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Volker Turau

Picture of Volker Turau
Prof. Dr. rer. nat. Volker Turau
Room 4.088, building E
Am Schwarzenberg-Campus 3
21073 Hamburg
phone+49 40 42878 - 3530
fax+49 40 427 - 3 - 10456
e-mail

I am professor at Hamburg Universtity of Technology since October 2002.


Program Committee Activities | Editorial Activities | CV | Ph.D. students

Books

Algorithmische Graphentheorie - 4., extended and revised edition
De Gruyter Studium, 2015, ISBN 978-3-110-41727-2 (Solutions)

Erdős number

My Erdős number is 3.

Teaching

Publications

Volker Turau. Amnesiac Flooding: Synchronous Stateless Information Dissemination. In Theory and Practice of Computer Science - 47th International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2021, Springer, January 2021, pp. 59–73.
@InProceedings{Telematik_sofsem_2021, author = {Volker Turau}, title = {Amnesiac Flooding: Synchronous Stateless Information Dissemination}, booktitle = {Theory and Practice of Computer Science - 47th International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2021}, pages = {59-73}, publisher = {Springer}, day = {25-29}, month = jan, year = 2021, location = {}, }
Abstract: A recently introduced stateless variant of network flooding for synchronous systems is called amnesiac flooding. Stateless protocols are advantageous in high volume applications, increasing performance by removing the load caused by retention of session information. In this paper we analyze the termination time of multi-source amnesiac flooding. We provide tight upper and lower bounds for the time complexity.
Marcus Venzke, Daniel Klisch, Philipp Kubik, Asad Ali, Jesper Dell Missier and Volker Turau. Artificial Neural Networks for Sensor Data Classification on Small Embedded Systems. Technical Report Report arXiv:2012.08403, arXiv.org e-Print Archive - Computing Research Repository (CoRR), Cornell University, December 2020.
@TechReport{Telematik_Venzke_ANNsES, author = {Marcus Venzke and Daniel Klisch and Philipp Kubik and Asad Ali and Jesper Dell Missier and Volker Turau}, title = {Artificial Neural Networks for Sensor Data Classification on Small Embedded Systems}, number = {Report arXiv:2012.08403}, institution = {arXiv.org e-Print Archive - Computing Research Repository (CoRR)}, address = {Cornell University}, month = dec, year = 2020, }
Abstract: In this paper we investigate the usage of machine learning for interpreting measured sensor values in sensor modules. In particular we analyze the potential of artificial neural networks (ANNs) on low-cost microcontrollers with a few kilobytes of memory to semantically enrich data captured by sensors. The focus is on classifying temporal data series with a high level of reliability. Design and implementation of ANNs are analyzed considering Feed Forward Neural Networks (FFNNs) and Recurrent Neural Networks (RNNs). We validate the developed ANNs in a case study of optical hand gesture recognition on an 8-bit microcontroller. The best reliability was found for an FFNN with two layers and 1493 parameters requiring an execution time of 36 ms. We propose a workflow to develop ANNs for embedded devices.
Florian Meyer, Ivonne Andrea Mantilla-Gonzales and Volker Turau. New CAP Reduction Mechanisms for IEEE 802.15.4 DSME to SupportFluctuating Traffic in IoT Systems. In Proceedings of 19th International Conference on Ad Hoc Networks and Wireless (AdHoc-Now 2020), Springer, October 2020, pp. 159–179. Bari, Italy / Virtually.
@InProceedings{Telematik_adhocnow_2020, author = {Florian Meyer and Ivonne Andrea Mantilla-Gonzales and Volker Turau}, title = {New CAP Reduction Mechanisms for IEEE 802.15.4 DSME to SupportFluctuating Traffic in IoT Systems}, booktitle = {Proceedings of 19th International Conference on Ad Hoc Networks and Wireless (AdHoc-Now 2020)}, pages = {159-179}, publisher = {Springer}, day = {19-21}, month = oct, year = 2020, location = {Bari, Italy / Virtually}, }
Abstract: In 2015, the IEEE 802.15.4 standard was expanded by theDeterministic and Synchronous Multi-Channel Extension (DSME) toincrease reliability, scalability and energy-efficiency in industrial appli-cations. The extension offers a TDMA/FDMA-based channel access,where time is divided into two alternating phases, a contention accessperiod (CAP) and a contention free period (CFP). During the CAP, transmission slots can be allocated offering an exclusive access to theshared medium during the CFP. The fractionτof CFP’s time slots ina dataframe is a critical value, because it directly influences agility andthroughput. A high throughput demands that the CFP is much longerthan the CAP, i.e., a high value ofτ, because application data is only sentduring the CFP. High agility is given if the expected waiting time to senda CAP message is short and that the length of the CAPs are long enoughto accommodate necessary GTS negotiations, i.e., a low value ofτ. OnceDSME is configured according to the needs of an application,τcan onlyassume one of two values and cannot be changed at run-time. In thispaper, we propose two extensions of DSME that allow to adoptτto thecurrent traffic pattern. We show theoretically and through simulationsthat the proposed extensions provide a high degree of responsiveness totraffic fluctuations while keeping the throughput high.

The complete list of publications is available separately.

Supervised Theses

Ongoing Theses

Completed Theses