Wireless network clustering method and device based on natural neighbor adaptive clustering

Abstract

The invention relates to a wireless network clustering method and device based on natural neighbor adaptive clustering. The method comprises the following steps: acquiring node information of all nodes in a wireless network of an aircraft cluster, forming a data set according to the node information, generating a clustering result of the wireless network according to the data set by using a natural neighbor-based adaptive clustering algorithm, calculating a cluster head in each cluster according to the clustering result, and calculating the cluster head in each cluster according to the clustering result. And a clustering result and cluster head information are distributed to all nodes through a wireless control link. By adopting the method, dynamic wireless network clustering can be realized.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a wireless network clustering method and device based on natural neighbor adaptive clustering. Background Art

[0002] Currently, with the increase in the number of nodes in wireless networks, traditional single-layer wireless network networking protocols face problems such as high network maintenance overhead and difficult convergence of routing algorithms, especially for mobile self-organizing network scenarios. Therefore, a clustering algorithm is used to divide large-scale wireless network nodes into multiple sub-clusters, and each sub-cluster selects a cluster head node. If the nodes in the cluster need to be transmitted across clusters, the data must be forwarded through the cluster head node of the cluster. The clustering algorithm can divide a single-layer wireless network into two levels, one is the networking layer within the cluster, and the other is the networking layer between cluster heads. The clustering algorithm can effectively reduce the complexity of network maintenance by dividing large-scale wireless networks into multiple sub-clusters.

[0003] The core of the clustering algorithm is to use the current state information of the wireless network, and divide the entire wireless network nodes into multiple subclusters through the consistency relationship and optimization objectives between different nodes, so that the nodes in the same subcluster have high consistency and different subclusters have high differentiation. The division of subclusters needs to consider the actual node task type, node location speed and other information, increase the rationality of subcluster division, and make the subcluster maintenance time longer. Traditional wireless network clustering methods mainly include static clustering and dynamic clustering. The static clustering method is manually set before the network is built, and the wireless network is divided into multiple subclusters according to information such as task type, but this method has the problem of insufficient dynamic adaptability. Dynamic clustering methods include multiple methods based on hop count, clustering, etc. Among them, clustering-based clustering algorithms have higher dynamic adaptability.

[0004] The clustering method based on clustering is mainly based on the status information of all nodes in the network, and divides the entire network into multiple clusters according to the consistency between nodes. For the clustering problem of large-scale wireless networks, it is first necessary to solve the problems of uneven node density distribution and position deviation nodes. Secondly, traditional clustering algorithms generally need to set the prior parameters of the response. The K-means algorithm needs to set the number of clusters in advance, and the DBSCAN algorithm needs to set the clustering distance Eps and density threshold MinPts in advance, which reduces the adaptability of the clustering algorithm in clustering applications. Therefore, designing a clustering method with adaptive capabilities can effectively improve the effect and dynamic adaptability of clustering, and has better adaptability to highly dynamic wireless networks such as drone cluster networking. Summary of the invention

[0005] Based on this, it is necessary to provide a wireless network clustering method and device based on natural neighbor adaptive clustering to address the above technical problems.

[0006] A wireless network clustering method based on natural neighbor adaptive clustering, the method comprising:

[0007] Obtaining node information of all nodes in the wireless network of the aircraft cluster, and forming a data set according to the node information; the node information includes: position, speed and remaining energy;

[0008] According to the data set, a clustering result of the wireless network is generated by using a natural neighbor adaptive clustering algorithm; wherein the natural neighbor algorithm is executed to obtain the number of natural neighbors of all nodes in the wireless network, the maximum number of natural neighbors among the natural neighbors and the core node with the smallest number among the maximum number of natural neighbors are searched, and according to the maximum natural neighbor distance and the maximum number of natural neighbors of the core node, the DBSCAN algorithm is used to perform clustering to obtain the clustering result;

[0009] According to the clustering result, the cluster head in each cluster is calculated, and the clustering result and the information of the cluster head are distributed to all nodes through a wireless control link.

[0010] In one of the embodiments, a clustering algorithm execution device is set in an aircraft cluster or a ground base station; the clustering algorithm execution device collects node information of all nodes in the wireless network of the aircraft cluster by using a wireless control link.

