NDS Group

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.


★ Feb 2024 – New members. We had multiple people joining the group early in 2024! Abhishek Duttagupta, who received a MSc from Trinity College Dublin, has joined as a PhD student in co-supervision with Claudio Fiandrino to investigate explainable reinforcement learning for network operation. David de Andrés Hernández, who received a MSc from Technical University of Munich, has joined as a PhD student to explore user-plane intelligence. And, Josué Miguel Aguilar, who received a BSc from Technological University of Havana, has joined as a research engineer. Welcome all!

★ Jan 2024 – Five papers accepted at IEEE INFOCOM 2024! We set a new record for the group, as its members have co-authored five papers that will be presented at the IEEE INFOCOM conference in Vancouver in May 2024. The works focus on understanding the adoption of the 5G technology, making joint packet- and flow-level inference feasible in programmable switches, using GPUs are hardware accelerators for vRAN workloads, reducing the energy consumption of vRANs via heterogeneous processors and improving the explainability of network traffic prediction. Congratulations to Leonardo, Sachit, André, Orlando, Akem, Beyza, Diego, Michele and the many collaborators for this achievement!

★ Oct 2023 – Two papers to be presented at ACM IMC 2024 and one at ACM MobiHoc 2023! We will present results at top ACM conferences this month, about the modeling of mobile traffic at the level of transport-layer sessions, the characterization of indoor cellular network demands, and the overbooking of network slices by mobile operators. Congrats to André, Stefanos and Sergi -along with all collaborators at Orange, University of Cambridge and Ranplan Wireless- for the excellent work!

★ Jun 2023 – Best demo award. We received the best demo award at IEEE NetSoft 2023, for a demonstration of our solutions for flow-level in-switch inference. Congratulations to Akem, Beyza and Michele!

★ May 2023 – New member. Diego Madariaga, a freshly graduated doctor from the University of Chile, joins the NDS group as a postdoctoral fellow and will co-lead our activities on network traffic analysis, characterization and modeling. Welcome Diego!

★ May 2023 – We launched the NetMob 2023 Data Challenge with Orange, which opens to the multidisciplinary research community an unprecedented high-resolution, multi-service dataset of mobile network traffic. The details of the challenge and application information are available at the official website. The challenge is associated with the NetMob 2023 conference, a friendly and accessible event that brings together a global community working on mobile network data analysis.

★ Mar 2023 – We organized a very successful 2023 edition of PAM, as a fully virtual and free event that attracted over 250 attendees. Videos of the talks can be found at the PAM 2023 YouTube account.

★ Dec 2022 – Three papers accepted at IEEE INFOCOM 2023! Alan and Antonio will present in NYC in may AutoManager, an improved neural network architecture for loss meta-learning in MANO tasks. Akem and Michele will showcase Flowrest, the very first classifier based on random forests that can operate at a flow level in real-world programmable switches. Finally, in a joint work with the Wireless Networking Group, Alan developed tools to explain and attack traffic forecasting models based on deep learning. Postprints to appear soon.

★ Nov 2022 – New paper accepted at the NativeNI 2022 workshop, held jointly with ACM CoNEXT 2022. The work, co-authored by Akem, Beyza and Michele, investigates the implementation and performance of hierachical machine learning models in programmable switches. Postprint to appear soon.

★ Sep 2022 – New paper presented at IEEE SECON 2022. Orlando presented his work on enhancing Voronoi tessellation as a representation for the spatial mapping of mobile network metadata. Full details are in the postprint, and supporting code is openly available in our GitHub repository.

★ Aug 2022 – New member. Beyza, a MSc graduate from Middle East Technical University in Turkey, joined the group to carry out her PhD studies on sustainable Network Intelligence. Welcome Beyza!

★ Aug 2022 – New paper accepted at IEEE Transactions on Networks and Service Management. In another step towards removing the barrier to mobile network traffic access by the research community, we extend our previous conditional generative models to produce dependable maps of traffic demands on a per-service basis. This provides researchers with realistic mobile data loads for the likes of video streaming, social media, messaging or Cloud applications. Again a joint activity with University of Edinburgh, Orange and Samsung. See the postprint for full details.

★ Mar 2022 – New paper accepted at IEEE WoWMoM 2022. In this collaboration with NEC, we show how a hybrid approach combining statistical modelling and machine learning through a joint AutoML training process can outperform pure deep learning architectures in forecasting tasks. We evaluate the proposed solution in practical anticipatory networking use cases, demonstrating the generality of the solution. See the postprint for full details.

★ Jan 2022 – New paper accepted at IEEE Percom 2022. This is another installment in our series of works on generating synthetic mobile network traffic, in collaboration with the usual team at University of Edinburgh, Orange and Samsung. This study focuses on the spatial dimension of the aggregate demands. See the postprint and associated open datasets.

