A wireless sensor network hierarchical routing optimization method and system based on quantum leafcutter mechanism

Abstract

The application relates to a wireless sensor network hierarchical routing optimization method and system based on a quantum mound beetle mechanism, relates to the technical field of wireless communication, and aims to solve the technical problems of unbalanced energy consumption and short network life of a wireless sensor network in a traditional method. The technical points of the application include the following steps: establishing a wireless sensor network system model; determining the number of clusters and dynamically clustering, and simultaneously establishing a network node dynamic clustering cost function; initializing quantum mound beetle groups and setting parameters, and calculating the organic matter decomposition degree of all quantum mound beetles to determine the global optimal quantum position of the quantum mound beetle groups; quantum position evolution of the quantum mound beetles and obtaining the global optimal quantum position by using a selection mechanism; quantum mound beetle mechanism evolution termination judgment, outputting the current wireless sensor network cluster center; wireless sensor network dynamic cluster head election; and the wireless sensor network performing stable data transmission while updating the network state. The application effectively improves the energy consumption efficiency of the wireless sensor network and effectively prolongs the network life.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, specifically to a hierarchical routing optimization method and system for wireless sensor networks based on the quantum dung beetle mechanism. Background Technology

[0002] Wireless sensor networks (WSNs) are multi-hop self-organizing networks composed of numerous miniature sensor nodes. They monitor various target information within the deployment area in real time. After processing the target information, the sensor nodes transmit it wirelessly. WSNs are widely used in military reconnaissance, medical care, and industrial production. However, due to the miniaturization of sensor structures, the capacity of the embedded batteries is limited. Furthermore, sensor nodes are typically deployed in inaccessible areas using small drones, making battery replacement and resupply extremely difficult. Therefore, resource constraints are a major problem in wireless sensor networks. Effective routing protocol design can effectively avoid excessive power consumption and short network lifespan.

[0003] Wireless sensor network routing protocols can be divided into two categories: planar routing and hierarchical routing. Planar routing protocols stipulate that all wireless sensors play the same role in the routing process, without introducing a hierarchical management mechanism, setting up local management nodes, and lacking effective resource allocation; they are only suitable for small-scale networks. Unlike planar routing protocols, in hierarchical routing protocols, the positions and functions of sensor nodes vary, and they can be basically divided into three categories: base stations, cluster heads, and intra-cluster nodes. In the logical view of hierarchical routing protocols, the network structure is horizontally clustered, with each cluster consisting of a cluster head and multiple intra-cluster nodes, and vertically layered, recursively communicating with the base station. Cluster heads perform local data compression during communication to reduce data redundancy, enabling more advanced data description forms for specific applications, effectively improving energy efficiency and network lifetime. With the continuous expansion of network scale, hierarchical routing protocols have become the mainstream protocols for wireless sensor networks at present.

[0004] A review of existing literature reveals that Heinzelman et al. proposed LETCH, a fully distributed, self-organizing, and adaptive clustering routing protocol for two-layer network architectures, in their paper "Energy-efficient communication protocol for wireless microsensor networks." Currently, various wireless sensor network routing protocols are based on this protocol. The network architecture using the LETCH protocol is clustered, with nodes within a cluster responsible for sensing the environment and communicating directly with the cluster head. The cluster head acts as a gateway, processing data and communicating directly with the base station. Because LETCH employs a completely random cluster head configuration, it suffers from issues such as node overload and reduced network lifespan. Sun Aijing et al., in their paper "WSN Clustering Routing Algorithm Based on PSO-Optimized Fuzzy C-Means" published in the *Journal of Communications* (2021, 42(03): 91-99), proposed PSOFCM, a hierarchical routing protocol for clustering based on particle swarm optimization of fuzzy C-means. First, the particle swarm optimization algorithm is used to dynamically and centrally build clusters to balance the load within each cluster. Then, the cat swarm algorithm is used to optimize the data relay transmission process. The results of the existing literature search show that existing WSN hierarchical routing protocols suffer from problems such as unbalanced load, unbalanced energy consumption, high computational complexity, and poor usability because the design of the routing method directly affects the network's working time.

[0005] In summary, existing hierarchical routing protocols still have many problems in cluster partitioning, especially in achieving optimal data transmission. There is a lack of methods that can leverage the advantages of hierarchical routing protocols over planar routing protocols in reducing energy consumption to design a simple, efficient, low-complexity, energy-efficient hierarchical routing method that can maximize network lifetime. Summary of the Invention

[0006] In view of this, the present invention proposes a hierarchical routing optimization method and system for wireless sensor networks based on the quantum dung beetle mechanism, in order to solve the technical problems of uneven energy consumption and short network lifetime in existing wireless sensor networks.

[0007] According to one aspect of the present invention, a hierarchical routing optimization method for wireless sensor networks based on the quantum dung beetle mechanism is proposed, comprising the following steps:

[0008] Step 1: Establish a wireless sensor network system model;

[0009] Step 2: Set and initialize network parameters to enable network operation;

[0010] Step 3: Determine the number of clusters and perform dynamic clustering, while establishing a dynamic clustering cost function for network nodes;

[0011] Step 4: Initialize the quantum dung beetle swarm and set parameters;

[0012] Step 5: Based on the mapping equation and the node dynamic clustering cost function, calculate the degree of organic matter decomposition of all quantum dung beetles and determine the global optimal quantum position of the quantum dung beetle colony;

[0013] Step Six: The quantum dung beetle performs rolling, breeding, foraging, and stealing behaviors, while using quantum rotation angles to evolve its quantum position;

[0014] Step 7: Use a selection mechanism to select the quantum position and the globally optimal quantum position of the next generation of quantum dung beetles;

[0015] Step 8: Determine if the quantum dung beetle mechanism evolution terminates, and output the current wireless sensor network cluster center;

[0016] Step 9: Dynamic Cluster Head Election in Wireless Sensor Networks;

[0017] Step 10: The wireless sensor network transmits stable data while updating the network status, and determines when the network operation has terminated.

