Wireless sensor network hierarchical routing optimization method and system based on quantum dung beetle mechanism

Abstract

The invention discloses a wireless sensor network hierarchical routing optimization method and system based on a quantum dung beetle mechanism, relates to the technical field of wireless communication, and is used for solving the technical problems of unbalanced energy consumption and short network life of a wireless sensor network in a traditional method. The method is technically characterized by comprising the following steps: establishing a wireless sensor network system model; determining a clustering number and dynamically clustering, and establishing a network node dynamic clustering cost function at the same time; a quantum dung beetle group is initialized, parameters are set, the organic matter decomposition degree of all quantum dung beetles is calculated, and the global optimal quantum position of the quantum dung beetle group is determined; the quantum positions of the quantum dung beetles are evolved, and a global optimal quantum position is obtained through a selection mechanism; determining quantum dung beetle mechanism evolution termination, and outputting a current wireless sensor network cluster center; performing dynamic cluster head election on the wireless sensor network; and the wireless sensor network performs stable data transmission and updates the network state at the same time. The energy consumption efficiency of the wireless sensor network is effectively improved, and the service life of the network is effectively prolonged.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method and system for optimizing hierarchical routing of wireless sensor networks based on a quantum dung beetle mechanism. Background Art

[0002] Wireless sensor network (WSN) is a multi-hop self-organizing network composed of a large number of micro sensor nodes. It monitors various target information in the node deployment area in real time. After the sensor node processes the target information, it sends it out through wireless communication. It has been widely used in military reconnaissance, medical care and industrial production. On the one hand, due to the miniaturization of sensor structure design, the capacity of the sensor embedded battery is limited; on the other hand, since sensor nodes usually use small drones to deploy sensors in areas that are difficult for humans to access, it is not easy to replace node batteries or secondary recharge is very difficult. Therefore, resource limitation is one of the most important problems in wireless sensor networks. Effective routing protocol design can effectively avoid the problems of excessive energy consumption and short network life.

[0003] Wireless sensor network routing protocols can be divided into two categories: flat routing and hierarchical routing. Flat routing protocols stipulate that all wireless sensors play the same role in the routing process, do not introduce a hierarchical management mechanism, do not set up local management nodes, lack effective resource allocation, and are only suitable for small-scale networks. Different from flat routing protocols, the positions and functions of sensor nodes in hierarchical routing protocols are different, and can basically be divided into three categories: base stations, cluster head nodes, and cluster nodes. In the logical view of hierarchical routing protocols, the network structure is clustered horizontally, and each cluster consists of a cluster head and multiple cluster nodes. The vertical layers are recursive, and finally communicate directly with the base station. The cluster head performs local data compression during the communication process to reduce data redundancy and realize a more advanced data description form for specific applications, which effectively improves energy efficiency and network life. With the continuous expansion of network scale, hierarchical routing protocols have become the mainstream protocols for wireless sensor networks at this stage.

[0004] Through the search of existing literature, it is found that Heinzelman et al. proposed a fully distributed, self-organizing and adaptive clustering routing protocol LETCH for a two-layer network architecture in "Energy-efficient communication protocol for wireless microsensor networks". At present, the design of various wireless sensor network routing protocols is based on this protocol. The network architecture using the LETCH protocol is clustered. The nodes in the cluster are responsible for sensing the environment and communicating directly with the cluster head. The cluster head performs the role of gateway, is responsible for processing data and communicating directly with the base station. Since LEACH adopts a completely random cluster head configuration method, LEACH has problems such as node overload and network life reduction to a certain extent. Sun Aijing et al. published "WSN Clustering Routing Algorithm Based on PSO Optimization Fuzzy C Mean" in Journal of Communications (2021, 42(03): 91-99), proposing a hierarchical routing protocol PSOFCM based on particle swarm optimization fuzzy C mean to achieve clustering. First, the particle swarm optimization algorithm is used to dynamically and centrally build clusters for the network to balance the load within the cluster, and then the cat swarm algorithm is used to optimize the data relay transmission process. The search results of existing literature show that the existing WSN hierarchical routing protocols have problems such as load imbalance, energy consumption imbalance, high computational complexity and poor practicability because the design of routing methods directly affects the working time of the network.

[0005] In summary, there are still many problems in the cluster division of the hierarchical routing protocol in the existing technology, especially the simultaneous optimization of data transmission has not been achieved. There is a lack of methods that can utilize the advantages of hierarchical routing protocols in reducing energy consumption compared to flat routing protocols to design a simple, efficient, low-complexity, energy-efficient hierarchical routing method that can maximize network life. Summary of the invention

[0006] In view of this, the present invention proposes a method and system for optimizing hierarchical routing of wireless sensor networks based on quantum dung beetle mechanism, so as to solve the technical problems of unbalanced energy consumption and short network life of wireless sensor networks in the prior art.