★ Jan 2022 – One paper accepted at The ACM Web Conference 2022. Congratulations to Sachit for his first paper as first author. The work uses extensive measurement data to investigate the interaction between consumption of mobile services and urbanization levels. It unveils a growing imbalance in the per-capita consumption of overall and per-app mobile data traffic during the past years between smaller and larger cities in France, and raises interesting questions on the presence of second-level digital divides in developed countries. See the postprint for full details.

★ Dec 2021 – Two papers accepted at IEEE INFOCOM 2022. Congratulations to Alan, André, Orlando and Sachit for the first publications of their PhD. The first work explores loss function meta-learning models aimed at supporting Intent-Based Networking, see the postprint. The second study analyzes the impact of measures enacted to limit the COVID-19 pandemic on mobile service usage at a national scale, in collaboration with Orange Innovation, see the postprint.

★ 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.

Network intelligence
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.

Scientific Direction

Marco Fiore
Dr. Marco FIORE
Team leader / Research Professor
More infoarrow_right_alt


antonio bazco-nogueras
Post-Doc Researcher
Post-Doc Researcher
Post-Doc Researcher
External PhD Student
PhD Student
PhD Student
PhD Student

Research Engineering & Support

Mohammaderfan JABBARI
Research Engineer
Josué Miguel Aguilar
Josué Miguel AGUILAR
Research Engineer

Current visitors

Genoveva GARCIA GARCIA | Visiting BSc student | Universidad Carlos III de Madrid | since Sep 2023

Alumni and past visitors

Daniel Alejandro AMARO RAMOS | Visiting MSc student | Universidad de La Habana | Sep 2023-Feb 2024
Prashant RAY | Visiting MSc student | Politecnico di Torino | Mar-Sep 2023
Stefanos BAKIRTZIS | Visiting PhD student | University of Cambridge / Ranplan Wireless | Jan-Dec 2022
Abhishek MISHRA | Visiting PhD student | Inria / École Polytecnique | May-Sep 2022
Benoît MATET | Visiting PhD student | Université Gustave Eiffel | May-Aug 2022
Théo COUTURIEUX | Visiting undergraduate student | ENSEEIHT | May-Jul 2021


Our activities are partially funded by the following research projects.

Optimized resource integration and global architecture for mobile infrastructure for 6G

Financed by: European Union (HORIZON-JU-SNS-2023)

Traffic Collection, Contextual Analysis, Data-driven Optimization for 5G

Financed by: CoCo5G project received funding from ANR, but IMDEA Networks participation is self-funded.

Explainable and robust AI for integration in next generation networked systems

Financed by: Ministry of Sciences and Innovation

Talent Attraction grant - One-year Extension

Financed by: Regional Govmt. of Madrid, Department of Education, Science, Universities and Spokesperson's office

Energy consumption measurements and optimization in mobile networks

Financed by: Ministry of Science and Innovation (MICIN/AEI) and the European Union "NextGenerationEU"/PRTR

Network Intelligence for zero-touch orchestration and anomaly detection

Financed by: Ministry of Economic Affairs and Digital Transformation, European Union NextGeneration-EU

Previous projects


Big dAta aNalYtics for radio Access Networks


Network intelligence for aDAptive and sElf-Learning MObile Networks


★  June 2022. Spanish media coverage about our research on second-level digital divides emerging from the quantity of mobile data usage in a developed country like France. Articles by DiCYT, Madrimasd, and iLeon.

★ Dec 2021. Media coverage on our research on correlations between mobile service consumption and socio-economic status indicators of education and income levels by El País, Phys.org, TeleMadrid, Yahoo! Noticias, SiNC, Newtral, and EurekAlert!.

★ Mar 2021. Media coverage on the H2020 ICT-52 DAEMON project we coordinate, by 6G World.



The NDS group supports the research community by developing open-access tools and datasets.

NDS group GitHub repository
We promote research reproducibility and verifiability. To this end, open source code produced in the context of our different research activities is provided in dedicated repositories on GitHub.

VoronoiBoost is a data-driven model that scales Voronoi cells to match the probabilistic spatial distribution of users associated to each base station. VoronoiBoost relies on the same input as traditional Voronoi decomposition, but provides a richer and more accurate rendering of where users are located. Details are in the original paper.

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. Additional datasets for spatial maps are also available here.

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 position in the analysis and modeling of 5G traffic
    PhD Student

    Deadline for receipt of applications: 30 June 2024

    More infoarrow_right_alt


  • Group leader: Marco Fiore
  • Email: marco.fiore@imdea.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)
  • Spain
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