[0018] Further, the wireless sensor network system described in step one is as follows: N wireless sensors are randomly and uniformly deployed within an M×M monitoring area. Once deployed, the nodes do not move over time and self-form a network. The base station has unlimited energy and computing power, while the sensors have limited energy and computing power. There are no obstacles within the monitoring area that could impede wireless signal transmission. A hybrid network architecture is supported, with a chain-like data transmission pattern. Sensors are densely deployed, and the response data generated by adjacent sensors to similar events are similar. Sensors self-regulate their wireless transmission power, and the wireless communication links are symmetrical. The wireless sensor network nodes consume energy through internal network communication and localized data processing. The internal network communication involves the routing and transmission of data packets or control packets within the network, and the localized data processing involves performing localized fusion and compression processing on the sensed data.

[0019] The wireless sensor network system model is established as follows:

[0020] A first-order radio model is used to simulate communication energy consumption. This first-order radio model includes a transmitting circuit, a power amplifier circuit, and a receiving circuit, as detailed below:

[0021] E RX (k)=kE elec E DA (k)=γkE da ,

[0022] Among them, E TX (k,d),E RX (k) and E DA(k) represents the energy consumed by the node in sending, receiving, and processing data, respectively; d represents the communication distance between nodes; k represents the amount of information sent by the node; γ represents the data fusion compression rate; E elec and E da These represent the energy consumed by the node in sending, receiving, and processing a unit of bit information, respectively. ε is the channel threshold. fs and ε mp These represent the power amplification energy consumption coefficients for free space and multipath fading channel models, respectively.

[0023] Furthermore, step two includes the following process:

[0024] The parameter set for the wireless sensor network in round t is defined as follows:

[0025]

[0026] Where, m d and m c These are communication data packets and control data packets, respectively. For the location of the base station; s n =[x n ,y n [ ] represents the position of the nth sensor node; Let N be the remaining energy of the nth sensor node during the t-th round of operation, where n∈[1,N], t∈[1,T], N is the number of sensor nodes, and T is the maximum number of rounds of operation for the wireless sensor network. The node is a bisexual node identifier, and l is a network coefficient. When q n =1 indicates that this sensor node can communicate with the base station as a cluster head or intra-cluster node. n =0 indicates that this sensor node can act as a cluster head to communicate with the base station or with the cluster head from other nodes within the cluster; This serves as the survival flag for the sensor node during the t-th round of runtime. When this sensor node is destroyed, it indicates that the sensor node has been destroyed. This indicates that the sensor node is alive.

[0027] Furthermore, step three includes the following process:

[0028] The number of clusters in the t-th round of network operation is determined as follows:

[0029]

[0030] Where round(·) is the nearest integer function; This indicates the number of surviving nodes; dynamic clustering is performed again when the network runs for the first time or the number of clusters changes; when t≠1 and c t =c t-1If this happens, proceed to step nine without performing dynamic clustering;

[0031] The node dynamic clustering cost function for the t-th round of network operation is established as follows:

[0032]

[0033] in, Represents the set of cluster centers. Indicates the first The position vector of the cluster center To find the first... All surviving nodes within a cluster are routed to the cluster center. The distance and.

[0034] Further, step four includes: setting the quantum dung beetle population size to h, the maximum number of iterations to G, and the iteration number to an integer g; when g = 1, the quantum positions of the quantum dung beetles in each dimension of the search space are initialized to uniformly random numbers in the interval [0,1]; at the g-th iteration, the quantum position of the i-th quantum dung beetle in the q-dimensional search space is... The j-th dimension of quantum position

[0035] Step five includes: In the g-th iteration, mapping each dimension of all quantum dung beetle quantum positions to the corresponding solution space, obtaining the mapped state of the quantum position as follows: The mapping equation for the j-th dimension variable is: Among them, w max,j and w min,j These are the upper and lower bounds of the j-th dimension of the quantum dung beetle's position, respectively;

[0036] Substitute the mapping state of the i-th quantum dung beetle's quantum position into the node dynamic clustering cost function to calculate the clustering cost value corresponding to the quantum dung beetle. The degree of organic matter decomposition at the i-th quantum dung beetle quantum position is measured, where the superscript Λ indicates that the matrix is ​​reconstructed in 2 rows, and it is stipulated that the smaller the cluster generation value, the greater the degree of organic matter decomposition of the quantum dung beetle.

[0037] Arrange all quantum dung beetles in descending order of their degree of organic matter decomposition. Define the quantum position with the highest degree of organic matter decomposition as the globally optimal quantum position of the quantum dung beetle group up to the (g+1)th generation.

[0038] Furthermore, step six includes defining the update equation for the j-th dimension quantum position of the i-th quantum dung beetle as:

[0039]

[0040] Defining ball rolling, reproduction, and foraging behaviors Definition of theft i = 1, 2, ..., h, j = 1, 2, ..., q; where, Let e1 be the j-th quantum rotation angle of the i-th quantum dung beetle in the (g+1)-th generation, e2 be the thieving factor, and e1 be the reproduction factor. A random number uniformly distributed within the interval [0,1]. The random number is uniformly distributed within the interval [-1, 1]. Mapping state of quantum position of quantum dung beetle The gradient of the j-th dimension, and They are respectively Only the j-th dimension becomes and The extent to which post-quantum dung beetles decompose organic matter, where ε is the differential factor;

[0041] Step seven involves: calculating the degree of organic matter decomposition by all quantum dung beetles at the initial and newly generated quantum positions, applying a greedy strategy, and selecting from the set of quantum positions... We selected h quantum positions with a high degree of organic matter decomposition as the quantum positions for the next generation of quantum dung beetles. The quantum dung beetles of generation g+1 are sorted in descending order of their organic matter decomposition degree. The globally optimal quantum position of the quantum dung beetle colony is then updated up to generation g+1.

[0042] Furthermore, step eight includes: if the quantum dung beetle mechanism evolution has not reached the maximum number of iterations G, then let g = g + 1 and continue the quantum dung beetle mechanism evolution; if the maximum number of iterations G is reached, then the quantum dung beetle mechanism evolution terminates, and the mapping state of the global optimal quantum position of the quantum dung beetle swarm is reconstructed and used as the cluster center of the current wireless sensor network. Output the results.