[0007] According to one aspect of the present invention, a method for optimizing hierarchical routing in wireless sensor networks based on a 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 make the network run;

[0010] Step 3: Determine the number of clusters and dynamically cluster them, and establish a dynamic clustering cost function for network nodes;

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

[0012] Step 5: According to the mapping equation and the node dynamic clustering cost function, the decomposition degree of all quantum dung beetles' organic matter is calculated to determine the global optimal quantum position of the quantum dung beetle group;

[0013] Step 6: The quantum dung beetle performs rolling, reproduction, foraging and stealing behaviors, and uses the quantum rotation angle to evolve the quantum position of the quantum dung beetle;

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

[0015] Step 8: The quantum dung beetle mechanism evolution is terminated and the current wireless sensor network cluster center is output;

[0016] Step 9: Dynamic cluster head election in wireless sensor network;

[0017] Step 10: The wireless sensor network performs stable data transmission and updates the network status at the same time, and determines that the network operation is terminated.

[0018] Furthermore, the wireless sensor network system described in step 1 is as follows: N wireless sensors are randomly and evenly deployed in an M×M monitoring area, and the nodes do not move over time after deployment and form a network by themselves; the energy and computing power of the base station are unlimited, and the energy and computing power of the sensor are limited; there are no obstacles in the monitoring area that may hinder the transmission of wireless signals; a hybrid network architecture is supported, and the data transmission mode is chain-shaped; the sensors are densely deployed, and the response data generated by adjacent sensors to similar events are similar; the sensors self-regulate the wireless transmission power, and the wireless communication links are symmetrical; the wireless sensor network nodes generate energy consumption through internal network communication and data localization processing, wherein the internal network communication is the routing and receiving of data packets or control packets within the network, and the data localization processing is the localization fusion compression processing performed 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. The first-order radio model includes a transmitting circuit, a power amplifier circuit, and a receiving circuit, as follows:

[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 nodes to send, receive and process data, d represents the communication distance between nodes, k represents the amount of information sent by nodes, γ represents the data fusion compression rate, and E elec and E da They represent the energy consumed by a node in sending, receiving and processing a unit bit of information respectively; is the channel threshold, ε fs and ε mp Represent the power amplification energy consumption coefficients of the free space and multipath fading channel models respectively.

[0023] Furthermore, the process of step 2 includes:

[0024] The parameter set for the wireless sensor network during the tth round of operation is set as:

[0025]

[0026] Among them, m d and m c They are communication data packets and control data packets respectively; is the base station location; s n =[x n ,y n ] is the position of the nth sensor node; is the residual energy of the nth sensor node in the tth round of operation, n∈[1,N], t∈[1,T], N is the number of sensor nodes, and T is the maximum number of rounds of operation of the wireless sensor network; is the bisexual node symbol, l is the network coefficient, when q n =1 indicates that this sensor node can communicate with the base station as a cluster head or a node within the cluster. n =0 means that this sensor node can communicate with the base station as a cluster head or with the cluster head as a node in the cluster; is the survival flag of the sensor node during the tth round of operation. When , it indicates that the sensor node is alive.

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

[0028] Determine the number of clusters when the network runs in round t:

[0029]

[0030] Among them, round(·) is the nearest integer function; Indicates the number of surviving nodes; when the network is running for the first time or the number of clusters changes, dynamic clustering is performed again; when t≠1 and c t =c t-1When , go to step 9 and do not perform dynamic clustering;

[0031] The node dynamic clustering cost function for the tth round of network operation is as follows:

[0032]

[0033] in, represents the set of cluster centers, Indicates The position vector of the cluster center, Indicates the first All surviving nodes in the cluster to the cluster center The distance and.

[0034] Furthermore, the process of step 4 includes: setting the size of the quantum dung beetle population to h, the maximum number of iterations to G, and the number of iterations to an integer g; when g = 1, the quantum position of the quantum dung beetle in each dimensional search space is initialized to a uniform random number 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 jth dimension of the quantum position

[0035] The process of step five includes: at the g-th iteration, each dimension of all quantum dung beetle quantum positions is mapped to the corresponding solution space, and the mapping state of the quantum position is obtained as follows: The mapping equation of the j-th dimension variable is: Among them, w max,j and w min,j are the upper and lower limits of the j-th dimension of the quantum dung beetle position respectively;

[0036] Substitute the mapping state of the quantum position of the i-th quantum dung beetle into the node dynamic clustering cost function to calculate the clustering cost value corresponding to the quantum dung beetle Measures the degree of organic matter decomposition of the i-th quantum dung beetle quantum position, where the superscript Λ indicates that the matrix is ​​reconstructed by 2 rows, and stipulates that the smaller the clustering cost, the greater the degree of organic matter decomposition of the quantum dung beetle;

