ICACT20250172 Slide.20
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ThatĄŻs all. Thank you for your listening.
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ICACT20250172 Slide.19
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To address the challenges of high mobility, interference, and large-scale coverage in UAV-mounted weapon networks, this paper proposes an anti-jamming weighted clustering algorithm (AJWCA). The algorithm uses K-means++ for balanced clustering, incorporates an interference factor, and introduces backup cluster heads for enhanced stability. Simulations demonstrate improved recovery, scalability, and adaptability,making AJWCA suitable for complex environments.
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ICACT20250172 Slide.18
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In the last section, I will summarize the results of the paper.
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ICACT20250172 Slide.17
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The cluster head duration of the AJWCA algorithm was simulated under varying interference power conditions and compared with the WCA algorithm. As shown in the figure, the proposed algorithm achieves a longer overall cluster head duration
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ICACT20250172 Slide.16
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After running the simulation for 3s, interference is simulated on cluster head node 24. The updating and maintenance process is shown in left figure. The backup cluster head node 3 assumes the role of the new cluster head, taking over the cluster.
After the simulation continues for another 5s, interference is simulated on cluster member node 10.The process is shown in right figure. Cluster head node 4 removes the failed node from the member list.
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ICACT20250172 Slide.15
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In the simulation, the network is divided into five clusters.First, as the left figure, the K-means++ algorithm is employed to partition the nodes into clusters based on their geographical locations.Second , as the right figure, a weighted algorithm is applied to select the cluster heads and backup cluster, ultimately forming the network topology.
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ICACT20250172 Slide.14
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Here are the simulation related settings. The simulation aims to evaluate the performance of the proposed anti-jamming weighted clustering scheme designed for UAV-mounted weapon self-organizing networks. In the left side, the table shows the parameters of simulation. And the figure shows the initial distribution of nodes without clustering.
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ICACT20250172 Slide.13
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To prove the investigated algorithm, simulation experiments are carried out in this part.
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ICACT20250172 Slide.12
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The forth step is dynamic update and maintenance of cluster structure. It can be mainly divided into the maintenance of cluster head and cluster member.
For cluster heads, The backup head listen for hello messages sent by current head. If the backup head fails to receive hello message from current head three times, it takes over as the new head.
For cluster members, the cluster head listen for hello messages sent by members. If the head fails to receive hello message from a specific member three times, it removes this member from the member list.
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ICACT20250172 Slide.11
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The third step is network topology generation. The selected cluster head nodes broadcast clustering message, and other nodes within the same partition reply with message to join the cluster, completing the generation of the network topology.
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ICACT20250172 Slide.10
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The second step is cluster formation. The cluster head influence factors include: remaining energy factor, movement similarity factor, average distance factor, and external interference factor.
After determining the four cluster head selection factors, the clustering weight of a candidate cluster head node is calculated using a weighted method.The node with the lowest weight is elected as the cluster head. And the node with the second lowest weight is selected as the backup cluster head.
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ICACT20250172 Slide.09
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This paper proposes a anti-jamming weighted clustering algorithm (AJWCA) for UAV-mounted weapon self-organizing network.The first step is cluster formation. After network initialization, the UAV-mounted weapons cluster network is divided into regions by K-means++ algorithm.The K-means++ algorithm considers the distance between the nodes when selecting the initial clustering center, which makes the inter-cluster interference is reduced.
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ICACT20250172 Slide.08
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The third part is about the Anti-Jamming Weighted Clustering Algorithm.
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ICACT20250172 Slide.07
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The network topology model considered in this paper is here. In UAV-mounted weapons networkŁŹthe nodes are categorized into cluster heads and cluster members through clustering algorithm, which together form a two-layer network, as shown in the left Figure. And the clustering algorithm process usually includes clustering after network initialization, cluster head selection, network topology generation, and maintenance after clustering. The basic steps are shown in the right Figure.
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ICACT20250172 Slide.06
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Then, IĄŻll show you the details of the UAV-Mounted Weapon Network Clustering Model.
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ICACT20250172 Slide.05
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To address the aforementioned issues, we propose a anti-jamming weighted clustering algorithm(AJWCA) for UAV-mounted weapon self-organizing network.
Partitioning nodes by K-means++algorithm to reduce the impact of inter cluster interference.
Introducing interference factors in the select of cluster heads to reduce the impact of external interference.
Using backup cluster head mechanism to accelerate the reconstruction of the topology.
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ICACT20250172 Slide.04
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With the development of technology, UAV-mounted weapon system can achieve precise strikes and coordinated operations. For UAV-mounted weapon systems ,the establishment of a reliable communication network is crucial. The network has the feature of multiple nodes, high mobility, large-scale coverage.
Based on the above features, cluster network is an effective means. But, the aforementioned research lacks consideration of interference issue.
Therefore, it is necessary to make corresponding improvements to the clustering.
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ICACT20250172 Slide.03
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LetĄŻs look at the introduction part first.
In this part, IĄŻll show you the research background, and my paper contribution.
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ICACT20250172 Slide.02
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My presentation is organized in 5 parts——Introduction, UAV-Mounted Weapon Network Clustering Model, Anti-Jamming Weighted Clustering Algorithm, Experiments and results, and Conclusions.
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ICACT20250172 Slide.01
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Hello, everyone! IĄŻm Siqi Li from Beijing Institute of Technology. IĄŻm so glad to be here to share you with my paper. The title is Anti-Jamming Weighted Clustering Algorithm for UAV-Mounted Weapon Self-Organizing Network.
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