[0043] Furthermore, step nine includes: electing a cluster head based on the dynamic clustering; using the cluster head node to collect and aggregate data from cluster members, and sending the data to the base station or acting as a relay node to forward data information; the cluster head indicators include the node's remaining energy and the distance between the node and the base station; with the goal of preventing premature node death, selecting a cluster node with higher energy to serve as the cluster head, and balancing the load within the cluster; with the goal of reducing cluster head energy consumption, selecting a node closer to the base station to serve as the cluster head.

[0044] During the data transmission phase, each surviving node corresponds to a data group, which includes its remaining energy and location information. After reading the data group, the cluster head selects a suitable node as the new cluster head and uses Time Division Multiple Access (TDMA) for stable data transmission with the goal of reducing node energy consumption.

[0045] Furthermore, step ten includes: all surviving nodes are assigned to c t Within a cluster, when a surviving node is the cluster head, the energy loss for stable data transmission is:

[0046]

[0047] in, This represents the number of non-bisexual nodes within the cluster containing the cluster head.

[0048] When a surviving node is an in-cluster node, stable data transmission identifies whether it is a bisexual node. If it is a non-bisexual node, the non-bisexual node sends the data group to the cluster head according to the TDMA time slot allocated by the cluster head. The cluster head then merges the data group using single-hop communication and sends it out. The energy loss is:

[0049]

[0050] in, This is the cluster head of the cluster to which this node belongs;

[0051] If it is a bipolar node, the bipolar node directly sends the data group using single-hop communication, and the energy loss is:

[0052]

[0053] After stable data transmission is completed, the network status is updated by updating the node liveness flag; the node liveness flag is:

[0054]

[0055] Network termination judgment: If t+1 = T or If the wireless sensor network reaches the maximum number of rounds T or all nodes are destroyed, the network will terminate. Otherwise, let t = t + 1, return to step three, and the network will enter the next round.

[0056] According to another aspect of the present invention, a hierarchical routing optimization system for wireless sensor networks based on the quantum dung beetle mechanism is proposed, comprising:

[0057] Network model building module: It is configured to build a model of a wireless sensor network system;

[0058] Network parameter setting module: It is configured to set and initialize network parameters to enable network operation;

[0059] Network optimization module: It is configured to determine the number of clusters and dynamically cluster them, while establishing a dynamic clustering cost function for network nodes;

[0060] Algorithm configuration module: It is configured to initialize the quantum dung beetle swarm and set parameters;

[0061] Algorithm optimization module: It is configured to calculate the degree of organic matter decomposition of all quantum dung beetles based on the mapping equation and the node dynamic clustering cost function, and determine the global optimal quantum position of the quantum dung beetle colony;

[0062] Population evolution module: It is configured for quantum dung beetles to perform ball rolling, reproduction, foraging and stealing behaviors, while using quantum rotation angles to evolve the quantum position of quantum dung beetles;

[0063] Global optimization module: It is configured to use a selection mechanism to select the quantum position and the globally optimal quantum position of the next generation of quantum dung beetles;

[0064] Cluster center output module: It is configured to determine the termination of the quantum dung beetle mechanism evolution and output the current wireless sensor network cluster center;

[0065] Dynamic cluster head election module: It is configured for dynamic cluster head election in wireless sensor networks;

[0066] Network update module: It is configured to perform stable data transmission and update the network status of the wireless sensor network, and determine when the network operation is terminated.

[0067] The beneficial technical effects of this invention are:

[0068] Compared with existing technologies, the hierarchical routing optimization method for wireless sensor networks based on the quantum dung beetle mechanism proposed in this invention outperforms existing benchmark protocols in terms of energy efficiency, network lifetime, and scalability. To avoid uneven clustering of nodes in the wireless sensor network, the quantum dung beetle mechanism is used for dynamic clustering, which features high convergence accuracy. To reduce cluster head energy consumption and prevent premature node death, the optimal cluster head is selected by considering two factors: the remaining energy of the node and the distance between the node and the base station, combined with the network state, which can further balance node load and network energy consumption. Simulation experiments demonstrate that the energy efficiency of the wireless sensor network using this invention is effectively improved, and the network lifetime is effectively extended.

[0069] Based on the study of the LEACH protocol, this invention proposes a hierarchical routing optimization method for wireless sensor networks based on the quantum dung beetle mechanism. This method operates in a round-based cycle, with each round comprising three stages: dynamic clustering, cluster head election, and data transmission. In the dynamic clustering stage, the quantum dung beetle mechanism is used to dynamically cluster wireless sensor network nodes, mimicking dung beetle behavior and incorporating quantum rotation angles to evolve the quantum dung beetle quantum state. In the cluster head election stage, the remaining energy of nodes and the distance between nodes and the base station are considered to elect the optimal cluster head. In the data transmission stage, the concept of a dual-nature node is introduced; this node can communicate directly with the base station as either a cluster head or an intra-cluster node. This hierarchical routing optimization method can effectively balance network energy consumption and extend network operating time.

[0070] This invention establishes a wireless sensor network system model, which includes a monitoring area, wireless sensors, base stations, a hybrid network architecture, and a chain-like data transmission mode. The wireless sensors have limited energy and computing power, are capable of self-regulating their wireless transmission power, exhibit symmetry in communication links, and the densely deployed sensor nodes do not move over time and self-form a network. The response data of adjacent sensors to similar events are similar. This invention utilizes this model to obtain the energy consumption of the wireless sensor network, initializes the network parameters to enable its operation, optimizes the network based on a quantum dung beetle mechanism to ensure stable data transmission, and updates the network state by updating the node survival flags. Attached Figure Description

[0071] The present invention can be better understood by referring to the description given below in conjunction with the accompanying drawings, which together with the following detailed description are included in and form part of this specification, and are used to further illustrate preferred embodiments of the invention and explain the principles and advantages of the invention.

[0072] Figure 1 This is a flowchart of a hierarchical routing optimization method for wireless sensor networks based on a quantum dung beetle mechanism, as described in this invention.

[0073] Figure 2 This is a graph showing the relationship between the number of surviving network nodes and the number of network operation rounds when the base station is deployed within the area of ​​the present invention.

[0074] Figure 3 This is a graph showing the relationship between the remaining network energy and the number of network operation rounds when the base station is deployed within the area of ​​the present invention.