[0011] In one of the embodiments, D_temp=D, label=1 is set, the distances between all nodes are calculated based on the node information in the data set, and the natural neighbor number NAN_num of all nodes in the wireless network is determined based on the distances.

[0012] In one embodiment, the maximum number of natural neighbors K among the natural neighbors and the core node d with the smallest number among the maximum number of natural neighbors are searched. K , get the core node d K The corresponding maximum natural neighbor distance dist;

[0013] Set the cluster distance to dist, the cluster density threshold to K, and use the DBSCAN algorithm to perform clustering to obtain the core node d K The cluster label n and the core node d K Dataset D with the same cluster label K , after executing all nodes, the clustering results are obtained.

[0014] In one embodiment, the data set D KThe clustering results of all nodes in the dataset D are marked as label, and the dataset D K The natural neighbor numbers of all nodes in are marked as -1, and D is deleted from the data set D_temp. K ;

[0015] Let label=label+1, and determine whether the length of the data set D_temp is equal to 0. If so, end.

[0016] In one embodiment, the average position of all nodes in the cluster is calculated.

[0017] Calculate the distance between all nodes in the cluster and the center location

[0018] Calculate the cluster head selection factor index η i for:

[0019] η i =ω 1 η Ni +ω 2 η Di +ω 3 η Ei

[0020] Among them, ω 1 ∈(0,1),ω 2 ∈(0,1) and ω 3 ∈(0,1) represents the weight factor, and ω 1 +ω 2 +ω 3 =1,η Ni =N i / N represents the centrality metric of node i, where N i represents the number of natural neighbors of the node, N represents the total number of nodes in the cluster where the node is located, represents the distance metric of node i, η Ei Indicates the current remaining energy percentage of node i;

[0021] Select the selection factor index η in the cluster i The largest node is the cluster head, and the selection factor index η is selected in the cluster i The second largest node serves as the backup cluster head.

[0022] In one embodiment, clustering results and cluster head information are distributed to all nodes through a wireless control link, so that the wireless network distributes data according to the clustering results; wherein intra-cluster data is forwarded according to a self-organizing network protocol, and inter-cluster data is forwarded through a cluster head node;

[0023] The cluster head node periodically sends cluster maintenance information. If the backup cluster head node fails to receive the maintenance information for multiple times in a row, the backup cluster head will be converted into a cluster head. If the member node fails to receive the maintenance information for multiple times in a row, it will leave the original sub-cluster and start joining a new sub-cluster.

[0024] A wireless network clustering device based on natural neighbor adaptive clustering, the device comprising:

[0025] An information acquisition module is used to acquire node information of all nodes in the wireless network of the aircraft cluster, and form a data set according to the node information; the node information includes: position, speed and remaining energy;

[0026] A clustering module is used to generate a clustering result of the wireless network according to the data set by using a natural neighbor adaptive clustering algorithm; wherein the natural neighbor algorithm is executed to obtain the number of natural neighbors of all nodes in the wireless network, the maximum number of natural neighbors among the natural neighbors and the core node with the smallest number among the maximum number of natural neighbors are searched, and the DBSCAN algorithm is used to perform clustering according to the maximum natural neighbor distance and the maximum number of natural neighbors of the core node to obtain a clustering result;

[0027] The distribution module is used to calculate the cluster head in each cluster according to the clustering result, and distribute the clustering result and the information of the cluster head to all nodes through the wireless control link.

[0028] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0029] Obtaining node information of all nodes in the wireless network of the aircraft cluster, and forming a data set according to the node information; the node information includes: position, speed and remaining energy;

[0030] According to the data set, a clustering result of the wireless network is generated by using a natural neighbor adaptive clustering algorithm; wherein the natural neighbor algorithm is executed to obtain the number of natural neighbors of all nodes in the wireless network, the maximum number of natural neighbors among the natural neighbors and the core node with the smallest number among the maximum number of natural neighbors are searched, and according to the maximum natural neighbor distance and the maximum number of natural neighbors of the core node, the DBSCAN algorithm is used to perform clustering to obtain the clustering result;

[0031] According to the clustering result, the cluster head in each cluster is calculated, and the clustering result and the information of the cluster head are distributed to all nodes through a wireless control link.

