Automated and Reproducible Application Traces Generation for IoT Applications

Nina Santi (1) , Rémy Grünblatt (1) , Brandon Foubert (1) , Aroosa Hameed (2) , John Violos (2) , Aris Leivadeas (2) , Nathalie Mitton (1)

1 FUN - Self-organizing Future Ubiquitous Network
2 ETS - Ecole de Technologie Supérieure [Montréal]

In this paper, we investigate and present how to generate application traces of IoT (Internet of Things) Applications in an automated, repeatable and reproducible manner. By using the FIT IoT-Lab large scale testbed and relying on state-of-the-art software engineering techniques, we are able to produce, collect and share artifacts and datasets in an automated way. This makes it easy to track the impact of software updates or changes in the radio environment both on a small scale, e.g. during a single day, and on a large scale, e.g. during several weeks. By providing both the source code for the trace generation as well as the resulting datasets, we hope to reduce the learning curve to develop such applications and encourage reusability as well as pave the way for the replication of our results. While we focus in this work on IoT networks, we believe such an approach could be of used in many other networking domains.

Mots clés: datasets; testbed; network; traces; reproducibility; automation; experiments; 802.15.4

Domaines: Calcul parallèle, distribué et partagé [cs.DC]; Informatique ubiquitaire; Réseaux et télécommunications [cs.NI]

Nina Santi, Rémy Grünblatt, Brandon Foubert, Aroosa Hameed, John Violos, et al.. Automated and Reproducible Application Traces Generation for IoT Applications. Q2SWinet 2021 - 17th ACM Symposium on QoS and Security for Wireless and Mobile Networks, Nov 2021, Alicante, Spain. pp.1-8, ⟨10.1145/3479242.3487321⟩. ⟨hal-03390693⟩