[0075] Figure 4 This is a graph showing the relationship between the number of network surviving nodes and the number of network operation rounds when the base station is deployed outside the base station area in this invention.

[0076] Figure 5This is a graph showing the relationship between the remaining network energy and the number of network operation rounds when the base station is deployed outside the designated area in this invention. Detailed Implementation

[0077] To enable those skilled in the art to better understand the present invention, exemplary embodiments or examples of the present invention will be described below in conjunction with the accompanying drawings. Obviously, the described embodiments or examples are merely some, not all, of the embodiments or examples of the present invention. All other embodiments or examples obtained by those skilled in the art based on the embodiments or examples of the present invention without inventive effort should fall within the scope of protection of the present invention.

[0078] This invention proposes a hierarchical routing optimization method for wireless sensor networks based on the quantum dung beetle mechanism, such as... Figure 1 As shown, the method includes the following steps:

[0079] Step 1: Establish a wireless sensor network system model;

[0080] Step 2: Set and initialize network parameters to enable network operation;

[0081] Step 3: Determine the number of clusters and perform dynamic clustering, while establishing a dynamic clustering cost function for network nodes;

[0082] Step 4: Initialize the quantum dung beetle swarm and set parameters;

[0083] Step 5: Based on the mapping equation and the node dynamic clustering cost function, calculate the degree of organic matter decomposition of all quantum dung beetles and determine the global optimal quantum position of the quantum dung beetle colony;

[0084] Step Six: The quantum dung beetle performs rolling, breeding, foraging, and stealing behaviors, while using quantum rotation angles to evolve its quantum position;

[0085] Step 7: Use a selection mechanism to select the quantum position and the globally optimal quantum position of the next generation of quantum dung beetles;

[0086] Step 8: Determine if the quantum dung beetle mechanism evolution terminates, and output the current wireless sensor network cluster center;

[0087] Step 9: Dynamic Cluster Head Election in Wireless Sensor Networks;

[0088] Step 10: The wireless sensor network transmits stable data while updating the network status, and determines when the network operation has terminated.

[0089] The method begins with step one. In step one, a wireless sensor network system model is established.

[0090] According to embodiments of the present invention, the following assumptions are made regarding the wireless sensor network system model: N wireless sensors are randomly and uniformly deployed within an M×M monitoring area; the nodes do not move over time after deployment and form a self-contained network; the base station has unlimited energy and computing power, while the sensors have limited energy and computing power; there are no obstacles within the monitoring area that could impede wireless signal transmission; a hybrid network architecture is supported, and the data transmission mode is chain-like; sensors are densely deployed, and the response data generated by adjacent sensors to similar events are similar; sensors can self-regulate their wireless transmission power, and the wireless communication links are symmetrical.

[0091] The energy consumption of wireless sensor network nodes mainly arises from two aspects: internal network communication and local data processing. Internal network communication refers to the routing and transmission of data packets or control packets within the network, while local data processing refers to the localized fusion and compression processing of sensed data. This invention uses a first-order radio model to simulate communication energy consumption. The model mainly includes a transmitting circuit, a power amplifier circuit, and a receiving circuit, specifically: E RX (k)=kE elec E DA (k)=γkE da , of which E TX (k,d),E RX (k) and E DA (k) represents the energy consumed by the node in sending, receiving, and processing data, respectively; d represents the communication distance between nodes; k represents the amount of information sent by the node; γ represents the data fusion compression rate; E elec and E da These represent the energy consumed by a node in transmitting, receiving, and processing a unit of bit information, respectively. Channel threshold. Where, ε fs and ε mp These represent the power amplification energy consumption coefficients of the free space and multipath fading channel models, respectively. When the communication distance between nodes is greater than the channel threshold, the multipath fading channel model is selected for wireless communication; otherwise, the free space channel model is selected for wireless communication.

[0092] Then proceed to step two, where network parameters are set and initialized to enable network operation.

[0093] According to an embodiment of the present invention, the parameter set for the t-th round of operation of the wireless sensor network is set using... It means that, where m d bit and m c The bit represents the size of communication data packets and control data packets. For the location of the base station, s n =[x n ,y n [ ] represents the position of the nth sensor node. This represents the remaining energy of the nth sensor node during the t-th round of operation, where n∈[1,N], t∈[1,T], N is the number of sensor nodes, and T is the maximum number of rounds the wireless sensor network can run. (Hybrid node flag) Where l is the network coefficient. For the channel threshold, when q n =1 indicates that this sensor node can communicate with the base station as a cluster head or intra-cluster node; when q n =0 indicates that this sensor node can act as a cluster head to communicate with the base station or for nodes within the cluster to communicate with the cluster head. The survival flag of the sensor node during round t. when When this indicates that the sensor node has been destroyed; when This indicates that the sensor node is alive. Initialize the wireless sensor network parameters; the network begins operation.

[0094] Then proceed to step three, in which the number of clusters is determined and dynamic clustering is performed, while a dynamic clustering cost function for network nodes is established.

[0095] According to an embodiment of the present invention, the number of clusters in the t-th round of network operation is determined by analyzing and calculating the energy consumption of the wireless sensor network. Where round(·) is the nearest integer function. This indicates the number of surviving nodes. Dynamic clustering is performed again when the network runs for the first time or when the number of clusters changes; when t≠1 and c t =c t-1 If the condition is met, proceed to step nine without dynamic clustering. Set the node dynamic clustering cost function for the t-th round of network operation as follows: in, Represents the set of cluster centers. Indicates the first The position vector of the cluster center To find the first... All surviving nodes within a cluster are routed to the cluster center. The distance and.

[0096] Then proceed to step four, in which the quantum dung beetle swarm is initialized and its parameters are set.

[0097] According to an embodiment of the present invention, a quantum dung beetle swarm is initialized and relevant parameters are set. The swarm size is set to h, the maximum number of iterations is G, and the number of iterations is denoted as an integer g. When g = 1, the quantum positions of the quantum dung beetles in each dimension of the search space are initialized to uniform random numbers in the interval [0,1]. At the g-th iteration, the quantum position of the i-th quantum dung beetle in the q-dimensional search space is... The j-th dimension of its quantum position

[0098] Then, step five is executed. In step five, the degree of organic matter decomposition of all quantum dung beetles is calculated based on the mapping equation and the node dynamic clustering cost function, and the global optimal quantum position of the quantum dung beetle colony is determined.

