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Ubiquitous Computing

Lupe

The Ubiquitous Computing paradigm describes a world in which electronic devices are not only unobtrusively embedded within the human’s environment but also in which electronic devices are interacting with each other to support humans in their daily life. Hence, Ubiquitous Computing deals with a wide range of research topics related to Pervasive Computing, Wearable Computing, Ambient Intelligence, Intelligent Environments, Automotive Computing and Smart Homes. Thereby, the context- and location-awareness of Ubiquitous Computing systems are the key enablers for many application scenarios such as carsharing, navigation systems, location-based marketing, home automation or context-aware services in general. The Ubiquitous Computing field is highly dependent on the underlying wireless communication technologies such as 5G networks, WLAN, Bluetooth, NFC etc., and many research questions deal with the optimization of protocols and algorithms that are applied over these wireless communication networks. 

Our research related to Ubiquitous Computing focuses on developing context-aware middlewares for smart mobile devices like smartphones, wearables or tablets. Our work encompasses the design and development of proactive context or location-based service infrastructures for indoor and outdoor environments as well the investigation of new methods for the datafication of mobile data. By enhancing context-aware systems with latest spatiotemporal data analytics techniques, we envision intelligent environments which are able to autonomically learn typical behavior patterns and adapt mobile applications according to situational needs. These self-adaptive systems come along with several interesting research challenges like automatic data processing and analysis, energy-efficient context-awareness and profile-based adaptation of mobile devices, which are all addressed within our research group.

Publications

Denisow, I. and Zickau, S. and Beierle, F. and Küpper, A. (2015). Dynamic Location Information in Attribute-based Encryption Schemes. Proceedings of the 9th International Conference on Next Generation Mobile Applications, Services and Technologies (NGMAST 2015). IEEE, 240-247.


Bareth, U. and Küpper, A. and Ruppel, P. (2010). geoXmart - a Marketplace for Geofence-Based Mobile Services. Proceedings of the IEEE 34th Annual Computer Software and Applications Conference (COMPSAC 2010). IEEE, 101-106.


Bareth, U. (2012). Privacy-aware and Energy-efficient Geofencing through Reverse Cellular Positioning. Proceedings of the 8th Intl. Conference on Wireless Communications and Mobile Computing Conference (IWCMC 2012). IEEE, 153-158.


Rodriguez Garzon, S. and Deva, B. and Pilz, G. and Medack, S. (2015). Infrastructure-assisted Geofencing: Proactive Location-based Services with Thin Mobile Clients and Smart Servers. Proceedings of the 2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering. IEEE Computer Society, 61-70.


Salem, M. and Deva, B. (2013). 3rd Party Geolocation Messaging: A Positioning Enabler Middleware for Realizing Context-aware Polling. Proceedings of the 6th International Conference on Mobile Wireless Middleware, Operating Systems, and Applications. IEEE, 100-109.


Uzun, A. and Salem, M. and Küpper, A. (2014). Exploiting Location Semantics for Realizing Cross-referencing Proactive Location-based Services. Proceedings of the IEEE 8th International Conference on Semantic Computing. IEEE Computer Society, 76-83.


Uzun, A. and Küpper, A. and Einsiedler, H.-J. (2011). Communicate Green - Energy Efficient Mobile Communication. Proceedings of the 1st Intl. Conference on Pervasive and Embedded Computing and Communication Systems. SciTePress, 302-305.


Clemens, K. (2015). Geocoding with OpenStreetMap Data. GEOProcessing 2015, The Seventh International Conference on Advanced Geographic Information Systems, Applications, and Services. IARIA.


Rodriguez Garzon, S. and Deva, B. (2019). Sensafety: Crowdsourcing the Urban Sense of Safety. Advances in Cartography and GIScience of the ICA. Copernicus Publications, 12.


Beierle, F. and Probst, T. and Allemand, M. and Zimmermann, J. and Pryss, R. and Neff, P. and Schlee, W. and Stieger, S. and Budimir, S. (2020). Frequency and duration of daily smartphone usage in relation to personality traits. Digital Psychology. facultas.


Uzun, A. and Neidhardt, E. and Küpper, A. (2013). OpenMobileNetwork - A Platform for Providing Estimated Semantic Network Topology Data. International Journal of Business Data Communications and Networking (IJBDCN). IGI Global, 46-64.


Salem, M. and Schonowski, J. and and Küpper, A. (2013). Citizen-centric Smart Cities: M-Technology for realizing Smart Participatory Urban Sensing in E-Government. International Journal of Information Technology & Computer Science (IJITCS). IISRC, 82-92.


Beierle, F. and Tran, V.T. and Allemand, M. and Neff, P. and Schlee, W. and Probst, T. and Zimmermann, J. and Pryss, R. (2019). What data are smartphone users willing to share with researchers? Designing and evaluating a privacy model for mobile data collection apps. Journal of Ambient Intelligence and Humanized Computing. Springer.


Beierle, F. and Tran, V.T. and Allemand, M. and Neff, P. and Schlee, W. and Probst, T. and Pryss, R. and Zimmermann, J. (2018). Context Data Categories and Privacy Model for Mobile Data Collection Apps. Procedia Computer Science, 18-25.


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