[0032] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0033] Obtaining node information of all nodes in the wireless network of the aircraft cluster, and forming a data set according to the node information; the node information includes: position, speed and remaining energy;

[0034] According to the data set, a clustering result of the wireless network is generated by using a natural neighbor adaptive clustering algorithm; wherein the natural neighbor algorithm is executed to obtain the number of natural neighbors of all nodes in the wireless network, the maximum number of natural neighbors among the natural neighbors and the core node with the smallest number among the maximum number of natural neighbors are searched, and according to the maximum natural neighbor distance and the maximum number of natural neighbors of the core node, the DBSCAN algorithm is used to perform clustering to obtain the clustering result;

[0035] According to the clustering result, the cluster head in each cluster is calculated, and the clustering result and the information of the cluster head are distributed to all nodes through a wireless control link.

[0036] The above-mentioned wireless network clustering method and device based on natural neighbor adaptive clustering can adaptively divide the wireless network into several sub-clusters according to the current network status, without specifying parameters such as the number of sub-clusters, the number of members in the cluster, and the cluster radius in advance. It has good adaptability to highly dynamic networks such as drone clusters, and can adapt to situations where the sub-cluster density is uneven. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A schematic diagram of a flow chart of a wireless network clustering method based on natural neighbor adaptive clustering in one embodiment;

[0038] Figure 2 FIG. 1 is an application environment diagram of a wireless network clustering method based on natural neighbor adaptive clustering in one embodiment;

[0039] Figure 3 A framework diagram of a wireless network clustering method based on natural neighbor adaptive clustering in another embodiment;

[0040] Figure 4 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0042] In one embodiment, Figure 1 As shown, a wireless network clustering method based on natural neighbor adaptive clustering is provided, comprising the following steps:

[0043] Step 102, obtaining node information of all nodes in the wireless network of the aircraft cluster, and forming a data set according to the node information.

[0044] Specifically, the node information includes: location, speed and remaining energy.

[0045] In the wireless network of aircraft clusters, the clustering algorithm execution device plays a key role. It can be set on a ground base station or a specific node of an aircraft cluster to collect information through a specific wireless control link. Each node uses the GPS positioning system and the inertial measurement unit IMU to obtain its own position and speed information. The control unit uses the power management system to obtain the remaining energy estimate and sends a data packet containing information such as node number, position, speed and remaining energy through the control channel at a cycle of 1 second. After collecting this information from each node, the clustering algorithm execution device integrates it into a data set D. This data set comprehensively records the status of all nodes, laying a solid data foundation for subsequent wireless network clustering and cluster head selection based on the natural neighbor adaptive clustering algorithm.

[0046] Step 104: Generate a clustering result of the wireless network based on the data set by using a natural nearest neighbor adaptive clustering algorithm.

[0047] A natural neighbor algorithm is executed to obtain the number of natural neighbors of all nodes in the wireless network, and the maximum number of natural neighbors among the natural neighbors and the core node with the smallest number among the maximum number of natural neighbors are searched. According to the maximum natural neighbor distance and the maximum number of natural neighbors of the core node, a DBSCAN algorithm is used for clustering to obtain a clustering result.

[0048] Step 106: Calculate the cluster head in each cluster according to the clustering result, and distribute the clustering result and the information of the cluster head to all nodes through the wireless control link.

[0049] In the above-mentioned wireless network clustering based on natural neighbor adaptive clustering, the wireless network can be adaptively divided into several sub-clusters according to the current network status. There is no need to specify parameters such as the number of sub-clusters, the number of members in the cluster, and the cluster radius in advance. It has good adaptability to highly dynamic networks such as drone clusters, and can adapt to uneven sub-cluster density.

[0050] In one of the embodiments, a clustering algorithm execution device is set in an aircraft cluster or a ground base station; the clustering algorithm execution device collects node information of all nodes in the wireless network of the aircraft cluster by using a wireless control link.

