Topology sensing of FANET under missing data

IF 4.4 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Zaixing Zhu, Tao Hu, Di Wu, Chengcheng Liu, Siwei Yang, Zhifu Tian
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引用次数: 0

Abstract

The topological structure of a flying ad hoc network (FANET) is crucial to understand, explain, and predict the behavior of unmanned aerial vehicle (UAV) swarms. Most studies focusing on topology sensing use perfect observations and complete datasets. However, the received signal dataset, being non-cooperative, commonly encounters instances of missing data, causing the performance of the existing algorithms to degrade. We investigate the issue of topology sensing of FANET based on external observations and propose a topology sensing method for FANET with missing data while introducing link-prediction methods to correct the topology inference results. First, we employ multi-dimensional Hawkes processes to model the communication event sequence in the network. Subsequently, to solve the problem in which the binary decision threshold is difficult to determine and cannot be adapted to the application scenario, we propose an extended multi-dimensional Hawkes model suitable for FANET and use the maximum likelihood estimation method for topology inference. Finally, to solve the problem of the low accuracy of inference results owing to missing data, we perform community detection on the observation network and combine the community detection and inference results to construct a mixed connection probability matrix, based on which we perform topology correction. The results of the analysis show that the topology sensing method proposed in this study is robust against missing data, indicating that it is an effective solution for solving this problem.
缺失数据下的 FANET 拓扑感知
飞行特设网络(FANET)的拓扑结构对于理解、解释和预测无人驾驶飞行器(UAV)群的行为至关重要。大多数关注拓扑感知的研究都使用完美的观测数据和完整的数据集。然而,接收到的信号数据集是非合作的,通常会遇到数据缺失的情况,导致现有算法的性能下降。我们研究了基于外部观测的 FANET 拓扑感知问题,并提出了一种适用于数据缺失的 FANET 拓扑感知方法,同时引入链路预测方法来修正拓扑推断结果。首先,我们采用多维霍克斯过程来模拟网络中的通信事件序列。随后,为了解决二元判定阈值难以确定且无法适应应用场景的问题,我们提出了适合 FANET 的扩展多维霍克斯模型,并使用最大似然估计方法进行拓扑推断。最后,为了解决数据缺失导致的推断结果准确性不高的问题,我们对观测网络进行了群落检测,并结合群落检测和推断结果构建了混合连接概率矩阵,在此基础上进行了拓扑校正。分析结果表明,本研究提出的拓扑感知方法对数据缺失具有鲁棒性,表明它是解决这一问题的有效方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computer Networks
Computer Networks 工程技术-电信学
CiteScore
10.80
自引率
3.60%
发文量
434
审稿时长
8.6 months
期刊介绍: Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.
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