Collaborative Research Center 876
The collaborative research center SFB876 brings together data mining and embedded systems. On the one hand, embedded systems can be further improved using machine learning. On the other hand, data mining algorithms can be realized in hardware, e.g. FPGAs, or run on GPGPUs. The restrictions of ubiquitous systems in computing power, memory, and energy demand new algorithms for known learning tasks. These resource bounded learning algorithms may also be applied on extremely large data bases on servers.
Project duration: 01.01.2015 - 31.12.2022
Project Composition of the SFB 876
The collaborative research center is divided into multiple projects. An extensive overview of all projects and their main objective can be found on the homepage of the SFB 876.
The Communication Networks Institute attends projects A4 and B4
News
Excellent wireless localization technology - CNI team wins IPIN competition
The CNI team (Janis Tiemann, Fabian Eckermann and Christian Wietfeld) won first prize in the "Indoor Mobile Robot Localization" category within the…
ComNets-Awards for Graduates of the Institute
The coveted awards by the "Verein der Freunde und Förderer der ComNets-Einrichtungen", who dedicated themselves to the research on future…
Collaborative Research Center with CNI-Participation Approved for 4 More Years
In the Collaborative Research Center 876 (SFB 876), the combination of data analysis (Data Mining/Big Data) with networked, embedded systems…
Successful Completion of Björn Dusza's Doctoral Studies in the Collaborative Research Center 876
Due to strongly increasing data traffic in mobile communications, the influence of the intensive use of mobile services on the battery life of…
Publications
Machine learning based context-predictive car-to-cloud communication using multi-layer connectivity maps for upcoming 5G networks
B. Sliwa, T. Liebig, R. Falkenberg, J. Pillmann, C. Wietfeld,
In 2018 IEEE 88th IEEE Vehicular Technology Conference, Chicago, USA, August 2018.
Performance comparison of dynamic vehicle routing methods for minimizing the global dwell time in upcoming smart cities
T. Vranken, B. Sliwa, C. Wietfeld, M. Schreckenberg,
In 2018 IEEE 88th IEEE Vehicular Technology Conference (VTC-Fall), Chicago, USA, August 2018.
Efficient and Reliable Car-to-Cloud Data Transfer Empowered by BBR-enabled Network Coding
J. Pillmann, D. Behnke, B. Sliwa, M. Priebe, C. Wietfeld,
In 2018 IEEE 88th IEEE Vehicular Technology Conference (VTC-Fall), Chicago, USA, August…
Machine Learning Based Uplink Transmission Power Prediction for LTE and Upcoming 5G Networks using Passive Downlink Indicators
R. Falkenberg, B. Sliwa, N. Piatkowski, C. Wietfeld,
In 2018 IEEE 88th IEEE Vehicular Technology Conference (VTC-Fall), Chicago, USA, August 2018.
Performance Analysis of Unsupervised LTE Device-to-Device (D2D) Communication
F. Eckermann, J. Freudenthal, C. Wietfeld,
In 2018 IEEE 88th IEEE Vehicular Technology Conference (VTC-Fall), Chicago, USA, August 2018.
Payload-size and Deadline-aware Scheduling for Upcoming 5G Networks: Experimental Validation in High-load Scenarios
S. Monhof, M. Haferkamp, B. Sliwa, C. Wietfeld,
In 2018 IEEE 88th IEEE Vehicular Technology Conference (VTC-Fall), Chicago, USA, August 2018.
Resource-efficient Transmission of Vehicular Sensor Data Using Context-aware Communication
B. Sliwa, T. Liebig, R. Falkenberg, J. Pillmann, C. Wietfeld,
In 19th IEEE International Conference on Mobile Data Management, Aalborg, Denmark,…
The AutoMat CVIM - A scalable data model for automotive big data marketplaces
J. Pillmann, B. Sliwa, C. Wietfeld,
In 19th IEEE International Conference on Mobile Data Management (MDM), Aalborg, Denmark, June 2018.
Improving the Robustness of Control-Grade Ultra-Wideband Localization
J. Tiemann, L. Koring, P. Gorczak, C. Wietfeld,
In 3rd IFAC Conference on Embedded Systems, Computer Intelligence and Telematics, Faro, Portugal