[0051] Specifically, Figure 2As shown, the clustering algorithm execution device can be placed on a ground base station and use the control channel of the device platform communication link to collect the status information of all nodes in the wireless network. First, the device node in the wireless network uses its own GPS positioning information and inertial measurement unit IMU information to obtain the node's own position information and speed information. At the same time, the control unit of the device node uses the power management system to obtain an estimate of the remaining energy of the entire device. In the wireless network discovery phase, each node in the network periodically uses the control channel to send a data packet containing information such as node number, location, speed and remaining energy. The clustering algorithm execution device collects the network status information of all nodes in the network to form a network status data set D. Specifically, the nodes in the network send node status information with a period of 1s. When the clustering algorithm execution device does not receive new node information within 1min, it is considered that all network status information has been collected and the clustering algorithm is executed.

[0052] In one of the embodiments, D_temp=D, label=1 is set, the distances between all nodes are calculated based on the node information in the data set, and the natural neighbor number NAN_num of all nodes in the wireless network is determined based on the distances.

[0053] Specifically, first, perform the initialization operation, set D_temp = D, label = 1, and use the natural neighbor algorithm to calculate the number of natural neighbors of all nodes. Specifically, in the process of calculating natural neighbors, the distances between all nodes in the network must be calculated, and the location of the clustering algorithm execution device is used as the coordinate origin to calculate the location coordinates of all nodes. Take any two nodes in the wireless network as an example, their locations are (100, 500, 300) m and (200, 500, 300) m, and their speeds are (50, 0, 0) m / s and (50, 10, 0) m / s, respectively. In the maximum communication distance R of the wireless network, max is 5000m, the maximum moving speed of the node is V max Under the condition of 50m / s, the equivalent distance between two nodes is 509.9m. The specific calculation refers to the following formula:

[0054]

[0055] in R max Represents the maximum communication distance between wireless network nodes, V max represents the maximum moving speed of wireless network nodes. The positions of any two nodes i and j in the wireless network are (x i ,y i ,z i ) and (x j ,y j ,z j), and the speeds are (v xi ,v yi ,v zi ) and (v xj ,v yj ,v zj ).

[0056] In one embodiment, the maximum number of natural neighbors K among the natural neighbors and the core node d with the smallest number among the maximum number of natural neighbors are searched. K , get the core node d K The corresponding maximum natural neighbor distance dist; set the cluster distance to dist, the cluster density threshold to K, and use the DBSCAN algorithm for clustering to obtain the core node d K The cluster label n and the core node d K Dataset D with the same cluster label K , after executing all nodes, the clustering results are obtained.

[0057] Specifically, if the currently obtained core node number is 5, the corresponding maximum number of neighbors is 10, and the corresponding maximum natural neighbor distance value is 1000, then the above parameters are passed to the subsequent steps.

[0058] In one embodiment, the data set D K The clustering results of all nodes in the dataset D are marked as label, and the dataset D K The natural neighbor numbers of all nodes in are marked as -1, and D is deleted from the data set D_temp. K ; Let label = label + 1, and determine whether the length of the data set D_temp is equal to 0. If so, end.

[0059] Specifically, using the parameter values ​​obtained in the above steps, the DBSCAN algorithm is executed on D_temp to obtain the clustering label value n of the core node under the parameter conditions of clustering distance Eps = 1000 and density threshold MinPts = 10. It is determined whether the clustering label value n of the core point 5 is equal to -1. If n is equal to -1, all the remaining nodes in D_temp are considered as noise points, and the clustering algorithm ends. According to the results obtained by the DBSCAN algorithm, the data set D with the same clustering label as the core point 5 is retrieved. K .

[0060] In one embodiment, the average position of all nodes in the cluster is calculated. Calculate the distance between all nodes in the cluster and the center location Calculate the cluster head selection factor index η i is: i =ω 1 η Ni+ω 2 η Di +ω 3 η Ei

[0061] Among them, ω 1 ∈(0,1),ω 2 ∈(0,1) and ω 3 ∈(0,1) represents the weight factor, and ω 1 +ω 2 +ω 3 =1,η Ni =N i / N represents the centrality metric of node i, where N i represents the number of natural neighbors of the node, N represents the total number of nodes in the cluster where the node is located, represents the distance metric of node i, η Ei Indicates the current remaining energy percentage of node i; select the selection factor index η in the cluster i The largest node is the cluster head, and the selection factor index η is selected in the cluster i The second largest node serves as the backup cluster head.

