Method and system for identifying critical nodes in ad hoc network

Abstract

Critical nodes are identified in an ad hoc network by determining node betweenness of each node according to a number of shortest paths in a target node pair passing through each node; constructing a node betweenness matrix based on all nodes; determining a betweenness centrality index of each node i as the sum of the values of elements in an i-th column of the matrix; determining a betweenness information entropy value of each node according to the betweenness matrix and the betweenness centrality index, the entropy value corresponding to normalised values of elements in the i-th column of the matrix; and multiplying the entropy of each node as a weighting coefficient by the betweenness centrality index to obtain a criticality evaluation index of each node. The network may be a drone cluster network, and the critical nodes are countered or attacked. The dynamic nature of the ad hoc network is considered, improving the identification of critical nodes that change with time. Dynamic node deletion simulation following attack is also disclosed.

Description

TECHNICAL FIELD

[0001] The present invention relates to the technical field of network monitoring, in particular to a method and a system for identifying critical nodes in an Ad Hoc network. BACKGROUND

[0002] An Ad Hoc network has been widely used in modem military communication and civil communication because nodes in the Ad Hoc network can arbitrarily communicate with each other and can arbitrarily move and be quickly constructed. In order to supervise or monitor the nodes in the Ad Hoc network, it is necessary to identify some nodes with greater criticality from the network topology. Identifying the critical nodes of the enemy network can narrow the monitoring range and reduce the amount of information processing, so that the attack range or reconnaissance range is more concentrated. Identifying the critical nodes of our own network can increase the stability and invulnerability of the network by increasing the backup of critical nodes. Most researchers mainly start with the static network. The criticality evaluation index value of each node in the current static network is calculated. Centrality indexes are common and intuitive, such as degree centrality, betweenness centrality and closeness centrality, which intuitively reflect the degree to which a node is located in the central position or connected with other nodes in the topology.

[0003] The degree centrality index is low in complexity, but can only reflect the local topological attribute of the node. The degree centrality index is suitable for a simple network structure where it appears easily that multiple nodes have the same degree centrality, and critical nodes have low distinguishing accuracy. The closeness centrality index has a higher calculation complexity than the degree centrality index, which is an average measure of the shortest distance between a node and other nodes and reflects the global attribute of the node. The betweenness centrality index reflects the proportion of nodes appearing on the shortest path of other node pairs, which provides the importance of nodes as a bridge and is also a global attribute. The betweenness centrality index has a higher distinguishing accuracy than the other two centrality indexes. In addition, these three centrality indexes do not consider the influence of dynamic networks, and are only suitable for identifying critical nodes in the static network. However, in reality, the Ad Hoc network often changes dynamically with time. SUMMARY

[0004] The objective of some embodiments of the present invention is to provide a method and a system for identifying critical nodes in an Ad Hoc network, which improves the identification accuracy of the critical nodes.

[0005] In order to achieve the above objective, the present invention provides the following technical solution.

[0006] A method for identifying critical nodes in an Ad Hoc network is provided, which includes:

[0007] based on a network topology of a target network at current time, determining node betweenness of each node according to a number of shortest paths in a target node pair passing through each node, where the target network is an Ad Hoc network, and the node is a mobile device;

[0008] constructing a node betweenness matrix including node betweenness of all nodes;

[0009] determining the betweenness centrality index value of each node according to the node betweenness matrix, where a betweenness centrality index of node i is a sum of values of elements in an / -th column of the node betweenness matrix;

[0010] determining a betweenness information entropy value of each node according to the node betweenness matrix and the betweenness centrality index value of each node, where the betweenness information entropy value of node i is information entropy of normalized values of the elements in the / -th column of the node betweenness matrix;

[0011] multiplying the betweenness information entropy value of each node as a weighting coefficient by the betweenness centrality index value of each node to obtain a criticality evaluation index of each node;

[0012] arranging the criticality evaluation index of each node in descending order, and outputting a predetermined number of nodes as critical nodes at the current time.

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

[0014] after arranging the criticality evaluation index of each node in descending order, and outputting a predetermined number of nodes as critical nodes at the current time, the method further includes:

[0015] countering the critical nodes at the current time.

