Universität Stuttgart

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    Generic templates for monitoring agents
    (2018) Weise, Marc
    This 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.
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    Generating code for distributed deployments of cyber-physical systems using the MechatronicUML
    (2022) Stürner, David
    Models 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.
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    Goal-driven context-sensitive production processes : a case study using BPMN
    (2016) Kar, Debasis
    The Fourth Industrial Revolution, also known as Industry 4.0 or Industrial Internet, predicts that Smart Factories driven by Internet of Things (IoT) and Cyber-Physical Systems, will reinvent the traditional manufacturing industry into a digitalized, a context-aware, and an automated manufacturing that will flourish with contemporary Information and Communication Technology (ICT). As the IoT are being deployed across production cites of the manufacturing companies, the need of decision making inside a business process based upon the received contextual data such as employee availability, machine status, etc. from the execution environment has transpired. Production processes need to be updated and optimized frequently to stay competitive in the market. Context-sensitive Adaptive Production Processes is an adept concept that illustrates how a business process can be context-sensitive keeping itself aligned with the abstract organizational goals. The notion of Context-sensitive Adaptive Production Processes leads us to Context-sensitive Execution Step (CES), a logical construct, that encompasses multiple alternative processes, albeit the best-fitting alternative can only be selected, optimized, and executed in runtime. Realization of the context-sensitive business processes requires a model-driven approach. Being Business Process Model and Notation (BPMN) the de-facto standard for business processes modeling, business experts of manufacturing companies can use custom CES construct of BPMN to model and execute context-sensitive business processes in a model-driven approach. This case study is based upon a scenario where there exists multiple alternatives to achieve the same goal in production, nevertheless all the alternatives are not suitable at a certain point of time as changes in business objectives and execution environment makes adaption tougher. Properties of intelligent production processes are different from traditional processes. Such properties along with the scrutinized properties of standard BPMN facilitates modeling CES integrated processes in BPMN. From the requirements inferred from these properties, standard BPMN is extended with extensions such that context-sensitive business processes can be modeled and executed seamlessly. Developed extensions include a new type of process construct and a new type of process definition that are technology agnostic. Thus, CES approach provides a comprehensive solution that makes production processes contextsensitive as well as goal-driven in unison.
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    Towards a neuro-symbolic approach for occupant activity recognition : combining temporal HTN planning with hidden Markov models
    (2025) Hösch, Peter
    The 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.
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    Personenbezogene Daten im Data Lake
    (2018) Ebinger, Felix
    Big-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.
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    Scheduling with uncertainty for Time-Sensitive Networking using robust optimization techniques and integer linear programming
    (2024) Bauer, Florian
    Application 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.
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    Ein Ansatz für IoT-Sicherheitstests basierend auf dem MQTT-Protokoll
    (2021) Chen, Kai
    Das 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.
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    Feature based volumetric terrain generation
    (2016) Becher, Michael
    Two-dimensional heightfields are the most common data structure used for storing and rendering of terrain in offline rendering and especially real-time computer graphics. By its very nature, a 2D heightfield cannot store terrain structures with multiple vertical layers such as overhangs and caves. This restriction is lifted if a volumetric data structure is chosen in place of a 2D heightfield. However, the workflow of manual modelling and editing of volumetric terrain usually involves a large number of minor edits and adjustments and is very time consuming. Therefore, I propose to use three-dimensional curve-based primitives to efficiently model prominent, large scale terrain features and present techniques for volumetric generation of a complete terrain surface from the sparse input data by means of diffusion-based algorithms. By combining an efficient, feature-based toolset with a volumetric terrain representation, the modelling workflow is accelerated and simplified while offering the full artistic freedom of volumetric terrain.
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    Concept for executing management operations on components of application instances
    (2019) Sowoidnich, Yannic
    A 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.