Wireless sensor network communication link construction method of redundant nodes

Abstract

The invention relates to the technical field of Internet of Things and wireless communication, and discloses a wireless sensor network communication link construction method of redundant nodes, which comprises the following steps: defining a node deployment area, and setting a sensor node set in the node deployment area; initializing algorithm parameters; calculating the maximum link of the network by adopting a maximum flow algorithm; initially deploying a network based on the sensor node set, and generating an initial solution; determining an algorithm optimization target according to the maximum flow network link; a teaching and learning optimization algorithm based on quantum entropy is adopted to carry out loop iteration of redundant node position optimization, and a better target solution set is obtained in each iteration; and when the maximum loop iteration is reached or a stop condition is met, outputting a solution set of the current iteration as optimal redundant node deployment, otherwise, repeating the previous step. According to the method, the stability of the network and the continuity of data transmission under the condition that partial nodes fail are improved, effective balance between global search and local search is also realized, and the convergence speed of the algorithm is accelerated.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things and wireless communication, and in particular to a method for constructing a wireless sensor network communication link of redundant nodes. Background Art

[0002] Wireless Sensor Network (WSN) is a multi-hop self-organizing information perception, collection and transmission system that can obtain detailed and accurate data in a variety of environments. In order to ensure the stable operation of WSN, it is crucial to ensure the reliability of its communication link. The traditional maximum flow algorithm can be used to evaluate and optimize the traffic distribution in the network, but it fails to fully consider the node layout in three-dimensional space and its impact on the overall network performance. In addition, in a complex environment, it is difficult to ensure sufficient link reliability of the network by relying solely on existing nodes. Therefore, it is necessary to introduce additional redundant nodes and reasonably deploy the locations of redundant nodes to improve the fault tolerance and robustness of the network. Summary of the invention

[0003] The purpose of the present invention is to solve the problem of insufficient communication connectivity and reliability of existing wireless sensor networks in complex environments. By introducing the maximum flow algorithm to evaluate and optimize the maximum connectivity of the network, and combining the teaching and learning algorithm with quantum entropy evaluation technology, the appropriate number of redundant nodes is dynamically and reasonably added to maximize the number of network links.

[0004] To achieve the above purpose, the technical solution adopted by the present invention is as follows: A method for establishing a wireless sensor network communication link of redundant nodes comprises the following steps: S1. Define the node deployment area and set the sensor node set within the node deployment area; S2, initialize algorithm parameters; S3, using the maximum flow algorithm to calculate the maximum flow network link; S4, deploying the network based on the sensor node set to generate an initial solution; S5. Determine the algorithm optimization target according to the maximum flow network link; S6. Using the quantum entropy-based teaching and learning optimization algorithm to perform cyclic iterations of redundant node position optimization, each iteration obtains a better target solution set; S7. When the maximum loop iteration is reached or the stopping condition is met, the solution set of the current iteration is output as the optimal redundant node deployment, otherwise S6 is repeated.

[0005] Furthermore, in S1, the node deployment area is defined as ,in l , w , hRespectively represent the number of divisions in length, width and height; set the sensor node set ,in Represents a preset node. Indicates the redundant node to be deployed.

[0006] Furthermore, in S4, randomly select Deploy redundant nodes in different locations , forming the initial solution set .

[0007] Furthermore, S3 includes: using the maximum flow algorithm to calculate the relationship between any two nodes in the sensor node set The maximum flow between , , and obtain the optimal connected path set, that is, the maximum flow network link.

[0008] Furthermore, in S5, the minimum value of the maximum flow network link is used as the algorithm optimization target fit i (t) , as follows: .

[0009] Furthermore, in S2, the initialization algorithm parameters include: initialization population size, maximum number of iterations, initial value of initialization teaching factor is 1, initial value of learning step is a random number between [0, 1], initial value of quantum entropy weight coefficient is 1, and initial value of number of iterations is 0.

