Room: Amphi Gaston Planté (stair 35, 1st floor), Cnam, 2 rue conté, 75003 Paris
Date: September 26, 2022. 14h-16h
Title: Impact of Later-Stages COVID-19 Response Measures on
Spatiotemporal Mobile Service Usage.
Speaker: André Zanella, IMDEA Networks, Spain
Abstract: The COVID-19 pandemic has affected our lives and how we use
network infrastructures in an unprecedented way. While early studies
have started shedding light on the link between COVID-19 containment
measures and mobile network traffic, we presently lack a clear
understanding of the implications of the virus outbreak, and of our
reaction to it, on the usage of mobile apps. We contribute to closing
this gap, by investigating how the spatiotemporal usage of mobile
services has evolved through different response measures enacted in
France during a continued seven-month period in 2020 and 2021.
Our work complements previous studies in several ways: it delves into
individual service dynamics, whereas previous studies have not gone
beyond broad service categories; it encompasses different types of
containment strategies, allowing to observe their diverse effects on
mobile traffic; it covers both spatial and temporal behaviors, providing
a comprehensive view on the phenomenon. These elements of novelty let us
lay new insights on how the demands for hundreds of different mobile
services are reacting to the new environment set forth by the pandemics.
Master Interns presentations
The seminar will be followed by the following students presentations:
- Ali Awarkeh : status of deployment of a softwarized LoraWAN testbed in Cnam Paris campus
- Augustin Clero: evaluation of SDN/NFV systems adoption for satellite communications.
- David Kule: status of deployment of an open 5G infrastructure in Cnam Paris campus
- Farnaz Ebrahimishad: evaluation of data-pipeling techniques for in-network AI
- Hussein Khalil: design of an SDN environment for NetFPGA SmartNIC operations.
- Zifeng Zhang: Leveraging on nove RISC-V boards for real-time video AI surveillance
- Zisen Xu: Filtering and dimensionality reduction for big 5G monitoring time series datasets