[0016] Preferably, node betweenness of node / is denoted as: jrf(z) = £^j = l,2,...,A,s = l,2,...,A,t = l,2,.^ Pst where pst indicates a number of shortest paths between node 5 and node / , pst(i) indicates a number of the shortest paths between node 5 and node t passing through node / , and N indicates a number of nodes.

[0017] Preferably, the betweenness information entropy value of node i is denoted as: > > v log? — bq) 6Lw)j’ where EQ) indicates a betweenness information entropy value of node / , B^Q) indicates node betweenness of node i, and BQ) indicates a betweenness centrality index value of node i.

[0018] Preferably, when the betweenness centrality index value of node / is 0, the betweenness information entropy value of node i is 0.

[0019] The present invention further discloses a system for identifying a critical node in an Ad Hoc network, including:

[0020] a module for determining node betweenness of each node, configured to, based on a network topology of a target network at the current time, determine node betweenness of each node according to a number of shortest paths in a target node pair passing through each node, where the target network is the Ad Hoc network, and the node is a mobile device;

[0021] a module for constructing a node betweenness matrix, configured to construct the node betweenness matrix including node betweenness of all nodes;

[0022] a module for determining a betweenness centrality index value of each node, configured to determine the betweenness centrality index value of each node according to the node betweenness matrix, where a betweenness centrality index of node / is a sum of values of elements in an / -th column of the node betweenness matrix;

[0023] a module for determining a betweenness information entropy value of each node, configured to determine the betweenness information entropy value of each node according to the node betweenness matrix and the betweenness centrality index value of each node, where a betweenness information entropy value of node / is information entropy of normalized values of the elements in the / -th column of the node betweenness matrix;

[0024] a module for determining a criticality evaluation index of each node, configured 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 a criticality evaluation index of each node;

[0025] a module for determining critical nodes, configured to arrange the criticality evaluation index of each node in descending order, and output a predetermined number of nodes as critical nodes at the current time.

[0026] The present invention further discloses an electronic device including a memory and a processor, where the memory is configured to store a computer program, and the processor is configured to run the computer program to cause the electronic device to execute the method for identifying the critical nodes in the Ad Hoc network.

[0027] The present invention further discloses a computer-readable storage medium storing a computer program, where the computer program, when executed by a processor, implements the method for identifying the critical nodes in the Ad Hoc network.

[0028] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects.

[0029] According to the present invention, the betweenness information entropy of the node as a weighting coefficient is multiplied by the betweenness centrality index value of the node to obtain a criticality evaluation index of the node, which improves the adaptability in a dynamic network compared with other centrality indexes and can effectively identify the critical nodes with a heavier routing and forwarding task in a dynamic Ad Hoc network. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to explain the technical solutions in the embodiments of the present invention or in the prior art more clearly, the drawings required in the embodiments will be briefly introduced. Apparently, the drawings in the following description are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without creative labor.

[0031] FIG. 1 is a flowchart of a method for identifying critical nodes in an Ad Hoc network according to an embodiment of the present invention.

[0032] FIG. 2 is a schematic block diagram of a dynamic node deletion simulation process according to an embodiment of the present invention.

[0033] FIG. 3 is a schematic diagram of partial sequential dynamic network topology simulation according to an embodiment of the present invention; in FIG. 3, (a) shows the initial network, (h) shows the network after one time step, (c) shows the network after two time steps, and (d) shows the network after three time steps.

[0034] FIG. 4 is a simulation result diagram of network efficiency corresponding to four critical node evaluation index algorithms with node deletion according to an embodiment of the present invention.

[0035] FIG. 5 is a schematic structure diagram of a system for identifying a critical node in an Ad Hoc network according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] The technical solutions in the embodiments of the present invention will be clearly and completely described with reference to the drawings in the embodiments of the present invention hereinafter. Apparently, the described embodiments are only some rather than all of the embodiments of the present invention. Based on the embodiment of the present invention, all other embodiments obtained by those skilled in the art without creative labor shall fall within the scope of protection of the present invention.