[0099] According to an embodiment of the present invention, during the g-th iteration, each dimension of all quantum dung beetle quantum positions is mapped to the corresponding solution space range to obtain the mapped state of the quantum position. The mapping equation for the j-th dimension variable is: Among them, w max,j and w min,j Let $\mathbf{j}$ be the upper and lower bounds of the j-th dimension of the quantum dung beetle's position, respectively. Substitute the mapping state of the i-th quantum dung beetle's quantum position into the node dynamic clustering cost function to calculate the clustering cost value corresponding to the quantum dung beetle. This is used to measure the degree of organic matter decomposition at the i-th quantum dung beetle quantum position. The superscript Λ indicates that the matrix is ​​reconstructed with two rows. It is stipulated that the smaller the cluster generation value, the greater the degree of organic matter decomposition in the quantum dung beetle. All quantum dung beetles are arranged in descending order of their degree of organic matter decomposition. The quantum position with the highest degree of organic matter decomposition is defined as the globally optimal quantum position of the quantum dung beetle group up to the (g+1)-th generation.

[0100] Then proceed to step six, in which the quantum dung beetle performs ball rolling, reproduction, foraging, and stealing behaviors, while using the quantum rotation angle to evolve the quantum dung beetle's quantum position.

[0101] According to embodiments of the present invention, the quantum dung beetle performs behaviors such as rolling a ball, reproducing, foraging, and stealing, while using a quantum rotation angle to evolve the quantum position of the quantum dung beetle. The update equation for the j-th dimension of the quantum position of the i-th quantum dung beetle is defined. Defining ball rolling, reproduction, and foraging behaviors Definition of theft in, Let e1 be the j-th quantum rotation angle of the i-th quantum dung beetle in the (g+1)-th generation, e2 be the thieving factor, and e1 be the reproduction factor. A random number uniformly distributed within the interval [0,1]. The random number is uniformly distributed within the interval [-1, 1]. Mapping state of quantum position of quantum dung beetle The gradient of the j-th dimension, and They are respectively Only the j-th dimension becomes and The degree to which the post-quantum dung beetle decomposes organic matter, ε being the differential factor.

[0102] Then proceed to step seven, in which a selection mechanism is used to select the quantum position and the globally optimal quantum position of the next generation of quantum dung beetles.

[0103] According to an embodiment of the present invention, a selection mechanism is used to select the quantum position of the next generation of quantum dung beetles and the globally optimal quantum position. The degree of organic matter decomposition of all quantum dung beetles at the initial and newly generated quantum positions is calculated, and a greedy strategy is applied to select the optimal quantum position from the set of quantum positions. The h quantum positions with the highest degree of organic matter decomposition were selected as the quantum positions for the next generation of quantum dung beetles. The quantum dung beetles of generation g+1 are sorted from highest to lowest degree of organic matter decomposition, and the globally optimal quantum position of the quantum dung beetle colony is updated up to generation g+1.

[0104] Then proceed to step eight, in which the quantum dung beetle mechanism evolution termination judgment is made, and the current wireless sensor network cluster center is output.

[0105] According to an embodiment of the present invention, if the quantum dung beetle mechanism evolution has not reached the maximum number of iterations G, then let g = g + 1, and continue the quantum dung beetle mechanism evolution; if the maximum number of iterations G is reached, then the quantum dung beetle mechanism evolution terminates, and the mapping state of the globally optimal quantum position of the quantum dung beetle swarm is reconstructed and used as the cluster center of the current wireless sensor network. Output.

[0106] Then proceed to step nine, in which the wireless sensor network performs dynamic cluster head election.

[0107] According to an embodiment of the present invention, after the network dynamic clustering is completed, the cluster head election phase begins. The cluster head node is primarily responsible for collecting and aggregating data from cluster members, as well as sending data to the base station or acting as a relay node to forward data information. The main indicators to consider when selecting a cluster head include the node's remaining energy and its distance from the base station. To prevent premature node death, selecting a cluster head with higher energy helps balance the cluster load; to reduce cluster head energy consumption, a node closer to the base station is selected. During the data transmission phase, each surviving node corresponds to a data set containing its remaining energy and location information. After reading this data set, the cluster head selects a suitable node as the new cluster head. To reduce node energy consumption, Time Division Multiple Access (TDMA) is used for stable data transmission.

[0108] Finally, step ten is executed. In step ten, the wireless sensor network performs stable data transmission and updates the network status, and determines that the network operation has terminated.

[0109] According to an embodiment of the present invention, after the wireless sensor network completes dynamic clustering and cluster head election, it enters a stable data transmission phase. All surviving nodes are assigned to c tWithin a cluster, when a surviving node is the cluster head, the energy loss for stable data transmission. This represents the number of non-bisexual nodes within the cluster containing the cluster head. When a surviving node is a cluster node, its stable data transmission has two scenarios depending on whether it is a bisexual node. If it is a non-bisexual node, the node sends the data group to the cluster head according to the TDMA time slot allocated by the cluster head. The cluster head then merges the data groups using single-hop communication before sending them out. Its energy loss... This is the cluster head of the cluster to which this node belongs; if it is a bisexual node, then this node uses single-hop communication to send data packets directly, and its energy consumption is low. After stable data transmission is completed, update the node's liveness flag. This updates the network status.

[0110] In this embodiment, the network termination condition is as follows: if t+1 = T or If the wireless sensor network reaches the maximum number of rounds T or all nodes are destroyed, the network will terminate. Otherwise, let t = t + 1, return to step three, and the network will enter the next round.

