Smart Urban Networks

As more people move towards cities, those cities are transformed into encapsulated ecosystems. Megacities (e.g., Tokyo) host more people than several states on a much smaller area.  For the first time ever, more people are living in urban areas than in rural areas.

To tackle the associated challenge we envision a pipeline of sensing and processing to connect people and things through smart services.

The pipeline as pictured above illustrates the three main research areas:

Things: We work with and on things to capture data at scale. From tiny sensors to powerful smartphones, we try to collect data in high quantity and quality. Example research questions are calibration, power efficiency, participatory sensing and incentive systems.

Connect: Data at scale requires networks at scale. We see different challenges in future networks spanning from “just” transmitting data most efficiently to more advanced networking concepts, e.g., moving processing to the edge/in-network or services to the users – also called fog computing and  cloudlets respectively. Our research currently focuses on protocol and resource locality and network virtualization through work in our collaborative research center.

Smart Services: Using advanced machine learning concepts, we want to offer the next generation of services on top of data and network. We are currently mostly focused on prediction. Current examples from our research are failure or behavior prediction. For smart services, we also research nowcasting and event detection on social media data.

For more details on our respective research visit the projects mentioned below or contact us at any time.


Group Leader

Ph.D. Student


  • Dr. Immanuel Schweizer
  • Dr. Axel Schulz
  • Dr. Kamill Panitzek

Current Projects

“MAKI – Multi-Mechanisms Adaptation for the Future Internet” creates an innovative premise for the communication systems of the future. Its aim is to be more adaptive to changes, particularly during ongoing operations. offers a comprehensive tracking suite to capture your personal digital life. No matter if you are mobile, on your laptop, desktop or in your social networks.
In this project we are building an urban sensor platform that collects, processes, and visualizes data from smartphones, wireless sensor networks, and sensor infrastructure.
Noisemap is a smartphone application designed to capture noise pollution. It empowers each individual carrying a smartphone to contribute noise measurements to a public map.
Resilient communication is important, especially in disaster response and recovery. Citymesh evaluates the use of wireless mesh networks to provide resilient first responder communication.
GTNA is a java tool, mainly developed by the P2P Networks group, which enables a fast and extendable analysis of graphs. The focus of GTNA is on metrics and models for network graph analysis.
MICI is a mashup that uses in­for­ma­tion from Linked Open Data for rule-based risk as­sess­ment, demon­strat­ed with live data pro­vid­ed by the Seat­tle fire brigade. It has been de­vel­oped to­geth­er with Heiko Paulheim from the Knowledge Engineering group. The project has been awarded the second prize at the co-located AI Mashup Challenge, at the 9th Extended Semantic Web Conference (ESWC) 2012.

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