Unmanned aerial vehicle communication network robustness evaluation method based on node attack
By constructing a drone network topology and implementing random and deliberate attack strategies, the robustness of the drone communication network is evaluated using assessment metrics. This solves the stability problem of the drone network under node attacks and improves the network's resilience and the continuity of information transmission.
Patent Information
- Application Number
- CN202511071824.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-01
- Publication Date
- 2025-11-18
AI Technical Summary
Robustness assessment of UAV communication networks under node attack or failure conditions is difficult to guarantee network stability and connectivity, making them vulnerable to malicious attacks that could lead to network paralysis. Existing technologies lack effective assessment methods.
By constructing a drone network topology, implementing random and deliberate attack strategies, and using metrics such as the maximum connected subgraph ratio, the reciprocal of the average path length, the reciprocal of the network diameter, and the average clustering coefficient, the robustness of the network is evaluated, vulnerable nodes are located, and guidance is provided for improving node layout and topology design.
It reduces the risk of network function loss when nodes fail, improves the resilience and stability of UAV communication networks in complex environments, and ensures the continuity and security of information transmission.
Smart Images
Figure CN120980540A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of complex networks, and in particular to a method for evaluating the robustness of a UAV communication network based on node attacks. BACKGROUND
[0002] Due to its flexibility and efficiency, UAVs are widely used in disaster relief, environmental monitoring, logistics distribution and other fields. However, UAV communication networks face complex challenges: nodes are highly dynamic, communication links are easily broken, and networks are vulnerable to attacks. Therefore, evaluating the robustness of the network under node attacks or failures is a key research direction to ensure the reliability of the communication network.
[0003] UAV clusters perform information collection, communication relay and other tasks in various application scenarios, and may be subject to malicious attacks on key nodes, causing the entire network to fail. To address these threats, robust network topology needs to be designed, and the ability to withstand attacks under different attack strategies needs to be evaluated to improve the applicability of the cluster. In disaster relief, UAV networks are used to coordinate search, locate survivors and deliver rescue information. When some nodes fail or run out of energy, it is crucial to maintain the connectivity of the remaining network, and robustness evaluation can help develop redundancy design and self-healing mechanisms to ensure uninterrupted rescue missions.
[0004] In addition, UAV nodes are vulnerable to hijacking attacks and other threats. By evaluating the robustness of UAV communication networks, key nodes and vulnerable links can be identified, and targeted protection measures can be developed to assist in dynamic routing design for UAV communication networks and improve the network's ability to adapt to random failures, providing important support for the stability, security and efficient operation of UAV networks, and playing a key role in communication support for various application scenarios. SUMMARY
[0005] In view of the above problems existing in the prior art and the need to improve the stability and invulnerability of UAV cluster networks in complex environments, the present application provides a method for evaluating the robustness of a UAV communication network based on node attacks, which analyzes the network performance under random failures and deliberate attacks, locates vulnerable nodes in the network topology, and provides guidance for improving node layout, topology design and key node protection, thereby reducing the risk of network function loss when nodes fail.
[0006] The present application provides a method for evaluating the robustness of a UAV communication network based on node attacks, comprising:
[0007] Step 1: Constructing a UAV network topology;
[0008] Step 2: Implementing two node attack strategies, random attacks and deliberate attacks;
[0009] Step 3: Establish evaluation indexes and analyze network robustness.
[0010] In some embodiments of the present application, the UAV network topology is constructed, including:
[0011] Based on the scale-free network and ER network model, a UAV communication network is simulated, and a topological graph representing the UAV communication network is randomly generated.
[0012] In some embodiments of the present application, different numbers of nodes and densities are set during the construction process to represent various typical UAV communication network scenarios.
[0013] In some embodiments of the present application, the two node attack strategies of random attack and deliberate attack are implemented, including:
[0014] Random failure randomly selects a number of nodes to be disabled to simulate unexpected node damage or communication interruption;
[0015] Deliberate attack sorts nodes according to their degree / centrality from high to low, and gradually removes the nodes to simulate hostile attacks on key nodes of the network, and analyzes the network's resistance to destruction.