[0037] The present invention aims to provide a method and a system for identifying critical nodes in an Ad Hoc network, which improve the identification accuracy of the critical nodes.

[0038] In order to make the above objective, features and advantages of the present invention clearer and more comprehensible, the present invention will be explained in further detail with reference to the drawings and specific embodiments hereinafter.

[0039] Because the nodes in the Ad Hoc network are generally mobile devices, the topology of the whole dynamic network is dynamic with the movement of the nodes. Therefore, in the process of identifying the actual critical nodes in the dynamic Ad Hoc network, the critical nodes may also change with time. If the betweenness centrality index of a node in the current network is larger, that is, the sum of the probabilities that the node is on the shortest path of each node pair is larger, it means that the routing and forwarding task undertaken by the node is likely heavier. Therefore, it can be considered that the node is more critical in the current network from the perspective of routing and forwarding. However, due to the dynamic nature of the Ad Hoc network, the betweenness centrality index of the current network cannot fully measure whether the criticality of the node is still large at the next time. In view of the above characteristics, the present invention takes into account the proportion distribution of the number of nodes to the number of the shortest paths of each node pair, and proposes to use the betweenness information entropy of the node to characterize the probability distribution of the node on the shortest paths of each node pair. The betweenness information entropy of the node as a weighting coefficient is multiplied by the betweenness centrality index of the node to obtain an evaluation index for identifying the critical nodes, that is, an entropy-weighted betweenness centrality index (criticality evaluation index), which makes up for the defect that the betweenness centrality index cannot characterize the betweenness distribution of the node, and can keep the global attribute information provided by the betweenness centrality index.

[0040] The variables in the criticality evaluation index include: (1) a node label i \ (2) a betweenness information entropy value E(i) of node i in the network; and (3) a betweenness centrality index value B(i) of node i in the network. The above betweenness information entropy value E(i) is multiplied by the betweenness centrality index value B(i) to obtain an identification evaluation index B(f) of the critical node proposed by the present invention, and the definition formula is: B(f) = E(i) -B(f).

[0041] Embodiment 1

[0042] It is assumed that the Ad Hoc network has a total of N free-moving nodes, the node label set is {1,2,..., N}, and (i,j) is used to denote a pair of nodes, where i and j are the node labels, i = 1,2,...,N, and j = 1,2,...,N. If (i,j) and (J,i) are regarded as different node pairs and i = j is taken into account, the network has a total of N2 node pairs, and the set of node pairs is: {{(1,1),(1,2),...,(1,A)},{(2,1)^^

[0043] As shown in FIG. 1, this embodiment provides a method for identifying critical nodes in an Ad Hoc network, which specifically includes steps 101-106.

[0044] In step 101, based on a network topology of a target network at current time, node betweenness of each node is determined according to the number of shortest paths in a target node pair passing through each node. The target network is the Ad Hoc network, and the node is a mobile device.

[0045] The target network is a drone cluster network, and the nodes in the drone cluster network are drones.

[0046] If there is a total of pst shortest paths in the target node pair (s,t), and the number of shortest paths passing through node i is pst(f) , then the node betweenness of node i is denoted as: 8st^=Ml.? = 1,2,....,N ,5 = 1,2,....,N 7 = 1,2,....,N, Pst where pst indicates the number of the shortest path between node 5 and node t, pst(i) indicates the number of the shortest paths between node 5 and node t passing through node i, and N indicates the number of nodes.

[0047] In step 102, a node betweenness matrix including node betweenness of all nodes is constructed.

[0048] In step 102, if s,t,i do not satisfy the mutually unequal condition or there is no path between node 5 and node t, then 3,,(i) = 0 is set. The betweenness of all nodes is denoted in a matrix form as: MD M2) ^2(2) - MN) - MAO M (1) M(2) - M(N) MD ^21(2) - mao MD M2) - MN) 8 = M(D ^2N (2) ••• M(A0 3m (1) ^vi(2) ••• M^) M(D 3m (2) - M(A0 _^NN (1) M / (2) ■■■  ^nn(N)_ where the node betweenness matrix 6 has a total of N columns and N2 rows, and has a total of N3 elements. The element in the / -th column of the node betweenness matrix is the node betweenness between node / and all node pairs. From the above definition, it can be seen that there are many 0 elements in the node betweenness matrix 6 .

