论文标题
基于定时元数据的网络拓扑推断
Network Topology Inference based on Timing Meta-Data
论文作者
论文摘要
考虑一个仅访问元数据的处理器，该处理器由网络中所有节点的数据包和确认（ACK）数据包组成。元数据报告每个数据包的源节点，而不是目标节点或数据包的内容。处理器的目的是仅根据此类信息来推断网络拓扑。先前的工作利用因果关系指标来确定哪些链接是活动的。如果两个节点的数据时间和ACK时间分别（例如节点1和节点2）是因果关系相关的，则可以将其视为节点1正在传达与节点2的证据（将ACK数据包发送到节点1）。本文始于观察到数据包损失可以削弱数据和ACK正时流之间的因果关系。为了消除此问题，引入了新的期望最大化（EM）算法 - EM-CAUSALITY DISCOVION算法（EM-CDA） - 将数据包损失视为潜在变量。 EM-CDA迭代数据包丢失和因果关系指标的评估。该方法通过在NS-3模拟平台上的无线传感器网络中的广泛实验进行验证。
Consider a processor having access only to meta-data consisting of the timings of data packets and acknowledgment (ACK) packets from all nodes in a network. The meta-data report the source node of each packet, but not the destination nodes or the contents of the packets. The goal of the processor is to infer the network topology based solely on such information. Prior work leveraged causality metrics to identify which links are active. If the data timings and ACK timings of two nodes -- say node 1 and node 2, respectively -- are causally related, this may be taken as evidence that node 1 is communicating to node 2 (which sends back ACK packets to node 1). This paper starts with the observation that packet losses can weaken the causality relationship between data and ACK timing streams. To obviate this problem, a new Expectation Maximization (EM)-based algorithm is introduced -- EM-causality discovery algorithm (EM-CDA) -- which treats packet losses as latent variables. EM-CDA iterates between the estimation of packet losses and the evaluation of causality metrics. The method is validated through extensive experiments in wireless sensor networks on the NS-3 simulation platform.
