maximum iteration number;
s64, dynamically optimizing teaching factors and learning step sizes through quantum entropy, so that the algorithm highlights global searching capability in the early stage and highlights local searching capability in the later stage, and global and local searching balance is achieved:
dynamic optimization teaching factor :
;
Wherein round is a rounding function;
dynamic optimization of learning step size :
;
S65, updating the optimized teaching factors and learning step sizes, and performing loop iteration to obtain a solution set meeting the algorithm optimization target after each iteration, namely the node layout scheme with the best current iteration。
According to the invention, S6 adopts a teaching and learning optimization algorithm based on quantum entropy, and the quantum entropy is introduced into the teaching and learning optimization algorithm to serve as a key factor for guiding new solution generation and step length control, so that more efficient global and local search balance is realized. In addition, a proper amount of redundant nodes are added through a quantum entropy teaching and learning optimization algorithm, the number of links is maximized, and the reliability of the network is enhanced.
The method comprises the steps of S7, when the maximum iteration times are reached or the stop condition that the entropy of the continuous 50 iterations is not changed significantly is met, outputting a solution set of the current iteration as the optimal redundant node deployment, otherwise repeating S6, and continuing iteration.
It should be noted that the foregoing embodiments are merely preferred embodiments of the present invention, and are not intended to limit the scope of the present invention, but are not intended to limit the scope of the present invention, and although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions described in the foregoing embodiments may be modified or some or all of the technical features may be replaced, and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, that is, the technical problems solved by the present invention are still consistent with the main design concept and spirit of the present invention, and all technical solutions are included in the scope of the present invention.
Claims (8)
1. A method for constructing a communication link of a wireless sensor network of a redundant node is characterized by comprising the following steps:
S1, defining a node deployment area, and setting a sensor node set in the node deployment area;
S2, initializing algorithm parameters;
s3, calculating a maximum flow network link by adopting a maximum flow algorithm;
s4, initially deploying a network based on the sensor node set, and generating an initial solution;
S5, determining an algorithm optimization target according to the maximum flow network link;
S6, performing cyclic iteration of redundant node position optimization by adopting a teaching and learning optimization algorithm based on quantum entropy, and obtaining a better target solution set by each iteration;
and S7, outputting the solution set of the current iteration as the optimal redundant node deployment when the maximum loop iteration is reached or the stop condition is met, otherwise, repeating the step S6.
2. The method for constructing a communication link of a wireless sensor network of a redundant node according to claim 1, wherein in S1, a node deployment area is defined asWherein l, w, h respectively represent the dividing number in the length, width and height directionsWhereinRepresenting a preset node point of the network,Representing redundant nodes to be deployed.
3. The method for constructing a communication link of a wireless sensor network of a redundant node according to claim 2, wherein in S4, the method is selected randomlyMultiple location deployment redundancy nodeConstitute an initial solution set。
4. The method for constructing a communication link of a wireless sensor network of a redundant node according to claim 2, wherein S3 comprises calculating any two nodes in the set of sensor nodes by using a maximum flow algorithmMaximum flow between,And obtaining the optimal communication path set, namely the maximum flow network link.
5. The method for constructing a communication link of a wireless sensor network of a redundant node according to claim 4, wherein in S5, a minimum value of a maximum flow network link is used as an algorithm optimization target fit i (t), specifically as follows:
。
6. The method for constructing a communication link of a wireless sensor network of a redundant node according to claim 3, wherein in S2, the initialization algorithm parameters include an initialization population size, a maximum iteration number, an initial value of an initialization teaching factor of 1, an initial value of a learning step of [0, 1], an initial value of a quantum entropy weight coefficient of 1, and an initial value of an iteration number of 0.
7. The method for constructing a communication link of a wireless sensor network of a redundant node according to claim 6, wherein S6 comprises:
S61, generating a new solution, namely a redundant node position set with a larger link number, by mutual cooperation of a teacher stage and a learning stage of the teaching and learning optimization algorithm:
In the teacher stage, for each of the initial solutions Generating a new solution:
;
Wherein, Is the optimal solution among the initial solutions,Is the average of all the solutions in the initial solution,Is the learning step size of the learning step,Is a teaching factor;
During the learning phase, two solutions are randomly selected from the initial solutions AndLearning each other to generate new solution:
;
Wherein f is a fitness function;
s62, calculating Euclidean probability p (x ' i) of a new solution x ' i through Euclidean distance measurement:
;
Wherein, Representing a new solutionAndA Euclidean distance metric between N represents the number of individuals in the population;
s63, collecting all new solutions into a new solution set Calculate the new solution set after each round of iterationQuantum entropy of (2):
;
Wherein, W q is a quantum entropy weight coefficient, W q linearly decreases with the number of iterations, and the decreasing formula is:
;
Wherein, Is the initial value of the initial quantum entropy weight coefficient, and the weight is set to be 1; is the final value of the quantum entropy weight coefficient, and the weight is 0.1; Maxitr is the maximum iteration number;
S64, dynamically optimizing teaching factors and learning step sizes through quantum entropy:
dynamic optimization teaching factor :
;
Wherein round is a rounding function;
dynamic optimization of learning step size :
;
S65, updating the optimized teaching factors and learning step sizes, and performing loop iteration to obtain a solution set meeting the algorithm optimization target after each iteration, namely the node layout scheme with the best current iteration。
8. The method for constructing a communication link of a wireless sensor network of a redundant node according to claim 6, wherein in S7, when the maximum iteration number is reached or a stop condition that the entropy of 50 consecutive iterations has no significant change is satisfied, the solution set of the current iteration is output as the optimal redundant node deployment, otherwise, repeating S6 to continue iteration.
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