This work proposes a framework to discover the topology of a non-collaborative packet-based wireless network using radio-frequency (RF) sensors. The methodology developed is blind, allowing topology sensing of a network whose key features (i.e., number of nodes, physical layer signals, and medium access control (MAC) and routing protocols) are unknown. Because of the wireless medium, over-the-air signals captured by the sensors are mixed; therefore, blind source separation (BSS) and measurement association are used to separate traffic patterns. Then, to infer the topology, we detect directed data flows among nodes by identifying causal relationships between the separated transmitted patterns. We propose causal inference methods such as Granger causality (GC), transfer entropy (TE), and conditional transfer entropy (CTE) that use the times series of traffic profiles, and a solution based on a neural network (NN) that exploits distilled time-based features. The framework is validated on an ad-hoc wireless network accounting for MAC protocol, packet collisions, nodes mobility, the spatial density of sensors, and channel impairments, such as path-loss, shadowing, and noise. Numerical results reveal that the proposed approach reaches a high probability of link detection and a moderate false alarm rate in mild shadowing regimes and low to moderate network nodes mobility.

中文翻译：

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无线网络拓扑盲推断


这项工作提出了一个框架，用于发现使用射频 (RF) 传感器的非协作基于数据包的无线网络的拓扑。所开发的方法是盲目的，允许对关键特征（即节点数量、物理层信号、媒体访问控制（MAC）和路由协议）未知的网络进行拓扑感知。由于无线介质的原因，传感器捕获的无线信号是混合的；因此，盲源分离（BSS）和测量关联用于分离流量模式。然后，为了推断拓扑，我们通过识别分离的传输模式之间的因果关系来检测节点之间的定向数据流。我们提出了使用流量曲线的时间序列的因果推理方法，例如格兰杰因果关系（GC）、转移熵（TE）和条件转移熵（CTE），以及基于利用蒸馏时间的神经网络（NN）的解决方案基于的功能。该框架在自组织无线网络上进行了验证，考虑了 MAC 协议、数据包冲突、节点移动性、传感器的空间密度以及通道损伤（例如路径损耗、阴影和噪声）。数值结果表明，所提出的方法在轻度阴影状态和低到中等网络节点移动性下达到了高链路检测概率和中等误报率。