Topics for theses and student projects
How does a transport protocol respond to changing traffic? Where should a computing task run: at the edge or in the cloud? These topics offer a way into those questions, from simulating conventional methods to developing learning-based decisions. Student projects also address network experiments, sensor data and multimodal analysis.
The bachelor’s topics focus on implementing and comparing existing methods. The master’s topics extend this to developing and evaluating reinforcement-learning approaches. A student project focuses on a working component with understandable tests. Scope, data and supervision are agreed in discussion to suit your degree programme and available time.
Bachelor’s theses
Network congestion control
How do conventional congestion-control methods perform when traffic conditions change? This thesis compares their behaviour through simulation and performance evaluation.
Tasks
- Review existing congestion-control approaches.
- Implement conventional algorithms in a simulation environment.
- Compare their performance under different network conditions.
Requirements: Basic knowledge of Python, Linux and Git.
Research context: RMDT · AI-RMDT
Task offloading in edge computing
Which tasks should run at the edge, and which should be sent to the cloud? This thesis investigates task-offloading strategies by evaluating task execution and resource use across edge and cloud environments.
Tasks
- Review existing task-offloading strategies.
- Implement basic offloading methods using simulation tools.
- Evaluate latency and resource utilisation.
- Compare the results under different workloads.
Requirements: Basic Python programming and an interest in distributed systems.
Research context: Networked systems and intelligent infrastructure
Master’s theses
Reinforcement learning for network congestion control
Can reinforcement learning adapt congestion control to changing traffic conditions? This thesis develops and evaluates an adaptive algorithm in simulation, comparing it with conventional methods.
Tasks
- Review existing congestion-control approaches.
- Develop and implement a reinforcement-learning algorithm in a simulation environment.
- Optimise network performance under dynamic traffic conditions.
- Compare the results with conventional methods.
Requirements: Knowledge of reinforcement learning and working knowledge of Python, Linux and Git.
Research context: AI-RMDT · AI and multimodal analysis
Reinforcement learning for edge computing
Can a learning-based decision improve where a task is executed? This thesis investigates reinforcement learning for task offloading, with the aim of improving task execution and resource utilisation across edge and cloud environments.
Tasks
- Review existing task-offloading strategies.
- Develop a reinforcement-learning approach using simulation tools.
- Compare the results with existing methods.
Requirements: Knowledge of reinforcement learning and working knowledge of Python, Linux and Git.
Research context: Networked systems and intelligent infrastructure · AI and multimodal analysis
Student projects
Making network experiments repeatable
Student project. Build an executable workflow for defined bandwidth, delay and loss profiles. Each run should keep its configuration, software version and measurement files together and reproduce the analysis steps from the same inputs. A small set of documented test cases is more useful than a large interface without a verifiable workflow. This topic suits students with some scripting experience.
Research context: RMDT · AI-RMDT · LTest · HighPerTimer · Experimental facilities
From a sensor node to a traceable measurement log
Student project. Connect a small sensor experiment to a receiver and a structured log. Detect missing messages, record transmission settings and show the relationship between the measured value, transmission and reception. The task can begin with simulated data and move to available devices by agreement. Initial experience with microcontrollers or Python is enough for a bounded starting task.
Research context: Networked systems and intelligent infrastructure · SmartLighting · Experimental facilities
A small test dataset with two sources
Student project. Create a manageable dataset with two related sources, such as synthetic movement sequences and short speech cues. Define timing, file formats and test cases, then build an analysis that reveals missing or contradictory associations. The aim is a well-documented component for later experiments. Programming experience and familiarity with data formats are helpful.
Research context: AI and multimodal analysis · AURORA · ReSeDiUm
Discuss a topic
To discuss a topic, contact Prof. Dr. Dmitry Kachan first: Dmitry.Kachan@hs-anhalt.de.
When enquiring, include your degree programme, the kind of work you are considering, your interests and experience, and your proposed timeframe. If you already have a question or a suitable dataset, briefly describe both. The contacts in the FILA team can help turn them into a workable task.