[0062] Specifically, take a subcluster containing 3 nodes as an example, whose positions and speeds are (100,500,300)m, (200,500,300)m, (100,400,300)m, and whose speeds are (10,0,0)m / s, (10,10,0)m / s, and (10,0,10)m / s, respectively. max is 5000m, the maximum moving speed of the node is V max Under the condition of 50m / s, the average position of all nodes in the sub-cluster can be calculated to be (633.3, 633.3, 466.7). The average position of the three nodes is (633.3, 633.3, 466.7), and the distances from the three nodes to the center point can be calculated to be 216, 408.3 and 288.7 respectively. To calculate the cluster head selection factor index, it is necessary to calculate the measurement results of three factors, including the centrality measurement index, the distance measurement index and the residual energy measurement index. First, select appropriate weight coefficients for the above three index factors. This implementation case sets ω 1 ,ω 2 and ω 3The values ​​of are 0.375, 0.375 and 0.25 respectively. Taking any node in the subcluster as an example, the number of natural neighbors of the node is 8, the total number of nodes in the subcluster is 20, the distance from the center of the subcluster is 200m, the maximum communication distance of the wireless network is 5000m, and the remaining energy is 80%, then the cluster head selection factor of the node is 0.71. The node with the largest index is selected as the cluster head, and the node with the second largest index is selected as the backup cluster head.

[0063] In one of the embodiments, clustering results and cluster head information are distributed to all nodes via a wireless control link, so that the wireless network distributes data according to the clustering results; wherein, intra-cluster data is forwarded according to a self-organizing network protocol, and inter-cluster data is forwarded via a cluster head node; the cluster head node periodically sends cluster maintenance information, and if a backup cluster head node fails to receive the maintenance information for multiple consecutive times, the backup cluster head is converted into a cluster head; if a member node fails to receive the maintenance information for multiple consecutive times, it leaves the cluster and performs the next round of clustering.

[0064] Specifically, during the network maintenance phase, the cluster head node periodically sends cluster maintenance information, indicating that the cluster head node is working normally. In this implementation case, the cluster head node can send cluster head maintenance information at a period of 1s. If the backup cluster head node does not receive maintenance information from the cluster head node for 10 consecutive times, it will change from the backup cluster head to the cluster head state. If a member node in the cluster does not receive maintenance information from the cluster head node for 10 consecutive times, it considers that it has detached from the current sub-cluster and enters the sub-cluster joining phase. During the sub-cluster joining phase, the member nodes in the cluster continuously listen to the cluster maintenance information sent by the surrounding nodes, and join the new sub-cluster after receiving new cluster maintenance information.

[0065] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0066] In one embodiment, a wireless network clustering device based on natural neighbor adaptive clustering is provided, comprising:

[0067] The information acquisition module 302 is used to acquire node information of all nodes in the wireless network of the aircraft cluster, and form a data set according to the node information; the node information includes: position, speed and remaining energy;

[0068] The clustering module 304 is used to generate a clustering result of the wireless network by using a natural neighbor adaptive clustering algorithm based on the data set; wherein the natural neighbor algorithm is executed to obtain the number of natural neighbors of all nodes in the wireless network, the maximum number of natural neighbors among the natural neighbors and the core node with the smallest number among the maximum number of natural neighbors are searched, and the DBSCAN algorithm is used to perform clustering according to the maximum natural neighbor distance and the maximum number of natural neighbors of the core node to obtain the clustering result;

[0069] The distribution module 306 is used to calculate the cluster head in each cluster according to the clustering result, and distribute the clustering result and the information of the cluster head to all nodes through the wireless control link.

[0070] In one of the embodiments, the information acquisition module 302 is further used to set up a clustering algorithm execution device in the aircraft cluster or in the ground base station; the clustering algorithm execution device uses a wireless control link to collect node information of all nodes in the wireless network of the aircraft cluster.