[0037] Arrange all quantum dung beetles in descending order according to the degree of organic matter decomposition, and define the quantum position with the highest degree of organic matter decomposition as the global optimal quantum position of the quantum dung beetle group until the g+1th generation as

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

[0039]

[0040] Rolling, breeding and foraging behavior defined Define stealing i=1,2,...,h,j=1,2,…,q;where, is the j-dimensional quantum rotation angle of the i-th quantum dung beetle of the g+1th generation, e1 is the reproduction factor, e2 is the stealing factor, is a random number uniformly distributed in the interval [0,1]. is a random number uniformly distributed in the interval [-1,1]. is the mapping state of the quantum position of the quantum dung beetle The gradient of the jth dimension in , and They are Only the jth dimension becomes and The degree of decomposition of organic matter by post-quantum dung beetles, ε is the differential factor;

[0041] The process of step seven includes: calculating the degree of organic matter decomposition of all quantum dung beetles at the initial and newly generated quantum positions, applying a greedy strategy, and selecting the optimal quantum position from the quantum position set. Select h quantum positions with a larger degree of organic matter decomposition as the quantum positions of the next generation of quantum dung beetles Sort the g+1 generation of quantum dung beetles according to the degree of organic matter decomposition from large to small, and update the quantum dung beetle group until the g+1 generation. The global optimal quantum position is

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

[0043] Furthermore, the process of step nine includes: performing cluster head election based on the dynamic clustering; using cluster head nodes to collect and aggregate data of cluster members, and sending data to the base station or forwarding data information as a relay node; the indicators of cluster heads include node residual energy and the distance between the node and the base station; with the goal of non-premature death of nodes, selecting nodes in the cluster with higher energy as cluster heads to balance the load in the cluster; with the goal of reducing cluster head energy consumption, selecting nodes that are closer to the base station as cluster heads;

[0044] Each surviving node in the data transmission phase corresponds to a data group, which includes its own remaining energy and location information; the cluster head reads the data group and 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, the process of step 10 includes: all surviving nodes are assigned to c t In a cluster, when a surviving node is the cluster head, the energy loss of stable data transmission is:

[0046]

[0047] in, is the number of non-binary nodes in the cluster where the cluster head is located;

[0048] When a surviving node is a node in the cluster, stable data transmission identifies whether it is a dual-sex node; if it is a non-dual-sex node, the non-dual-sex node sends the data group to the cluster head according to the TDMA time slot allocated by the cluster head, and the cluster head uses a single-hop communication method to merge the data group and send it out. The energy loss is:

[0049]

[0050] in, is the cluster head of the cluster where the node is located;

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

[0052]

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

[0054]

[0055] Network operation termination judgment: if t+1=T or It means that the wireless sensor network runs to the maximum number of rounds T or all nodes die, and the network terminates; otherwise, let t = t + 1, return to step 3, and the network enters the next round of operation.

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

[0057] A network model building module: configured to build a wireless sensor network system model;

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

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

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

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

[0062] Group evolution module: It configures the quantum dung beetles to perform rolling, reproduction, foraging and stealing behaviors, and uses the quantum rotation angle to evolve the quantum position of the quantum dung beetles;

[0063] A global optimization module: configured to select the quantum position of the next generation of quantum dung beetles and the global optimal quantum position using a selection mechanism;

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

[0065] Dynamic cluster head election module: it is configured to perform dynamic cluster head election in wireless sensor networks;

[0066] Network update module: It is configured to perform stable data transmission on the wireless sensor network while updating the network status and determining the termination of network operation.

[0067] The beneficial technical effects of the present invention are:

[0068] Compared with the prior art, the proposed method of wireless sensor network hierarchical routing optimization based on quantum dung beetle mechanism is superior to the existing benchmark protocols in terms of energy efficiency, network life and scalability. In order to avoid uneven clustering of wireless sensor network nodes, the quantum dung beetle mechanism is used for dynamic clustering, which has the characteristics of high convergence accuracy. In order to reduce cluster head energy consumption and avoid premature node death, the two factors of node residual energy and node-base station distance are considered and combined with the network status to select the optimal cluster head, which can further balance the node load and network energy consumption. Simulation experiments have proved that the energy efficiency of the wireless sensor network using the present invention is effectively improved and the network life is effectively extended.

[0069] Based on the study of LEACH protocol, this invention proposes a hierarchical routing optimization method for wireless sensor networks based on quantum dung beetle mechanism, which runs in a "round" cycle, and each round includes three stages: dynamic clustering, cluster head election and data transmission. In the dynamic clustering stage, the quantum dung beetle mechanism is used to realize the dynamic clustering of wireless sensor network nodes, which is inspired by the behavior of dung beetles and combined with the quantum rotation angle to evolve the quantum dung beetle quantum state; in the cluster head election stage, the node residual energy and the distance between the node and the base station are considered to elect the optimal cluster head; in the data transmission stage, the concept of dual-sex nodes is introduced, and this node can communicate directly with the base station as a cluster head or a node within the cluster. This hierarchical routing optimization method can effectively balance network energy consumption and extend the network working time.