[0016] In some embodiments of the present application, when establishing evaluation indexes and analyzing network robustness, the following indexes are selected as network robustness evaluation indexes, including: maximum connected subgraph proportion, reciprocal of average path length, reciprocal of network diameter, and average clustering coefficient.
[0017] The network robustness is,
[0018]
[0019] The closer the robustness is to 1, the smaller the impact of node attack on network performance, and the closer it is to 0, the greater the impact of node attack on network performance.
[0020] In some embodiments of the present application, when the maximum connected subgraph proportion is selected for network robustness evaluation, it is specifically:
[0021] The ratio of the largest connected subgraph in the network after the attack to the total number of nodes in the original network is used to measure the overall connectivity.
[0022]
[0023] In some embodiments of the present application, when the reciprocal of the average path length is selected for network robustness evaluation, it is specifically:
[0024] The reciprocal of the average path length of the remaining network after node failure is used to evaluate the information transmission efficiency, where the average path length is:
[0025]
[0026] In some embodiments of the present application, when the reciprocal of the network diameter is selected for network robustness evaluation, specifically:
[0027] The reciprocal of the longest path length in the network after node failure reflects the overall transmission performance of the network, wherein the network diameter is:
[0028] D = max i,j∈N (d(i,j)))).
[0029] In some embodiments of the present application, when the average clustering coefficient is selected for network robustness evaluation, specifically:
[0030] Evaluate the degree of influence of node failure on network structure;
[0031]
[0032] Compared with the prior art, the beneficial effects of the unmanned aerial vehicle communication network robustness evaluation method based on node attack provided by the embodiments of the present application are that: by analyzing the network performance under random failure and deliberate attack, the vulnerable nodes in the network topology are located, and guidance is provided for improving node layout, topology design and key node protection, thereby reducing the risk of network function loss when the nodes fail. BRIEF DESCRIPTION OF DRAWINGS
[0033] Figure 1 The system workflow diagram in the unmanned aerial vehicle communication network robustness evaluation method based on node attack provided by the embodiments of the present application;
[0034] Figure 2 The initial unmanned aerial vehicle communication network example diagram in the unmanned aerial vehicle communication network robustness evaluation method based on node attack provided by the embodiments of the present application;
[0035] Figure 3 The simulation random failure process schematic diagram in the unmanned aerial vehicle communication network robustness evaluation method based on node attack provided by the embodiments of the present application;
[0036] Figure 4 The simulation deliberate attack process schematic diagram in the unmanned aerial vehicle communication network robustness evaluation method based on node attack provided by the embodiments of the present application. DETAILED DESCRIPTION
[0037] In order for those skilled in the art to better understand the technical solutions of the present application, the present application will be described in detail below in combination with the drawings and specific embodiments.
[0038] The various schemes and features of the present application are described herein with reference to the accompanying drawings.
[0039] These and other characteristics of the present application will become patently apparent as the description proceeds in conjunction with the accompanying drawings, by way of non-limiting examples.
[0040] It is also to be understood that, while the application has been described in terms of certain specific examples, in the appended claims, is intended to cover all possible equivalents of the specific examples taught and hence claimed, without limitation.
[0041] The above and other aspects, features and advantages of the present application will become apparent from the following detailed description, taken in conjunction with the accompanying drawings, which illustrate several
[0042] Specific embodiments of the present application are described in detail below with reference to the attached drawing figures; however, these embodiments are merely examples of the application, which can be embodied in various forms. Well-known and / or redundant functions and structures are not described in detail to avoid obscuring the application unnecessarily. Therefore, specific structural and functional details disclosed herein are not to be interpreted in a limiting manner, but merely for the purpose of teaching a person skilled in the art to use the application in substantially any manner.
[0043] The specification can use phrases such as "in one embodiment", "in another embodiment", "in yet another embodiment", or "in other embodiments", which can refer to one or more of the same or different embodiments of the application.