[0049] In step 103, a betweenness centrality index value of each node is determined according to the node betweenness matrix. The betweenness centrality index of node / is the sum of values of elements in the / -th column of the node betweenness matrix.

[0050] The betweenness centrality index value B(i) of node / is the column sum of the / -th column of the node betweenness matrix 6 : 5=1 t=l

[0051] In step 104, a betweenness information entropy value of each node is determined according to the node betweenness matrix and the betweenness centrality index value of each node. The betweenness information entropy value of node / is information entropy of normalized values of the elements in the / -th column of the node betweenness matrix.

[0052] The betweenness information entropy value of node / is denoted as: > > v log? — 62Lw)j’ where E(i) indicates the betweenness information entropy value of node / , 3., (i) indicates the node betweenness of node / , and B(i) indicates the betweenness centrality index value of node i.

[0053] If the betweenness centrality index value of node / is 0, the betweenness information entropy value of node / is 0. That is to say, if B(i) = 0, E(i) = 0.

[0054] In step 105, the betweenness information entropy value of each node as a weighting coefficient is multiplied by the betweenness centrality index value of each node to obtain a criticality evaluation index of each node.

[0055] The criticality evaluation index of node z is denoted as: 5( / ) = 5( / )-5( / ),7 = 1,2,..., A; where 5( / ) indicates the criticality evaluation index of node z , £( / ) indicates the betweenness information entropy value of node z , 5( / ) indicates the betweenness centrality index value of node z, and N indicates the number of nodes.

[0056] In step 106, the criticality evaluation index of each node is arranged in descending order, and a predetermined number of nodes are output as critical nodes at current time.

[0057] The criticality of each node can be measured by using the criticality evaluation index 5( / ) of the node i . The larger the index value of the node, the greater the criticality. In order to identify the most critical M(M <N) nodes, the elements in {5( / ), / = 1,2,...,N} are arranged in descending order from left to right, and the node labels corresponding to the first M index values are the identified critical nodes.

[0058] After step 106, the method for identifying the critical nodes in the Ad Hoc network further includes:

[0059] countering the critical nodes at the current time, specifically attacking the drones corresponding to the critical nodes at the current time.

[0060] According to the present invention, the betweenness information entropy of the node as a weighting coefficient is multiplied by the betweenness centrality index value of the node to obtain an index for identifying the critical nodes, which improves the adaptability in the dynamic network compared with other centrality indexes and can effectively identify the critical nodes with heavier routing and forwarding tasks in the dynamic Ad Hoc network.

[0061] In order to observe the effect of the critical node identification, dynamic node deletion simulation is carried out, and the schematic diagram of the simulation process is shown in FIG. 2. The network efficiency 77 is used to measure the connectivity of a network. The calculation formula is to divide the sum of the reciprocals of the shortest distances among all node pairs by the product of the total number N of nodes and the total number N of nodes minus 1: 1 A(A-l) / = 1,2,..., ^,7 = 1,2,..., N. '=1 7=1 av

