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Advancements in micro-electronics, sensor technologies, wireless networks, software technologies and many other areas basically enable every object on the Earth to be connected to the Internet. Its services and data can be made available to application and business processes. Wikipedia states: "The Internet of Things refers to uniquely identifiable objects and their virtual representations in an Internet-like structure". Behind this technical term is a fundamental technological revolution that will enable computing systems to gain a deep and real-time understanding of what is happening in the real-world, as well as enable application and services to influence social activities and work processes.

The resulting Smart-X systems (Smart Home, Smart City, Smart Nation, Smart Car, …) will be characterized through the following features:

  • Smart-through-Insights (StI): the ability to observe phenomena in the real world with new unprecedented timeliness and level of details. This will give users new insights into their systems.
  • Smart-through-new.KNowledge (StK): combining real-time data with advanced analytic functions will enhance the understanding of processes and systems and generate new knowledge at a previously unachievable level.
  • Smart-through-Mashup (StM): through the ability to exchange, sell and trade those new data, IoT systems achieve new levels of visualization, integration and deep understanding compared to old systems.
  • Smart-through-Services (StS): the generated, aggregated and processed data enable the creation of new services which will offer customers previously not achievable levels  in service, quality and novel features.
  • Smart-through Optimization (StO): a large set of data, that is brought together from various sources, analyzed and mashed together, can be combined with advanced algorithms for optimally increasing cost effectiveness as well as precision of ICT systems.

    As an example, IoT technologies will deliver much better data about a smart city then ever before (StI), use analytics to help decision makers in understanding the situation in a city (StU), enable to combine real-time sensor data with social media data (StM), create new services (e.g. city apps for citizens) (StS), and optimize the overall processes in the city (StO). All this can be used to improve the comfort, safety, and security in many areas including smart living, smart home, smart cities and  smart government.

Research at NEC Laboratories Europe focuses on two areas:

Research Area 1: IoT Analytics

In the world of IoT, high volume data having high variety are generated from connected devices (sensors, public display, city system, mobile devices, robots) via modern network infrastructure. The information from such raw data can be processed with  modern data analytics technologies to generate new knowledge about the observed systems, behavior patterns and rules that were not previously known.  Our research work focuses on tackling these challenges. We focus on the analysis of human behavior data based on new metrics and behavioral patterns, such as understanding customer engagement with interactive content/services and detecting crowd level of a place based on collected sensor data. Applications of this technology are examined in Broadcast services for Second Screen devices, Safety Systems for Smart Cities, Retail services for personalized shopping, and service discovery in Cloud-based service marketplaces.  We are developing two specific technologies: Unsupervised Anomaly Detection for Large Data Streams, and Pattern Mining for User Behaviour.

Research Area 2: IoT Control and Platforms

Our research focuses on platforms and platform components that enable effective and scalable processing of IoT data and use it for controlling the IoT itself as well as the systems connected to it. One example is the processing of geo-tagged streamed data (Geostreaming). The target is to provide scalable algorithms which can utilize the elasticity of Cloud infrastructures to deal with Geostreaming problems. One set of typical problems is comparing events from moving objects against pre-defined static geo-fencing or against dynamic  moving areas (dynamic geofences). Another set of problems is to analyze the geo-tagged streams through combining the location properties with the sensor data itself, e.g. to detect cluster of areas with similar values.

The expected size of the Internet-of-Things requires that a support control mechanism for the IoT is also considered. Ultimately, this means that the infrastructure of the Internet-of-Things has to follow principles of self-organization. We are conducting research in two directions: (a)self-optimization of IoT systems in terms of energy consumption, communication load, elastic utilization of available resources, and data flows; and (b) realization of a resource scheduling system that enables users and applications to formulate high-level tasks which are then automatically broken down into concrete actuation or processing commands by an intelligent middleware and executed according to a dynamic schedule.


Selected Publications


  • F.-J. Wu: A Sensor-assisted Emergency Guiding System: Sensor-centric or User-centric? Accepted for IEEE Transactions on Vehicular Technology, 2017
  • B. Cheng, G. Solmaz, F. Cirillo, E. Kovacs, K. Terasawa, und A. Kitazawa: FogFlow: Easy Programming of IoT Services Over Cloud and Edges for Smart Cities. Accepted for IEEE Internet of Things Journal, 2017



  • Tobias Jacobs, Salvatore Longo: A Study of Caching Strategies for Web Service Discovery, IEEE International Conference on Web Services (ICWS’15) 2015.
  • Bin Cheng, Apostolos Papageorgiou, Flavio Cirillo, Ernö Kovacs: GeeLytics: Geo-distributed edge analytics for large scale IoT systems based on dynamic topology, 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), 2015.
  • Ernö Kovacs, Apostolos Papageorgiou, Bin Cheng: Real-Time Data Reduction at the Network Edge of Internet-of-Things, SystemsConference: 11th International Conference on Network and Service Management, Oct 2015.
  • Bin Cheng, Salvatore Longo, Flavio Cirillo, Martin Bauer, Ernoe Kovacs: Building a Big Data Platform for Smart Cities: Experience and Lessons from Santander, 4th IEEE International Congress on Big Data (BigData Congress 2015), New York, July 2015.


  • Apostolos Papageorgiou, Manuel Zahn, Ernoe Kovacs: Auto-configuration System and Algorithms for Big Data-enabled Internet-of-Things Platforms, in: IEEE Proceedings of the 3rd International Congress on Big Data, Alaska, Anchorage, June 2014.


  • Alessandro Bassi, Martin Bauer, Martin Fiedler, Thorsten Kramp, Rob van Kranenburg, Sebastian Lange, Stefan Meissner (Editors): Enabling Things to Talk, Springer, ISBN 978-3-642-40402-3, November 2013


  • Alex Gluhak, Manfred Hauswirth, Srdjan Krco (Ericsson Serbia), Nenad Stojanovic, Martin Bauer, Rasmus Nielsen, Stephan Haller, Neeli Prasad, Vinny Reynolds, Oscar Corcho: Blueprint for a Real-World Internet, Towards the Future Internet - Emerging Trends from European Research, LNCS 6656, Springer-Verlag Berling Heidelberg, April 2011, p.67-80
  • Martin Strohbach, Martin Bauer, Miquel Martin, Benjamin Hebgen: Managing Advertising Context, in: Pervasive Advertising, Jörg Müller, Florian Alt, Daniel Michelis (Eds.), Springer HCI Series, July 2011


  • Martin Bauer, Naoko Ito, Ernö Kovacs, Anett Schülke, Carmen Criminisi, Laurent-Walter Goix, Massimo Valla, The Context API in the OMA Next Generation Service Interface, 14th International Conference on Intelligence in Next Generation Networks (ICIN) “Weaving Applications into the Network Fabric”, Berlin, October 2010


  • Ioannis G. Nikolakopoulos, Charalampos Z. Patrikakis, Antonio Cimmino, Martin Bauer, Henning Olesen, On the Personalization of Personal Networks - Service Provision Based on User Profiles, Journal of Universal Computer Science, Vol. 15, No. 12, pp. 2353-2372, 2009
  • Martin Strohbach, Jesús Bernat Vercher, Martin Bauer: A case for IMS - Harvesting the Power of Ubiquitous Sensor Networks, Vehicular Technology Magazine, IEEE , vol.4, no.1, pp.57-64, March 2009

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