[0044] The embodiments of the application provide a method for evaluating the robustness of a UAV communication network based on node attacks, as shown in Figures 1 to 4 The method comprises the following steps:
[0045] Step 1: Constructing a UAV network topology, comprising: simulating a UAV communication network based on a scale-free network and an ER network model, randomly generating a topology graph representing the UAV communication network, and setting different numbers and densities of nodes during the construction process to represent various typical UAV communication network scenarios;
[0046] Step 2: Implementing two node attack strategies, random attack and deliberate attack, comprising: randomly selecting a number of nodes to be disabled to simulate unexpected node damage or communication interruption; deliberately attacking the nodes in descending order of degree / betweenness centrality, and gradually removing the nodes to simulate hostile attacks on key nodes of the network, and analyzing the network's resistance to damage;
[0047] Step 3: Establish evaluation indexes and analyze network robustness, select the following indexes as network robustness evaluation indexes, the indexes include: maximum connected subgraph proportion, reciprocal of average path length, reciprocal of network diameter and average clustering coefficient;
[0048] When the maximum connected subgraph proportion is selected for network robustness evaluation, specifically:
[0049] The proportion of the largest connected subgraph in the network after the attack to the total number of nodes in the original network is used to measure the overall connectivity;
[0050]
[0051] When the reciprocal of the average path length is selected for network robustness evaluation, specifically:
[0052] The reciprocal of the average path length of the remaining network after node failure is used to evaluate the information transmission efficiency, where the average path length is:
[0053]
[0054] When the reciprocal of the network diameter is selected for network robustness evaluation, specifically:
[0055] The reciprocal of the longest path length in the network after node failure, which reflects the overall transmission performance of the network, where the network diameter is:
[0056] D = max i,j∈N (d(i,j))).
[0057] When the average clustering coefficient is selected for network robustness evaluation, specifically:
[0058] Evaluate the influence of node failure on network structure;
[0059]
[0060] The network robustness is,
[0061]
[0062] The closer the robustness is to 1, the smaller the impact of node attack on network performance, and the closer to 0, the greater the impact of node attack on network performance.
[0063] In order to facilitate the understanding of the above technical solutions, the following will be described in detail with specific examples, as follows:
[0064] Step S1: Based on the scale-free network and ER network model, the UAV communication network is simulated, and the topological graph representing the UAV communication network is randomly generated. In the construction process, different node numbers and densities are set to represent various typical UAV communication network scenarios. Taking the randomly generated 20-node ER random graph as an example, the UAV network topology is shown in Figure 2 .
[0065] Step S2: Randomly select several nodes to make them fail to simulate unexpected node damage or communication interruption. Taking the network generated in the previous step as an example, the topology changes of the 5-node random failure process are shown in Figure 3 ; deliberate attack according to the degree of nodes from high to low, and gradually remove these nodes to simulate the destruction of key nodes of the network by hostile attacks, and analyze the network damage resistance. Taking the network generated in the previous step as an example, the topology changes of the 5-node deliberate attack process are shown in Figure 4 .
[0066] Step S3: Network robustness after random failure:
[0067] 1) The ratio of the largest connected subgraph to the total number of nodes in the original network, which can be used to measure the overall connectivity, Figure 3 The ratio of the largest connected subgraph in the network is
[0068] rLCC = [1, 0.95, 0.85, 0.8, 0.75, 0.7]
[0069] 2) The reciprocal of the average path length of the remaining network after node failure, which can be used to evaluate the information transmission efficiency, Figure 3 The reciprocal of the average path length in the network is
[0070]
[0071] 3) The reciprocal of the network diameter, which can reflect the overall transmission performance of the network, Figure 3 The reciprocal of the network diameter in the network is
[0072]
[0073] 4) The average clustering coefficient, which evaluates the influence of node failure on network structure, Figure 3 The average clustering coefficient in the network is
[0074]
[0075] Combining the above four indicators, Figure 3 The network robustness in the network is
[0076] robustness=[1, 0.878, 0.425, 0.4, 0.375, 0.35]
[0077] Robustness of network after implementing deliberate attack:
[0078] 1) Maximum connected subgraph proportion: the proportion of the largest connected subgraph in the network after attack to the total number of nodes in the original network, which can be used to measure the overall connectivity, Figure 4 The maximum connected subgraph proportions are in turn
[0079] rLCC=[1, 0.6414, 0.325, 0.25, 0.175, 0.1]
[0080] 2) Reciprocal of average path length: the reciprocal of the average path length of the remaining network after node failure, which can be used to evaluate the information transmission efficiency, Figure 4 The reciprocals of average path lengths are in turn
[0081]
[0082] 3) Reciprocal of network diameter: the reciprocal of the longest path length in the network after node failure, which can reflect the overall transmission performance of the network, Figure 4 The reciprocals of network diameters are in turn
[0083]
[0084] 4) Average clustering coefficient: to evaluate the influence of node failure on network structure, Figure 4 The average clustering coefficients are in turn
[0085]
[0086] Combining the above four indexes, Figure 4 The network robustnesses are in turn,
[0087] robustness=[1, 0.6414, 0.325, 0.25, 0.175, 0.1]
[0088] It can be known from the above technical solutions that the unmanned aerial vehicle communication network robustness evaluation method based on node attack provided by the above embodiments of the present application can analyze the network performance under random failure and deliberate attack, locate the vulnerable nodes in the network topology, and provide guidance for improving node layout, topology design and key node protection, so as to reduce the risk of network function loss when the nodes fail.