[0071] In one embodiment, the clustering module 304 is further configured to set D_temp=D, label=1, calculate the distances between all nodes according to the node information in the data set, and determine the natural neighbor number NAN_num of all nodes in the wireless network according to the distances.

[0072] In one embodiment, the clustering module 304 is further configured to search for the maximum natural neighbor number K among the natural neighbor numbers and the core node d with the smallest number among the maximum natural neighbor numbers. K , get the core node d K The corresponding maximum natural neighbor distance dist;

[0073] Set the cluster distance to dist, the cluster density threshold to K, and use the DBSCAN algorithm to perform clustering to obtain the core node d K The cluster label n and the core node d K Dataset D with the same cluster label K , after executing all nodes, the clustering results are obtained.

[0074] In one embodiment, the clustering module 304 is further configured to cluster the data set D K The clustering results of all nodes in the dataset D are marked as label, and the dataset D K The natural neighbor numbers of all nodes in are marked as -1, and D is deleted from the data set D_temp.K ;

[0075] Let lable = lable + 1, and determine whether the length of the dataset D_temp is equal to 0. If so, end.

[0076] In one embodiment, the clustering module 304 is further configured to calculate the average position of all nodes within the cluster

[0077]

[0078] Calculate the distance between all nodes within the cluster and the central position

[0079] Calculate the cluster head selection factor index η i It is:

[0080] η i = ω 1 η Ni + ω 2 η Di + ω 3 η Ei

[0081] Where ω 1 ∈(0, 1), ω 2 ∈(0, 1), and ω 3 ∈(0, 1) represent weight factors, and ω 1 + ω 2 + ω 3 = 1, η Ni = N i / N represents the centrality metric index of node i, where N i represents the natural neighbor number of this node, and N represents the total number of nodes in the cluster where this node is located. represents the distance metric index of node i, and η Ei represents the current remaining energy percentage of node i;

[0082] Select the node with the largest cluster selection factor index η i in the cluster as the cluster head, and select the node with the second largest cluster selection factor index η i in the cluster as the standby cluster head.

[0083] In one embodiment, the distribution module 306 is further configured to distribute the clustering result and the information of the cluster head to all nodes through a wireless control link, so that the wireless network distributes data according to the clustering result; among them, the data within the cluster is forwarded according to the ad hoc network protocol, and the data between clusters is forwarded through the cluster head node;

[0084] The cluster head node periodically sends cluster maintenance information. If the backup cluster head node fails to receive the maintenance information for multiple times in a row, the backup cluster head will be converted into a cluster head. If the member node fails to receive the maintenance information for multiple times in a row, it will leave the original sub-cluster and start joining a new sub-cluster.

[0085] For the specific definition of the wireless network clustering device based on natural neighbor adaptive clustering, please refer to the definition of the wireless network clustering method based on natural neighbor adaptive clustering in the above text, which will not be repeated here. Each module in the above-mentioned wireless network clustering device based on natural neighbor adaptive clustering can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0086] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a wireless network clustering method based on natural neighbor adaptive clustering is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse, etc.

[0087] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0088] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method in the above embodiment when executing the computer program.

[0089] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method in the above embodiment are implemented.

[0090] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0091] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0092] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A wireless network clustering method based on natural neighbor adaptive clustering, characterized in that: The method comprises: Obtaining node information of all nodes in the aircraft cluster wireless network, and forming a data set according to the node information; the node information includes: position, speed, and remaining energy; According to the data set, a clustering result of the wireless network is generated by using a natural neighbor adaptive clustering algorithm; wherein the natural neighbor algorithm is executed to obtain the number of natural neighbors of all nodes in the wireless network, the maximum number of natural neighbors among the natural neighbors and the core node with the smallest number among the maximum number of natural neighbors are searched, and according to the maximum natural neighbor distance and the maximum number of natural neighbors of the core node, the DBSCAN algorithm is used to perform clustering to obtain the clustering result; According to the clustering result, the cluster head in each cluster is calculated, and the clustering result and the information of the cluster head are distributed to all nodes through a wireless control link.