[0070] The present invention establishes a wireless sensor network system model, which includes a monitoring area, wireless sensors, base stations, a hybrid network architecture, and a chain data transmission mode; wherein the wireless sensors have limited energy and computing power, can self-regulate wireless transmission power, the communication link is symmetrical, the densely deployed sensor nodes do not move over time and self-form a network, and the response data of adjacent sensors to similar events are similar. The present invention uses the model to obtain the energy consumption of the wireless sensor network, initializes the parameters of the wireless sensor network to make the wireless sensor network run, and then optimizes the wireless sensor network based on the quantum dung beetle mechanism, so that the wireless sensor network can transmit data stably, and updates the state of the wireless sensor network by updating the survival flag of the node. BRIEF DESCRIPTION OF THE DRAWINGS

[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 this specification and form a part of this specification, and are used to further illustrate the preferred embodiments of the present invention and explain the principles and advantages of the present invention.

[0072] Figure 1 This is a flow chart of a method for optimizing hierarchical routing in wireless sensor networks based on a quantum dung beetle mechanism according to the present invention.

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

[0074] Figure 3 It is a curve diagram showing the relationship between network residual energy and network operation round number when the base station is deployed in the area of ​​the present invention.

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

[0076] Figure 5This is a curve diagram showing the relationship between network residual energy and network operation round number when the base station is deployed outside the area in the present invention. DETAILED DESCRIPTION

[0077] In order to enable those skilled in the art to better understand the scheme of the present invention, exemplary implementations or embodiments of the present invention will be described below in conjunction with the accompanying drawings. Obviously, the described implementations or embodiments are only implementations or embodiments of a part of the present invention, not all of them. Based on the implementations or embodiments of the present invention, all other implementations or embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0078] The embodiment of the present invention proposes a method for optimizing hierarchical routing in wireless sensor networks based on the quantum dung beetle mechanism. Figure 1 As shown, the method comprises the following steps:

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

[0080] Step 2: Set and initialize network parameters to make the network run;

[0081] Step 3: Determine the number of clusters and dynamically cluster them, and establish a dynamic clustering cost function for network nodes;

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

[0083] Step 5: According to the mapping equation and the node dynamic clustering cost function, the decomposition degree of all quantum dung beetles' organic matter is calculated to determine the global optimal quantum position of the quantum dung beetle group;

[0084] Step 6: The quantum dung beetle performs rolling, reproduction, foraging and stealing behaviors, and uses the quantum rotation angle to evolve the quantum position of the quantum dung beetle;

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

[0086] Step 8: The quantum dung beetle mechanism evolution is terminated and the current wireless sensor network cluster center is output;

[0087] Step 9: Dynamic cluster head election in wireless sensor network;

[0088] Step 10: The wireless sensor network performs stable data transmission and updates the network status at the same time, and determines that the network operation is terminated.

[0089] The method starts from step 1. In step 1, a wireless sensor network system model is established.

[0090] According to the embodiments of the present invention, the present invention makes the following assumptions on the wireless sensor network system model: N wireless sensors are randomly and evenly deployed in an M×M monitoring area, and the nodes do not move over time after deployment and form a network by themselves; the energy and computing power of the base station are infinite, and the energy and computing power of the sensor are limited; there are no obstacles in the monitoring area that may hinder the transmission of wireless signals; a hybrid network architecture can be supported, and the data transmission mode is chain-shaped; the sensors are densely deployed, and the response data generated by adjacent sensors to similar events are similar; the sensors can self-regulate the wireless transmission power, and the wireless communication link is symmetrical.

[0091] The energy consumption of wireless sensor network nodes mainly comes from two aspects: internal network communication and data localization processing. Internal network communication refers to the routing and receiving of data packets or control packets within the network, and data localization processing refers to the localization, fusion and compression processing of sensor data. The present 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 , where E TX (k,d),E RX (k) and E DA (k) represents the energy consumed by nodes to send, receive and process data, d represents the communication distance between nodes, k represents the amount of information sent by nodes, γ represents the data fusion compression rate, and E elec and E da They represent the energy consumed by a node to send, receive and process a unit bit of information. Among them, ε fs and ε mp They 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, step 2 is executed, in which network parameters are set and initialized to make the network run.