[0111] Further experiments were conducted to verify the technical effects of the present invention:

[0112] The experimental parameters were set as follows: the hierarchical routing optimization method was denoted as QDBO; the low-power adaptive clustering routing protocol was denoted as LEACH; and the hierarchical routing protocol based on particle swarm optimization fuzzy C-means clustering was denoted as PSOFCM, specifically referring to the "WSN clustering routing algorithm based on PSO optimization fuzzy C-means" published by Sun Aijing et al. in the *Journal of Communications*. In LEACH, the cluster head probability was set to 0.08, and the rotation period was round(1 / 0.08), where round(·) is the nearest integer function. In PSOFCM, the maximum number of iterations was set to 1000, the fuzzy factor to 2, and the algorithm termination condition threshold to 10. -6 In the proposed QDBO, h = 20, G = 300, and w max,j =100, w min,j =0, e1=-0.4, e2=0.02, ε=1. The wireless sensor network is deployed in a two-dimensional plane with a monitoring area of ​​100m×100m. All sensor nodes have the same initial conditions in terms of energy, computing power, and communication capabilities. The number of sensor nodes is set to N=100, and the energy consumption per bit of data transmission and reception is E. elec =50nJ / bit, energy consumption per unit bit of data processing E da =5nJ / bit, power amplification energy consumption coefficient ε in free-space channel model fs = 10pJ / (bit·m)2 ), power amplification energy consumption coefficient ε of multipath fading channel model mp = 0.0013pJ / (bit·m) 4 ), data packet size m d =4000 bits, control packet size m c =100bit, data fusion compression ratio γ=0.4, network coefficient l=0.4, maximum number of network rounds T=4000, location of base station when deployed within the monitoring area Location when deployed outside the monitoring area The nodes are deployed with an initial energy of 1J.

[0113] like Figures 2 to 5 The simulation results show that the proposed hierarchical routing optimization method for wireless sensor networks based on the quantum dung beetle mechanism has better energy efficiency and network lifetime than the benchmark protocol, and can be applied as a network-level energy-saving method to practical static wireless sensor networks.

[0114] Another embodiment of the present invention proposes a hierarchical routing optimization system for wireless sensor networks based on the quantum dung beetle mechanism, the system comprising:

[0115] Network model building module: It is configured to build a model of a wireless sensor network system;

[0116] Network parameter setting module: It is configured to set and initialize network parameters to enable network operation;

[0117] Network optimization module: It is configured to determine the number of clusters and dynamically cluster them, while establishing a dynamic clustering cost function for network nodes;

[0118] Algorithm configuration module: It is configured to initialize the quantum dung beetle swarm and set parameters;

[0119] Algorithm optimization module: It is configured to calculate the degree of organic matter decomposition of all quantum dung beetles based on the mapping equation and the node dynamic clustering cost function, and determine the global optimal quantum position of the quantum dung beetle colony;

[0120] Population evolution module: It is configured for quantum dung beetles to perform ball rolling, reproduction, foraging and stealing behaviors, while using quantum rotation angles to evolve the quantum position of quantum dung beetles;

[0121] Global optimization module: It is configured to use a selection mechanism to select the quantum position and the globally optimal quantum position of the next generation of quantum dung beetles;

[0122] Cluster center output module: It is configured to determine the termination of the quantum dung beetle mechanism evolution and output the current wireless sensor network cluster center;

[0123] Dynamic cluster head election module: It is configured for dynamic cluster head election in wireless sensor networks;

[0124] Network update module: It is configured to perform stable data transmission and update the network status of the wireless sensor network, and determine when the network operation is terminated.

[0125] The functionality of the hierarchical routing optimization system for wireless sensor networks based on the quantum dung beetle mechanism described in this embodiment of the invention can be explained by the aforementioned hierarchical routing optimization method for wireless sensor networks based on the quantum dung beetle mechanism. Therefore, for the parts not detailed in the system embodiment, please refer to the above method embodiment, and they will not be repeated here.

[0126] Although the invention has been described with respect to a limited number of embodiments, those skilled in the art will understand from the foregoing description that other embodiments are conceivable within the scope of the invention described herein. The disclosure of the invention is illustrative and not restrictive, and the scope of the invention is defined by the appended claims.