2. The method according to claim 1, characterized in that Get the node information of all nodes in the wireless network of the aircraft cluster, including: A clustering algorithm execution device is set in an aircraft cluster or a ground base station; the clustering algorithm execution device collects node information of all nodes in the wireless network of the aircraft cluster by using a wireless control link.

3. The method according to claim 1, characterized in that Execute the natural neighbor algorithm to obtain the number of natural neighbors of all nodes in the wireless network, including: Set D_temp=D, label=1, calculate the distances between all nodes according to the node information in the data set, and determine the natural neighbor number NAN_num of all nodes in the wireless network according to the distances.

4. The method according to claim 3, characterized in that Searching for the maximum number of natural neighbors among the natural neighbors and the core node with the smallest number among the maximum number of natural neighbors, clustering using the DBSCAN algorithm according to the maximum natural neighbor distance and the maximum number of natural neighbors of the core node, and obtaining a clustering result, including: Search for the maximum number of natural neighbors K among the natural neighbors and the core node d with the smallest number among the maximum number of natural neighbors. K , get the core node d K The corresponding maximum natural neighbor distance dist; Set the cluster distance to dist, the cluster density threshold to K, and use the DBSCAN algorithm to perform clustering to obtain the core node d K The cluster label n and the core node d K Dataset D with the same cluster label K , after executing all nodes, the clustering results are obtained.

5. The method according to claim 4, characterized in that The method further comprises: The dataset D K The clustering results of all nodes in the dataset D are marked as label, and the dataset D K The natural neighbor numbers of all nodes in are marked as -1, and D is deleted from the data set D_temp. K ; Let label=label+1, and determine whether the length of the data set D_temp is equal to 0. If so, end.

6. The method according to claim 5, characterized in that According to the clustering result, the cluster head in each cluster is calculated, including: Calculate the average position of all nodes in the cluster Calculate the distance between all nodes in the cluster and the center location Calculate the cluster head selection factor index η i for: or i =ω1η Ni +ω2η Di +ω3η Ei Among them, ω1∈(0,1), ω2∈(0,1) and ω3∈(0,1) represent weight factors, and ω1+ω2+ω3=1, η Ni =N i N represents the centrality metric of node i, where N i represents the number of natural neighbors of the node, N represents the total number of nodes in the cluster where the node is located, represents the distance metric of node i, η Ei Indicates the current remaining energy percentage of node i; Select the selection factor index η in the cluster i The largest node is the cluster head, and the selection factor index η is selected in the cluster i The second largest node serves as the backup cluster head.

7. The method according to any one of claims 1 to 6, characterized in that: The clustering results and cluster head information are distributed to all nodes through the wireless control link, including: The clustering results and cluster head information are distributed to all nodes through the wireless control link, so that the wireless network can distribute data according to the clustering results; the data within the cluster is forwarded according to the self-organizing network protocol, and the data between clusters is forwarded through the cluster head node; The cluster head node periodically sends cluster maintenance information. If the backup cluster head node fails to receive the maintenance information for multiple times in a row, the backup cluster head will be converted into a cluster head. If the member node fails to receive the maintenance information for multiple times in a row, it will leave the original sub-cluster and start joining a new sub-cluster.

8. A wireless network clustering device based on natural neighbor adaptive clustering, characterized in that: The device comprises: An information acquisition module is used to acquire node information of all nodes in the wireless network of the aircraft cluster, and form a data set according to the node information; the node information includes: position, speed and remaining energy; A clustering module is used to generate a clustering result of the wireless network according to the data set by using a natural neighbor adaptive clustering algorithm; wherein the natural neighbor algorithm is executed to obtain the number of natural neighbors of all nodes in the wireless network, the maximum number of natural neighbors among the natural neighbors and the core node with the smallest number among the maximum number of natural neighbors are searched, and the DBSCAN algorithm is used to perform clustering according to the maximum natural neighbor distance and the maximum number of natural neighbors of the core node to obtain a clustering result; The distribution module is used to calculate the cluster head in each cluster according to the clustering result, and distribute the clustering result and the information of the cluster head to all nodes through the wireless control link.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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