Integrating Terrestrial and Non-Terrestrial Networks (NTNs) within Beyond-5G (B5G) and future 6G ecosystems represents a transformative advancement in achieving ubiquitous, resilient, and scalable communication services. NTNs, including Low Earth Orbit (LEO) satellites, Unmanned Aerial Vehicles (UAVs), and High Altitude Platform Systems (HAPS), extend traditional terrestrial networks by providing continuous connectivity in remote, underserved, and connection-critical scenarios such as disaster-hit regions and rural areas. This thesis deals with an end-to-end cloud-native framework that leverages cutting-edge technologies, including Multi-Access Edge Computing (MEC), Software Defined Networking (SDN), Network Function Virtualization (NFV), blockchain, and advanced AI/ML models, to enhance service availability, security, and Quality of Service (QoS) in 3D NTN environments. The research first explores the deployment of disaggregated Next-Generation Radio Access Networks (NGRANs) across terrestrial and non-terrestrial domains using a Kubernetes-based architecture. A Graph Neural Network (GNN) model is developed to monitor and manage these networks, detecting link failures and optimizing traffic routing paths between terrestrial and satellite components. The GNN model achieves an 85% accuracy in link failure detection and significantly reduces end-to-end delays in NTN deployments, highlighting the potential of AI-driven network management in enhancing overall network resilience. To address the challenge of dynamic resource management in NTNs, this thesis investigates the implementation of functional splits, such as F1 and E1 interfaces, between terrestrial control units (gNB-CU) and satellite-based distributed units (gNB-DU). The study employs Long Short-Term Memory (LSTM) neural networks to predict resource utilization, specifically CPU, memory, and bandwidth of satellite payloads. These predictive models enable proactive monitoring and resource allocation decisions, ensuring efficient use of limited computational resources and maintaining high levels of network performance. Security remains a critical concern in NTNs due to decentralized and open 5G satellite communications. A blockchain-based authentication framework is proposed to mitigate these risks, enhancing the security of data exchanges and remote firmware updates in LEO satellite constellations. Blockchain technology provides a decentralized, transparent, and immutable security framework, improving authentication efficiency and protecting against unauthorized access, though with trade-offs in network performance, such as increased latency and reduced throughput. This approach makes the hybrid B5G NTN network secure, reinforcing the integrity and confidentiality of communication channels, which is essential for emerging services and applications. Furthermore, this thesis comprehensively evaluates MEC-based experimental testbeds that demonstrate service resiliency in NTNs during terrestrial network outages. The MEC deployments allow seamless transitions to satellite access networks, ensuring service continuity and improving QoS. These testbeds showcase the capability of cloud native technologies in maintaining service availability, highlighting their critical role in resilient NTN networks. The findings of this thesis demonstrate that integrating cloud-native architectures, blockchain-based security mechanisms, and advanced AI/ML models significantly enhances the resilience, security, and resource efficiency of NTNs. These innovations pave the way for robust, adaptive, and secure communication systems, supporting the seamless deployment of critical B5G and 6G applications across diverse and challenging environments. This research provides valuable insights into designing and implementing resilient NTNs, setting the foundation for future advancements in global connectivity and intelligent network management.

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Machine Learning (ML) and Artificial Intelligence (AI) impact many aspects of human life, from recommending a significant other to assist the search for extraterrestrial life. The area develops rapidly and exiting unexplored design spaces are constantly laid bare. The focus in this work is one of these areas; ML systems where decisions concerning ML model training, usage and selection of target domain lay in the hands of domain experts.  This work is then on ML systems that function as a tool that augments and/or enhance human capabilities. The approach presented is denoted Human In Command ML (HIC-ML) systems. To enquire into this research domain design experiments of varying fidelity were used. Two of these experiments focus on augmenting human capabilities and targets the domains commuting and sorting batteries. One experiment focuses on enhancing human capabilities by identifying similar hand-painted plates. The experiments are used as illustrative examples to explore settings where domain experts potentially can: independently train an ML model and in an iterative fashion, interact with it and interpret and understand its decisions.  HIC-ML should be seen as a governance principle that focuses on adding value and meaning to users. In this work, concrete application areas are presented and discussed. To open up for designing ML-based products for the area an abstract model for HIC-ML is constructed and design guidelines are proposed. In addition, terminology and abstractions useful when designing for explicability are presented by imposing structure and rigidity derived from scientific explanations. Together, this opens up for a contextual shift in ML and makes new application areas probable, areas that naturally couples the usage of AI technology to human virtues and potentially, as a consequence, can result in a democratisation of the usage and knowledge concerning this powerful technology.

Adaptive Large Neighborhood Search (ALNS) is a fairly recent yet popular single-solution heuristic for solving discrete optimization problems. Even though the heuristic has been a popular choice for researchers in recent times, the parallelization of this algorithm is not widely studied in the literature compared to the other classical metaheuristics. To extend the existing literature, this study proposes several different parallel schemes to parallelize the basic/sequential ALNS algorithm. More specifically, seven different parallel schemes are employed to target different characteristics of the ALNS algorithm and the capability of the local computers. The schemes of this study are implemented in a master-slave architecture to manage and assign loads in processors of the local computers. The overall goal is to simultaneously explore different areas of the search space in an attempt to escape the local minima, taking effective steps toward the optimal solution and, to the end, accelerating the convergence of the ALNS algorithm. The performance of the schemes is tested by solving a capacitated vehicle routing problem (CVRP) with available wellknown test instances. Our computational results indicate that all the parallel schemes are capable of providing a competitive optimality gap in solving CVRP within our investigated test instances. However, the parallel scheme (scheme 1), which runs the ALNS algorithm independently within different slave processors (e.g., without sharing any information with other slave processors) until the synchronization occurs only when one of the processors meets its predefined termination criteria and reports the solution to the master processor, provides the best running time with solving the instances approximately 10.5 times faster than the basic/sequential ALNS algorithm. These findings are applied in a real-life fulfillment process using mixed-mode delivery with trucks and drones. Complex but optimized routes are generated in a short time that is applicable to perform last-mile delivery to customers.

This work shows the application of artificial intelligence (AI) on invasion game tracking data to realize a fast (sub-second) and adaptable search engine for sports scenes, scene ratings based on machine learning (ML) and computer-generated solutions using reinforcement learning (RL). We provide research results for all three areas. Benefits are expected for accelerated video analysis at professional sports clubs. / Diese Arbeit zeigt die Anwendung von künstlicher Intelligenz (KI) auf Invasionsspielverfolgungsdaten, um eine schnelle (unter einer Sekunde) und anpassungsfähige Suchmaschine für Sportszenen zu realisieren, Szenenbewertungen auf der Grundlage von maschinellem Lernen (ML) und computergenerierte Lösungen unter Verwendung von Verstärkungslernen (RL). Wir stellen Forschungsergebnisse für alle drei Bereiche vor. Es werden Vorteile für eine beschleunigte Videoanalyse in Profisportvereinen erwartet.