The Networks Data Science (NDS) group at IMDEA Networks Institute is led by Dr. Marco Fiore, and 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. Following this methodology, we address research problems along two directions. On the one hand, we characterize, model and forecast the complex dynamics of mobile data traffic, and use the derived insights to improve the design and operation of mobile network architectures. On the other hand, we leverage the rich information available in mobile network metadata to solve challenging problems in social science with a computational, data-driven approach. Our research builds on large-scale measurement data collected in the operational systems of major mobile network providers.
The NDS group is active in the following research areas.
Mobile traffic characterization - We investigate how individual mobile services are consumed at large (metropolitan and national) scales, by studying 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 [CoNEXT'17]. We also work towards identifying complex commonalities and diversities in the way mobile services are used at different times or in different geographical areas [WWW'19].
Data-driven mobile networking - We design data-driven algorithms for network intelligence, i.e., the effective, anticipatory and automated orchestration of network resources. To this end, we apply artificial intelligence, machine learning, statistical methods and optimization to traffic data collected in large-scale operational mobile networks. Among notable contributions, we assess the limits of current and future technologies, such as resource utilization efficiency in network slicing [MobiCom'18]. We also develop original solutions for network management, such as predictors of the capacity to be allocated to network slices to minimize operation costs [INFOCOM'19,INFOCOM'20].
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 [TMC'17,INFOCOM'17], estimate the dynamics of population density in cities at order-of-minute granularity [TMC'19], or study how income relates to mobile app usage [NetMob'19].
Human mobility analysis from mobile phone data - We analyze individual and mass mobility of people through the lenses of mobile network metadata. Our activities focus on data cleansing, and include characterizing and mitigating the limitations of sparse, irregularly sampled positioning information available to operators [EPJ Data Science'19], drawing laws of the relationship between the location sampling frequency and the quality of captured human trajectories [GLOBECOM'17], and removing noise generated by scattered device associations to base stations [WoWMoM'19].