Overview
Assignment of theses
The final theses in the AG Riehle are usually not pure literature theses but include the development of small prototypes or the collection and analysis of data. Below you will find a list of suggested topics. It is also possible to submit your own topic suggestions, as long as they can be assigned to one of the following topics:
- Sensors and Internet-of-Things (LoRa, etc.)
- Cloud architectures (IT infrastructures, virtualization, orchestration, etc.)
- Data management and data analytics
Please refrain from inquiries that cannot be assigned to any of the above areas.
Topics for theses
Developing Assessment Mechanisms for Evaluating GenAI-based IoT Applications
For: Bachelor, Master
This thesis develops comprehensive assessment mechanisms for evaluating Generative AI-based Internet of Things applications. Evaluating artifacts powered by nondeterministic AI algorithms presents inherent complexity, requiring sophisticated frameworks that address both technical performance and practical effectiveness. The research establishes multi-dimensional evaluation criteria encompassing accuracy, latency, scalability, user satisfaction, and interpretability. These metrics are benchmarked against established standards while adapting recent frameworks for assessing large language models to address the unique temporal and contextual characteristics of IoT data streams. Through systematic review of existing assessment methodologies and analysis of current GenAI-IoT applications, this work proposes novel evaluation approaches tailored to this emerging field. The outcome includes evaluation methodologies, practical guidelines, and tools designed to measure artifact performance in Design Science Research contexts. These assessment mechanisms support iterative refinement cycles and provide structured approaches for validating GenAI-IoT integration effectiveness across diverse deployment scenarios.
Supervisor: Jun.-Prof. Dr. Dennis Riehle
Conceptualization of an Interaction Model linking IoT Applications with GenAI.
For: Bachelor, Master
This thesis conceptualizes an interaction model that enables integration between Internet of Things applications and Generative AI technologies. The primary focus is establishing conceptual underpinnings necessary to link IoT sensor data with GenAI-driven processes and interfaces, particularly enabling autonomous and agentic GenAI applications in the IoT domain. The research explores technical foundations including grammar-constrained LLM generation, intermediate prompting approaches using logical programming languages, and emerging standards like Model Context Protocol. These methods address challenges such as ingesting Open Data into standardized platforms, managing heterogeneous data formats, and enabling LLMs to interact with complex distributed systems. The framework incorporates dialog-based interaction models to ensure alignment with natural user query patterns and IoT data interpretation needs.Through systematic analysis of existing interaction models and their limitations, this work proposes a novel framework that facilitates effective communication and collaboration between IoT devices and GenAI systems. The outcome is a conceptual model that bridges technical requirements with user-facing scenarios, establishing foundations for intelligent, context-aware IoT systems enhanced by generative AI capabilities.
Supervisor: Jun.-Prof. Dr. Dennis Riehle
Derivation of System Requirements for GenAI-based IoT Data Processing Systems
For: Bachelor, Master
This thesis derives comprehensive system requirements for integrating Generative AI technologies with Internet of Things data processing systems. The research draws on multiple sources: literature-based findings, empirical studies of sensor-based use cases in smart city contexts, and prior IoT artifacts from related work. Normative guidelines are established through existing theoretical frameworks, while ethical and societal considerations are integrated throughout the design process, following established practices for multi-user data platforms. The research identifies key challenges, opportunities, and design considerations for developing effective GenAI-based IoT solutions. Through systematic analysis of current technologies, use cases, and theoretical foundations, this work establishes a set of design objectives and requirements aligned with the eDSR methodology. These requirements serve as a foundation for future development in this emerging field, providing guidance for creating efficient and responsible GenAI-IoT integration architectures.
Supervisor: Jun.-Prof. Dr. Dennis Riehle
Literature Review on the Integration of Internet of Things (IoT) and Large Language Models (LLMs)
For: Bachelor, Master
This thesis conducts a comprehensive literature review mapping the landscape of opportunities and challenges when Generative AI interacts with IoT-based systems. While GenAI applications have demonstrated value across various domains, numerous technical and conceptual issues remain unexplored at this intersection. Key challenges include managing the volume and velocity of IoT sensor readings for real-time integration, addressing the unique temporal and contextual characteristics of sensor data that complicate standard GenAI retrieval approaches, and mitigating hallucination phenomena where models generate factually incorrect information. Ethical dimensions around data privacy, fairness, and accountability are examined, particularly in sensitive domains like smart healthcare. The research employs structured literature reviews to identify dominant research streams and gaps in peer-reviewed work, with Smart City applications serving as a primary domain for IoT-based use cases. Qualitative methods including idea-generation workshops and stakeholder interviews map the problem space, while analysis of prototype artifacts and log data provides contextualized, real-world problem evidence. This exploratory, inductive approach follows the eDSR methodology's first echelon.
Supervisor: Jun.-Prof. Dr. Dennis Riehle
Application for a topic
Please proceed as follows when applying for thesis supervision:
- Contact the respective tutor responsible for your topic via e-mail.
- Briefly explain your motivation for the targeted topic.
- In the attachment to your e-mail, send an excerpt of your previous academic achievements so that we can see which subjects you have successfully completed.
- Indicate the date when you would like to write the thesis (from/until).
We will be happy to explain the detailed terms of reference to you in a personal meeting afterwards. Please note: A condition for supervision is the acceptance of a research proposal written by you. You will receive the necessary assistance from your chosen tutor.
Templates
Research proposal
For all theses, a research proposal according to our template must be submitted before starting the work. Please send this proposal to your tutor for approval.
The research proposal is divided into the sections "Motivation of the thesis", "Formulation of objectives" and "Methodological approach". The proposal should already be provided with several literature sources, especially in the area of motivation, and should consist of a total of 1-2 pages of text.
Writing the thesis
The processing time for your thesis can be found in your examination regulations; as a rule, this is six months. When writing your thesis, you must adhere to the layout guidelines of our working group, for which we will provide you with appropriate templates: