A method and system for identifying key nodes in an AdHoc network

Abstract

The application discloses a key node identification method and system in an Ad Hoc network, and relates to the technical field of network monitoring. The method comprises the following steps: determining the node betweenness of each node based on the shortest path number of the shortest path between target nodes and passing through each node according to the network topology of the target network at the current moment; constructing a node betweenness matrix composed of the node betweenness of all nodes; determining the betweenness centrality index value of each node according to the node betweenness matrix; determining the betweenness information entropy value of each node according to the node betweenness matrix and the betweenness centrality index value of each node; multiplying the betweenness information entropy value of each node by the betweenness centrality index value of each node as a weighted coefficient to obtain the key degree evaluation index of each node; and arranging the key degree evaluation index of each node in descending order, and outputting the first set number of nodes as the key nodes at the current moment. The application improves the identification accuracy of the key nodes.

Description

Technical Field

[0001] This invention relates to the field of network monitoring technology, and in particular to a method and system for identifying key nodes in an Ad Hoc network. Background Technology

[0002] Ad Hoc networks, due to their ability for nodes to communicate freely, move freely, and be rapidly constructed, have found increasingly widespread applications in modern military and civilian communications. To monitor or supervise nodes in Ad Hoc networks, it is necessary to identify a subset of critical nodes from the network topology. Identifying critical nodes in an enemy network can narrow the monitoring scope, reduce information processing load, and make the attack or reconnaissance range more focused; identifying critical nodes in one's own network can increase network stability and resilience by adding backups of these critical nodes. Most researchers primarily focus on static networks, evaluating the criticality of each node within the current static network by calculating key performance indicators. Common and intuitive indicators include centrality metrics such as degree centrality, betweenness centrality, and proximity centrality. These indicators directly reflect the degree to which a node is centrally located in the topology or connected to other nodes.

[0003] Degree centrality has low complexity but only reflects the local topological properties of a node. It is suitable for simpler network structures and is prone to having multiple nodes with the same degree centrality, resulting in low accuracy in distinguishing key nodes. Proximity centrality has higher computational complexity than degree centrality and is an average measure of the shortest distance between a node and other nodes, reflecting the node's global properties. Betweenness centrality reflects the proportion of a node appearing on the shortest path between other node pairs, providing information on the node's importance as a "bridge." It is a global property and has higher accuracy than the other two centrality metrics. Furthermore, none of these three centrality metrics consider dynamic networks and are only suitable for identifying key nodes in static networks. However, in reality, Ad Hoc networks often change dynamically over time. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for identifying key nodes in Ad Hoc networks, which improves the accuracy of key node identification.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A method for identifying key nodes in an Ad Hoc network, comprising:

[0007] Based on the current network topology of the target network, the node betweenness of each node is determined according to the number of shortest paths between target node pairs passing through each node; the target network is an Ad Hoc network, and the nodes are mobile devices.

[0008] Construct a node betweenness matrix consisting of the betweennesses of all nodes;

[0009] The betweenness centrality index of each node is determined based on the node betweenness matrix: the betweenness centrality index of node i is the sum of the values ​​of the elements in the i-th column of the node betweenness matrix;

[0010] The betweenness information entropy value of each node is determined based on the node betweenness matrix and the betweenness centrality index value of each node: the betweenness information entropy value of node i is the information entropy after normalizing the value of the element in the i-th column of the node betweenness matrix;

[0011] The criticality evaluation index of each node is obtained by multiplying the betweenness information entropy value of each node as a weighting coefficient with the betweenness centrality index value of each node.

[0012] The criticality evaluation indicators of each node are sorted in descending order, and the first set number of nodes are output as the critical nodes at the current moment.

[0013] Optionally, the target network is a drone swarm network, and the nodes in the drone swarm network are drones;

[0014] After sorting the criticality evaluation indicators of each node in descending order and outputting the first set number of nodes as the critical nodes at the current moment, the following also includes:

[0015] Countermeasures against key points at the current moment.

[0016] Optionally, the node betweenness of node i is represented as:

[0017]

[0018] Where, p st p represents the data of the shortest path between node s and node t. st (i) represents the number of nodes i that the shortest path between nodes s and t passes through, and N represents the number of nodes.

[0019] Optionally, the betweenness information entropy value of node i is represented as:

[0020]

[0021] Where E(i) represents the betweenness information entropy value of node i, δ st (i) represents the betweenness number of node i, and B(i) represents the betweenness centrality index value of node i.

[0022] Optionally, when the betweenness centrality index of node i is 0, the betweenness information entropy of node i is set to 0.

[0023] This invention also discloses a key node identification system in an Ad Hoc network, comprising:

[0024] The node betweenness determination module is used to determine the node betweenness of each node based on the network topology of the target network at the current time and the number of shortest paths between target node pairs passing through each node; the target network is an Ad Hoc network and the nodes are mobile devices.

[0025] The node betweenness matrix construction module is used to construct a node betweenness matrix consisting of the betweennesses of all nodes.

[0026] The betweenness centrality index value determination module for each node is used to determine the betweenness centrality index value of each node based on the node betweenness matrix: the betweenness centrality index of node i is the sum of the values ​​of the elements in the i-th column of the node betweenness matrix;

[0027] The betweenness information entropy value determination module for each node is used to determine the betweenness information entropy value of each node based on the node betweenness matrix and the betweenness centrality index value of each node: the betweenness information entropy value of node i is the information entropy after normalizing the value of the element in the i-th column of the node betweenness matrix;

[0028] The criticality evaluation index determination module for each node is used to multiply the betweenness information entropy value of each node as a weighting coefficient with the betweenness centrality index value of each node to obtain the criticality evaluation index of each node.

[0029] The critical node determination module is used to sort the criticality evaluation indicators of each node in descending order and output the first set number of nodes as the critical nodes at the current moment.

[0030] The present invention also discloses an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the key node identification method in the Ad Hoc network.

[0031] The present invention also discloses a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for identifying key nodes in the Ad Hoc network.

[0032] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0033] This invention uses the node betweenness information entropy as a weighting coefficient and multiplies it with the node's betweenness centrality index value to obtain a key node identification index. Compared with other centrality indices, this improves the adaptability in dynamic networks and can effectively identify key nodes with heavy routing and forwarding tasks in dynamic Ad Hoc networks. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a schematic diagram of a method for identifying key nodes in an Ad Hoc network according to an embodiment of the present invention;

[0036] Figure 2 This is a schematic block diagram illustrating the dynamic node deletion simulation process provided in an embodiment of the present invention.

[0037] Figure 3 This is a partial time-series dynamic network topology simulation diagram provided in an embodiment of the present invention; wherein, Figure 3 In the diagram, (a) represents the initial network, (b) represents the network after 1 time step, (c) represents the network after 2 time steps, and (d) represents the network after 3 time steps.

[0038] Figure 4 Simulation results of network efficiency as nodes are deleted, corresponding to the four key node evaluation index algorithms provided in the embodiments of the present invention.

[0039] Figure 5 This is a schematic diagram of a key node identification system in an Ad Hoc network provided by an embodiment of the present invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] The purpose of this invention is to provide a method and system for identifying key nodes in Ad Hoc networks, which improves the accuracy of key node identification.

[0042] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0043] Since nodes in Ad Hoc networks are generally mobile devices, the topology of the entire dynamic network is dynamic as nodes move. Therefore, in the actual identification of critical nodes in dynamic Ad Hoc networks, critical nodes may also change over time. If a node has a high betweenness centrality index in the network at the current moment, meaning the sum of probabilities that the node is on the shortest path of all node pairs is high, it indicates that the node is likely to bear a heavier routing and forwarding task. Therefore, from the perspective of routing and forwarding, the node can be considered to be of high criticality in the current network. However, due to the dynamic nature of Ad Hoc networks, the betweenness centrality index of the current network cannot fully measure whether the criticality of the node will still be high in the next moment. To address the aforementioned characteristics, this invention considers the distribution of the proportion of nodes on the shortest paths of each node pair. It proposes using node betweenness information entropy to characterize the probability distribution of nodes on the shortest paths of each node pair. The node betweenness information entropy is used as a weighting coefficient multiplied by the betweenness centrality index of the node as an evaluation index for key node identification, namely the entropy-weighted betweenness centrality index (keyness evaluation index). This overcomes the deficiency of the betweenness centrality index in being unable to characterize the distribution of node betweenness, while also retaining the global attribute information provided by the betweenness centrality index.

[0044] The variables in the criticality evaluation index include: (1) node label i; (2) the betweenness information entropy value E(i) of node i in the network; and (3) the betweenness centrality index value B(i) of node i in the network. Multiplying the betweenness information entropy value E(i) and the betweenness centrality index value B(i) above, we obtain the critical node identification evaluation index proposed in this invention. The definition is:

[0045] Example 1

[0046] Suppose there are N freely moving nodes in an Ad Hoc network, with node labels set {1,2,...,N}. Let (i,j) represent a pair of nodes, where i and j are node labels, i = 1,2,...,N, j = 1,2,...,N. Treating (i,j) and (j,i) as distinct node pairs and considering the case where i = j, the network has a total of N nodes. 2 Group of node pairs, the set of node pairs is:

[0047] {{(1,1),(1,2),...,(1,N)},{(2,1),(2,2),...,(2,N)},...,{(N,1),(N,2),...,(N,N)}}.

[0048] like Figure 1 As shown, this embodiment provides a method for identifying key nodes in an Ad Hoc network, which specifically includes the following steps:

[0049] Step 101: Based on the network topology of the target network at the current moment, determine the node betweenness of each node according to the number of shortest paths between target node pairs passing through each node; the target network is an Ad Hoc network, and the node is a mobile device.

[0050] The target network is a drone swarm network, and the nodes in the drone swarm network are drones.

[0051] Suppose there are a total of p between the target node pairs (s,t). st There are p shortest paths, where the number of shortest paths passing through node i is p. st (i), then the node betweenness of node i is expressed as:

[0052]

[0053] Where, p st p represents the data of the shortest path between node s and node t. st (i) represents the number of nodes i that the shortest path between nodes s and t passes through, and N represents the number of nodes.

[0054] Step 102: Construct a node betweenness matrix consisting of the node betweennesses of all nodes.

[0055] In step 102, if s, t, i do not satisfy the condition that they are not equal, or if there is no path between node s and node t, let δ st (i) = 0. The betweenness numbers of all nodes can be expressed in matrix form as:

[0056]

[0057] The node betweenness matrix δ has N columns and N... 2 Line, total N 3 The node betweenness matrix has several elements, with the i-th column representing the betweenness of node i with all node pairs. As defined above, the node betweenness matrix δ contains a significant number of zero elements.

[0058] Step 103: Determine the betweenness centrality index value of each node based on the node betweenness matrix: the betweenness centrality index of node i is the sum of the values ​​of the elements in the i-th column of the node betweenness matrix.

[0059] The betweenness centrality index B(i) of node i is the column sum of the i-th column of the node betweenness matrix δ:

[0060]

[0061] Step 104: Determine the betweenness information entropy value of each node based on the node betweenness matrix and the betweenness centrality index value of each node: the betweenness information entropy value of node i is the information entropy after normalizing the value of the element in the i-th column of the node betweenness matrix.

[0062] The betweenness information entropy value of node i is expressed as:

[0063]

[0064] Where E(i) represents the betweenness information entropy value of node i, δ st (i) represents the betweenness number of node i, and B(i) represents the betweenness centrality index value of node i.

[0065] When the betweenness centrality index of node i is 0, let the betweenness information entropy of node i be 0, that is, if B(i) = 0, then let E(i) = 0.

[0066] Step 105: Multiply the betweenness information entropy value of each node as a weighting coefficient with the betweenness centrality index value of each node to obtain the criticality evaluation index of each node.

[0067] The criticality evaluation index for node i is expressed as:

[0068]

[0069] in, Let E(i) represent the criticality evaluation index of node i, E(i) represent the betweenness information entropy value of node i, B(i) represent the betweenness centrality index value of node i, and N represent the number of nodes.

[0070] Step 106: Sort the criticality evaluation indicators of each node in descending order, and output the first set number of nodes as the critical nodes at the current moment.

[0071] Using the criticality evaluation index B(i) of node i, we can obtain the measurement value of the criticality of each node. The larger the index value, the more critical the node is considered. To identify the most critical M (M < N) nodes, we will... The elements in the table are arranged in descending order from left to right, and the node numbers corresponding to the first M index values ​​are the key nodes identified.

[0072] Following step 106, a method for identifying key nodes in an Ad Hoc network further includes:

[0073] Countermeasures can be taken against key nodes at the current moment, specifically including attacks on drones corresponding to key nodes at the current moment.

[0074] This invention uses the node betweenness information entropy as a weighting coefficient and multiplies it with the node's betweenness centrality index value to obtain a key node identification index. Compared with other centrality indices, this improves the adaptability in dynamic networks and can effectively identify key nodes with heavy routing and forwarding tasks in dynamic Ad Hoc networks.

[0075] To observe the effectiveness of key node identification, a dynamic node deletion simulation was performed. The simulation process is illustrated in the diagram below. Figure 2 As shown. Network efficiency η is used to measure the connectivity of a network, and is calculated as the shortest distance d between all pairs of nodes. ij The sum of the reciprocals of N divided by the product of the total number of nodes N and the total number of nodes N minus 1:

[0076]

[0077] In a dynamic attack simulation, an initial Ad Hoc network is first randomly generated. The network has 50 nodes randomly distributed across a 100×100 unit plane, with a communication distance of 20 between nodes (meaning any two nodes connected by a distance less than 20 are linked). For this initial network, at each time step, 20 nodes are randomly selected and moved. The horizontal movement distance of the selected nodes is a normally distributed random variable with a mean of 0 and a variance of 4, and the vertical movement distance is a normally distributed random variable with a mean of 0 and a variance of 3. Assuming a total of 19 time steps are elapsed, 20 network topologies (including the initial network) can be obtained. The initial network of the first simulation and its corresponding network topologies after 1, 2, and 3 time steps are shown below. Figure 3As shown. During dynamic node deletion simulation, the network efficiency value of the initial network is first calculated. Then, using the index calculation method (criticality evaluation index calculation method) proposed in this invention, the index value of each node in the initial network is calculated, obtaining the node label with the largest index value. If multiple nodes have the largest index value, one node is randomly selected as the node with the highest criticality. The most critical node in the initial network is deleted from the network topology graph after one time step, and its connected edges are also deleted, resulting in the current network graph after deleting the most critical node at the previous time step. The network efficiency after deleting the node is calculated, obtaining the network efficiency after one time step. Then, using the index calculation method proposed in this invention, the index values ​​of all remaining nodes in the network after one time step and after deleting the most critical node in the initial network are calculated, obtaining the node label with the largest index value. This process is repeated, deleting the n+1 most critical nodes obtained after n time steps from the network topology graph after n+1 time steps, and deleting all their connected edges, obtaining the current network graph, calculating the network efficiency, and obtaining the most critical node in the current network according to the critical node identification method proposed in this scheme. Therefore, the initial number of network nodes is 50, and the number of network nodes after n time steps is 50-n. After one simulation, the network efficiency η0 of the initial network and the network efficiencies η1, η2, ..., η1 after 1 to 19 time steps are obtained. 19 Assuming a total of 1000 simulations, we can obtain 1000 sets of the 20 network efficiency values ​​listed above in chronological order. After the 1000 simulations are completed, we calculate the average network efficiency value for each time step, denoted as . There are 20 values ​​in total.

[0078] The key node evaluation metrics are degree centrality, betweenness centrality, proximity centrality, and the entropy-weighted betweenness centrality proposed in this scheme. The simulation results of the network efficiency are shown in the figure below. Figure 4 As shown. From Figure 4 As can be seen, all three centrality evaluation metrics and the improved metrics proposed in this invention can identify nodes that have a significant impact on network efficiency under dynamic network conditions. However, it is obvious that the key nodes identified by the improved metrics method proposed in this invention have a greater impact on network efficiency in dynamic networks than the other three centrality metrics. That is, the network efficiency after deleting the key nodes obtained by the improved metrics method proposed in this invention is lower than that of the other centrality metrics. Therefore, it can be seen that this invention is superior to the other three centrality metrics in identifying key nodes in dynamic networks.

[0079] Example 2

[0080] like Figure 5 As shown, this embodiment provides a key node identification system in an Ad Hoc network, including:

[0081] The node betweenness determination module 201 is used to determine the node betweenness of each node based on the network topology of the target network at the current time and the number of shortest paths between target node pairs passing through each node; the target network is an Ad Hoc network and the nodes are mobile devices.