[0010] Further, the S6 includes: S61. Through the cooperation between the teacher phase and the learning phase of the teaching and learning optimization algorithm, a new solution is generated, that is, a set of redundant node positions with a larger number of links: In the teacher phase, for each solution in the initial solution , generate new solutions : ; in, is the optimal solution among the initial solutions, is the average value of all solutions in the initial solution, is the learning step size, is the teaching factor; In the learning phase, two solutions are randomly selected from the initial solutions. and Learn from each other and generate new solutions : ; in, fis the fitness function; S62. New solution calculated by Euclidean distance metric x ' i The Euclidean probability p(x ' i ) : ; in, New interpretation and The Euclidean distance between them is measured, and N represents the number of individuals in the population; S63. All new solution sets are combined into a new solution set , calculate the new solution set after each round of iteration The quantum entropy of : ; in, W q is the quantum entropy weight coefficient, W q As the number of iterations decreases linearly, the decreasing formula is: ; in, is the initial value of the initial quantum entropy weight coefficient, and the weight is set to 1; is the final value of the quantum entropy weight coefficient, and the weight is set to 0.1; is the current number of iterations; Maxitr is the maximum number of iterations; S64. Dynamically optimize teaching factors and learning steps through quantum entropy: Dynamically optimize teaching factors : ; Among them, round is the rounding function; Dynamically optimize learning step size : ; S65, update the optimization teaching factor and learning step size and then perform cyclic iterations. After each iteration, a solution set that meets the algorithm optimization goal is obtained, which is the best node layout solution for the iteration. .

[0011] Furthermore, in S7, when the maximum number of iterations is reached or the stopping condition that the entropy does not change significantly after 50 consecutive iterations is met, the solution set of the current iteration is output as the optimal redundant node deployment; otherwise, S6 is repeated to continue the iteration.

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention enhances network connectivity and reliability: By introducing the maximum flow algorithm to evaluate the connectivity of the existing network and combining it with the redundant node addition strategy, the present invention enhances the number of communication links in the wireless sensor network in complex environments. This enhancement not only improves the success rate of data transmission, but also increases the network's tolerance to faults, ensuring that even if some nodes fail, the entire network can still maintain efficient operation.

[0013] 2. The present invention achieves an effective balance between global and local search: by combining the teaching and learning optimization algorithm with the application of quantum entropy optimization technology, the present invention can automatically adjust the balance between global exploration and local development during the search process. Specifically, in the early stages of the algorithm, higher quantum entropy encourages extensive exploration behavior, which helps avoid falling into local optimal solutions; as the number of iterations increases, the influence of quantum entropy gradually weakens, and the algorithm turns to more sophisticated local searches, which speeds up convergence and increases the probability of finding the global optimal solution. This method effectively solves the problem of premature convergence of traditional optimization algorithms and provides a more robust solution.

[0014] 3. The present invention improves resource utilization efficiency: it focuses on the reasonable deployment of redundant nodes, aiming to maximize the number of links. By calculating the impact of the redundant node set position on the overall network link construction and evaluating the quality of the solution based on quantum entropy, it ensures that the newly added redundant nodes can most effectively improve network connectivity. In addition, this method also helps to extend the life of the network and reduce maintenance costs, providing strong support for large-scale deployment in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0017] like Figure 1 As shown, the present invention provides a method for establishing a wireless sensor network communication link of redundant nodes, characterized in that it includes the following steps: S1. Define the node deployment area and set the sensor node set within the node deployment area; S2, initialize algorithm parameters; S3, use the maximum flow algorithm to calculate the maximum link of the network; S4, initially deploying the network based on the sensor node set to generate an initial solution; S5. Determine the algorithm optimization target according to the maximum flow network link; S6. Using the quantum entropy-based teaching and learning optimization algorithm to perform cyclic iterations of redundant node position optimization, each iteration obtains a better target solution set; S7. When the maximum loop iteration is reached or the stopping condition is met, the solution set of the current iteration is output as the optimal redundant node deployment, otherwise S6 is repeated.

[0018] The present invention not only improves the stability of the network and the continuity of data transmission when some nodes fail, but also achieves an effective balance between global and local search, accelerates the algorithm convergence speed and increases the probability of finding the global optimal solution. In addition, the present invention focuses on resource utilization efficiency, ensuring that the addition of redundant nodes can most effectively improve network performance while minimizing energy consumption and other resource costs, thereby extending the network life and reducing maintenance costs.

[0019] S1 in the present invention is a three-dimensional space node deployment: the node deployment area is divided into l × w × h Pixels form a point set R a ,Sensor nodes perform regional monitoring in the node deployment area, ,in l , w , h Respectively represent the number of divisions in length, width and height; set the sensor node set ,in Represents a preset node. Indicates the redundant node to be deployed.