Claims

1. A hierarchical routing optimization method for wireless sensor networks based on the quantum dung beetle mechanism, characterized in that, Includes the following steps: Step 1: Establish a wireless sensor network system model; Step 2: Set and initialize network parameters to enable network operation; Step 3: Determine the number of clusters and perform dynamic clustering, while establishing a dynamic clustering cost function for network nodes; Step 4: Initialize the quantum dung beetle swarm and set parameters; Step 5: Based on the mapping equation and the node dynamic clustering cost function, calculate the degree of organic matter decomposition of all quantum dung beetles and determine the global optimal quantum position of the quantum dung beetle colony; Step Six: The quantum dung beetle performs rolling, breeding, foraging, and stealing behaviors, while using quantum rotation angles to evolve its quantum position; Step 7: Use a selection mechanism to select the quantum position and the globally optimal quantum position of the next generation of quantum dung beetles; Step 8: Determine if the quantum dung beetle mechanism evolution terminates, and output the current wireless sensor network cluster center; Step 9: Dynamic Cluster Head Election in Wireless Sensor Networks; Step 10: The wireless sensor network transmits stable data while updating the network status, and determines when the network operation has terminated. The wireless sensor network system mentioned in step one is: in Randomly and evenly deployed within the monitoring area A network of wireless sensors, where nodes remain stationary after deployment and self-organize; base stations possess unlimited energy and computing power, while sensors have limited energy and computing power; there are no obstacles within the monitoring area that could impede wireless signal transmission; a hybrid network architecture is supported, with data transmission occurring in a chain-like pattern; sensors are densely deployed, and adjacent sensors generate similar response data to the same type of event; sensors self-regulate their wireless transmission power, and the wireless communication links are symmetrical; wireless sensor network nodes consume energy through internal network communication and localized data processing, where internal network communication involves the routing and transmission of data packets or control packets within the network, and localized data processing involves performing localized fusion and compression processing on the sensed data; The wireless sensor network system model is established as follows: A first-order radio model is used to simulate communication energy consumption. This first-order radio model includes a transmitting circuit, a power amplifier circuit, and a receiving circuit, as detailed below: ， ， ， in, , and These represent the energy consumed by the node in sending, receiving, and processing data, respectively. Indicates the communication distance between nodes. This indicates the amount of information sent by the node. Indicates the data fusion compression rate. and These represent the energy consumed by the node in sending, receiving, and processing a unit of bit information, respectively. , is the channel threshold. and These represent the power amplification energy consumption coefficients for free space and multipath fading channel models, respectively. Step two includes the following process: Setting up a wireless sensor network The parameter set for the wheel operation is as follows: ， in, and These are communication data packets and control data packets, respectively. Location of the base station; For the first The location of each sensor node; For the first Round of running The remaining energy of each sensor node, , , The number of sensor nodes. This represents the maximum number of rounds that a wireless sensor network can run. This is a marker for bisexual nodes. For network coefficients, when This indicates that the sensor node can communicate with the base station as a cluster head or intra-cluster node. This indicates that the sensor node can act as a cluster head to communicate with the base station or that nodes within the cluster can communicate with the cluster head. , for the first The liveness flag of the sensor node during round-trip operation, when When this sensor node is destroyed, it indicates that the sensor node has been destroyed. This indicates that the sensor node is alive; Step three includes the following process: Determine the network number The number of clusters during round-robin operation is: ， in, It is a function that rounds down to the nearest integer. This indicates the number of surviving nodes; dynamic clustering is performed again when the network runs for the first time or when the number of clusters changes; when and If this happens, proceed to step nine without performing dynamic clustering; Establishing the network The node dynamic clustering cost function during round-robin operation is as follows: ， in, Represents the set of cluster centers. Indicates the first The position vector of the cluster center , To find the first... All surviving nodes within a cluster are routed to the cluster center. The distance and; Step four includes: setting the quantum dung beetle population size to... The maximum number of iterations is The number of iterations is marked as an integer. ;when At that time, the quantum position of the quantum dung beetle in each dimension of the search space is initialized to... Uniform random numbers within the interval; the first During the nth iteration, the 1st A quantum dung beetle in The quantum position in the dimensional search space is The first quantum position dimension , , ; Step five includes the following process: In the next iteration, each dimension of all quantum dung beetle quantum positions is mapped to the corresponding solution space, resulting in the mapped state of the quantum position: , No. The mapping equation for the dimensional variable is: , , ,in, and The positions of the quantum dung beetle are respectively Upper and lower bounds of a dimension; The first The mapped state of each quantum dung beetle's quantum position is substituted into the node dynamic clustering cost function to calculate the clustering cost value corresponding to the quantum dung beetle. , measuring the first The degree of organic matter decomposition at each quantum position of a dung beetle, where the superscript... The matrix is ​​reconstructed in 2 rows, and it is stipulated that the smaller the cluster generation value, the greater the degree of decomposition of organic matter in the quantum dung beetle; Arrange all quantum dung beetles in descending order of their degree of organic matter decomposition, and define the quantum position with the highest degree of organic matter decomposition as the quantum dung beetle group, up to the [number missing]th [number missing]. The global optimal quantum position up to date is ; Step six includes defining the first... Only the quantum dung beetle The quantum position update equation is: ， Defining ball rolling, reproduction, and foraging behaviors Defining theft , , ;in, For the first The generation The first quantum dung beetle 3D quantum rotation angle For reproductive factors, For theft factor, for Uniformly distributed random numbers within an interval. for Uniformly distributed random numbers within an interval. Mapping state of quantum position of quantum dung beetle The Middle Gradient of dimension , , , and They are respectively Only the middle Dimensional change and The extent to which post-quantum dung beetles decompose organic matter, The differential factor; Step seven involves: calculating the degree of organic matter decomposition by all quantum dung beetles at the initial and newly generated quantum positions, applying a greedy strategy, and selecting from the set of quantum positions... Select before The quantum position of a high degree of organic matter decomposition, as the quantum position of the next generation of quantum dung beetle. , ;Will The quantum dung beetles are sorted from highest to lowest degree of organic matter decomposition, and the quantum dung beetle colony is updated until the [number missing]th generation. The global optimal quantum position up to date is .

2. The hierarchical routing optimization method for wireless sensor networks based on the quantum dung beetle mechanism according to claim 1, characterized in that, Step eight includes: if the quantum dung beetle mechanism evolution has not reached the maximum number of iterations... Then let Continue the evolution of the quantum dung beetle mechanism; if the maximum number of iterations is reached... If the quantum dung beetle mechanism evolution terminates, the mapping state of the global optimal quantum position of the quantum dung beetle swarm is reconstructed and used as the cluster center of the current wireless sensor network. Output the results.

3. The hierarchical routing optimization method for wireless sensor networks based on the quantum dung beetle mechanism according to claim 2, characterized in that, Step nine includes: performing cluster head election based on the dynamic clustering; using the cluster head node to collect and aggregate data from cluster members, and sending data to the base station or acting as a relay node to forward data information; the cluster head indicators include the node's remaining energy and the node's distance from the base station; with the goal of preventing premature node death, selecting a cluster node with higher energy to serve as the cluster head, and balancing the load within the cluster; with the goal of reducing cluster head energy consumption, selecting a node closer to the base station to serve as the cluster head. During the data transmission phase, each surviving node corresponds to a data group, which includes its remaining energy and location information. After reading the data group, the cluster head selects a suitable node as the new cluster head and uses Time Division Multiple Access (TDMA) for stable data transmission with the goal of reducing node energy consumption.

4. The hierarchical routing optimization method for wireless sensor networks based on the quantum dung beetle mechanism according to claim 3, characterized in that, Step ten includes: all surviving nodes are assigned to Within a cluster, when a surviving node is the cluster head, the energy loss for stable data transmission is: ， in, This represents the number of non-bisexual nodes within the cluster containing the cluster head. When a surviving node is an in-cluster node, stable data transmission identifies whether it is a bisexual node. If it is a non-bisexual node, the non-bisexual node sends the data group to the cluster head according to the TDMA time slot allocated by the cluster head. The cluster head then merges the data group using single-hop communication and sends it out. The energy loss is: ， in, This is the cluster head of the cluster to which this node belongs; If it is a bipolar node, the bipolar node directly sends the data group using single-hop communication, and the energy loss is: ； After stable data transmission is completed, the network status is updated by updating the node liveness flag; the node liveness flag is: ； Network termination judgment: If the following conditions are met or This indicates that the wireless sensor network has run to the maximum number of rounds. Either all nodes die, and the network terminates; otherwise, let Return to step three, and the network will enter the next round of operation.

