Universität Stuttgart
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Item Open Access Generic templates for monitoring agents(2018) Weise, MarcThis thesis presents an agent-centric approach for monitoring IT resources, which enables the execution of preprocessing and aggregation steps directly on the target systems in order to limit data transfers to a central server and allow a local event detection and treatment. To keep the agent behavior definition as simple as possible, an extendable template model is introduced which can be used to define Monitoring Pipelines by chaining individual processing steps. Furthermore this work demonstrates how a graphical editor can be implemented which also allows non-experts in the field of monitoring to create and modify Monitoring Templates.Item Open Access Generating code for distributed deployments of cyber-physical systems using the MechatronicUML(2022) Stürner, DavidModels are applied in engineering disciplines to describe systems from a higher level of abstraction. In Model-Driven Software Engineering (MDSE), formal models are used to design and verify software systems and to infer platform-specific models and implementations. The MechatronicUML is an MDSE method specifically designed for distributed cyber-physical systems (CPS). This thesis explores how the MechatronicUML may be used for generating code. The exact state of previous code generation approaches is not precisely known. The objective of this thesis is to design and implement a MechatronicUML-based code generator for distributed deployments of CPS. Previous code generation approaches are analyzed for this purpose and one approach is selected and extended to support a particular type of robot car as a target platform. A taxonomy for model-based code generation is proposed to structure the analysis of the previous approaches. Based on the selected previous approach, a code generator is presented and implemented. Additionally, an automotive application scenario is used as a case study for evaluating the concept and the implementation of the presented code generator. This code generator supports modeling the distributed deployment of a CPS with the MechatronicUML and generates platform-specific source code which can be successfully compiled and deployed on the Arduino-based robot cars. Ultimately, the thesis presents a proof of concept to generate the code for a distributed CPS based on the MechatronicUML.Item Open Access Analysis of water volume change of the lakes and reservoirs in the Mississippi River basin using Landsat imagery and satellite altimetry(2021) Wang, LingkeIn recent years, the demand for freshwater has been steadily increasing owing to population growth and economic expansion. Surface waters such as lakes and reservoirs function as a dominant factor in mankind's freshwater provision. Analysis of changes in their water storage is consequently vital for understanding of the global water cycle and water resources. However, the water volume changes in lakes or reservoirs cannot be measured directly from space, but can be inferred from lake areas and lake water levels. Lake area can be measured globally from space but lake water level is not easy to be obtained globally. Because the number of in situ stations is few, and in situ data are only accessible for some lakes with few measurement epochs, despite in situ stations can measure lake water level and provide high accuracy observations. Although the altimetry technique can generate the time series of the water level for the majority of lakes, they are not global coverage due to the distance between satellite tracks and the gap between different missions. Therefore, in situ data and satellite altimetry measurements of water levels of lakes and reservoirs are not always available. For example, there are only 22 lakes or reservoirs in this study covered by satellite altimetry or in situ stations out of 90 research cases in Mississippi River Basin. Then, in case of unavailable in situ data or altimetry measurements, this research proposes an alternative method to estimate the water level through Digital Elevation Model (DEM). Because satellite imagery offers global coverage and DEM is the global digital representation of the land surface elevation with respect to any reference datum, this study allows for the evaluation of global water volume changes by acquiring lake area data from space and lake height data from DEM. Therefore, the objective of this study is that changes in water volume in lakes or reservoirs can be successfully monitored even when in situ data and satellite altimetry measurements are not available for lakes or reservoirs. Hereby, we investigate 90 lakes and reservoirs in the Mississippi River Basin and develop an alternative remote sensing technique to monitor the water volume changes by combining the improved water mask with DEM. Meanwhile, we propose practical methods to detect the shoreline pixels of the water body from improved water mask. Given the assumption that all pixels in the shoreline should have the same height, four water level estimation models are developed, including water level estimation model based on statistical analysis, frequency maps, change pixels and pixel pair analysis. To this end, the study estimates the time series of lake height from water level estimation model and obtains the time series of lake surface area from HydroSat. Subsequently, this study builds the unique function between the lake water level and the lake surface area and then develops the function between the lake water volume change and the lake surface area. Finally, this study analyses