Researchers at IMDEA Networks are organized in research groups and laboratories. These will evolve and adapt according to our working priorities, ensuring the relevance and innovative character of our scientific work. Each of our professors leads a research group, and at the same time several professors may join their research groups to form a larger lab on a particular focus area.
The Global Computing Group explores distributed computing systems and social networks in the widest sense. Among others it has the following lines of research: (i) research on volunteer and crowd-based computing, with emphasis on game-theoretic aspects; (ii) research on techniques for energy efficiency in networks and data centers; (iii) analysis of online social networks using big data, machine learning, and natural language processing technologies.
The Internet Analytics Group designs and develops novel techniques to analyze the behavior, use, and abuse of networked systems and applications. The group research interests range from traffic analysis and network measurements to practical security and privacy aspects in emerging networking technologies. The group engages and collaborates with users, industry and regulators to identify and address problems of societal, industrial and academic interest from a practical angle.
The NETCOM research group currently focuses its research activities on the following main areas: (i) Network Architectures, (ii) Communication Protocols, (iii) Wireless and Mobile Networks, (iv) Peer-to-Peer Systems and (v) Distributed Services. Current main focus includes 5G networks (with the participation to the EU project TYPES, coordinated by IMDEA Networks) and SDN-based architectures (with the participation on the recently concluded EU project NetIDE).
The NetEcon group at IMDEA Networks studies economics of networked systems and its interplay with technology and security. Based on measurements, data analysis, and modeling, the group explores the roles and interactions of various entities in the Internet ecosystem. We also develop innovative business models for cost reduction, revenue increase, and social utility maximization.
The members of the OppArch Lab focus on solutions for extremely high-performing opportunistic architectures for highly dense cellular networks and for infrastructure-less wireless services. Specifically, the OppArch Lab targets analysis, design, implementation and experimental evaluation of energy-efficient, robust, fair and high-throughput communication protocols for: (i) SDN/SDR-based cellular networks, (ii) context-aware services, and (iii) cloud-based data centers.
The Pervasive Wireless Systems Group has the goal to provide truly pervasive and seamless communication using innovative wireless systems. The group has currently three main lines of research: (i) visible light communication networks, (ii) mobile indoor localization, and (iii) collaborative wideband spectrum monitoring. The approach of the group is to build and deploy solid systems that support the theoretical and simulation analysis.
The Wireless Networking Group performs research on all aspects of wireless networking and communication. Some of our focus areas are millimeter wave networking and extremely high frequency communication, interference management and coordination mechanisms, network coding, mobile network resource and traffic optimization, and wireless transport protocols. We specifically target experimentation in testbeds in addition to analysis and simulation.
The Networks Data Science group at IMDEA Networks Institute carries out research at the interface of mobile networking and data science, along two directions. On the one hand, we characterize the dynamics of mobile data traffic, and use the insights to improve the design and operation of mobile network architectures. On the other hand, we leverage metadata from mobile networks in computational social science applications. In both cases, our research builds on large-scale measurement data collected in operational systems.
The Data Transparency Group (DTG) is employing a mix of network measurements, distributed systems building, algorithms, and machine learning to study problems and propose solutions to transparency issues related to data privacy, the economics of data, information and disinformation spread, and automated decision making via machine learning algorithms. The objective of the group is to tackle important problems on the forefront of the interplay between technology, society, public policy, and economics. On all of the above we take a holistic approach that goes from fundamental thinking and rethinking, all the way to actual code running on large systems and devices, including all the business challenges for transforming visions and ideas to real world services.