[0093] According to an embodiment of the present invention, the parameter set for setting the wireless sensor network during the tth round of operation is Indicates that, m d bit and m c bit is the size of the communication data packet and control data packet. is the base station location, s n =[x n ,y n ] is the position of the nth sensor node, is the residual energy of the nth sensor node in the tth round of operation, n∈[1,N], t∈[1,T], N is the number of sensor nodes, and T is the maximum number of rounds of operation of the wireless sensor network. Where l is the network coefficient, is the channel threshold, when q n =1 indicates that this sensor node can communicate with the base station as a cluster head or a node within the cluster; n = 0 means that this sensor node can communicate with the base station as a cluster head or with the cluster head as a node in the cluster. Survival flag of the sensor node in the tth round of operation when When , it means that the sensor node dies; when When , it means that the sensor node is alive. Initialize the wireless sensor network parameters and the network starts running.

[0094] Then, step three is executed. In step three, the number of clusters is determined and clustering is performed dynamically, and a cost function for dynamic clustering of network nodes is established.

[0095] According to an embodiment of the present invention, the number of clusters in the tth round of network operation is determined by analyzing and calculating the energy consumption of the wireless sensor network. Among them, round(·) is the nearest integer function, Indicates the number of surviving nodes. When the network is running for the first time or the number of clusters changes, dynamic clustering is performed again; when t≠1 and c t =c t-1 When , go to step 9 and do not perform dynamic clustering. Set the node dynamic clustering cost function when the network is running in round t as in, represents the set of cluster centers, Indicates The position vector of the cluster center, Indicates the first All surviving nodes in the cluster to the cluster center The distance and.

[0096] Then execute step 4, in which the quantum dung beetle swarm is initialized and parameters are set.

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

[0098] Then execute step five. In step five, according to the mapping equation and the node dynamic clustering cost function, the decomposition degree of all quantum dung beetle organic matter is calculated to determine the global optimal quantum position of the quantum dung beetle group.

[0099] According to an embodiment of the present invention, at the g-th iteration, each dimension of all quantum dung beetle quantum positions is mapped into the corresponding solution space to obtain the mapping state of the quantum position The mapping equation of the j-th dimension variable is Among them, w max,j and w min,j are the upper and lower limits of the j-th dimension of the quantum dung beetle position. Substitute the mapping state of the i-th quantum dung beetle quantum position into the node dynamic clustering cost function to calculate the clustering cost value corresponding to the quantum dung beetle. It is used to measure the degree of organic matter decomposition of the quantum position of the i-th quantum dung beetle, where the superscript Λ indicates that the matrix is ​​reconstructed in 2 rows, and the smaller the cluster generation value, the greater the degree of organic matter decomposition of the quantum dung beetle. All quantum dung beetles are arranged from large to small according to the degree of organic matter decomposition, and the quantum position with the largest degree of organic matter decomposition is defined as the global optimal quantum position of the quantum dung beetle group until the g+1th generation.

[0100] Then, step six is ​​executed, in which the quantum dung beetle performs rolling, reproduction, foraging, and stealing behaviors, while using the quantum rotation angle to evolve the quantum position of the quantum dung beetle.

[0101] According to an embodiment of the present invention, the quantum dung beetle performs rolling, reproduction, foraging and stealing behaviors, and uses the quantum rotation angle to evolve the quantum position of the quantum dung beetle. Define the update equation of the j-dimensional quantum position of the i-th quantum dung beetle: Rolling, breeding and foraging behavior defined Define stealing in, is the j-dimensional quantum rotation angle of the i-th quantum dung beetle of the g+1th generation, e1 is the reproduction factor, e2 is the stealing factor, is a random number uniformly distributed in the interval [0,1]. is a random number uniformly distributed in the interval [-1,1]. is the mapping state of the quantum position of the quantum dung beetle The gradient of the jth dimension in , and They are Only the jth dimension becomes and The degree of decomposition of organic matter by post-quantum dung beetles, ε is the differential factor.

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

[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 global 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 quantum position set. Select h quantum positions with a larger degree of organic matter decomposition as the quantum positions of the next generation of quantum dung beetles Sort the g+1 generation of quantum dung beetles from large to small according to the degree of organic matter decomposition, and update the global optimal quantum position of the quantum dung beetle group until the g+1 generation.

[0104] Then, step eight is executed. In step eight, 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 does not reach the maximum number of iterations G, g=g+1 is set to continue the quantum dung beetle mechanism evolution; if the maximum number of iterations G is reached, the quantum dung beetle mechanism evolution is terminated, and the mapping state of the global optimal quantum position of the quantum dung beetle group is reconstructed as the cluster center of the current wireless sensor network. Output.

[0106] Then, step nine is executed. In step nine, a dynamic cluster head election is performed in the wireless sensor network.

[0107] According to an embodiment of the present invention, after the network is dynamically clustered, it enters the cluster head election phase. The cluster head node is mainly responsible for data collection and aggregation of cluster members, as well as sending data to the base station or forwarding data information as a relay node. The indicators that need to be considered as a cluster head mainly include the remaining energy of the node and the distance between the node and the base station. In order to avoid premature death of nodes, selecting nodes in the cluster with higher energy as cluster heads helps to balance the load within the cluster; in order to reduce the energy consumption of the cluster head, nodes that are closer to the base station are selected as cluster heads. In the data transmission phase, each surviving node corresponds to a data group, which contains its own remaining energy and location information. After the cluster head reads the data group, it selects a suitable node as the new cluster head. In order 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 at the same time, and determines that the network operation is terminated.

