Aeronautical ad hoc networks (AANETs) dramatically increase the Internet access rates of aircraft by widening the coverage area thanks to the air-to-air links established. However, the mobility and atmospheric effects on AANETs increase air-to-air link breakages, leading to frequent aircraft replacement and reducing link quality. These broken air-to-air links should be transferred to other aircraft to enable the sustainability of the AANET topology. At that point, the wrong and late transfer decisions of broken links make topology sustainability challenging by reducing packet transfer success and increasing end-to-end latency, respectively. Despite these challenges, to the best of our knowledge, air-to-air link transfers between aircraft in AANETs have not been investigated by any study to enable the topology sustainability. This article proposes to utilize learning vector quantization in three phases - winning cluster selection, intra-clus-ter link determination, and attribute update - to enable the sustainability of AANET topology, which is in the form of aircraft clusters. In winning cluster selection, we consider each cluster in the topology as a pattern. Then we aim to find the best matching cluster of an aircraft observing air-to-air link breakage through pattern classification. Then we take airplanes in a cluster pattern as weight vectors with location and queuing delay attributes to determine the intra-cluster links of newly assigned aircraft. Here, the aircraft is modeled according to a G/G/1 queuing system for delay attribute calculations. Finally, according to the freshly established intra-cluster links, we update weight vectors' attributes using the throughput rate as the learning rate. We simulate the proposed system by utilizing OMNET++ and Weka tools with realistic air traffic data obtained from flight radar databases. In these simulations, we can reduce the end-to-end latency of AANET topology by 25 percent and increase the packet transfer rate by 31 percent compared to the methodologies in the literature.

中文翻译：

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基于学习矢量量化的集群 AANET 拓扑可持续性

由于建立了空对空链接，航空自组织网络 (AANET) 通过扩大覆盖区域来显着提高飞机的互联网接入率。然而，AANET 的机动性和大气效应会增加空对空链路的中断，导致频繁更换飞机并降低链路质量。这些断开的空对空链路应该转移到其他飞机上，以实现 AANET 拓扑的可持续性。在这一点上，断开链路的错误和延迟传输决策分别通过减少数据包传输成功和增加端到端延迟使拓扑可持续性具有挑战性。尽管存在这些挑战，但据我们所知，AANET 中飞机之间的空对空链路传输尚未被任何研究调查以实现拓扑可持续性。本文建议在三个阶段利用学习向量量化 - 获胜集群选择、集群内链路确定和属性更新 - 以实现飞机集群形式的 AANET 拓扑的可持续性。在获胜集群选择中，我们将拓扑中的每个集群视为一个模式。然后我们的目标是通过模式分类找到观察空对空链路断裂的飞机的最佳匹配集群。然后我们将集群模式中的飞机作为具有位置和排队延迟属性的权重向量来确定新分配飞机的集群内链路。在这里，飞机根据 G/G/1 排队系统建模，用于延迟属性计算。最后，根据新建立的簇内链接，我们更新权重向量 使用吞吐率作为学习率的属性。我们通过使用 OMNET++ 和 Weka 工具以及从飞行雷达数据库中获得的真实空中交通数据来模拟所提出的系统。在这些模拟中，与文献中的方法相比，我们可以将 AANET 拓扑的端到端延迟降低 25%，并将数据包传输率提高 31%。