[0020] S2 in the present invention is the initialization of algorithm parameters: initialization population size PS , maximum number of iterations Maxitr , and initialize the teaching factor The initial value is 1, the learning step length The initial value is a random number between [0, 1], and the quantum entropy weight coefficient The initial value is 1, the number of iterations t The initial value of is 0. Teaching factor It is used to generate new solutions in the teacher phase, and its value changes with the number of iterations. It is used to generate new solutions in the learning phase, and its value also changes with the number of iterations. The value of decreases linearly with the number of iterations to achieve a natural transition from exploration to exploitation.

[0021] S4 of the present invention is the initial deployment of the network and the generation of the initial solution: Existing nodes, i.e. pre-set nodes ; Random selection Deploy redundant nodes in different locations , forming the initial solution set , thereby ensuring maximum connectivity of the network.

[0022] S3 of the present invention is the calculation of the maximum flow network link: the maximum flow algorithm (Edmonds-Karp method) is used to calculate the maximum flow between any two nodes in the sensor node set. The maximum flow between , , and obtain the best connected path set, i.e. the maximum flow network link. The overall connectivity of the network is calculated based on this.

[0023] S5 of the present invention is the determination of the algorithm optimization target: the overall optimization target is the minimum value of the maximum flow value set between any two nodes. fit i (t) , as follows: .

[0024] The calculation of the solution set with the highest fitness value in S6 of the present invention includes: S61. Through the cooperation between the teacher phase and the learning phase of the teaching and learning optimization algorithm, a new solution is generated, that is, a set of redundant node positions with a larger number of links: In the teacher phase, for each solution in the initial solution , generate new solutions : ; in, is the optimal solution among the initial solutions, is the average value of all solutions in the initial solution, is the learning step size, is the teaching factor; In the learning phase, two solutions are randomly selected from the initial solutions. and Learn from each other and generate new solutions : ; in, f is the fitness function; S62. New solution calculated by Euclidean distance metric x' i The Euclidean probability p(x ' i ) : ; in, New interpretation and The Euclidean distance between them is measured, and N represents the number of individuals in the population; The probabilities are calculated based on the Euclidean distance metric between solutions. If two solutions are very close, they may be considered similar and assigned a lower probability; conversely, solutions that are farther away are assigned a higher probability to encourage exploration of a wider solution space.

[0025] S63. All new solution sets are combined into a new solution set , calculate the new solution set after each round of iteration The quantum entropy of : ; in, W q is the quantum entropy weight coefficient, W q As the number of iterations decreases linearly, the decreasing formula is: ; in, is the initial value of the initial quantum entropy weight coefficient, and the weight is set to 1; is the final value of the quantum entropy weight coefficient, and the weight is set to 0.1; is the current number of iterations; Maxitr is the maximum number of iterations; S64. Dynamically optimize the teaching factor and learning step size through quantum entropy, so that the algorithm can highlight the global search ability in the early stage and the local search ability in the later stage, achieving a balance between global and local search: Dynamically optimize teaching factors : ; Among them, round is the rounding function; Dynamically optimize learning step size : ; S65, update the optimization teaching factor and learning step size and then perform cyclic iterations. After each iteration, a solution set that meets the algorithm optimization goal is obtained, which is the best node layout solution for the iteration. .

[0026] The S6 of the present invention adopts a teaching and learning optimization algorithm based on quantum entropy, and introduces quantum entropy as a key factor in guiding the generation of new solutions and step size control in the teaching and learning optimization algorithm, thereby achieving a more efficient global and local search balance. In addition, the quantum entropy teaching and learning optimization algorithm is used to increase an appropriate amount of redundant nodes, maximize the number of links, and enhance network reliability.

[0027] S7 described in the present invention is the end or continuation of the loop iteration: when the maximum number of iterations is reached or the stopping condition that the entropy has no significant change after 50 consecutive iterations is met, the solution set of the current iteration is output as the optimal redundant node deployment; otherwise, S6 is repeated to continue the iteration.