[0062] In a dynamic attack simulation, first, an initial Ad Hoc network is randomly generated. The total number of nodes in the network is 50, and nodes are randomly distributed on a plane with an area of 100x100 units. The communication distance between nodes is 20. That is to say, if a geographical position between two nodes is less than 20, there is a connected edge. For the initial network, 20 nodes are randomly selected for random movement every time step. The horizontal movement distance of the selected nodes is a normal distribution random variable with a mean value of 0 and a variance of 4, and the vertical movement distance is a normal distribution random variable with a mean value of 0 and a variance of 3. If it takes a total of 19 time steps, a total of 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 in FIG. 3. When dynamic node deletion simulation is performed, the network efficiency of the initial network is first calculated, and then the index value of each node in the initial network is calculated according to the index calculation method (criticality evaluation index calculation method) proposed by the present invention to obtain 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 largest criticality. The obtained most critical node in the initial network is deleted from the network topology after 1 time step, and its connected edges are deleted to obtain the current network diagram after deleting the most critical node at the last time. The network efficiency after deleting the most critical node is calculated to obtain the network efficiency after 1 time step, and then index values of all remaining nodes in the network after 1 time step and deleting the most critical node in the initial network are calculated according to the index calculation method provided by the present invention to obtain the node label with the largest index value. Subsequently, in a similar way, n + \ most critical nodes obtained after n time steps are deleted from the network topology after n +1 time steps and all connected edges thereof are deleted, so as to obtain the current network diagram, calculate the network efficiency, and obtain the most critical node of the current network according to the method for identifying the critical node proposed in this technical solution. Therefore, the number of nodes in the initial network is 50, and the number of nodes in the network after n time steps is 50- n . After one simulation, the network efficiency ?70 of the initial network and 19 network efficiencies after 1 to 19 time steps 77^772,- -,^19 are obtained. If a total of 1000 simulations are carried out, 1000 groups of the above 20 network efficiencies arranged in time sequence can be obtained. After 1000 simulations, the average values of network efficiencies at various time steps are calculated and denoted as a total of 20 values rjQ, , 772,...,    .

[0063] The evaluation indexes of the critical nodes are degree centrality, betweenness centrality, closeness centrality and the entropy-weighted betweenness centrality proposed in this technical solution. The simulation results of the corresponding network efficiency are shown in FIG. 4. As can be seen from FIG. 4, the three centrality evaluation indexes and the improved index proposed by the present invention can identify nodes that have great influence on the network efficiency under the dynamic change of the network. It is obvious that the critical nodes identified by the improved index method proposed by the present invention have greater influence on the network efficiency in the dynamic network than those identified by the other three centrality indexes, that is, the network efficiency after deleting the critical nodes simulated by the improved index method proposed by the present invention is lower than that after deleting the critical nodes simulated by other centrality indexes. Therefore, it can be seen that the improved index method proposed by the present invention is superior to the other three centrality indexes in the identification of critical nodes in the dynamic network.

[0064] Embodiment 2

[0065] As shown in FIG. 5, this embodiment provides a system for identifying critical nodes in an Ad Hoc network, including:

[0066] a module for determining node betweenness of each node 201, configured to, based on a network topology of a target network at current time, determine node betweenness of each node according to the number of shortest paths in a target node pair passing through each node, where the target network is the Ad Hoc network, and the node is a mobile device;

[0067] a module for constructing a node betweenness matrix 202, configured to construct a node betweenness matrix including node betweenness of all nodes;

[0068] a module for determining a betweenness centrality index value of each node 203, configured to determine a betweenness centrality index value of each node according to the node betweenness matrix, where a betweenness centrality index of node i is a sum of values of elements in the / -th column of the node betweenness matrix;

[0069] a module for determining a betweenness information entropy value of each node 204, configured to determine the betweenness information entropy value of each node according to the node betweenness matrix and the betweenness centrality index value of each node, where the betweenness information entropy value of node i is information entropy of normalized values of the elements in the / -th column of the node betweenness matrix;

[0070] a module for determining a criticality evaluation index of each node 205, configured 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;

[0071] a module for determining critical nodes 206, configured to arrange the criticality evaluation index of each node in descending order, and output a predetermined number of nodes as critical nodes at current time.

[0072] Embodiment 3

[0073] This embodiment provides an electronic device including a memory and a processor. The memory is configured to store a computer program, and the processor is configured to run the computer program to cause the electronic device to execute the method for identifying the critical node in the Ad Hoc network according to Embodiment 1.

[0074] This embodiment further provides a computer-readable storage medium storing a computer program. The computer program, when executed by a processor, implements the method for identifying the critical nodes in the Ad Hoc network according to Embodiment 1.

[0075] In this specification, various embodiments are described in a progressive way. Each embodiment focuses on the difference from other embodiments, and the same and similar parts between the embodiments may refer to each other. Since the system disclosed in an embodiment corresponds to the method disclosed in another embodiment, the description thereof is relatively simple, and reference can be made to the method description.