[0109] According to the embodiment of the present invention, after the wireless sensor network completes dynamic clustering and cluster head election, it enters the stable data transmission phase. All surviving nodes are assigned to c tIn a cluster, when a surviving node is the cluster head, the energy loss of its stable data transmission is the number of non-dual nodes in the cluster where the cluster head is located; when a surviving node is a node in the cluster, its stable data transmission has the following two situations depending on whether it is a dual node. If it is a non-dual 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 uses a single-hop communication method to merge the data group and send it out. Its energy loss is the cluster head of the cluster where the node is located; if it is a dual-sex node, the node uses a single-hop communication method to send the data group directly, and its energy loss is After stable data transmission is completed, the node survival flag is updated This updates the network status.

[0110] In this embodiment, the network operation termination judgment condition is as follows: if t+1=T or This indicates that the wireless sensor network has run to the maximum number of rounds T or all nodes have died, and the network has terminated; otherwise, let t = t + 1, return to step 3, and the network enters the next round of operation.

[0111] The technical effects of the present invention are further verified by experiments:

[0112] The experimental parameters are set as follows: the hierarchical routing optimization method is denoted as QDBO; the low-power adaptive clustering routing protocol is denoted as LEACH; the hierarchical routing protocol based on particle swarm optimization fuzzy C-means clustering is denoted as PSOFCM, which refers 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 is set to 0.08, the rotation period is round(1 / 0.08), where round(·) is the nearest integer function; in PSOFCM, the maximum number of iterations is set to 1000, the fuzzy factor is 2, and the algorithm termination condition threshold is 10 -6 In the proposed QDBO, we set 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. In terms of energy, computing power and communication capabilities, the sensor nodes have the same initial conditions. Set the number of sensor nodes N=100, and the energy consumption per bit of data transmission and reception E elec =50nJ / bit, energy consumption per bit of data processing E da =5nJ / bit, free space channel model power amplification energy consumption coefficient ε fs =10pJ / (bit·m2 ), the power amplification energy consumption coefficient ε of the multipath fading channel model mp =0.0013pJ / (bit·m 4 ), packet size m d =4000bit, control packet size m c =100bit, data fusion compression rate γ = 0.4, network coefficient l = 0.4, maximum number of network operation rounds T = 4000, base station is deployed in the monitoring area Position 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 shown indicate that the energy efficiency and network lifetime of the wireless sensor network hierarchical routing optimization method based on the quantum dung beetle mechanism proposed in the present invention are superior to those of the benchmark protocol, and can be applied as a network-level energy-saving method to actual static wireless sensor networks.

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

[0115] A network model building module: configured to build a wireless sensor network system model;

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

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

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

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

[0120] Group evolution module: It configures the quantum dung beetles to perform rolling, reproduction, foraging and stealing behaviors, and uses the quantum rotation angle to evolve the quantum position of the quantum dung beetles;

[0121] A global optimization module: configured to select the quantum position of the next generation of quantum dung beetles and the global optimal quantum position using a selection mechanism;

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

[0123] Dynamic cluster head election module: it is configured to perform dynamic cluster head election in wireless sensor networks;

[0124] Network update module: It is configured to perform stable data transmission on the wireless sensor network while updating the network status and determining the termination of network operation.

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

[0126] Although the present invention has been described according to a limited number of embodiments, it will be apparent to those skilled in the art, with the benefit of the above description, that other embodiments are contemplated within the scope of the invention thus described. The disclosure of the present invention is intended to be illustrative rather than restrictive of the scope of the invention, which is defined by the appended claims.

Claims

1. A method for optimizing hierarchical routing in wireless sensor networks based on quantum dung beetle mechanism, characterized in that: The following steps are involved: Step 1: Establish a wireless sensor network system model; Step 2: Set and initialize network parameters to make the network run; Step 3: Determine the number of clusters and dynamically cluster them, and establish a dynamic clustering cost function for network nodes; Step 4: Initialize the quantum dung beetle swarm and set parameters; Step 5: According to the mapping equation and the node dynamic clustering cost function, the decomposition degree of all quantum dung beetles' organic matter is calculated to determine the global optimal quantum position of the quantum dung beetle group; Step 6: The quantum dung beetle performs rolling, reproduction, foraging and stealing behaviors, and uses the quantum rotation angle to evolve the quantum position of the quantum dung beetle; Step 7: Use the selection mechanism to select the quantum position of the next generation of quantum dung beetles and the global optimal quantum position; Step 8: The quantum dung beetle mechanism evolution is terminated and the current wireless sensor network cluster center is output; Step 9: Dynamic cluster head election in wireless sensor network; Step 10: The wireless sensor network performs stable data transmission and updates the network status at the same time, and determines that the network operation is terminated.