the water volume changes of lakes and reservoirs in the Mississippi River Basin using this alternative remote sensing method. Four water level estimation models are proposed and evaluated. They are respectively based on statistical analysis, frequency maps, change pixels and pixel pair analysis. As a result of their actions, the first model based on statistical analysis, with an average correlation of 0.62 and an average RMSE of 0.91 meters, functions in the majority of situations and demonstrates excessive outlier removal in some cases. The second model based on frequency maps is more general than the first, with an average correlation of 0.66 and an average RMSE of 1.11 meters. The average correlation for the third model based on change pixels is 0.71, and the average RMSE is 0.99 meters. The resulting model based on pixel pair analysis obtains a mean correlation of 0.67 and a mean RMSE of 1.00 meters. Finally, these models behave differently in different seasons, so they exhibit distinct monthly behaviour. To conclude, the above validation results show that this alternative method can be used in different lakes and reservoirs in case of absence of water level observation data, and achieve to monitor the water volume changes during a long period.Item Open Access Reconstruction of μXRCT data sets using the ASTRA toolbox(2020) Voland, PaulItem Open Access Towards a neuro-symbolic approach for occupant activity recognition : combining temporal HTN planning with hidden Markov models(2025) Hösch, PeterThe problem of occupant activity recognition has gained in relevance due to demographic shifts and growing environmental concerns where context-sensitive applications promise to help. The prevalent approach to this problem is based around the use of supervised machine learning, which faces challenges due to its requirement for large amounts of annotated training data and its tendency to overfit. Using preexisting common sense or expert knowledge, usually in the form of ontologies, presents another option, but carries its own set of shortcomings. Recently, the usage of hierarchical task network planning as an alternative to this ontological approach has been proposed. Hybrid systems that utilize both machine learning and preexisting knowledge promise to preserve the strength of both approaches while alleviating their drawbacks. We propose a new hybrid occupant activity system using hierarchical task network planning to support the training of a Hidden Markov Model, which, to the best of our knowledge, has not been done before. In addition, we evaluate the system on real sensor data in order to find out how much merits this new design has. Hereby we attempt and compare multiple approaches to the problem. Although not all methods improve the performance, the results show that the basic idea is sound and can generate measurable improvements.Item Open Access Personenbezogene Daten im Data Lake(2018) Ebinger, FelixBig-Data-Analysen bieten Wettbewerbsvorteile, ermöglichen Innovationen und können zu einer höheren Qualität von Produkten oder Serviceleistungen beitragen. Insbesondere die Analyse von Kundendaten und des Kundenverhaltens eröffnet vielfältige Möglichkeiten, um dem Kunden auf ihn zugeschnittene Angebote zu unterbreiten und um so zu höheren Umsätzen und zu einer höheren Kundenzufriedenheit beizutragen. Für die dafür benötigten Daten werden geeignete Speichersysteme benötigt. Ein solches System stellt der Data Lake dar. Neben der gut skalierenden und günstigen Speicherung von Daten ist auch die Auswertung der Daten mittels explorativer Analysen bereits im Design angelegt. Gleichzeitig steht aber auch der Schutz, genauer der fehlende Schutz der Privatsphäre, des Einzelnen bei Big Data Verarbeitungen im Mittelpunkt der öffentlichen Aufmerksamkeit und Kritik. Insbesondere wird vor dem so entstehenden „gläsernen Menschen“ und den daraus resultierenden gesellschaftlichen Folgen gewarnt. Die sich daraus ergebenden Fragen, in welchem Umfang und auf welche Art personenbezogene Daten verarbeitet werden dürfen, bedürfen, neben einer ethisch-moralischen, vor allem einer rechtlichen Antwort. Die europäische Datenschutzgrundverordnung stellt hierzu den rechtlichen Rahmen dar, in dem personenbezogene Daten verarbeitet werden dürfen. In dieser Arbeit werden die gesetzlichen Anforderungen mit dem Konzept des Data Lakes abgeglichen und es wird aufgezeigt, wo Herausforderungen beim Design und bei der Implementierung eines Data Lakes entstehen (z.B. Transparenz, Zweckbindung, Recht auf Löschung). Zudem werden Lösungsansätze für diese Herausforderungen entwickelt und vorgestellt. Aus den einzelnen Lösungsansätzen werden zwei Lösungskonzepte für einige der identifizierten Herausforderungen entwickelt. Eines der Konzepte, ein Metadaten-Modell, wird dabei prototypisch umgesetzt und anhand von Use Cases beispielhaft getestet.Item Open Access Scheduling with uncertainty for Time-Sensitive Networking using robust optimization techniques and integer linear programming(2024) Bauer, FlorianApplication services depend on the network to guarantee reliability, which is critical for safety and correct operation. Time-Sensitive Networking is a technology for reliable real-time communication of time-sensitive applications. While many schedulers exist that provide reliability for wired Time-Sensitive Networks (TSN) with the assumption of deterministic packet delays, scheduling for wireless TSN with uncertain packet delays has received significantly less attention. This work leverages the methodology of Robust Optimization (RO) to propose a robust scheduling approach that ensures provable reliability