[0076] In this specification, some specific embodiments are applied to illustrate the principle and implementation of the present invention, and the description of the above embodiments is only used to help illustrate the method and core ideas of the present invention. In addition, those of ordinary skill in the art can make various modifications in terms of specific implementations and application scope in accordance with the ideas of the present invention. To sum up, the content of the specification should not be construed as limitations to the present invention.

Claims

1. A method for identifying critical nodes in an Ad Hoc network, comprising:based on a network topology of a target network at current time, determining node betweenness of each node according to a number of shortest paths in a target node pair passing through each node, wherein the target network is the Ad Hoc network, and the node is a mobile device;constructing a node betweenness matrix comprising node betweenness of all nodes;determining a betweenness centrality index value of each node according to the node betweenness matrix, wherein a betweenness centrality index of node i is a sum of values of elements in an / -th column of the node betweenness matrix;determining a betweenness information entropy value of each node according to the node betweenness matrix and the betweenness centrality index value of each node, wherein a betweenness information entropy value of node i is information entropy of normalized values of the elements in the / -th column of the node betweenness matrix;multiplying the betweenness information entropy value of each node as a weighting coefficient by the betweenness centrality index value of each node to obtain a criticality evaluation index of each node;arranging the criticality evaluation index of each node in descending order, and outputting a predetermined number of nodes as critical nodes at the current time.

2. The method according to claim 1, wherein the target network is a drone cluster network, and nodes in the drone cluster network are drones;wherein after arranging the criticality evaluation index of each node in descending order, and outputting a predetermined number of nodes as the critical nodes at the current time, the method further comprises:countering the critical nodes at the current time.

3. The method according to claim 1, wherein the node betweenness of node / is denoted as:3st(i) = ^^-,i = 1,2,...,N,s = 1,2,...,N,t = 1,2,...,N',Pstwhere pst indicates a number of shortest paths between node 5 and node t, p,!1! indicates a number of shortest paths between node 5 and node t passing through node i, and N indicates a number of nodes.

4. The method according to claim 1, wherein the betweenness information entropy value of node / is denoted as:N N5=1 t=llog2BQ)where E(i) indicates a betweenness information entropy value of node / , indicatesnode betweenness of node i, and B(i) indicates a betweenness centrality index value of node / .

5. The method according to claim 4, wherein when the betweenness centrality index value of node / is 0, the betweenness information entropy value of node / is 0.

6. A system for identifying critical nodes in an Ad Hoc network, comprising:a module for determining node betweenness of each node, configured to, based on a network topology of a target network at current time, determine node betweenness of each node according to a number of shortest paths in a target node pair passing through each node, wherein the target network is the Ad Hoc network, and the node is a mobile device;a module for constructing a node betweenness matrix, configured to construct the node betweenness matrix comprising node betweenness of all nodes;a module for determining a betweenness centrality index value of each node, configured to determine the betweenness centrality index value of each node according to the node betweenness matrix, wherein a betweenness centrality index of node / is a sum of values of elements in an / -th column of the node betweenness matrix;a module for determining a betweenness information entropy value of each node, configured to determine the betweenness information entropy value of each node according to the node betweenness matrix and the betweenness centrality index value of each node, wherein a betweenness information entropy value of node / is information entropy of normalized values of the elements in the / -th column of the node betweenness matrix;a module for determining a criticality evaluation index of each node, configured to multiply the betweenness information entropy value of each node as a weighting coefficient by the betweenness centrality index value of each node to obtain a criticality evaluation index of each node;a module for determining critical nodes, configured to arrange the criticality evaluation index of each node in descending order, and output a predetermined number of nodes as critical nodes at the current time.

7. An electronic device, comprising a memory and a processor, wherein the memory is configured to store a computer program, and the processor is configured to run the computer program to cause the electronic device to execute the method for identifying the critical nodes in the Ad Hoc network according to any of claims 1 to 5.

8. A computer-readable storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the method for identifying thecritical nodes in the Ad Hoc network according to any of claims 1 to 5.

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