Dr. rer. nat. Stefan Geißler
Telefon | (0931) 31-83134 |
Telefax | (0931) 31-86632 |
stefan.geissler@informatik.uni-wuerzburg.de | |
Raum | A209 |
Anschrift | Lehrstuhl für Informatik III Am Hubland |
Research Interests
My research interests include the following topics:
- Software-defined Networking (SDN) and Network Function Virtualization (NFV)
- Internet of Things
- 5G Mobile Communication
- Traffic Measurements and Traffic Modeling
- Performance Evaluation and Modeling of Communication Systems
- Simulation of Communication Systems and Network Applications
- Queuing Theory and Discrete-time Analysis
Recent Publications
2023[ to top ]
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Uplink-based Live Session Model for Stalling Prediction in Video Streaming.. In: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium : IEEE, 2023, pp. 1–9:
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Efficient graph-based gateway placement for large-scale LoRaWAN deployments. In: Computer Communications vol. 204, Elsevier (2023), pp. 11–23:
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Towards Understanding the Signaling Traffic in 5G Core Networks.. In: Würzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks (WueWoWAS’23), 2023:
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Paving the Way for an Energy Efficient and Sustainable Future Internet of Things.. In: Würzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks (WueWoWAS’23), 2023:
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Power to the Applications: The Vision of Continuous Decentralized Autoscaling.. In: 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) : IEEE, 2023, pp. 281–283:
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Towards Understanding the Global IPX Network from an MVNO Perspective.. In: Würzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks (WueWoWAS’23), 2023:
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MVNOCoreSim: A Digital Twin for Virtualized IoT-centric Mobile Core Networks. In: IEEE Internet of Things Journal, IEEE (2023):
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Performance evaluation of next-generation data plane architectures and their components.. In: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium : IEEE, 2023, pp. 1–6:
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Machine Learning Based Study of QoE Metrics in Twitch. tv Live Streaming.. In: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium : IEEE, 2023, pp. 1–7:
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