for both wired and wireless TSN. An uncertainty set defines the range of possible values, ensuring that the schedule remains feasible under all possible realizations within this set. As uncertainty sets are a key component in RO, we introduce methods to compute boxed and polytope uncertainty sets containing possible packet delays based on a set of given reliability requirements. A scheduler is deemed robust if it satisfies the given reliability constraints for all possible packet delays within the computed uncertainty set. Although robustness can be achieved through strict isolation and conservative filtering of packets, we demonstrate that several limitations prevent known robust schedulers from fully exploiting arbitrary uncertainty set shapes. As certain problem instances are unsolvable using simple boxed uncertainty sets, we indicate the need for schedulers that can utilize complex shapes of uncertainty sets rather than boxes. In response to this challenge, we introduce Uncertain No-Wait Packet Scheduling (UNWPS), a scheduler capable of computing robust schedules, and prove that UNWPS is robust against arbitrary upper-bounded boxed and polytope uncertainty sets. We assess the influence of uncertainty sets on the quality of the resulting UNWPS schedules, compare their performances to the performance of other robust scheduling approaches across various exemplary TSN networks and message stream configurations and carry out simulations conducted using the DetCom simulation framework to validate the robustness of UNWPS empirically.Item Open Access Ein Ansatz für IoT-Sicherheitstests basierend auf dem MQTT-Protokoll(2021) Chen, KaiDas Internet der Dinge (IoT) besteht aus einer stark wachsenden Anzahl an vernetzten Geräten und gewinnt immer mehr an Bedeutung. Aufgrund der Komplexität und Heterogenität der verwendeten Technologien existieren im IoT-Bereich viele Sicherheitsprobleme. MQTT ist das meist verwendete IoT-spezifische Protokoll für die Kommunikation, wodurch es einen attraktiven Angriffspunkt darstellt. Daher muss die Sicherheit bei MQTT-Systemen gewährleistet sein. Durch eine Literaturrecherche wurden als Hauptprobleme im Zusammenhang mit der Sicherheit von MQTT die unsichere Standardkonfiguration der Broker, sowie Schwachstellen im Umgang mit fehlerhaften Paketen identifiziert. Das Ziel dieser Arbeit ist, einen Testansatz zu entwerfen, der die Sicherheitsprobleme von MQTT-Broker-Implementierungen mittels automatisierten Sicherheitstests untersucht. Der Ansatz, genannt MQTT-AIO, besteht aus drei Testkomponenten und ist in der Lage, die Konfiguration des Brokers zu analysieren, Angriffe basierend auf Angriffsmustern auszuführen und weitere Schwachstellen mithilfe von Fuzzing zu finden. Eine weitere Komponente überwacht das System während des Testprozesses und zeichnet relevante Daten auf. Die Ergebnisse der Testdurchläufe werden als Bericht ausgegeben und können weiter analysiert werden. Der Testansatz MQTT-AIO wird im Rahmen dieser Masterarbeit prototypisch implementiert und anhand einer Fallstudie validiert.Item Open Access Concept for executing management operations on components of application instances(2019) Sowoidnich, YannicA large field of technologies exist for orchestrating cloud applications. Many of them focus on automated deployment techniques, rather than continous management of application instances. Executing operations for deploying applications is different from executing management operations, due to their dependencies to the application state. Proper state management is important to guarantee valid execution of management operations. Cloud providers such as Amazon have embedded functions for managing cloud applications, but they come with major drawbacks. They increase vendor-dependency and they do not support multi-cloud deployments. Technologies like Chef, Puppet or Terraform work with declarative process models, which cannot be used for non-state-changing operations and they mostly only allow simple operations. It is impossible to execute more customized fine grained operations with those technologies. Also, most of these management tools only support executing operations on the whole application, not on specific components of the application. The objective of this thesis is to find a way for executing management operations on running application instances by combining the information of the deployment model with the instance model of the application. The conceptual approach proposed in this thesis will consider and solve above addressed issues, as well as ensuring proper state management of application instances. The practical feasibility of this concept is validated by a prototypical implementation based on the TOSCA standard and the OpenTOSCA ecosystem.Item Open Access Quantenunterstütztes Clustering mit hybriden neuronalen Netzen(2021) Wundrack, PhilippMaschinelles Lernen und Quantencomputer sind zwei aktuelle Forschungsthemen, die großes Potenzial haben. Aktuell wird erforscht, wie diese beiden Gebiete kombiniert werden können, um voneinander zu profitieren. In diesen Bereich fällt die vorliegende Arbeit. In dieser Arbeit wird untersucht, ob hybride neuronale Netze genutzt werden können, um die Ergebnisse von Clustering-Algorithmen zu verbessern. Hierzu wird auf den Daten Dimensionsreduktion mit hybriden Autoencodern durchgeführt, bevor die Daten den Clustering-Algorithmen übergeben werden. Als Ergebnis konnte festgestellt werden, dass für bestimmte Datensätze Clustering-Algorithmen bessere Cluster erstellen können, wenn Dimensionsreduktion mit hybriden Autoencodern durchgeführt wurde, anstatt mit klassischen Autoencodern oder PCA.