2. According to claim 1, a method for optimizing hierarchical routing in wireless sensor networks based on quantum dung beetle mechanism is characterized in that: The wireless sensor network system described in step 1 is as follows: N wireless sensors are randomly and evenly deployed in an M×M monitoring area. After being deployed, the nodes do not move over time and form a network by themselves; the energy and computing power of the base station are unlimited, while the energy and computing power of the sensor are limited; there are no obstacles in the monitoring area that may hinder the transmission of wireless signals; Support hybrid network architecture, data transmission mode is chain-like; sensors are densely deployed, and the response data generated by adjacent sensors to similar events are similar; sensors self-regulate wireless transmission power, and wireless communication links are symmetrical; wireless sensor network nodes generate energy consumption through internal network communication and data localization processing, the internal network communication is the routing and receiving of data packets or control packets within the network, and the data localization processing is the localized fusion and compression processing of 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. The first-order radio model includes a transmitting circuit, a power amplifier circuit, and a receiving circuit, as follows: Among them, E TX (k,d),E RX (k) and E DA (k) represents the energy consumed by nodes to send, receive and process data, d represents the communication distance between nodes, k represents the amount of information sent by nodes, γ represents the data fusion compression rate, and E elec and E da They represent the energy consumed by a node in sending, receiving and processing a unit bit of information respectively; is the channel threshold, ε fs and ε mp Represent the power amplification energy consumption coefficients of the free space and multipath fading channel models respectively.

3. According to claim 2, a method for optimizing hierarchical routing in wireless sensor networks based on quantum dung beetle mechanism is characterized in that: The process of step 2 includes: The parameter set for the wireless sensor network during the tth round of operation is set as: Among them, m d and m c They are communication data packets and control data packets respectively; is the base station location; s n =[x n ,y n ] is the position of the nth sensor node; is the residual energy of the nth sensor node in the tth round of operation, n∈[1,N], t∈[1,T], N is the number of sensor nodes, and T is the maximum number of rounds of operation of the wireless sensor network; is the bisexual node symbol, l is the network coefficient, when q n =1 indicates that this sensor node can communicate with the base station as a cluster head or a node within the cluster. n =0 means that this sensor node can communicate with the base station as a cluster head or with the cluster head as a node in the cluster; is the survival flag of the sensor node during the tth round of operation. When , it indicates that the sensor node is alive.

4. According to claim 3, a method for optimizing hierarchical routing in wireless sensor networks based on quantum dung beetle mechanism is characterized in that: The process of step three includes: The number of clusters when the network runs in round t is determined as: Among them, round(·) is the nearest integer function; Indicates the number of surviving nodes; when the network is running for the first time or the number of clusters changes, dynamic clustering is performed again; when t≠1 and c t =c t-1 When , go to step 9 and do not perform dynamic clustering; The node dynamic clustering cost function for the tth round of network operation is as follows: in, represents the set of cluster centers, Indicates The position vector of the cluster center, Indicates the first All surviving nodes in the cluster to the cluster center The distance and.

