Network Topology Inference based on Timing Meta-Data

TL;DR

This paper introduces an EM-based causality discovery algorithm to infer network topology from timing meta-data, effectively handling packet losses in wireless sensor networks.

Contribution

It presents a novel EM-CDA algorithm that accounts for packet losses when inferring network links from timing data, improving accuracy over prior causality-based methods.

Findings

The EM-CDA algorithm outperforms existing causality methods in simulated wireless networks.

Packet loss modeling significantly enhances topology inference accuracy.

Extensive NS-3 simulations validate the effectiveness of the proposed approach.

Abstract

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.