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Tools for Collection, Context Inference, Trajectory
Il y a 4 mois
**Niveau de diplôme exigé **:Bac + 4 ou équivalent
**Autre diplôme apprécié **:Master or PhD
**Fonction **:Ingénieur scientifique contractuel
**Niveau d'expérience souhaité **:De 3 à 5 ans
**A propos du centre ou de la direction fonctionnelle**:
- Le centre de recherche Inria de Saclay a été créé en 2008. Sa dynamique s’inscrit dans le développement du plateau de Saclay, en partenariat étroit d’une part avec le pôle de l’_**_Université Paris-Saclay_**_ et d’autre part avec le pôle de l’_**_Institut Polytechnique de Paris_**_. Afin de construire une politique de site ambitieuse, le centre Inria de Saclay a signé en 2021 des accords stratégiques avec ces deux partenaires territoriaux privilégiés._
- Le centre compte, dont 27 sont communes avec l’Université Paris-Saclay ou l’Institut Polytechnique de Paris. Son action mobilise _**_plus de 600 personnes_**_, scientifiques et personnels d’appui à la recherche et à l’innovation, issues de 54 nationalités._
**Contexte et atouts du poste**:
A research-engineer position is available in the Inria TRiBE team in the Saclay Center in collaboration with the FUN team of Inria Lille. This position is funded by the project Mob Sci-Data FAcotry of the National French PEPR project MOBIDEC.
If needed, travels to Lille are foreseen for this post.
**Mission confiée**:
The research community faces a crucial lack of meaningful user mobility data today. Available data are generally very focused on a single kind of mobility (e.g., road traffic) or are sparse. Most of the existing datasets, such as Call Detail Records (CDR) logged by cellular operators [3, 4], Access Point measurements logged by the Internet provider [5], or check-ins collected by popular Location-Based Online Social Networks (LBOSN) [6] providing information of a large population, have limited accuracy in both spatial and temporal dimensions, which induces incomplete trajectories and consequently, missing interactions among end-users (i.e., wireless contacts among users). Besides their sparse spacial and coarse temporal features, such datasets either do not bring collection-related contextual information (e.g., LBOSN) or lack fine-grained information, allowing the comprehension of the involved environment conditions and contact inference.
The alternative is to extract data directly from smartphones, thanks to their high penetration among the population, or from wearable devices capturing sent-in-the-air public frames emitted by wireless devices. Although small-scale, such data captures fine-grained contextual information and environmental conditions, providing opportunities for users to contact inference.
This position aims to provide the community with tools to collect or generate new kinds of heterogeneous data and the means to publish and populate the datasets to be made available for the research community. The FUN and TRiBE teams have already designed and proposed different tools, such as network traffic dataset generators and tools to collect network traffic intrusively and privacy-compliantly. These tools are necessary for inferring user mobility.
The person to be recruited will make these existing tools more open and usable while enriching them.
References:
[1] Automated and Reproducible Application Traces Generation for IoT Applications, Nina Santi, Rémy Grünblatt, Brandon Foubert, Aroosa Hameed, John Violos, Aris Leivadeas, Nathalie Mitton Q2SWinet 2021 - 17th ACM Symposium on QoS and Security for Wireless and Mobile Networks, Nov 2021, Alicante, Spain. pp.1-8,
[2] Dataset Collection of Multi-Communication Technologies Monitored in Different Mobility Contexts Jana Koteich, Nathalie Mitton Under Submission
[3] G. Chen. Human Habits Investigation: from Mobility Reconstruction to Mobile Traffic Prediction. Ph.d. thesis, University Paris-Saclay, April 2018.
[4] G. Chen, S. Hoteit, A. C. Viana, M. Fiore, and C. Sarraute. Enriching sparse mobility information in Call Detail Records. Computer Communications, 122:44-58, 2018.
[5] T. Henderson, D. Kotz, and I. Abyzov. The Changing Usage of a Mature Campus-wide Wireless Network. In ACM Mobicom, pages 187-201, September 2004.
[6] T. H. Silva, A. C. Viana, F. Benevenuto, L. Villas, J. Salles, A. Loureiro, and D. Quercia. Urban computing leveraging location-based social network data: a survey. ACM Computing Surveys, 2019.
[7] F. D. de Mello Silva, A. K. Mishra, A. C. Viana, N. Achir, A. Fladenmuller and L. H. M. K. Costa, "Performance Analysis of a Privacy-Preserving Frame Sniffer on a Raspberry Pi," 2022 6th Cyber Security in Networking Conference (CSNet), Rio de Janeiro, Brazil, 2022, pp. 1-7, doi: 10.1109/CSNet56116.2022.9955615.
[8] L. Jouans, A. C. Viana, N. Achir and A. Fladenmuller, "Associating the Randomized Bluetooth MAC Addresses of a Device," 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 2021, pp. 1-6, doi: 10.1109/CCNC49032.2021.9369628.
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