5. The method for optimizing hierarchical routing in wireless sensor networks based on quantum dung beetle mechanism according to claim 4 is characterized in that: The process of step 4 includes: setting the size of the quantum dung beetle population to h, the maximum number of iterations to G, and the number of iterations to an integer g; when g = 1, the quantum position of the quantum dung beetle in each dimensional search space is initialized to a uniform random number 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 jth dimension of the quantum position i=1,2,…,h, j=1,2,…,q; The process of step five includes: at the g-th iteration, each dimension of all quantum dung beetle quantum positions is mapped to the corresponding solution space, and the mapping state of the quantum position is obtained as follows: The mapping equation of the j-th dimension variable is: i=1,2,…,h,j=1,2,…,q,where w max,j and w min,j are the upper and lower limits of the j-th dimension of the quantum dung beetle position respectively; Substitute the mapping state of the quantum position of the i-th quantum dung beetle into the node dynamic clustering cost function to calculate the clustering cost value corresponding to the quantum dung beetle Measures the degree of organic matter decomposition of the i-th quantum dung beetle quantum position, where the superscript Λ indicates that the matrix is ​​reconstructed by 2 rows, and stipulates that the smaller the clustering cost, the greater the degree of organic matter decomposition of the quantum dung beetle; Arrange all quantum dung beetles in descending order according to the degree of organic matter decomposition, and define the quantum position with the highest degree of organic matter decomposition as the global optimal quantum position of the quantum dung beetle group until the g+1th generation as 6. The method for optimizing hierarchical routing in wireless sensor networks based on quantum dung beetle mechanism according to claim 5 is characterized in that: The process of step six includes: defining the update equation of the j-dimensional quantum position of the i-th quantum dung beetle as: Rolling, breeding and foraging behavior defined Define stealing i=1,2,…,h,j=1,2,…,q;where, is the j-dimensional quantum rotation angle of the i-th quantum dung beetle of the g+1th generation, e1 is the reproduction factor, e2 is the stealing factor, is a random number uniformly distributed in the interval [0,1]. A random number uniformly distributed in the interval, is the mapping state of the quantum position of the quantum dung beetle The gradient of the jth dimension in , and They are Only the jth dimension becomes and The degree of decomposition of organic matter by post-quantum dung beetles, ε is the differential factor; The process of step seven includes: calculating the degree of organic matter decomposition of all quantum dung beetles at the initial and newly generated quantum positions, applying a greedy strategy, and selecting the optimal quantum position from the quantum position set. Select h quantum positions with a larger degree of organic matter decomposition as the quantum positions of the next generation of quantum dung beetles i=1,2,…,h; Sort the g+1 generation of quantum dung beetles according to the degree of organic matter decomposition from large to small, and update the quantum dung beetle group until the g+1 generation. The global optimal quantum position is 7. The method for optimizing hierarchical routing in wireless sensor networks based on quantum dung beetle mechanism according to claim 6 is characterized in that: The process of step eight includes: if the quantum dung beetle mechanism evolution has not reached the maximum number of iterations G, then set g=g+1 and continue the quantum dung beetle mechanism evolution; if the maximum number of iterations G is reached, the quantum dung beetle mechanism evolution is terminated, and the mapping state of the global optimal quantum position of the quantum dung beetle group is reconstructed as the cluster center of the current wireless sensor network. Output.

8. The method for optimizing hierarchical routing in wireless sensor networks based on quantum dung beetle mechanism according to claim 7, characterized in that: The process of step nine includes: performing cluster head election based on the dynamic clustering; using cluster head nodes to collect and aggregate data of cluster members, and sending data to the base station or forwarding data information as a relay node; the indicators of cluster heads include the remaining energy of the node and the distance between the node and the base station; with the goal of non-premature death of nodes, selecting nodes in the cluster with higher energy as cluster heads to balance the load in the cluster; with the goal of reducing the energy consumption of cluster heads, selecting nodes that are closer to the base station as cluster heads; Each surviving node in the data transmission phase corresponds to a data group, which includes its own remaining energy and location information; the cluster head reads the data group and 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.

9. The method for optimizing hierarchical routing in wireless sensor networks based on quantum dung beetle mechanism according to claim 8, characterized in that: Step 10 includes: all surviving nodes are assigned to c t In a cluster, when a surviving node is the cluster head, the energy loss of stable data transmission is: in, is the number of non-binary nodes in the cluster where the cluster head is located; When a surviving node is a node in the cluster, stable data transmission identifies whether it is a dual-sex node; if it is a non-dual-sex node, the non-dual-sex node sends the data group to the cluster head according to the TDMA time slot allocated by the cluster head, and the cluster head uses a single-hop communication method to merge the data group and send it out. The energy loss is: in, is the cluster head of the cluster where the node is located; If it is a dual-sex node, the dual-sex node directly sends the data group using a single-hop communication method, and the energy loss is: After stable data transmission is completed, the network status is updated by updating the node survival flag; the updated node survival flag is: Network operation termination judgment: if t+1=T or It means that the wireless sensor network runs to the maximum number of rounds T or all nodes die, and the network terminates; otherwise, let t = t + 1, return to step 3, and the network enters the next round of operation.

10. A wireless sensor network hierarchical routing optimization system based on quantum dung beetle mechanism, characterized in that: include: A network model building module: configured to build a wireless sensor network system model; Network parameter setting module: it is configured to set and initialize network parameters to enable network operation; Network optimization module: configured to determine the number of clusters and dynamically cluster, and to establish a dynamic clustering cost function for network nodes; Algorithm setting module: it is configured to initialize the quantum dung beetle swarm and set parameters; Algorithm optimization module: It is configured to calculate the decomposition degree of all quantum dung beetle organic matter according to the mapping equation and the node dynamic clustering cost function, and determine the global optimal quantum position of the quantum dung beetle group; Group evolution module: It configures the quantum dung beetles to perform rolling, reproduction, foraging and stealing behaviors, and uses the quantum rotation angle to evolve the quantum position of the quantum dung beetles; A global optimization module: configured to select the quantum position of the next generation of quantum dung beetles and the global optimal quantum position using a selection mechanism; Cluster center output module: It is configured to judge the termination of quantum dung beetle mechanism evolution and output the current wireless sensor network cluster center; Dynamic cluster head election module: it is configured to perform dynamic cluster head election in wireless sensor networks; Network update module: It is configured to perform stable data transmission on the wireless sensor network while updating the network status and determining the termination of network operation.

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