Open Access

Die Hochschule Offenburg nimmt jedes Jahr mit dem Team magmaOffenburg am RoboCup teil. Hierbei tritt das Offenburger Team in der 3D-Simulationsliga gegen Teams aus der ganzen Welt im Fußball mit autonomen Robotern an. Nun soll der Torwart von Team magmaOffenburg mit Hilfe von Deep Reinforcement Learning verbessert werden. Hierfür wird der Algorithmus PPO unter der Nutzung der RL-X Bibliothek verwendet. Im Laufe dieser Arbeit werden mehrere Versuche durchgeführt, bei denen Modelle für Torwartbewegungen entstehen. Eine dieser Bewegungen kann auf einem sehr schwer abzusichernden Bereich des Tors, in welchem der bisherige Torwart nur 5 Prozent der Bälle halten konnte, nun 36 Prozent der Bälle halten. Diese Arbeit erläutert des Weiteren die Integration dieses Modells für den Torwart in das Spiel von Team magmaOffenburg. Hierbei konnte beim Testen gegen das eigene, vorherige Team eine Reduktion von durchschnittlich 0,135 Gegentoren pro Spiel erreicht werden. Schlussendlich befasst sich diese Arbeit auch noch mit dem Weitertrainieren dieses Modells auf einem erweiterten Torbereich per Curriculum, um die Entscheidungslogik des Torwarts teilweise in das gelernte Modell zu integrieren. Hierdurch konnten in einer Spielserie jedoch nur minimal bessere Ergebnisse von 0,08 Gegentoren weniger als beim bisherigen Torwart erzielt werden.

The increasing integration of digital technologies in modern smart grids has significantly improved the efficiency, reliability, and automation of energy distribution. However, this transformation has also introduced critical cybersecurity risks, making smart grids vulnerable to cyber threats such as malware attacks, Distributed Denial of Service (DDoS), and intrusion attempts. Traditional security mechanisms, while effective in conventional IT systems, struggle to protect smart grids due to their complex interconnection of operational technology (OT) and IT systems. This thesis looks at cybersecurity challenges in smart grids by analyzing vulnerabilities in smartmeters, Supervisory Control and Data Acquisition (SCADA) systems, and communication networks. A detailed review of existing security approaches, including encryption, authentication protocols, anomaly detection, and intrusion detection systems, highlights their limitations in securing smart grid infrastructure. To address these challenges, this research proposes a cybersecurity framework that combines three defense mechanisms: (1) Digital Immune System – An AI-driven anomaly detection system that continuously learns from grid data to identify and neutralize threats in real time. (2) Genetic Algorithms for Cyber Defense – A self-optimizing security mechanism that evolves security configurations to improve grid resilience. (3) Decentralized AI Collectives – A distributed defense system where multiple AI agents collaborate to detect and mitigate cyberattacks without reliance on a central authority. This integrated defense mechanism ensures real-time threat detection, automated response, and adaptive security evolution. The proposed approach is validated through simulations, demonstrating its effectiveness in mitigating cyber threats and improving the overall security of smart grid systems. This research contributes to the field of enterprise and IT security by presenting a comprehensive, adaptive, and scalable cybersecurity solution tailored for smart grids.

With the advancement of technology in 21st century, the marketspace of various categories like Consumer electronics, Automotive, Healthcare & medical devices, Industrial Automation, Telecommunication and Défense & Aerospace is being disrupted with the fast-paced changing technology. Companies aiming on faster real-time processing, AI integration and better energy efficiency. Embedded systems have played a major role in the growth of such industries. An Embedded system is a computer system designed for a specific function within a larger mechanical or electrical system [1]. With an aim to perform a specific task, the devices are designed using a microprocessor or microcontroller with required memory, Input/Output interface and an integrated development environment for software coding. With the advent of technology, Embedded systems gained popularity for performing specific task at a faster rate, slowly and steadily the manual labour/machines are reduced/replaced with the addition of technology. Sensors/electronic devices have also played a pivotal role as a ground for an Embedded system. With the development of devices, the researchers have also stress on finding more efficiency, memory, affordability, reliability, complexity and reusability of the devices. IoT devices rely on embedded systems for data processing and control. With the popularity of IoT devices like Smart home devices (Amazon Echo, Philips Smart bulbs), wearables devices, smart agricultural devices, connected vehicles and transport devices, the embedded system has gained grounds in the technological space and transforming every day’s life. With the success of embedded system in various categories the demand and the requirements grew from customers and complex device requirement for multitasking with efficiency became a necessity. Traditionally, C was used for most of the embedded devices due to its efficiency, portability and hardware access as C provided control to low level hardware using pointers and allowed manipulation of peripheral settings. C also provided the advantage of saving space as the code size is not large and uses embedded compilers like GCC, Keil etc. Furthermore, it is compatible with Real time operating system. With the enhancement of embedded hardware from 1960s [2], it provided room to perform complex tasks. With C, the code modularity and its reusability, code maintainability, bugs detection was not very efficient for complex embedded system, hence it allowed programming languages like C++ which supports object-oriented programming concept to gain grounds as it allows the code to use features like Encapsulation, Inheritance and Polymorphism. Additionally, it allowed better bug detection & correction along with code maintainability. The work will reflect the advantages and disadvantages of C++ in resource restricted environment and provide better code optimization across the real time application.

Blockchain technology, renowned for its foundational attributes of decentralization, security, and immutability, offers substantial potential for diverse applications. At the heart of blockchain functionality are consensus mechanisms, crucial for preserving the decentralized integrity of the network. However, traditional consensus algorithms like Proof of Work (PoW), Proof of Stake (PoS), and Byzantine Fault Tolerance (BFT) typically require significant computational and communication resources, which may not be feasible for resource-limited environments. The purpose of this paper is to explore hybrid consensus algorithms that integrate conventional consensus mechanisms with advanced nonlinear data structures. We comprehensively analyze a wide range of hybrid consensus mechanisms, emphasizing their architectural design, operational efficiencies, and ability to address both consensus-specific vulnerabilities and network-level threats, such as Sybil attacks, double-spending, and partitioning attacks. To achieve this, we employ a set of comprehensive evaluation criteria for blockchain technologies, namely, validation, IoT, real-time processing, application suitability, security, and implementation. These criteria help assess the adaptability and efficacy of each mechanism in diverse operational contexts. Through this examination, the paper seeks to illuminate the significant contributions and implications of hybrid consensus mechanisms, guiding stakeholders, researchers, and developers toward making informed decisions about optimizing blockchain technology for their specific needs and inspiring the development of innovative solutions.

Arbeitsergebnisse der Künstlerischen Forschung BECOMING RIVER