[0028] Finally, it should be noted that the above embodiments are only preferred embodiments of the present invention to illustrate the technical solutions of the present invention, rather than limiting them, and certainly not limiting the patent scope of the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. 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. In other words, any changes or modifications made to the main design concept and spirit of the present invention that have no substantive significance, and the technical problems they solve are still consistent with the present invention, should be included in the protection scope of the present invention. In addition, the direct or indirect application of the technical solutions of the present invention in other related technical fields is also included in the patent protection scope of the present invention.

Claims

1. A method for establishing a wireless sensor network communication link with redundant nodes, characterized in that: The following steps are involved: S1. Define the node deployment area and set the sensor node set within the node deployment area; S2, initialize algorithm parameters; S3, using the maximum flow algorithm to calculate the maximum flow network link; S4, initially deploying the network based on the sensor node set to generate an initial solution; S5. Determine the algorithm optimization target according to the maximum flow network link; S6. Using the quantum entropy-based teaching and learning optimization algorithm to perform cyclic iterations of redundant node position optimization, each iteration obtains a better target solution set; S7. When the maximum loop iteration is reached or the stopping condition is met, the solution set of the current iteration is output as the optimal redundant node deployment, otherwise S6 is repeated.

2. The method for establishing a wireless sensor network communication link with redundant nodes according to claim 1, characterized in that: In S1, the node deployment area is defined as ,in l , w , h Respectively represent the number of divisions in length, width and height; set the sensor node set ,in Represents a preset node. Indicates the redundant node to be deployed.

3. The method for establishing a wireless sensor network communication link of redundant nodes according to claim 2, characterized in that: In S4, randomly select Deploy redundant nodes in each location , forming the initial solution set .

4. The method for establishing a wireless sensor network communication link of redundant nodes according to claim 2, characterized in that: S3 includes: using the maximum flow algorithm to calculate any two nodes in the sensor node set The maximum flow between , , and obtain the optimal connected path set, that is, the maximum flow network link.

5. The method for establishing a wireless sensor network communication link of redundant nodes according to claim 4, characterized in that: In S5, the minimum value of the maximum flow network link is used as the algorithm optimization target fit i (t) , as follows: 。 6. The method for establishing a wireless sensor network communication link with redundant nodes according to claim 3, characterized in that: In S2, the initialization algorithm parameters include: the initialization population size, the maximum number of iterations, the initial value of the initialization teaching factor is 1, the initial value of the learning step is a random number between [0, 1], the initial value of the quantum entropy weight coefficient is 1, and the initial value of the number of iterations is 0.

7. The method for establishing a wireless sensor network communication link of redundant nodes according to claim 6, characterized in that: The S6 includes: S61. Through the cooperation between the teacher phase and the learning phase of the teaching and learning optimization algorithm, a new solution is generated, that is, a set of redundant node positions with a larger number of links: In the teacher phase, for each solution in the initial solution , generate a new solution : ； in, is the optimal solution among the initial solutions, is the average value of all solutions in the initial solution, is the learning step size, is the teaching factor; In the learning phase, two solutions are randomly selected from the initial solutions. and Learn from each other and generate new solutions : ； in, f is the fitness function; S62. New solution calculated by Euclidean distance metric x ' i The Euclidean probability p(x ' i ) : ； in, New interpretation and The Euclidean distance between them is measured, and N represents the number of individuals in the population; S63. All new solution sets are combined into a new solution set , calculate the new solution set after each round of iteration The quantum entropy of : ； in, W q is the quantum entropy weight coefficient, W q As the number of iterations decreases linearly, the decreasing formula is: ； in, is the initial value of the initial quantum entropy weight coefficient, and the weight is set to 1; is the final value of the quantum entropy weight coefficient, and the weight is set to 0.1; is the current number of iterations; Maxitr is the maximum number of iterations; S64. Dynamically optimize teaching factors and learning steps through quantum entropy: Dynamically optimize teaching factors : ； Among them, round is the rounding function; Dynamically optimize learning step size : ； S65, update the optimization teaching factor and learning step size and then perform cyclic iterations. After each iteration, a solution set that meets the algorithm optimization goal is obtained, which is the best node layout solution for the iteration. .

8. The method for establishing a wireless sensor network communication link of redundant nodes according to claim 6, characterized in that: In S7, when the maximum number of iterations is reached or the stopping condition that the entropy does not change significantly after 50 consecutive iterations is met, the solution set of the current iteration is output as the optimal redundant node deployment; otherwise, S6 is repeated to continue the iteration.

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