[0089] The above examples are only exemplary embodiments of the present application, and are not intended to limit the present application, and the protection scope of the present application is defined by the claims. Those skilled in the art can make various modifications or equivalent replacements to the present application within the spirit and protection scope of the present application, and such modifications or equivalent replacements are also considered to fall within the protection scope of the present application.
Claims
1. A robustness evaluation method for unmanned aerial vehicle (UAV) communication networks based on node attacks, characterized in that, include: Step 1: Construct the drone network topology; Step 2: Implement two node attack strategies: random attack and deliberate attack; Step 3: Establish evaluation metrics and analyze network robustness.
2. The robustness evaluation method for UAV communication networks based on node attacks according to claim 1, characterized in that, The construction of the drone network topology includes: The UAV communication network is simulated based on scale-free network and ER network models, and a topology diagram representing the UAV communication network is randomly generated.
3. The robustness evaluation method for UAV communication networks based on node attacks according to claim 2, characterized in that, Different numbers and densities of nodes are set during the construction process to represent various typical drone communication network scenarios.
4. The robustness evaluation method for UAV communication networks based on node attacks according to claim 3, characterized in that, The two node attack strategies, random attack and deliberate attack, include: Random failure: Randomly select several nodes and cause them to fail to simulate unexpected node damage or communication interruption. The deliberate attack is sorted from high to low according to the degree / betweenness centrality of the nodes, and the nodes are removed step by step to simulate the damage of the hostile attack to the network's critical nodes and analyze the network's resilience.
5. The robustness evaluation method for UAV communication networks based on node attacks according to claim 4, characterized in that, When establishing evaluation metrics and analyzing network robustness, the following metrics are selected as network robustness evaluation metrics: the proportion of the largest connected subgraph, the reciprocal of the average path length, the reciprocal of the network diameter, and the average clustering coefficient. Network robustness is, The closer the robustness is to 1, the smaller the impact of node attacks on network performance; the closer it is to 0, the greater the impact of node attacks on network performance.
6. The robustness evaluation method for UAV communication networks based on node attacks according to claim 5, characterized in that, When selecting the proportion of the largest connected subgraph for network robustness evaluation, the specific steps are as follows: The ratio of the largest connected subgraph in the network after the attack to the total number of nodes in the original network is used to measure overall connectivity.
7. The robustness evaluation method for UAV communication networks based on node attacks according to claim 6, characterized in that, When selecting the reciprocal of the average path length for network robustness evaluation, the specific steps are as follows: The reciprocal of the average path length of the remaining network after a node failure is used to evaluate information transmission efficiency, where the average path length is:
8. The robustness evaluation method for UAV communication networks based on node attacks according to claim 7, characterized in that, When selecting the reciprocal of the network diameter for network robustness evaluation, the specific steps are as follows: The reciprocal of the longest path length in the network after a node failure reflects the overall transmission performance of the network, where the network diameter is: D=max i,j∈N (d(i,j)))。 9. The robustness evaluation method for UAV communication networks based on node attacks according to claim 8, characterized in that, When selecting the average clustering coefficient for network robustness evaluation, the specific steps are as follows: Assess the impact of node failures on the network structure;