IEEE Student Fellowship for Outstanding Contributions to Machine Learning in Communications based on research in DFG SFB 876

11.09.2018 -

in

News
SFB 876

We are very happy to announce that Benjamin Sliwa has received the 2018 Transportation Electronics Fellowship Award "For Outstanding Student Research Contributions to Machine Learning in Vehicular Communications and Intelligent Transportation Systems" of the Vehicular Technology Society (VTS) within the IEEE, which grants two student fellowships per year worldwide. The bestowal took place at the IEEE Vehicular Technology Conference (VTC) Fall 2018, in August in Chicago, USA, which is the flagship conference of the VTS. Within his research in project B4 "Analysis and Communication for Dynamic Traffic Prognosis" of the Collaborative Research Centre (SFB 876), Benjamin Sliwa has presented pioneering work in using machine learning for resource-efficient data transmissions within cellular vehicular networks.

Recent publications:

B. Sliwa, N. Piatkowski, M. Haferkamp, D. Dorn, C. Wietfeld, "Leveraging the Channel as a Sensor: Real-time Vehicle Classification Using Multidimensional Radio-fingerprinting", In 2018 IEEE 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, Hawaii, USA, November 2018. (accepted for presentation).

B. Sliwa, T. Liebig, R. Falkenberg, J. Pillmann, C. Wietfeld, "Machine Learning based Context-predictive Car-to-cloud Communication Using Multi-layer Connectivity Maps for Upcoming 5G Networks", In 2018 IEEE 88th IEEE Vehicular Technology Conference (VTC-Fall), Chicago, USA, August 2018.

B. Sliwa, T. Liebig, R. Falkenberg, J. Pillmann, C. Wietfeld, "Efficient Machine-type Communication using Multi-metric Context-awareness for Cars used as Mobile Sensors in Upcoming 5G Networks", In IEEE Vehicular Technology Conference (VTC-Spring), Porto, Portugal, Juni 2018. (Best Student Paper Award).

DFG SFB 876 project B4: "Analysis and Communication for Dynamic Traffic Prognosis"