[0082] Node betweenness matrix construction module 202 is used to construct a node betweenness matrix composed of the node betweennesses of all nodes;

[0083] The betweenness centrality index value determination module 203 is used to determine the betweenness centrality index value of each node based on the node betweenness matrix: the betweenness centrality index of node i is the sum of the values ​​of the elements in the i-th column of the node betweenness matrix;

[0084] The betweenness information entropy value determination module 204 is used to determine the betweenness information entropy value of each node based on the node betweenness matrix and the betweenness centrality index value of each node: the betweenness information entropy value of node i is the information entropy after normalizing the value of the element in the i-th column of the node betweenness matrix.

[0085] The criticality evaluation index determination module 205 is used to multiply the betweenness information entropy value of each node as a weighting coefficient with the betweenness centrality index value of each node to obtain the criticality evaluation index of each node.

[0086] The critical node determination module 206 is used to sort the criticality evaluation indicators of each node in descending order and output the first set number of nodes as the critical nodes at the current moment.

[0087] Example 3

[0088] This example provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor runs the computer program to enable the electronic device to perform the key node identification method in the Ad Hoc network according to Embodiment 1.

[0089] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the key node identification method in the Ad Hoc network as described in Embodiment 1.

[0090] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0091] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for identifying key nodes in an Ad Hoc network, characterized in that, include: Based on the network topology of the target network at the current moment, the node betweenness of each node is determined according to the number of shortest paths between target node pairs passing through each node. The target network is an Ad Hoc network, and each node in the target network is a mobile device; Construct a node betweenness matrix consisting of the betweennesses of all nodes; Determine the betweenness centrality index value of each node according to the node betweenness matrix: the betweenness centrality index of node i is the sum of the element values in the i-th column of the node betweenness matrix. i i ​​ The betweenness information entropy value of each node is determined based on the node betweenness matrix and the betweenness centrality index value of each node: Node i The entropy value of the betweenness information is the first of the betweenness matrices of the nodes. i Information entropy after normalizing column element values; The criticality evaluation index of each node is obtained by multiplying the betweenness information entropy value of each node as a weighting coefficient with the betweenness centrality index value of each node. The criticality evaluation indicators of each node are sorted in descending order, and the first set number of nodes are output as the critical nodes at the current moment.

2. The method for identifying key nodes in an Ad Hoc network according to claim 1, characterized in that, The target network is a drone swarm network, and the nodes in the drone swarm network are drones; After sorting the criticality evaluation indicators of each node in descending order and outputting the first set number of nodes as the critical nodes at the current moment, the following also includes: Countermeasures against key points at the current moment.

3. The method for identifying key nodes in an Ad Hoc network according to claim 1, characterized in that, node The node betweenness is represented as: ； in, Represents a node s and nodes t Data on the shortest path between them. Represents a node s and nodes t The shortest path between them passes through the nodes Quantity, N Indicates the number of nodes.

4. The method for identifying key nodes in an Ad Hoc network according to claim 1, characterized in that, node The betweenness information entropy value is expressed as: ； in, Represents a node The betweenness information entropy value, Represents a node The node betweenness, Represents a node The betweenness centrality index value.

5. The method for identifying key nodes in an Ad Hoc network according to claim 4, characterized in that, When node When the betweenness centrality index is 0, let the node The betweenness information entropy value is 0.

6. A key node identification system in an Ad Hoc network, characterized in that, include: The node betweenness determination module is used to determine the node betweenness of each node based on the network topology of the target network at the current time and the number of shortest paths between target node pairs passing through each node; the target network is an AdHoc network, and each node in the target network is a mobile device. The node betweenness matrix construction module is used to construct a node betweenness matrix consisting of the betweennesses of all nodes. The module for determining the betweenness centrality index value of each node is used to determine the betweenness centrality index value of each node based on the node betweenness matrix: Node The betweenness centrality index is the first of the node betweenness matrices. The sum of the column element values; The module for determining the betweenness information entropy value of each node is used to determine the betweenness information entropy value of each node based on the node betweenness matrix and the betweenness centrality index value of each node: Node The entropy value of the betweenness information is the first of the betweenness matrices of the nodes. Information entropy after normalizing column element values; The criticality evaluation index determination module for each node is used to multiply the betweenness information entropy value of each node as a weighting coefficient with the betweenness centrality index value of each node to obtain the criticality evaluation index of each node. The critical node determination module is used to sort the criticality evaluation indicators of each node in descending order and output the first set number of nodes as the critical nodes at the current moment.

7. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the critical node identification method in an Ad Hoc network according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the method for identifying key nodes in an Ad Hoc network as described in any one of claims 1 to 5.

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