5. A hierarchical routing optimization system for wireless sensor networks based on the quantum dung beetle mechanism, characterized in that, include: Network model building module: It is configured to build a wireless sensor network system model; the wireless sensor network system is: in Randomly and evenly deployed within the monitoring area Each wireless sensor node, once deployed, remains stationary and self-organizes into a network; the base station has unlimited energy and computing power, while the sensors have limited energy and computing power; there are no obstacles within the monitoring area that could impede the transmission of wireless signals. The system supports a hybrid network architecture with a chain-like data transmission pattern; sensors are densely deployed, and the response data generated by adjacent sensors to similar events are similar; sensors self-regulate their wireless transmission power, and the wireless communication links are symmetrical; wireless sensor network nodes consume energy through internal network communication and localized data processing. Internal network communication involves the routing and transmission of data packets or control packets within the network, and localized data processing involves performing localized fusion and compression processing on the sensed data. The wireless sensor network system model is established as follows: A first-order radio model is used to simulate communication energy consumption. This first-order radio model includes a transmitting circuit, a power amplifier circuit, and a receiving circuit, as detailed below: ， ， ， in, , and These represent the energy consumed by the node in sending, receiving, and processing data, respectively. Indicates the communication distance between nodes. This indicates the amount of information sent by the node. Indicates the data fusion compression rate. and These represent the energy consumed by the node in sending, receiving, and processing a unit of bit information, respectively. , is the channel threshold. and These represent the power amplification energy consumption coefficients for free space and multipath fading channel models, respectively. Network parameter setting module: It is configured to set and initialize network parameters to enable network operation; it sets the wireless sensor network parameters. The parameter set for the wheel operation is as follows: ， in, and These are communication data packets and control data packets, respectively. Location of the base station; For the first The location of each sensor node; For the first Round of running The remaining energy of each sensor node, , , The number of sensor nodes. This represents the maximum number of rounds that a wireless sensor network can run. This is a marker for bisexual nodes. For network coefficients, when This indicates that the sensor node can communicate with the base station as a cluster head or intra-cluster node. This indicates that the sensor node can act as a cluster head to communicate with the base station or that nodes within the cluster can communicate with the cluster head. , for the first The liveness flag of the sensor node during round-trip operation, when When this sensor node is destroyed, it indicates that the sensor node has been destroyed. This indicates that the sensor node is alive; Network optimization module: It is configured to determine the number of clusters and dynamically cluster them, while establishing a dynamic clustering cost function for network nodes; determining the network's... The number of clusters during round-robin operation is: ， in, It is a function that rounds down to the nearest integer. This indicates the number of surviving nodes; dynamic clustering is performed again when the network runs for the first time or when the number of clusters changes; when and If this happens, proceed to step nine without performing dynamic clustering; Establishing the network The node dynamic clustering cost function during round-robin operation is as follows: ， in, Represents the set of cluster centers. Indicates the first The position vector of the cluster center , To find the first... All surviving nodes within a cluster are routed to the cluster center. The distance and; Algorithm configuration module: It is configured to initialize the quantum dung beetle colony and set parameters; the size of the quantum dung beetle colony is set to... The maximum number of iterations is The number of iterations is marked as an integer. ;when At that time, the quantum position of the quantum dung beetle in each dimension of the search space is initialized to... Uniform random numbers within the interval; the first During the nth iteration, the 1st A quantum dung beetle in The quantum position in the dimensional search space is The first quantum position dimension , , ; Algorithm optimization module: It is configured to calculate the degree of organic matter decomposition of all quantum dung beetles based on the mapping equation and the node dynamic clustering cost function, and determine the globally optimal quantum position of the quantum dung beetle colony; In the next iteration, each dimension of all quantum dung beetle quantum positions is mapped to the corresponding solution space, resulting in the mapped state of the quantum position: , No. The mapping equation for the dimensional variable is: , , ,in, and The positions of the quantum dung beetle are respectively Upper and lower bounds of a dimension; The first The mapped state of each quantum dung beetle's quantum position is substituted into the node dynamic clustering cost function to calculate the clustering cost value corresponding to the quantum dung beetle. , measuring the first The degree of organic matter decomposition at each quantum position of a dung beetle, where the superscript... The matrix is ​​reconstructed in 2 rows, and it is stipulated that the smaller the cluster generation value, the greater the degree of decomposition of organic matter in the quantum dung beetle; Arrange all quantum dung beetles in descending order of their degree of organic matter decomposition, and define the quantum position with the highest degree of organic matter decomposition as the quantum dung beetle group, up to the [number missing]th [number missing]. The global optimal quantum position up to date is ; The group evolution module is configured to enable quantum dung beetles to perform ball-rolling, reproduction, foraging, and stealing behaviors, while using quantum rotation angles to evolve the quantum position of the quantum dung beetle; defining the first... Only the quantum dung beetle The quantum position update equation is: ， Defining ball rolling, reproduction, and foraging behaviors Defining theft , , ;in, For the first The generation The first quantum dung beetle 3D quantum rotation angle For reproductive factors, For theft factor, for Uniformly distributed random numbers within an interval. for Uniformly distributed random numbers within an interval. Mapping state of quantum position of quantum dung beetle The Middle Gradient of dimension , , , and They are respectively Only the middle Dimensional change and The extent to which post-quantum dung beetles decompose organic matter, The differential factor; The global optimization module is configured to use a selection mechanism to choose the quantum position of the next generation of quantum dung beetles and the globally optimal quantum position; calculate the degree of organic matter decomposition of all quantum dung beetles at the initial and newly generated quantum positions, and apply a greedy strategy to select the optimal quantum position from the set of quantum positions. Select before The quantum position of a high degree of organic matter decomposition, as the quantum position of the next generation of quantum dung beetle. , ;Will The quantum dung beetles are sorted from highest to lowest degree of organic matter decomposition, and the quantum dung beetle colony is updated until the [number missing]th generation. The global optimal quantum position up to date is ; Cluster center output module: It is configured to determine the termination of the quantum dung beetle mechanism evolution and output the current wireless sensor network cluster center; Dynamic cluster head election module: It is configured for dynamic cluster head election in wireless sensor networks; Network update module: It is configured to perform stable data transmission and update the network status of the wireless sensor network, and determine when the network operation is terminated.

Images