Sniffer deployment in urban area for human trajectory reconstruction and contact tracing

TL;DR

This paper proposes a method for deploying sniffers at urban street intersections using graph theory heuristics to collect large, heterogeneous mobility datasets for better human trajectory and contact tracing analysis.

Contribution

It introduces an optimization heuristic based on graph theory for efficient placement of sniffers in urban environments.

Findings

Heuristic improves sniffer placement efficiency

Enables collection of large, diverse mobility datasets

Supports enhanced contact tracing and trajectory analysis

Abstract

To study the propagation of information from individual to individual, we need mobility datasets. Existing datasets are not satisfactory because they are too small, inaccurate or target a homogeneous subset of population. To draw valid conclusions, we need sufficiently large and heterogeneous datasets. Thus we aim for a passive non-intrusive data collection method, based on sniffers that are to be deployed at some well-chosen street intersections. To this end, we need optimization techniques for efficient placement of sniffers. We introduce a heuristic, based on graph theory notions like the vertex cover problem along with graph centrality measures.