The Networks Data Science (NDS) group at IMDEA Networks Institute, led by Dr. Marco Fiore, carries out research at the interface of mobile networking and data science, by applying and tailoring tools from artificial intelligence, machine learning, statistical analysis and data mining to the metadata that flows through modern mobile network architectures. We address research problems on the characterization, modeling and forecasting of the complex dynamics of mobile data traffic, and use the derived insights to improve the design and operation of mobile network architectures. We also leverage the rich information available in mobile network metadata to solve challenging problems in social sciences with a computational, data-driven approach. Our research generally builds on large-scale measurement data collected in the operational systems of major mobile network providers, and is carried out in cooperation with leading academic and industrial partners.
★ Dec 2021 – Two papers accepted at IEEE INFOCOM 2022. Congratulations to Alan, André, Orlando and Sachit for the first publications of their PhD. The works explore meta-learning models for Intent-Based Networking and the impact of COVID-19 on mobile service usage. Preprints will be out soon.
★ Dec 2021 – New paper presented at ACM CoNEXT. In order to remove the current access barrier to spatiotemporal mobile traffic data for research purposes, we propose SpectraGAN, a deep learning model capable of generating realistic mobile traffic demands for target urban areas from publicly available local information such as demographics or land use. SpectraGAN is the result of a joint effort with the University of Edinburgh, Orange and Samsung. A video presentation of the tool as well as several open datasets generated with SpectraGAN are available.
★ Nov 2021 – We have openings for two new PhD students in the group. The positions are in the area of experimental Artificial Intelligence for the support of next-generation mobile network functionalities, are offered within the context of collaborations with industry and international research projects, and are fully funded. Find more information on the positions and application instructions in the Jobs section below.
★ Nov 2021 – New paper accepted at Journal of the Royal Society Interface. We investigate geographical unevenness in mobile service usage across areas characterized by different income or education levels, and find a gap so evident that aggregated traffic alone allows to accurately infer the socioeconomic status of a small area. Joint work with Universidad Carlos III de Madrid, Orange Labs, and MIT. The work received news coverage from El País, a major newspaper in Spain.
The current interest to the group include the following research areas.
Mobile traffic analysis and generation
We study how mobile services are consumed at large (metropolitan and national) scales, by exploring measurement data recorded in production networks. We aim at unveiling the specific dynamics of traffic demands generated by a variety of apps in space and time, including the complex commonalities and diversities in the way mobile services are used in different times or geographical areas, or the way mobile traffic consumption is affected by special events or circumstances such as the COVID-19 pandemic. We also develop models for the generation of synthetic datasets of spatiotemporal mobile network traffic that can support data-driven research.
We design data-driven algorithms for the effective, anticipatory and automated orchestration of network resources. To this end, we apply artificial intelligence, machine learning, statistical methods and optimization tools to traffic data collected in large-scale operational mobile networks. We perform assessments of the limits of current and future technologies, such as resource utilization efficiency in network slicing, when confronted with real-world traffic loads. We also develop original solutions for network management hinging on deep learning models, such as predictors of the capacity to be allocated to network slices to minimize operation costs, or lightweight approaches to service classification from traffic aggregates. Our network intelligence solutions are based on custom model designs that contribute to advancing the sate of the art in machine learning for practical engineering tasks.
Remote sensing with mobile network metadata
We take advantage of the rich information embedded in data generated within mobile network architectures to solve problems in social sciences. The nearly ubiquitous coverage and high level of detail of individual and aggregate metadata available to operators allow developing data-driven models that largely improve the current state-of-the-art in disciplines like geography, demographics or economics. As representative examples, we employ mobile network metadata to produce cartographies of mixed land use in urban areas, estimate the dynamics of population density in cities at order-of-minute granularity, or predict the socioeconomic status of urban neighborhoods.
We are an international team that is young and rapidly growing. Meet us below.
Our activities are partially funded by the following research projects.
