Reverse Engineering Human Mobility in Large-scale Natural Disasters

August 07, 2017 · Entered Twilight · 🏛 International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

"Last commit was 8.0 years ago (≥5 year threshold)"

Evidence collected by the PWNC Scanner

Repo contents: .gitattributes, .gitignore, CONTRIBUTING.md, HISTORY.txt, LICENSE.txt, README.md, README.txt, compile.bat, compile.sh, data, default_settings.txt, doc, ee, eval, example_settings, exp.py, lib, one.bat, one.sh, portauprince_settings.txt, src, tacloban_settings.txt, target, toolkit, wdm_settings

Authors Milan Stute, Max Maass, Tom Schons, Matthias Hollick arXiv ID 1708.02151 Category cs.NI: Networking & Internet Cross-listed cs.CY Citations 17 Venue International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems Repository https://github.com/seemoo-lab/natural-disaster-mobility ⭐ 8 Last Checked 1 month ago

Abstract

Delay/Disruption-Tolerant Networks (DTNs) have been around for more than a decade and have especially been proposed to be used in scenarios where communication infrastructure is unavailable. In such scenarios, DTNs can offer a best-effort communication service by exploiting user mobility. Natural disasters are an important application scenario for DTNs when the cellular network is destroyed by natural forces. To assess the performance of such networks before deployment, we require appropriate knowledge of human mobility. In this paper, we address this problem by designing, implementing, and evaluating a novel mobility model for large-scale natural disasters. Due to the lack of GPS traces, we reverse-engineer human mobility of past natural disasters (focusing on 2010 Haiti earthquake and 2013 Typhoon Haiyan) by leveraging knowledge of 126 experts from 71 Disaster Response Organizations (DROs). By means of simulation-based experiments, we compare and contrast our mobility model to other well-known models, and evaluate their impact on DTN performance. Finally, we make our source code available to the public.