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
Four publications addressing recent topics in automotive communication networks presented at IEEE VTC-Fall 2019
Key results of the vehicular research projects "SFB 876 (B4): Analysis and Communication for Dynamic Traffic Prognosis" and "Intelligente…
Workshop Series on "Resource constraints in data science - Recent research results and future directions within the SFB 876"
The SFB 876 has kicked off a series of workshops with dedicated themes about recent advancements in data science, artificial intelligence and adjacent…
IEEE Student Fellowship for Outstanding Contributions to Machine Learning in Communications based on research in DFG SFB 876
We are very happy to announce that Benjamin Sliwa has received the 2018 Transportation Electronics Fellowship Award "For Outstanding Student Research…
Bernhard-Walke Dissertationspreis for CNI-Alumnus Niklas Goddemeier
This year, the award, that includes prize money of 1,500 euros, for the best Ph.D. thesis of all Communication Networks Institutes of the "Freunde und…
5G-Initiative for Germany - TU Dortmund Is a Part of It
5G-Initiative for Germany - TU Dortmund is a part of it: The Federal Minister for Traffic and Digital Infrastructure has started an initiative, that…
Awards for Staff Members of the Institute
At the end of 2016, we are delighted that two scientists of the institute have been honored with notable awards for their research. Dr.-Ing. Christoph…
Publications
A Novel System Architecture for Small-Scale Motion Sensing Exploiting 5G mmWave Channels
S. Häger, S. Böcker, S. Jamali, T. Reinsch, C. Wietfeld
In 2021 IEEE Globecom Workshops (GC Workshops), Madrid, Spain, December 2021
Accurate Multi-Zone UWB TDOA Localization utilizing Cascaded Wireless Clock Synchronization
J. Friedrich, J. Tiemann, C. Wietfeld
In 11th International Conference IPIN, Lloret de Mar, Spain, December 2021. (Best Paper Award)
Modeling and simulation of reconfigurable intelligent surfaces for hybrid aerial and ground-based vehicular communications
K. Heimann, B. Sliwa, M. Patchou, C. Wietfeld
In Proceedings of the 24th International ACM Conference MSWiM 2021
On the Benefits of Demand-Based Planning and Configuration of Private 5G Networks
C. Bektas, C. Schüler, R. Falkenberg, P. Gorczak, S. Böcker, C. Wietfeld
In 2021 IEEE VNC, Virtual Event, November 2021.
Rapid Network Planning of Temporary Private 5G Networks with Unsupervised Machine Learning
C. Bektas, S. Böcker, B. Sliwa, C. Wietfeld
In 2021 IEEE 94th Vehicular Technology Conference (VTC-Fall), Virtual Event, September 2021
Pushing the Limits: Resilience Testing for Mission-Critical Machine-Type Communication
C. Arendt, M. Patchou, S. Böcker, J. Tiemann, C. Wietfeld
In 2021 IEEE 94th Vehicular Technology Conference (VTC-Fall), Virtual Event, September 2021
Machine Learning-Enabled Data Rate Prediction for 5G NSA Vehicle-to-Cloud Communications
B. Sliwa, H. Schippers, C. Wietfeld
In IEEE 4th 5G World Forum (5GWF), Virtual, October 2021
Towards Machine Learning-Enabled Context Adaption for Reliable Aerial Mesh Routing
C. Schüler, B. Sliwa, C. Wietfeld
In 2021 IEEE 94th VTC-Fall, Virtual Event, September 2021.
How Green Networking May Harm Your IoT Network: Impact of Transmit Power Reduction at Night on NB-IoT Performance
P. Jörke, C. Wietfeld
In 2021 IEEE 7th World Forum on Internet of Things (WF-IoT), New Orleans, USA, June 2021
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Location & approach
The campus of TU Dortmund University is located close to interstate junction Dortmund West, where the Sauerlandlinie A 45 (Frankfurt-Dortmund) crosses the Ruhrschnellweg B 1 / A 40. The best interstate exit to take from A 45 is “Dortmund-Eichlinghofen” (closer to South Campus), and from B 1 / A 40 “Dortmund-Dorstfeld” (closer to North Campus). Signs for the university are located at both exits. Also, there is a new exit before you pass over the B 1-bridge leading into Dortmund.
To get from North Campus to South Campus by car, there is the connection via Vogelpothsweg/Baroper Straße. We recommend you leave your car on one of the parking lots at North Campus and use the H-Bahn (suspended monorail system), which conveniently connects the two campuses.
TU Dortmund University has its own train station (“Dortmund Universität”). From there, suburban trains (S-Bahn) leave for Dortmund main station (“Dortmund Hauptbahnhof”) and Düsseldorf main station via the “Düsseldorf Airport Train Station” (take S-Bahn number 1, which leaves every 20 or 30 minutes). The university is easily reached from Bochum, Essen, Mülheim an der Ruhr and Duisburg.
You can also take the bus or subway train from Dortmund city to the university: From Dortmund main station, you can take any train bound for the Station “Stadtgarten”, usually lines U41, U45, U 47 and U49. At “Stadtgarten” you switch trains and get on line U42 towards “Hombruch”. Look out for the Station “An der Palmweide”. From the bus stop just across the road, busses bound for TU Dortmund University leave every ten minutes (445, 447 and 462). Another option is to take the subway routes U41, U45, U47 and U49 from Dortmund main station to the stop “Dortmund Kampstraße”. From there, take U43 or U44 to the stop “Dortmund Wittener Straße”. Switch to bus line 447 and get off at “Dortmund Universität S”.
The AirportExpress is a fast and convenient means of transport from Dortmund Airport (DTM) to Dortmund Central Station, taking you there in little more than 20 minutes. From Dortmund Central Station, you can continue to the university campus by interurban railway (S-Bahn). A larger range of international flight connections is offered at Düsseldorf Airport (DUS), which is about 60 kilometres away and can be directly reached by S-Bahn from the university station.
The H-Bahn is one of the hallmarks of TU Dortmund University. There are two stations on North Campus. One (“Dortmund Universität S”) is directly located at the suburban train stop, which connects the university directly with the city of Dortmund and the rest of the Ruhr Area. Also from this station, there are connections to the “Technologiepark” and (via South Campus) Eichlinghofen. The other station is located at the dining hall at North Campus and offers a direct connection to South Campus every five minutes.
The facilities of TU Dortmund University are spread over two campuses, the larger Campus North and the smaller Campus South. Additionally, some areas of the university are located in the adjacent “Technologiepark”.
Site Map of TU Dortmund University (Second Page in English).