Network intelligence for aDAptive and sElf-Learning MObile Networks
Financed by: European Union H2020-ICT-2020-2 (Information and Communication Technology) Grant
Big dAta aNalYtics for radio Access Networks
Financed by: European Union H2020-MSCA-ITN-2019 (Marie Curie Innovative Training Networks) Grant
Transactions on Data Privacy. Volume 13 , August 2020
The 39th IEEE International Conference on Computer Communications (IEEE INFOCOM 2020). Online (previously Toronto, Canada). July 2020
The 18th Mediterranean Communication and Computer Networking Conference (IEEE MedComNet 2020). Arona, Italy. June 2020
IEEE Communications Magazine. 10.1109/MCOM.001.1900653. Volume 58 , June 2020
IEEE 91st Vehicular Technology Conference (VTC2020-Spring). Antwerp, Belgium. May 2020
IEEE Journal on Selected Areas in Communications. DOI: 10.1109/JSAC.2019.2959245. Volume 38 , IEEE. ISSN: 0733-8716. February 2020
IEEE Transactions on Network and Service Management. DOI: 10.1109/TNSM.2019.2923265. Volume 16 , IEEE Communications Society. ISSN: 1932-4537. September 2019
Ad Hoc Networks. https://doi.org/10.1016/j.adhoc.2019.03.007. Elsevier. ISSN: 1570-8705. June 2019
The 2nd International Workshop on Network Intelligence (NI 2019), in conjunction with the 38th IEEE International Conference on Computer Communications (IEEE INFOCOM 2019). Paris, France. April 2019
The 38th IEEE International Conference on Computer Communications (IEEE INFOCOM 2019). Paris, France. April 2019
The NDS group supports the research community by developing open-access tools and datasets.
Synthetic spatiotemporal mobile traffic datasets
Datasets of synthetic spatiotemporal mobile traffic generated with the SpectraGAN deep learning model from publicly available context data. The datasets are available for five cities in Germany over a time span of three weeks each. The work was carried out in collaboration with Kai Xu, Rajkarn Singh and Mahesh Marina.
Dynamic population density datasets
Open datasets of time-varying urban population distributions extracted from mobile network metadata in the cities of Milan, Turin and Rome, in Italy. The dynamic population data was derived from mobile network metadata, and validated against attendance at public events. The work was carried out in collaboration with Ghazaleh Khodabandelou, Vincent Gauthier and Mounim el Yacoubi.
Madrid highway road traffic datasets
Several traces of weekly road traffic on three highways close to Madrid, Spain, generated using a dedicated microsimulator, which is also available at the webpage above. These are quite rare datasets of realistic traffic in freeways that mimic car in- and out-flows observed via in-situ measurements. The work was carried out in collaboration with Marco Gramaglia, Oscar Trullols-Cruces, and Maria Calderon.
Bologna Ringway road traffic dataset
A one-hour trace of road traffic in the center of Bologna, in Italy, generated using the SUMO microsimulator. The simulation is based on origin-destination matrices developed by the iTetris and Colombo projects funded by the European Commission. The work was carried out in collaboration with Luca Bedogni, Marco Gramaglia, Andrea Vesco, Jerome Haerri, and Francesco Ferrero.
Köln road traffic dataset
A complete vehicular mobility trace of the city of Cologne, Germany, generated through OpenStreetMap, SUMO and the TAPASCologne traffic demand dataset. The dataset covers 24 hours of road traffic during a typical working day, with over 700,000 individual car trips reproduced. The work was carried out jointly with Sandesh Uppoor.
We are always looking for motivated undergraduate, graduate, doctoral and postdoctoral students willing to visit and work with us.
★ Applications for internships and master theses are welcome on a continuous basis.
★ Doctors in topics related to our research activities and with a strong publication record are invited to join us on postdoctoral positions funded by the European Commission via Marie Skłodowska-Curie Actions.
★ Funded openings for PhD and postdoctoral fellowships in the group are listed below if available: please consider applying if you have a degree in Computer Science or related field, strong programming skills, and enthusiasm for interdisciplinary research.
PhD positions in experimental AI for networksPhd Student
Deadline for receipt of applications: 15 January 2022
- Group leader: Marco Fiore
- Email: firstname.lastname@example.org
- Contact phone: +34 91 481 6926
- Fax: +34 91 481 696
Office & Postal Address
- IMDEA Networks Institute
- Avda. del Mar Mediterraneo, 22
- 28918 Leganes (Madrid)